PMCC PMCC

Search tips
Search criteria

Advanced
Results 1-25 (1112629)

Clipboard (0)
None

Related Articles

1.  The Distribution of Infectious Related Symptoms in an Internet-based Syndromic Surveillance System in Rural China 
Objective
To describe the distribution of the infectious related symptoms in an internet-based syndromic surveillance system reported by doctors in village health stations, township and county hospitals in rural Jiangxi Province, China, and to identify the major infectious diseases for syndromic surveillance in different levels of health facility.
Introduction
Syndromic surveillance system, which collects non-specific syndromes in the early stages of disease development, has great advantages in promoting early detection of epidemics and reducing the burden of disease confirmation (1). It is especially effective for surveillance in resource-poor settings, where laboratory confirmation is not possible or practical (2). Integrating syndromic surveillance with traditional case report system may generate timely, effective and sensitive information for early warning and control of infectious diseases in rural China (3). A syndromic surveillance system (ISSC) has been implemented in rural Jiangxi Province of China since August 2011.
Methods
Doctors and health workers in the healthcare surveillance units of ISSC, including village health station, township hospital and county hospital, used an internet-based electronic system to collect information of daily outpatients, which included 10 categories of infectious disease related symptoms, i.e., cough, fever, sore throat, diarrhea, headache, rash, nausea/vomit, mucocutaneous hemorrhage, convulsion and disturbance of consciousness. The data from August 1st to December 31st 2011 were extracted from database and analyzed using SPSS 16.0. The combination of symptoms was also analyzed to identify patients with the syndrome of influenza-like illness (ILI) and fever-gastrointestinal syndrome (FGS). ILI were composed by fever (>=38 degree centigrade) plus cough or fever plus sore throat, and FGS were defined as fever plus vomit or diarrhea.
Results
Two county hospitals (CH), 4 township hospitals (TH) and 50 village health stations (VHS) were selected as surveillance unites in the pilot study during 2011/8/1 to 2011/12/31. In total, 152270 outpatient visits were reported, and 35395 patients had a chief complain of at least one surveillance symptom. Of these symptomatic patients, 24130 (68.2%) were from VHS, 4995 (14.1%) from TH and 6810 (19.2%) from CH. The proportion of patients with targeting symptom accounted for 15.5%, 66.4% and 23.9% of total outpatients in CH, TH and VHS respectively. The first 3 reported symptoms were cough (61.8%), fever (28.4%), and sore throat (23.4%), whereas mucocutaneous hemorrhage, convulsion and disturbance of consciousness were the least frequently reported symptoms in all surveillance units. Overall 3582 ILI and 1160 FGS cases were reported accounting for 35% and 11% of fever cases respectively. Of the reported ILI and FGS cases, 75% ILI and 55.9% FGS cases were reported by health workers in the VHS.
Conclusions
Cough, fever and sore throat were the top surveillance symptoms, and the respiratory infectious diseases had more chance to be reported in syndromic surveillance system in rural Jiangxi Province. Training on infectious disease diagnosis especially respiratory diseases for village health workers should be enhanced since large numbers of patients are likely to visit the village health stations.
PMCID: PMC3692925
Syndromic surveillance; rural; influenza-likes illness; fever-gastrointestinal syndrome
2.  The Organizational Structures and Human Resources Allocation of Infectious Disease Surveillance System in Rural China 
Objective
To understand the structure and capacity of current infection disease surveillance system, and to provide baseline information for developing syndromic surveillance system in rural China.
Introduction
To meet the long-term needs of public health and social development of China, it is in urgency to establish a comprehensive response system and crisis management mechanism for public health emergencies. Syndromic surveillance system has great advantages in promoting early detection of epidemics and reducing the burden of disease outbreak confirmation (1). The effective method to set up the syndromic surveillance system is to modify existing case report system, improve the organizational structures and integrate new function with the traditional system.
Methods
Since August 2011, an integrated syndromic surveillance project (ISSC) has been implemented in China. Before the launching of the project, a cross-sectional study was carried out in Fengxin County and Yongxiu County of Jiangxi province during October 11 to 18, 2010. Institution information were investigated in the county hospital, township hospital and County Center for Disease Control and Prevention (CDC) to understand the performance of existing case report system for notifiable infectious diseases with regard to its structure, capacity and data collection procedure. Health care workers from each township hospital and village health station were questionnaire interviewed for information on qualification of human resources, basic healthcare delivery condition, hardware and software needs for ISSC.
Results
An internet-based real-time (quasi real-time) case report system for notifiable infectious diseases, based on the three-tier public health service System, had been established in these two counties since 2004. The farthest end of net user in case report system was township hospital. Blood routine test, urine routine test, B ultrasound and electrocardiogram were available in all township hospitals. There was no laboratory equipment in village health stations in these two counties. All the township hospitals in these two counties were equipped with land-line telephones and desktop computers. The internet covers all township hospitals in both counties. Most clinical doctors in township hospital(TH) and village health station(VHS) were male. The age of doctors ranged from 21 to 72 years old, with the average at 42 and median at 40 years. The village health workers were significantly older, less educated and served in health care longer than the township hospital doctors. In Yongxiu County, 95.6% of the village health stations were equipped with computers, including private-owned computers, and 80.7% of them had access to the internet; while in Fengxin County, 66.5% of the village health stations possessed computers, among which most were private property of village doctors, and only 44.2% of them had access to the internet.
Conclusions
The current case report system, with full coverage and stable human resource, has established a solid basis for developing syndromic surveillance system in rural China. The syndromic surveillance system could play its role in early detection of infectious disease outbreaks in rural area where laboratory service for infectious disease diagnosis are not available. However, the lack of computerized patient registration in village and township health care facilities and incomplete internet coverage in rural area and relatively low quality of human resource in village level should be taken into consideration seriously before establishing the syndromic surveillance system in rural China.
PMCID: PMC3692936
Syndromic surveillance; rural area; human resources; case report system
3.  Selecting Targeted Symptoms/Syndromes for Syndromic Surveillance in Rural China 
Objective
To select the potential targeted symptoms/syndromes as early warning indicators for epidemics or outbreaks detection in rural China.
Introduction
Patients’ chief complaints (CCs) as a common data source, has been widely used in syndromic surveillance due to its timeliness, accuracy and availability (1). For automated syndromic surveillance, CCs always classified into predefined syndromic categories to facilitate subsequent data aggregation and analysis. However, in rural China, most outpatient doctors recorded the information of patients (e.g. CCs) into clinic logs manually rather than computers. Thus, more convenient surveillance method is needed in the syndromic surveillance project (ISSC). And the first and important thing is to select the targeted symptoms/syndromes.
Methods
Epidemiological analysis was conducted on data from case report system in Jingmen City (one study site in ISSC) from 2004 to 2009. Initial symptoms/syndromes were selected by literature reviews. And finally expert consultation meetings, workshops and field investigation were held to confirm the targeted symptoms/syndromes.
Results
10 kinds of infectious diseases, 6 categories of emergencies, and 4 bioterrorism events (i.e. plague, anthrax, botulism and hemorrhagic fever) were chose as specific diseases/events for monitoring (Table 1). Two surveillance schemes were developed by reviewing on 565 literatures about clinical conditions of specific diseases/events and 14 literatures about CCs based syndromic surveillance. The former one was to monitor symptoms (19 initial symptoms), and then aggregation or analysis on single or combined symptom(s); and the other one was to monitor syndromes (9 initial syndromes) directly (Table 2). The consultation meeting and field investigation identified three issues which should be considered: 1) the abilities of doctors especially village doctors to understand the definitions of symptoms/syndromes; 2) the workload of data collection; 3) the sensitive and specific of each symptom/syndrome. Finally, Scheme 1 was used and 10 targeted symptoms were determined (Table 2).
Conclusions
We should take the simple, stability and feasibility of operation, and also the local conditions into account before establishing a surveillance system. Symptoms were more suitable for monitoring compared to syndromes in resource-poor settings. Further evaluated and validated would be conducted during implementation. Our study might provide methods and evidences for other developing countries with limited conditions in using automated syndromic surveillance system, to construct similar early warning system.
PMCID: PMC3692788
Syndromic surveillance; Chief complaint; Early warning
4.  ISS-An Electronic Syndromic Surveillance System for Infectious Disease in Rural China 
PLoS ONE  2013;8(4):e62749.
Background
syndromic surveillance system has great advantages in promoting the early detection of epidemics and reducing the necessities of disease confirmation, and it is especially effective for surveillance in resource poor settings. However, most current syndromic surveillance systems are established in developed countries, and there are very few reports on the development of an electronic syndromic surveillance system in resource-constrained settings.
Objective
this study describes the design and pilot implementation of an electronic surveillance system (ISS) for the early detection of infectious disease epidemics in rural China, complementing the conventional case report surveillance system.
Methods
ISS was developed based on an existing platform ‘Crisis Information Sharing Platform’ (CRISP), combining with modern communication and GIS technology. ISS has four interconnected functions: 1) work group and communication group; 2) data source and collection; 3) data visualization; and 4) outbreak detection and alerting.
Results
As of Jan. 31st 2012, ISS has been installed and pilot tested for six months in four counties in rural China. 95 health facilities, 14 pharmacies and 24 primary schools participated in the pilot study, entering respectively 74256, 79701, and 2330 daily records into the central database. More than 90% of surveillance units at the study sites are able to send daily information into the system. In the paper, we also presented the pilot data from health facilities in the two counties, which showed the ISS system had the potential to identify the change of disease patterns at the community level.
Conclusions
The ISS platform may facilitate the early detection of infectious disease epidemic as it provides near real-time syndromic data collection, interactive visualization, and automated aberration detection. However, several constraints and challenges were encountered during the pilot implementation of ISS in rural China.
doi:10.1371/journal.pone.0062749
PMCID: PMC3633833  PMID: 23626853
5.  Monitoring the Impact of Influenza by Age: Emergency Department Fever and Respiratory Complaint Surveillance in New York City 
PLoS Medicine  2007;4(8):e247.
Background
The importance of understanding age when estimating the impact of influenza on hospitalizations and deaths has been well described, yet existing surveillance systems have not made adequate use of age-specific data. Monitoring influenza-related morbidity using electronic health data may provide timely and detailed insight into the age-specific course, impact and epidemiology of seasonal drift and reassortment epidemic viruses. The purpose of this study was to evaluate the use of emergency department (ED) chief complaint data for measuring influenza-attributable morbidity by age and by predominant circulating virus.
Methods and Findings
We analyzed electronically reported ED fever and respiratory chief complaint and viral surveillance data in New York City (NYC) during the 2001–2002 through 2005–2006 influenza seasons, and inferred dominant circulating viruses from national surveillance reports. We estimated influenza-attributable impact as observed visits in excess of a model-predicted baseline during influenza periods, and epidemic timing by threshold and cross correlation. We found excess fever and respiratory ED visits occurred predominantly among school-aged children (8.5 excess ED visits per 1,000 children aged 5–17 y) with little or no impact on adults during the early-2002 B/Victoria-lineage epidemic; increased fever and respiratory ED visits among children younger than 5 y during respiratory syncytial virus-predominant periods preceding epidemic influenza; and excess ED visits across all ages during the 2003–2004 (9.2 excess visits per 1,000 population) and 2004–2005 (5.2 excess visits per 1,000 population) A/H3N2 Fujian-lineage epidemics, with the relative impact shifted within and between seasons from younger to older ages. During each influenza epidemic period in the study, ED visits were increased among school-aged children, and each epidemic peaked among school-aged children before other impacted age groups.
Conclusions
Influenza-related morbidity in NYC was highly age- and strain-specific. The impact of reemerging B/Victoria-lineage influenza was focused primarily on school-aged children born since the virus was last widespread in the US, while epidemic A/Fujian-lineage influenza affected all age groups, consistent with a novel antigenic variant. The correspondence between predominant circulating viruses and excess ED visits, hospitalizations, and deaths shows that excess fever and respiratory ED visits provide a reliable surrogate measure of incident influenza-attributable morbidity. The highly age-specific impact of influenza by subtype and strain suggests that greater age detail be incorporated into ongoing surveillance. Influenza morbidity surveillance using electronic data currently available in many jurisdictions can provide timely and representative information about the age-specific epidemiology of circulating influenza viruses.
Don Olson and colleagues report that influenza-related morbidity in NYC from 2001 to 2006 was highly age- and strain-specific and conclude that surveillance using electronic data can provide timely and representative information about the epidemiology of circulating influenza viruses.
Editors' Summary
Background.
Seasonal outbreaks (epidemics) of influenza (a viral infection of the nose, throat, and airways) send millions of people to their beds every winter. Most recover quickly, but flu epidemics often disrupt daily life and can cause many deaths. Seasonal epidemics occur because influenza viruses continually make small changes to the viral proteins (antigens) that the human immune system recognizes. Consequently, an immune response that combats influenza one year may provide partial or no protection the following year. Occasionally, an influenza virus with large antigenic changes emerges that triggers an influenza pandemic, or global epidemic. To help prepare for both seasonal epidemics and pandemics, public-health officials monitor influenza-related illness and death, investigate unusual outbreaks of respiratory diseases, and characterize circulating strains of the influenza virus. While traditional influenza-related illness surveillance systems rely on relatively slow voluntary clinician reporting of cases with influenza-like illness symptoms, some jurisdictions have also started to use “syndromic” surveillance systems. These use electronic health-related data rather than clinical impression to track illness in the community. For example, increased visits to emergency departments for fever or respiratory (breathing) problems can provide an early warning of an influenza outbreak.
Why Was This Study Done?
Rapid illness surveillance systems have been shown to detect flu outbreaks earlier than is possible through monitoring deaths from pneumonia or influenza. Increases in visits to emergency departments by children for fever or respiratory problems can provide an even earlier indicator. Researchers have not previously examined in detail how fever and respiratory problems by age group correlate with the predominant circulating respiratory viruses. Knowing details like this would help public-health officials detect and respond to influenza epidemics and pandemics. In this study, the researchers have used data collected between 2001 and 2006 in New York City emergency departments to investigate these aspects of syndromic surveillance for influenza.
What Did the Researchers Do and Find?
The researchers analyzed emergency department visits categorized broadly into a fever and respiratory syndrome (which provides an estimate of the total visits attributable to influenza) or more narrowly into an influenza-like illness syndrome (which specifically indicates fever with cough and/or sore throat) with laboratory-confirmed influenza surveillance data. They found that emergency department visits were highest during peak influenza periods, and that the affect on different age groups varied depending on the predominant circulating viruses. In early 2002, an epidemic reemergence of B/Victoria-lineage influenza viruses caused increased visits among school-aged children, while adult visits did not increase. By contrast, during the 2003–2004 season, when the predominant virus was an A/H3N2 Fujian-lineage influenza virus, excess visits occurred in all age groups, though the relative increase was greatest and earliest among school-aged children. During periods of documented respiratory syncytial virus (RSV) circulation, increases in fever and respiratory emergency department visits occurred in children under five years of age regardless of influenza circulation. Finally, the researchers found that excess visits to emergency departments for fever and respiratory symptoms preceded deaths from pneumonia or influenza by about two weeks.
What Do These Findings Mean?
These findings indicate that excess emergency department visits for fever and respiratory symptoms can provide a reliable and timely surrogate measure of illness due to influenza. They also provide new insights into how different influenza viruses affect people of different ages and how the timing and progression of each influenza season differs. These results, based on data collected over only five years in one city, might not be generalizable to other settings or years, warn the researchers. However, the present results strongly suggest that the routine monitoring of influenza might be improved by using electronic health-related data, such as emergency department visit data, and by examining it specifically by age group. Furthermore, by showing that school-aged children can be the first people to be affected by seasonal influenza, these results highlight the important role this age group plays in community-wide transmission of influenza, an observation that could influence the implementation of public-health strategies such as vaccination that aim to protect communities during influenza epidemics and pandemics.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0040247.
• US Centers for Disease Control and Prevention provides information on influenza for patients and health professionals and on influenza surveillance in the US (in English, Spanish, and several other languages)
• World Health Organization has a fact sheet on influenza and on global surveillance for influenza (in English, Spanish, French, Russian, Arabic, and Chinese)
• The MedlinePlus encyclopedia contains a page on flu (in English and Spanish)
• US National Institute of Allergy and Infectious Diseases has a feature called “focus on flu”
• A detailed report from the US Centers for Disease Control and Prevention titled “Framework for Evaluating Public Health Surveillance Systems for Early Detection of Outbreaks” includes a simple description of syndromic surveillance
• The International Society for Disease Surveillance has a collaborative syndromic surveillance public wiki
• The Anthropology of the Contemporary Research Collaboratory includes working papers and discussions by cultural anthropologists studying modern vital systems security and syndromic surveillance
doi:10.1371/journal.pmed.0040247
PMCID: PMC1939858  PMID: 17683196
6.  Use of Control Bar Matrix for Outbreak Detection in Syndromic Surveillance System 
Objective
To develop and test the method of incorporating different control bars for outbreak detection in syndromic surveillance system.
Introduction
Aberration detection methods are essential for analyzing and interpreting large quantity of nonspecific real-time data collected in syndromic surveillance system. However, the challenge lies in distinguishing true outbreak signals from a large amount of false alarm (1). The joint use of surveillance algorithms might be helpful to guide the decision making towards uncertain warning signals.
Methods
A syndromic surveillance project (ISSC) has been implemented in rural Jiangxi Province of China since August 2011. Doctors in the healthcare surveillance units of ISSC used an internet-based electronic system to collect information of daily outpatients, which included 10 infectious related symptoms. From ISSC database, we extracted data of fever patients reported from one township hospital in GZ town between August 1st and December 31st, 2011 to conduct an exploratory study. Six different control bar algorithms, which included Shewart, Moving Average (MA), Exponentially Weighted Moving Average (EWMA) and EARS’ C1, C2, C3, were prospectively run among historical time series of daily fever count to simulate a real-time outbreak detection. Each control bar used 7 days’ moving baseline with a lag of 2 days [the baseline for predicting Day(t) starts from Day(t-9) to Day(t-3), C1 method used a lag of zero day]. We set the threshold of μ+2σ for Shewart and MA, and 2.1 for EWMA C1, C2 and C3. An alarm was triggered when the observed data exceeded threshold, and the detailed information of each patient were checked for signal verification. Microsoft Excel 2007 was used to calculate the simulation results.
Results
During the 5 months, GZ township hospital reported 514 outpatients with fever symptom, with an average of 3.4 per day. All control bars were simultaneously operated among daily counts of fever cases. Of the 153 days on surveillance, 29 triggered alarms by at least one of the control bars. Nine days triggered alarms from >= 3 control bars while on one day (12/30) all 6 algorithms raised alarms. Figure 1 shows the date, fever count, algorithm and warning level (color) of each alarm, which we called a control bar matrix. It can be seen that C3 and EWMA present a higher sensitiveness towards tiny data change whereas C1, C2 and MA focus on large increase of data. C3 also had a memory effect on recent alarms. No infectious disease epidemic or outbreak event was confirmed within the signals. Most fever patients on the nine high-warning days (red and purple) were diagnosed as upper level respiratory infection. However, we discovered that the sharp increase of fever cases on 12/30 was attributed to 5 duplicate records mistakenly input by the staff in GZ hospital.
Conclusions
By combining control bars with different characteristics, the matrix has potential ability to improve the specificity of detection while maintaining a certain degree of sensitivity. With alarms categorized into hierarchical warning levels, public health staffs can decide which alarm to investigate according to the required sensitivity of surveillance system and their own capacity of signal verification. Though we did not find any outbreak event in the study, the possibility of localized influenza epidemic on high-warning days cannot be wiped out, and the matrix’s ability to detect abnormal data change was apparent. The proper combination, baseline and threshold of control bars will be further explored in the real-time surveillance situation of ISSC.
PMCID: PMC3692756
Syndromic surveillance; matrix; control bar; signal
7.  Using Medications Sales from Retail Pharmacies for Syndromic Surveillance in Rural China 
Objective
To use an unconventional data - pharmaceutical sales surveillance for the early detection of respiratory and gastrointestinal epidemics in rural China.
Introduction
Drug sales data as an early indicator in syndromic surveillance has attracted particular interest in recent years (1, 2), however previous studies were mostly conducted in developed countries or areas. In China, many people (around 60%) choose self-medication as their first option when they encounter a health problem (3), and electronic sales information system is gradually used by retail pharmacies, which makes drug sales data become a promising data source for syndromic surveillance in China.
Methods
This experimental study was conducted in four rural counties in central China. From Apr. 1st 2012, there are 56 retail pharmacies joined the study, including 21 county pharmacies and 35 township pharmacies. 123 drugs were selected under surveillance based on the analysis of local historical sales volume and consultation with local pharmacists, including 19 antibiotics, 15 antidiarrheal medications, 9 antipyretics, 41 compound cold medicine, and 39 cough suppressants. Daily sales volume of the selected drugs was recorded into the database by pharmacy staff at each participating unit via electronic file importing or manual entering. Figure 1 showed the user interface for data viewing, query and export. Field training and supervision were regularly conducted to ensure the data quality.
Results
From Apr. 1st to Jun. 30th 2012, there were 103814 sales records reported in the system, including 44464 (42.83%) records from county pharmacies and 59350 (57.17%) from township pharmacies. Among all surveillance drugs, the sales of compound cold medicine accounted for the largest proportion (43.42%), followed by antibiotics (22.52 %), cough suppressants (18.50%), antidiarrheal drugs (9.49%) and antipyretics (6.06 %). More than 80% data were reported into the system within 24 hours after the sales date, and the reporting timeliness of county pharmacies improved with time (table 1). Missing report rate was less than 5% for all surveillance units. Several reporting mistakes were found during the first three-month implementation, which might be due to system bugs, data provider unfamiliar with the system especially when manual reporting, data providers’ carelessness, and some pharmacies reluctant to share sales data amongst others.
Conclusions
Although the current reporting timeliness and completeness are satisfying, it is noteworthy the quality of data is not stable during the beginning phase of the implementation. Further validation of the data will be required. To ensure the accuracy of data and the effective and sustainable deployment of the system, it is imperative to establish a data sharing policy between pharmacies and public health agencies, and achieve automated data collection to avoid additional human labor involvement.
PMCID: PMC3692793
Syndromic surveillance; Medication sales; Developing settings
8.  Incorporation of School Absenteeism Data into the Maryland Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) 
Objective
The state of Maryland has incorporated 100% of its public school systems into a statewide disease surveillance system. This session will discuss the process, challenges, and best practices for expanding the ESSENCE system to include school absenteeism data as part of disease surveillance. It will also discuss the plans that Maryland has for using this new data source, as well as the potential for further expansion.
Introduction
Syndromic surveillance offers the potential for earlier detection of bioterrorism, outbreaks, and other public health emergencies than traditional disease surveillance. The Maryland Department of Health and Mental Hygiene (DHMH) Office of Preparedness and Response (OP&R) conducts syndromic surveillance using the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE). Since its inception, ESSENCE has been a vital tool for DHMH, providing continuous situational awareness for public health policy decision makers. It has been established in the public health community that syndromic surveillance data, including school absenteeism data, has efficacy in monitoring disease, and specifically, influenza activity. Schools have the potential to play a major role in the spread of disease during an epidemic. Therefore, having school absenteeism data in ESSENCE would provide the opportunity to monitor schools throughout the school year and take appropriate actions to mitigate infections and the spread of disease.
Methods
DHMH partnered with the Maryland State Department of Education (MSDE), local health departments, and local school systems to incorporate school absenteeism data into the syndromic surveillance program. There are 24 local public school systems and 24 local health departments in the state of Maryland. OP&R contacted each local school superintendent and each local health officer to arrange a joint meeting to discuss the expansion of the ESSENCE program to include school absenteeism data. Once the meetings were arranged, OP&R epidemiologists traveled to each local jurisdiction and presented their plan for the ESSENCE expansion. At each meeting were representatives from the local health department, as well as school health, school attendance, and school IT staff. This allowed all questions and concerns to be addressed from both sides. In addition to the targeted meetings and presentations, the Secretary of Health issued an executive order which required all local school systems to sign a memorandum of understanding (MOU) with DHMH. This MOU detailed the data elements to be shared with the ESSENCE program and the process by which this would be shared. While this order made data contribution mandatory, the site visits by the OP&R staff created a working relationship and partnership with the local jurisdictions. Data was collected from all public schools in the state including elementary, middle, and high schools.
Results
As of June 30, 2012, Maryland became the first state in the United States to incorporate 100% of its public school systems (1,424 schools) into ESSENCE. Each school system reports absenteeism data daily via an automated secure FTP (sFTP) transfer to DHMH. Due to its unique properties, Johns Hopkins Applied Physics Laboratory (JHUAPL) designed a new detection algorithm in ESSENCE specifically for this data source. OP&R epidemiologist review and analyze this data for disease surveillance purposes in conjunction with other data sources in ESSENCE (emergency department chief complaints, poison control center data, thermometer sales data, and over-the-counter medication sales data). Integrating school absenteeism data will provide a more complete analysis of potential public health threats. The process by which Maryland incorporated their public school systems’ data could potentially be used as a best practice for other jurisdictions. Not only was DHMH able to obtain data from all public schools in the state, but the process also enhanced collaboration between local health departments and public school systems.
PMCID: PMC3692827
ESSENCE; Surveillance; Absenteeism
9.  An Epidemiological Network Model for Disease Outbreak Detection 
PLoS Medicine  2007;4(6):e210.
Background
Advanced disease-surveillance systems have been deployed worldwide to provide early detection of infectious disease outbreaks and bioterrorist attacks. New methods that improve the overall detection capabilities of these systems can have a broad practical impact. Furthermore, most current generation surveillance systems are vulnerable to dramatic and unpredictable shifts in the health-care data that they monitor. These shifts can occur during major public events, such as the Olympics, as a result of population surges and public closures. Shifts can also occur during epidemics and pandemics as a result of quarantines, the worried-well flooding emergency departments or, conversely, the public staying away from hospitals for fear of nosocomial infection. Most surveillance systems are not robust to such shifts in health-care utilization, either because they do not adjust baselines and alert-thresholds to new utilization levels, or because the utilization shifts themselves may trigger an alarm. As a result, public-health crises and major public events threaten to undermine health-surveillance systems at the very times they are needed most.
Methods and Findings
To address this challenge, we introduce a class of epidemiological network models that monitor the relationships among different health-care data streams instead of monitoring the data streams themselves. By extracting the extra information present in the relationships between the data streams, these models have the potential to improve the detection capabilities of a system. Furthermore, the models' relational nature has the potential to increase a system's robustness to unpredictable baseline shifts. We implemented these models and evaluated their effectiveness using historical emergency department data from five hospitals in a single metropolitan area, recorded over a period of 4.5 y by the Automated Epidemiological Geotemporal Integrated Surveillance real-time public health–surveillance system, developed by the Children's Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology on behalf of the Massachusetts Department of Public Health. We performed experiments with semi-synthetic outbreaks of different magnitudes and simulated baseline shifts of different types and magnitudes. The results show that the network models provide better detection of localized outbreaks, and greater robustness to unpredictable shifts than a reference time-series modeling approach.
Conclusions
The integrated network models of epidemiological data streams and their interrelationships have the potential to improve current surveillance efforts, providing better localized outbreak detection under normal circumstances, as well as more robust performance in the face of shifts in health-care utilization during epidemics and major public events.
Most surveillance systems are not robust to shifts in health care utilization. Ben Reis and colleagues developed network models that detected localized outbreaks better and were more robust to unpredictable shifts.
Editors' Summary
Background.
The main task of public-health officials is to promote health in communities around the world. To do this, they need to monitor human health continually, so that any outbreaks (epidemics) of infectious diseases (particularly global epidemics or pandemics) or any bioterrorist attacks can be detected and dealt with quickly. In recent years, advanced disease-surveillance systems have been introduced that analyze data on hospital visits, purchases of drugs, and the use of laboratory tests to look for tell-tale signs of disease outbreaks. These surveillance systems work by comparing current data on the use of health-care resources with historical data or by identifying sudden increases in the use of these resources. So, for example, more doctors asking for tests for salmonella than in the past might presage an outbreak of food poisoning, and a sudden rise in people buying over-the-counter flu remedies might indicate the start of an influenza pandemic.
Why Was This Study Done?
Existing disease-surveillance systems don't always detect disease outbreaks, particularly in situations where there are shifts in the baseline patterns of health-care use. For example, during an epidemic, people might stay away from hospitals because of the fear of becoming infected, whereas after a suspected bioterrorist attack with an infectious agent, hospitals might be flooded with “worried well” (healthy people who think they have been exposed to the agent). Baseline shifts like these might prevent the detection of increased illness caused by the epidemic or the bioterrorist attack. Localized population surges associated with major public events (for example, the Olympics) are also likely to reduce the ability of existing surveillance systems to detect infectious disease outbreaks. In this study, the researchers developed a new class of surveillance systems called “epidemiological network models.” These systems aim to improve the detection of disease outbreaks by monitoring fluctuations in the relationships between information detailing the use of various health-care resources over time (data streams).
What Did the Researchers Do and Find?
The researchers used data collected over a 3-y period from five Boston hospitals on visits for respiratory (breathing) problems and for gastrointestinal (stomach and gut) problems, and on total visits (15 data streams in total), to construct a network model that included all the possible pair-wise comparisons between the data streams. They tested this model by comparing its ability to detect simulated disease outbreaks implanted into data collected over an additional year with that of a reference model based on individual data streams. The network approach, they report, was better at detecting localized outbreaks of respiratory and gastrointestinal disease than the reference approach. To investigate how well the network model dealt with baseline shifts in the use of health-care resources, the researchers then added in a large population surge. The detection performance of the reference model decreased in this test, but the performance of the complete network model and of models that included relationships between only some of the data streams remained stable. Finally, the researchers tested what would happen in a situation where there were large numbers of “worried well.” Again, the network models detected disease outbreaks consistently better than the reference model.
What Do These Findings Mean?
These findings suggest that epidemiological network systems that monitor the relationships between health-care resource-utilization data streams might detect disease outbreaks better than current systems under normal conditions and might be less affected by unpredictable shifts in the baseline data. However, because the tests of the new class of surveillance system reported here used simulated infectious disease outbreaks and baseline shifts, the network models may behave differently in real-life situations or if built using data from other hospitals. Nevertheless, these findings strongly suggest that public-health officials, provided they have sufficient computer power at their disposal, might improve their ability to detect disease outbreaks by using epidemiological network systems alongside their current disease-surveillance systems.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0040210.
Wikipedia pages on public health (note that Wikipedia is a free online encyclopedia that anyone can edit, and is available in several languages)
A brief description from the World Health Organization of public-health surveillance (in English, French, Spanish, Russian, Arabic, and Chinese)
A detailed report from the US Centers for Disease Control and Prevention called “Framework for Evaluating Public Health Surveillance Systems for the Early Detection of Outbreaks”
The International Society for Disease Surveillance Web site
doi:10.1371/journal.pmed.0040210
PMCID: PMC1896205  PMID: 17593895
10.  Characterization of Regional Influenza Seasonality Patterns in China and Implications for Vaccination Strategies: Spatio-Temporal Modeling of Surveillance Data 
PLoS Medicine  2013;10(11):e1001552.
Cécile Viboud and colleagues describe epidemiological patterns of influenza incidence across China to support the design of a national vaccination program.
Please see later in the article for the Editors' Summary
Background
The complexity of influenza seasonal patterns in the inter-tropical zone impedes the establishment of effective routine immunization programs. China is a climatologically and economically diverse country, which has yet to establish a national influenza vaccination program. Here we characterize the diversity of influenza seasonality in China and make recommendations to guide future vaccination programs.
Methods and Findings
We compiled weekly reports of laboratory-confirmed influenza A and B infections from sentinel hospitals in cities representing 30 Chinese provinces, 2005–2011, and data on population demographics, mobility patterns, socio-economic, and climate factors. We applied linear regression models with harmonic terms to estimate influenza seasonal characteristics, including the amplitude of annual and semi-annual periodicities, their ratio, and peak timing. Hierarchical Bayesian modeling and hierarchical clustering were used to identify predictors of influenza seasonal characteristics and define epidemiologically-relevant regions. The annual periodicity of influenza A epidemics increased with latitude (mean amplitude of annual cycle standardized by mean incidence, 140% [95% CI 128%–151%] in the north versus 37% [95% CI 27%–47%] in the south, p<0.0001). Epidemics peaked in January–February in Northern China (latitude ≥33°N) and April–June in southernmost regions (latitude <27°N). Provinces at intermediate latitudes experienced dominant semi-annual influenza A periodicity with peaks in January–February and June–August (periodicity ratio >0.6 in provinces located within 27.4°N–31.3°N, slope of latitudinal gradient with latitude −0.016 [95% CI −0.025 to −0.008], p<0.001). In contrast, influenza B activity predominated in colder months throughout most of China. Climate factors were the strongest predictors of influenza seasonality, including minimum temperature, hours of sunshine, and maximum rainfall. Our main study limitations include a short surveillance period and sparse influenza sampling in some of the southern provinces.
Conclusions
Regional-specific influenza vaccination strategies would be optimal in China; in particular, annual campaigns should be initiated 4–6 months apart in Northern and Southern China. Influenza surveillance should be strengthened in mid-latitude provinces, given the complexity of seasonal patterns in this region. More broadly, our findings are consistent with the role of climatic factors on influenza transmission dynamics.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Every year, millions of people worldwide catch influenza, a viral disease of the airways. Most infected individuals recover quickly but seasonal influenza outbreaks (epidemics) kill about half a million people annually. These epidemics occur because antigenic drift—frequent small changes in the viral proteins to which the immune system responds—means that an immune response produced one year provides only partial protection against influenza the next year. Annual vaccination with a mixture of killed influenza viruses of the major circulating strains boosts this natural immunity and greatly reduces the risk of catching influenza. Consequently, many countries run seasonal influenza vaccination programs. Because the immune response induced by vaccination decays within 4–8 months of vaccination and because of antigenic drift, it is important that these programs are initiated only a few weeks before the onset of local influenza activity. Thus, vaccination starts in early autumn in temperate zones (regions of the world that have a mild climate, part way between a tropical and a polar climate), because seasonal influenza outbreaks occur in the winter months when low humidity and low temperatures favor the transmission of the influenza virus.
Why Was This Study Done?
Unlike temperate regions, seasonal influenza patterns are very diverse in tropical countries, which lie between latitudes 23.5°N and 23.5°S, and in the subtropical countries slightly north and south of these latitudes. In some of these countries, there is year-round influenza activity, in others influenza epidemics occur annually or semi-annually (twice yearly). This complexity, which is perhaps driven by rainfall fluctuations, complicates the establishment of effective routine immunization programs in tropical and subtropical countries. Take China as an example. Before a national influenza vaccination program can be established in this large, climatologically diverse country, public-health experts need a clear picture of influenza seasonality across the country. Here, the researchers use spatio-temporal modeling of influenza surveillance data to characterize the seasonality of influenza A and B (the two types of influenza that usually cause epidemics) in China, to assess the role of putative drivers of seasonality, and to identify broad epidemiological regions (areas with specific patterns of disease) that could be used as a basis to optimize the timing of future Chinese vaccination programs.
What Did the Researchers Do and Find?
The researchers collected together the weekly reports of laboratory-confirmed influenza prepared by the Chinese national sentinel hospital-based surveillance network between 2005 and 2011, data on population size and density, mobility patterns, and socio-economic factors, and daily meteorological data for the cities participating in the surveillance network. They then used various statistical modeling approaches to estimate influenza seasonal characteristics, to assess predictors of influenza seasonal characteristics, and to identify epidemiologically relevant regions. These analyses indicate that, over the study period, northern provinces (latitudes greater than 33°N) experienced winter epidemics of influenza A in January–February, southern provinces (latitudes less than 27°N) experienced peak viral activity in the spring (April–June), and provinces at intermediate latitudes experienced semi-annual epidemic cycles with infection peaks in January–February and June–August. By contrast, influenza B activity predominated in the colder months throughout China. The researchers also report that minimum temperatures, hours of sunshine, and maximum rainfall were the strongest predictors of influenza seasonality.
What Do These Findings Mean?
These findings show that influenza seasonality in China varies between regions and between influenza virus types and suggest that, as in other settings, some of these variations might be associated with specific climatic factors. The accuracy of these findings is limited by the short surveillance period, by sparse surveillance data from some southern and mid-latitude provinces, and by some aspects of the modeling approach used in the study. Further surveillance studies need to be undertaken to confirm influenza seasonality patterns in China. Overall, these findings suggest that, to optimize routine influenza vaccination in China, it will be necessary to stagger the timing of vaccination over three broad geographical regions. More generally, given that there is growing interest in rolling out national influenza immunization programs in low- and middle-income countries, these findings highlight the importance of ensuring that vaccination strategies are optimized by taking into account local disease patterns.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/ 10.1371/journal.pmed.1001552.
This study is further discussed in a PLOS Medicine Perspective by Steven Riley
The UK National Health Service Choices website provides information for patients about seasonal influenza and about influenza vaccination
The World Health Organization provides information on seasonal influenza (in several languages) and on influenza surveillance and monitoring
The US Centers for Disease Control and Prevention also provides information for patients and health professionals on all aspects of seasonal influenza, including information about vaccination; its website contains a short video about personal experiences of influenza.
Flu.gov, a US government website, provides access to information on seasonal influenza and vaccination
Information about the Chinese National Influenza Center, which is part of the Chinese Center for Disease Control and Prevention: and which runs influenza surveillance in China, is available (in English and Chinese)
MedlinePlus has links to further information about influenza and about vaccination (in English and Spanish)
A recent PLOS Pathogens Research Article by James D. Tamerius et al. investigates environmental predictors of seasonal influenza epidemics across temperate and tropical climates
A study published in PLOS ONE by Wyller Alencar de Mello et al. indicates that Brazil, like China, requires staggered timing of vaccination from Northern to Southern states to account for different timings of influenza activity.
doi:10.1371/journal.pmed.1001552
PMCID: PMC3864611  PMID: 24348203
11.  Applying Zero-inflated Mixed Model to School Absenteeism Surveillance in Rural China 
Objective
To describe and explore the spatial and temporal variability via ZIMM for absenteeism surveillance in primary school for early detection of infectious disease outbreak in rural China.
Introduction
Absenteeism has great advantages in promoting the early detection of epidemics1. Since August 2011, an integrated syndromic surveillance project (ISSC) has been implemented in China2. Distribution of the absenteeism generally are asymmetry, zero inflation, truncation and non-independence3. For handling these encumbrances, we should apply the Zero-inflated Mixed Model (ZIMM).
Methods
Data for this study was obtained from the web-based data of ISSC in 62 primary schools in two counties of Jiangxi province, China from April 1th, 2012 to June 30st, 2012. The ZIMM was used to explore: 1)the temporal and spatial variability regarding occurrence and intensity of absenteeism simultaneously, and 2) the heterogeneity among the reporting primary schools by introducing random effects into the intercepts. The analyse was processed in the SAS procedure NLMIXED4.
Results
The total 4914 absenteeism events were reported in the 62 primary schools in the study period. The rate of zero report was 49.88% (Fig. 1). According to ZIMM, there are fixed and random effect parameters in this model (Table 1). Firstly, for the fixed parameters, the spatial variable (county) was not significantly different both the occurrence and intensity model, while for the temporal variable (month), the probability of absenteeism occurrence was significantly different over three months (β=−0.165, p =0.026), suggesting a decreasing of school absenteeism from April to June. Meanwhile, a statistical significant difference in the intensity of absenteeism was also found over the three months (β=−0.073, p=0.007). Secondly, the random effect of intensity model was statistically significance (p=0.008), which strongly indicated a heterogeneity in intensity of absenteeism among the surveillance schools. Whereas the random effect of occurrence model by logistic regression showed a non-statistical difference (p=0.774) among the schools suggesting the homogeneity in the occurrence of absenteeism.
Conclusions
School absenteeism data has greater uncertain than many other sources and easier fluctuate by some factors such as holiday, season, family status and geographic distribution. Thus, the spatial and temporal dynamics should be taken into account in controlling fluctuate of absenteeism. Moreover, school absenteeism data are correlated within each school due to repeated measures. Applying the ZIMM, the occurrences and intensity of absenteeism could be evaluated to reduce the bias and improve the prediction precision. The ZIMM is an appropriate tool for health authorities in decision making for public health events.
PMCID: PMC3692942
surveillance; absenteeism; zero-inflated mixed model; occurrence; intensity
12.  Syndromic Surveillance from a Local Perspective – A Review of the Literature 
Objective
Review of the origins and evolution of the field of syndromic surveillance. Compare the goals and objectives of public health surveillance and syndromic surveillance in particular. Assess the science and practice of syndromic surveillance in the context of public health and national security priorities. Evaluate syndromic surveillance in practice, using case studies from the perspective of a local public health department.
Introduction
Public health disease surveillance is defined as the ongoing systematic collection, analysis and interpretation of health data for use in the planning, implementation and evaluation of public health, with the overarching goal of providing information to government and the public to improve public health actions and guidance [1,2]. Since the 1950s, the goals and objectives of disease surveillance have remained consistent [1]. However, the systems and processes have changed dramatically due to advances in information and communication technology, and the availability of electronic health data [2,3]. At the intersection of public health, national security and health information technology emerged the practice of syndromic surveillance [3].
Methods
To better understand the current state of the field, a review of the literature on syndromic surveillance was conducted: topics and keywords searched through PubMed and Google Scholar included biosurveillance, bioterrorism detection, computerized surveillance, electronic disease surveillance, situational awareness and syndromic surveillance, covering the areas of practice, research, preparedness and policy. This literature was compared with literature on traditional epidemiologic and public health surveillance. Definitions, objectives, methods and evaluation findings presented in the literature were assessed with a focus on their relevance from a local perspective, particularly as related to syndromic surveillance systems and methods used by the New York City Department of Health and Mental Hygiene in the areas of development, implementation, evaluation, public health practice and epidemiological research.
Results
A decade ago, the objective of syndromic surveillance was focused on outbreak and bioterrorism early-event detection (EED). While there have been clear recommendations for evaluation of syndromic surveillance systems and methods, the original detection paradigm for syndromic surveillance has not been adequately evaluated in practice, nor tested by real world events (ie, the systems have largely not ‘detected’ events of public health concern). In the absence of rigorous evaluation, the rationale and objectives for syndromic surveillance have broadened from outbreak and bioterrorism EED, to include all causes and hazards, and to encompass all data and analyses needed to achieve “situational awareness”, not simply detection. To evaluate current practices and provide meaningful guidance for local syndromic surveillance efforts, it is important to understand the emergence of the field in the broader context of public health disease surveillance. And it is important to recognize how the original stated objectives of EED have shifted in relation to actual evaluation, recommendation, standardization and implementation of syndromic systems at the local level.
Conclusions
Since 2001, the field of syndromic surveillance has rapidly expanded, following the dual requirements of national security and public health practice. The original objective of early outbreak or bioterrorism event detection remains a core objective of syndromic surveillance, and systems need to be rigorously evaluated through comparison of consistent methods and metrics, and public health outcomes. The broadened mandate for all-cause situation awareness needs to be focused into measureable public health surveillance outcomes and objectives that are consistent with established public health surveillance objectives and relevant to the local practice of public health [2].
PMCID: PMC3692931
evaluation; biosurveillance; situational awareness; syndromic surveillance; local public health
13.  Syndromic Surveillance Based on Emergency Visits: A Reactive Tool for Unusual Events Detection 
Objective
To show with examples that syndromic surveillance system can be a reactive tool for public health surveillance.
Introduction
The late health events such as the heat wave of 2003 showed the need to make public health surveillance evolve in France. Thus, the French Institute for Public Health Surveillance has developed syndromic surveillance systems based on several information sources such as emergency departments (1). In Reunion Island, the chikungunya outbreak of 2005–2006, then the influenza pandemic of 2009 contributed to the implementation and the development of this surveillance system (2–3). In the past years, this tool allowed to follow and measure the impact of seasonal epidemics. Nevertheless, its usefulness for the detection of minor unusual events had yet to be demonstrated.
Methods
In Reunion Island, the syndromic surveillance system is based on the activity of six emergency departments. Two types of indicators are constructed from collected data: - Qualitative indicators for the alert (every visit whose diagnostic relates to a notifiable disease or potential epidemic disease);- Quantitative indicators for the epidemic/cluster detection (number of visits based on syndromic grouping).
Daily and weekly analyses are carried out. A decision algorithm allows to validate the signal and to organize an epidemiological investigation if necessary.
Results
Each year, about 150 000 visits are registered in the six emergency departments that is 415 consultations per day on average. Several unusual health events on small-scale were detected early.
In August 2011, the surveillance system allowed to detect the first autochthonous cases of measles, a few days before this notifiable disease was reported to health authorities (Figure 1). In January 2012, the data of emergency departments allowed to validate the signal of viral meningitis as well as to detect a cluster in the West of the island and to follow its trend. In June 2012, a family foodborne illness was detected from a spatio-temporal cluster for abdominal pain by the surveillance system and was confirmed by epidemiological investigation (Figure 2).
Conclusions
Despite the improvement of exchanges with health practitioners and the development of specific surveillance systems, health surveillance remains fragile for the detection of clusters or unusual health events on small scale. The syndromic surveillance system based on emergency visits has proved to be relevant for the identification of signals leading to health alerts and requiring immediate control measures. In the future, it will be necessary to develop these systems (private practitioners, sentinel schools) in order to have several indicators depending on the degree of severity.
PMCID: PMC3692799
Syndromic surveillance; Unusual event detection; Reunion Island
14.  Adaptation of GUARDIAN for Syndromic Surveillance During the NATO Summit 
Objective
To develop and implement a framework for special event surveillance using GUARDIAN, as well as document lessons learned post-event regarding design challenges and usability.
Introduction
Special event driven syndromic surveillance is often initiated by public health departments with limited time for development of an automated surveillance framework, which can result in heavy reliance on frontline care providers and potentially miss early signs of emerging trends. To address timelines and reliability issues, automated surveillance system are required.
Methods
The North Atlantic Treaty Organization (NATO) summit was held in Chicago, IL, May 19–21, 2012. During the NATO summit, the Chicago Department of Public Health (CDPH) was charged with collecting and analyzing syndromic surveillance data from emergency department (ED) visits that may indicate a man-made or naturally occurring infectious disease threat.
Ten days prior to the NATO summit surveillance period, Rush University Medical Center (RUMC) received a guidance document from CDPH outlining the syndromes for systematic surveillance, specifically febrile rash illness, localized cutaneous lesion, acute febrile respiratory illness, gastrointestinal illness, botulism-like illness, hemorrhagic illness, along with unexplained deaths or severe illness potentially due to infectious disease and cases due to toxins or suspected poisoning. RUMC researchers collected relevant ICD-9 codes for each syndrome category.
GUARDIAN (1), an automated surveillance system, was programmed to scan patient charts and match free text using National Library of Medicine free-text term to unique medical concept, which were further mapped to relevant ICD-9 codes. The baselines were developed using ED patient data from 1/1/2010 to 12/31/2011. Statistical references were established for unsmoothed, 24 hour counts (Baseline = Average; Threshold = +2 standard deviations).
During the NATO surveillance timeframe (May 13–26, 2012) automated results with prior reporting period’s counts, reference statistics, and charts were electronically sent to CDPH. In addition, ED charge nurses made manual surveillance reports by telephone at least daily. Open lines of communication were maintained between RUMC and CDPH during the event to discuss potential positive cases. In addition, a post-event debriefing was conducted to document lessons learned.
Results
The automated GUARDIAN surveillance reports not only provided timely counts of potentially positive cases for each syndrome but also provided trend analysis with baseline measures. The GUARDIAN User Interface was used to explain what data points could trigger positive cases. The Epic system was used to review patient charts, if further explanation was necessary. The observed counts never exceeded +2 standard deviations during the NATO surveillance period for any of the syndromes.
Based on the debriefing meeting between RUMC and CDPH, the top three achievements and lessons learned were as follows: Quick turnaround time (∼ 10 days) from surveillance concept development to automated implementation using GUARDIANSurveillance data was timely and reliableAdditional statistical information was beneficial to put trends in contextSystem may be too sensitive resulting in false alarms and additional investigative burden on public health departmentsNeed for development of user-interfaces with drill down capabilities to patient level dataClinicians don’t necessarily utilize exact terminology used in ICD-9 codes which could result in undetected cases.
Conclusions
This exercise successfully highlights rapid development and implementation of special event driven automated surveillance as well as collaborative approach between front-line entities such as emergency departments, surveillance researchers, and the department of public health. In addition, valuable lessons learned with potential solutions are documented for further refinements of such surveillance activities.
PMCID: PMC3692845
Emergency department; NATO Summit; automated surveillance
15.  Utility of System Generated Syndromic Surveillance Alerts to Detect Reportable Disease Outbreaks 
Objective
In light of recent outbreaks of pertussis, the ability of Florida Department of Health’s (FDOH) Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE-FL) to detect emergent disease outbreaks was examined. Through a partnership with the Johns Hopkins University Applied Physics Laboratory (JHU/APL), FDOH developed a syndromic surveillance system, ESSENCE-FL, with the capacity to monitor reportable disease case data from Merlin, the FDOH Bureau of Epidemiology’s secure webbased reporting and epidemiologic analysis system for reportable diseases. The purpose of this evaluation is to determine the utility and application of ESSENCE-FL system generated disease warnings and alerts originally designed for use with emergency department chief complaint data to reportable disease data to assist in timely detection of outbreaks in promotion of appropriate response and control measures.
Introduction
Reportable disease case data are entered into Merlin by all 67 county health departments in Florida and assigned confirmed, probable, or suspect case status. De-identified reportable disease data from Merlin are sent to ESSENCE-FL once an hour for further analysis and visualization using tools in the surveillance system. These data are available for ad hoc queries, allowing users to monitor disease trends, observe unusual changes in disease activity, and to provide timely situational awareness of emerging events. Based on system algorithms, reportable disease case weekly tallies are assigned an awareness status of increasing intensity from normal to an alert category. These statuses are constantly scrutinized by county and state level epidemiologists to guide disease control efforts in a timely manner, but may not signify definitive actionable information.
Methods
Within the ESSENCE-FL query portal, the Merlin Reportable Diseases Data Source was selected with a weekly time resolution by event date. Case Classification included all confirmed, probable and suspect cases, reported and not yet reported, during the time period of week 35, 2011, to week 35, 2012. The ESSENCE Weighted Moving Average (EWMA 1.2) detector was used to classify weekly counts as either of normal, warning or alert status based on previous weeks’ counts, indicating the possibility of an emerging outbreak. These weekly statuses were then compared with outbreaks reported in Merlin’s fully integrated outbreak reporting system and with outbreak reports submitted to EpiCom, Florida’s EpiX or health alert network. An ESSENCE-FL generated warning or alert was considered valid if a corresponding outbreak of 2 or more epi-linked pertussis cases were reported in either Merlin’s outbreak module or in EpiCom. For the sake of brevity in this abstract, the analysis of pertussis is presented, while other reportable disease conditions of immediate interest will be presented at the conference.
Results
Examination of 494 pertussis cases reported from September 2011 to September 2012 showed that of 53 weeks, 38 weeks contained normal case counts, 11 weeks generated warnings, and 4 weeks produced alerts. The number of warnings that corresponded to actual outbreaks was 6 of 11, whereas 2 of the 4 alerts matched reported outbreaks. Of the remaining 38 weeks, 12 had outbreaks reported with no warning or alert generated by ESSENCE-FL. When comparing confirmed outbreak status with ESSENCE-FL weekly data count status, warning/alert versus normal, it was found that the sensitivity of ESSENCE-FL to detect a true outbreak was 40.0% while the specificity was 78.8%. This comparison generated a positive-predictive value of 53.3% and a negative predictive value of 68.4%.
Conclusions
The ability of ESSENCE-FL to act as a first alert system for emerging disease events using Merlin reportable disease data should be considered with constraint. While warnings or alerts about potential pertussis outbreaks were generated correctly about half the time, the nearly one-third of reported outbreaks with no warning or alert makes the utility of the alerts questionable as far as initiating immediate action without prior verification of the alert. Florida does not currently have a requirement for centrally documenting all outbreaks, so it is likely that outbreaks occurred but were not recorded, precluding verification of all outbreaks.
PMCID: PMC3692865
Syndromic; Surveillance; Outbreaks
16.  Scoping Review on Search Queries and Social Media for Disease Surveillance: A Chronology of Innovation 
Background
The threat of a global pandemic posed by outbreaks of influenza H5N1 (1997) and Severe Acute Respiratory Syndrome (SARS, 2002), both diseases of zoonotic origin, provoked interest in improving early warning systems and reinforced the need for combining data from different sources. It led to the use of search query data from search engines such as Google and Yahoo! as an indicator of when and where influenza was occurring. This methodology has subsequently been extended to other diseases and has led to experimentation with new types of social media for disease surveillance.
Objective
The objective of this scoping review was to formally assess the current state of knowledge regarding the use of search queries and social media for disease surveillance in order to inform future work on early detection and more effective mitigation of the effects of foodborne illness.
Methods
Structured scoping review methods were used to identify, characterize, and evaluate all published primary research, expert review, and commentary articles regarding the use of social media in surveillance of infectious diseases from 2002-2011.
Results
Thirty-two primary research articles and 19 reviews and case studies were identified as relevant. Most relevant citations were peer-reviewed journal articles (29/32, 91%) published in 2010-11 (28/32, 88%) and reported use of a Google program for surveillance of influenza. Only four primary research articles investigated social media in the context of foodborne disease or gastroenteritis. Most authors (21/32 articles, 66%) reported that social media-based surveillance had comparable performance when compared to an existing surveillance program. The most commonly reported strengths of social media surveillance programs included their effectiveness (21/32, 66%) and rapid detection of disease (21/32, 66%). The most commonly reported weaknesses were the potential for false positive (16/32, 50%) and false negative (11/32, 34%) results. Most authors (24/32, 75%) recommended that social media programs should primarily be used to support existing surveillance programs.
Conclusions
The use of search queries and social media for disease surveillance are relatively recent phenomena (first reported in 2006). Both the tools themselves and the methodologies for exploiting them are evolving over time. While their accuracy, speed, and cost compare favorably with existing surveillance systems, the primary challenge is to refine the data signal by reducing surrounding noise. Further developments in digital disease surveillance have the potential to improve sensitivity and specificity, passively through advances in machine learning and actively through engagement of users. Adoption, even as supporting systems for existing surveillance, will entail a high level of familiarity with the tools and collaboration across jurisdictions.
doi:10.2196/jmir.2740
PMCID: PMC3785982  PMID: 23896182
disease; surveillance; social media; review
17.  Surveillance of Infection Severity: A Registry Study of Laboratory Diagnosed Clostridium difficile 
PLoS Medicine  2012;9(7):e1001279.
Iryna Schlackow and colleagues investigated whether electronic systems providing early warning of changing severity of infectious conditions can be established using routinely collected laboratory hospital data. They showed that for Clostridium difficile infection, these systems perform better than those monitoring mortality.
Background
Changing clinical impact, as virulent clones replace less virulent ones, is a feature of many pathogenic bacterial species and can be difficult to detect. Consequently, innovative techniques monitoring infection severity are of potential clinical value.
Methods and Findings
We studied 5,551 toxin-positive and 20,098 persistently toxin-negative patients tested for Clostridium difficile infection between February 1998 and July 2009 in a group of hospitals based in Oxford, UK, and investigated 28-day mortality and biomarkers of inflammation (blood neutrophil count, urea, and creatinine concentrations) collected at diagnosis using iterative sequential regression (ISR), a novel joinpoint-based regression technique suitable for serial monitoring of continuous or dichotomous outcomes. Among C. difficile toxin-positive patients in the Oxford hospitals, mean neutrophil counts on diagnosis increased from 2003, peaked in 2006–2007, and then declined; 28-day mortality increased from early 2006, peaked in late 2006–2007, and then declined. Molecular typing confirmed these changes were likely due to the ingress of the globally distributed severe C. difficile strain, ST1. We assessed the generalizability of ISR-based severity monitoring in three ways. First, we assessed and found strong (p<0.0001) associations between isolation of the ST1 severe strain and higher neutrophil counts at diagnosis in two unrelated large multi-centre studies, suggesting the technique described might be useful elsewhere. Second, we assessed and found similar trends in a second group of hospitals in Birmingham, UK, from which 5,399 cases were analysed. Third, we used simulation to assess the performance of this surveillance system given the ingress of future severe strains under a variety of assumptions. ISR-based severity monitoring allowed the detection of the severity change years earlier than mortality monitoring.
Conclusions
Automated electronic systems providing early warning of the changing severity of infectious conditions can be established using routinely collected laboratory hospital data. In the settings studied here these systems have higher performance than those monitoring mortality, at least in C. difficile infection. Such systems could have wider applicability for monitoring infections presenting in hospital.
Editor's Summary
Background
The ability of bacteria to cause infection and disease (that is, their virulence) is in part determined by bacterial genetic makeup. At any time, there is a great deal of genetic diversity within common kinds of bacteria, which naturally generate new variants all the time. Sometimes organisms arise with a genetic makeup that causes more severe infection in humans and even death. For example, in 2005, spread of a particularly virulent strain (called 027/ST1) of the toxin-producing bacterium Clostridium difficile caused a global epidemic of C. difficile infection.
Why Was This Study Done?
In health care settings, general methods of detecting changing virulence to enable the early recognition, control, and optimal management of increasingly severe infections would be highly beneficial. Changing virulence of a bacterial infection is often measured using death rates, but only a small proportion of those with infection die from it, so this may not be the most sensitive method of monitoring. Consequently, in this study the researchers investigated whether the changing virulence of C. difficile infection could be tracked by using common clinical measurements (white blood cell count, neutrophil count, blood urea, and creatinine concentrations, which reflect how unwell patients are) as the basis of an infection-severity surveillance scheme.
What Did the Researchers Do and Find?
The researchers examined all C. difficile toxin tests obtained from the microbiological lab serving hospitals in Oxford, UK, between February 1, 1998 and August 1, 2009. They also identified all inpatients aged over 18 years with a positive C. difficile test when admitted to hospitals over this time, examined their laboratory blood tests, and noted recorded deaths. The researchers used patients with C. difficile toxin-negative samples as a control group. To further validate their analysis, the researchers also undertook a similar analysis in a hospital in another UK city (Birmingham).
The researchers used statistical models to estimate changes in potential biomarkers (neutrophils, creatinine, and urea) with reference to the C. difficile 2005 global epidemic and post-infection death rates. The researchers used another statistical model (an iterative sequential regression technique) to estimate how soon any changes in biomarker/mortality trends would have been detected. Finally, to evaluate these severity-monitoring techniques, the researchers performed two simulation studies in which they assumed a more severe strain of C. difficile was introduced into a hospital.
Using these methods, the researchers found that in patients who were positive for C. difficile toxin, average neutrophil counts on diagnosis increased from 2003, peaked in 2006–2007, and then declined. They also found that 28-day deaths from C. difficile infection increased from early 2006, peaked in late 2006–2007, and then declined. Furthermore, laboratory tests (molecular typing) confirmed these changes were likely due to the severe C. difficile strain. The simulation model derived from the observed data suggested the performance of biomarker-based detection was notably higher than that of monitoring deaths post-infection.
What Do These Findings Mean?
These findings suggest that passively monitoring the severity of infection using routinely measured clinical biomarkers is feasible and can potentially detect important shifts in the virulence of human pathogens, such as C. difficile. Furthermore, such a surveillance system is superior to, and has obvious advantages over, monitoring deaths from infection — provided biomarker data is available. It could be used to provide an early trigger for more detailed investigations of patient characteristics, complemented by studies of bacterial genetic makeup, with the aim of tailoring policy and optimizing treatment of infection. Although this study monitored only one bacterial species, as changes in virulence are common to most human pathogens, this surveillance of severity technique could be applied to other organisms.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001279.
Wikipedia provides information about bacterial virulence (note that Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
The World Health Organization provides the latest news on infection outbreaks and epidemics
The Centers for Disease Control and Prevention provide information on C. difficile for health professionals and patients
Trends in C. difficile disease in England and Wales are reported by the Health Protection Agency
doi:10.1371/journal.pmed.1001279
PMCID: PMC3409138  PMID: 22859914
18.  Syndromic Surveillance for Outbreak Detection and Investigation 
Objective
For the purpose of developing a national system of outbreak surveillance, we compared local outbreak signals in three sources of syndromic data – telephone triage of acute gastroenteritis (Swedish Health Care Direct 1177), web queries about symptoms of gastrointestinal illness (Stockholm County’s website for healthcare information), and OTC pharmacy sales of anti-diarrhea medication.
Introduction
A large part of the applied research on syndromic surveillance targets seasonal epidemics, e.g. influenza, winter vomiting disease, rotavirus and RSV, in particular when dealing with preclinical indicators, e.g. web traffic (Hulth et al, 2009). The research on local outbreak surveillance is more limited. Two studies of teletriage data (NHS Direct) have shown positive and negative results respectively (Cooper et al, 2006; Smith et al, 2008). Studies of OTC pharmacy sales have reported similar equivocal performance (Edge et al, 2004; Kirian and Weintraub, 2010). As far as we know, no systematic comparison of data sources with respect to multiple point-source outbreaks has so far been published (cf. Buckeridge, 2007). In the current study, we evaluated the potential of three data sources for syndromic surveillance by analyzing the correspondence between signal properties and point-source outbreak characteristics.
Methods
The extracted data streams were compared with respect to nine waterborne and foodborne outbreaks in Sweden in 2007–2011. The analysis consisted of three parts: (1) the validation of outbreak signals by comparing signal counts during outbreak and baseline periods, (2) the estimation of detection limits by modeling signal rates (signal-to-case ratios), and (3) the evaluation of early warning potential by means of signal detection analysis.
Results
The four largest outbreaks generated strong and clear outbreak signals in the 1177 triage data. The two largest outbreaks produced signals in OTC sales of anti-diarrhea. No signals could be identified in the web query data. The outbreak detection limit based on triage data was about 100–1000 cases. For two outbreaks, triage data on diarrhea provided outbreak signals early on, weeks and months respectively, potentially serving the purpose of early warning.
Conclusions
The sensitivity and specificity were highest for telephone triage data on patient symptoms. It provided the most promising source of syndromic data for surveillance of point-source outbreaks. Currently, a project has been initialized to develop and implement a national system in Sweden for daily syndromic surveillance based on 1177 Health Care Direct, supporting regional and local outbreak detection and investigation.
PMCID: PMC3692926
syndromic surveillance; outbreak detection; point-source outbreak; outbreak investigation; data analysis
19.  The Surveillance Window – Contextualizing Data Streams 
Objective
The goal of this project is the evaluation of data stream utility in integrated, global disease surveillance. This effort is part of a larger project with the goal of developing tools to provide decision-makers with timely information to predict, prepare for, and mitigate the spread of disease.
Introduction
Los Alamos National Laboratory has been funded by the Defense Threat Reduction Agency to determine the relevance of data streams for an integrated global biosurveillance system. We used a novel method of evaluating the effectiveness of data streams called the “surveillance window”. The concept of the surveillance window is defined as the brief period of time when information gathered can be used to assist decision makers in effectively responding to an impending outbreak. We used a stepwise approach to defining disease specific surveillance windows; Timeline generation through historical perspectives and epidemiological simulations.Identifying the surveillance windows between changes in “epidemiological state” of an outbreak.Data streams that are used or could have been used due to their availability during the generated timeline are identified. If these data streams fall within a surveillance window, and provide both actionable and non-actionable information, they are deemed to have utility.
Methods
Figure 1 shows the overall approach to using this method for evaluating data stream types. Our first step was identifying a list of priority diseases to build surveillance windows for and our primary sources were our SME panel, CDC priorities, as well as DOD priorities. We also conducted a literature review to support our selection of diseases. We ensured that there was representation of human, animal and plant diseases and there was enough data available for selected outbreaks to facilitate evaluation of all data stream types identified. We then selected representative outbreaks for diseases to generate a timeline for defining surveillance windows. Surveillance windows were then defined (based on four specific biosurveillance goals developed by LANL) and information for applicable data streams was collected for the duration of the outbreak. A data stream was deemed useful if it was determined to be available within the defined surveillance window. In addition, evaluation of the ideal use case of the data streams was performed. In essence, if used more effectively could this data stream provide greater support to understanding, detection, warning or management of disease outbreaks or event situations?
Results
Results presented in this abstract are from retrospective analyses of historical outbreaks selected as being representative of FMD, Ebola, Influenza and E.coli. Graphs indicating case counts and geographical spread were combined and a timeline was created to determine the length of time between changes in “epidemiological state” that defined various surveillance windows. This timeline was then populated with durations when data streams were used during the outbreak. Results showed varying surveillance windows times are dependent on disease characteristics. In turn, epidemiology of the disease affected the occurrence of data streams on the timeline.
Conclusions
Surveillance window based evaluation of data streams during disease outbreaks helped identify data streams that are of significance for developing an effective biosurveillance system. Some data streams were identified to have high utility for early detection and early warning regardless of disease, while others were more disease and operations specific. This work also identified data streams currently not in use that could be exploited for faster outbreak detection. Key useful data streams that are underlying to all disease categories and thus important for integration into global biosurveillance programs will be presented here.
PMCID: PMC3692758
Surveilliance Windows; Data streams; Biosurveilliance
20.  An Integrated Syndromic Surveillance System for Monitoring Scarlet Fever in Taiwan 
Objective
To develop an integrated syndromic surveillance system for timely monitoring and early detection of unusual situations of scarlet fever in Taiwan, since Hong Kong, being so close geographically to Taiwan, had an outbreak of scarlet fever in June 2011.
Introduction
Scarlet fever is a bacterial infection caused by group A streptococcus (GAS). The clinical symptoms are usually mild. Before October, 2007, case-based surveillance of scarlet fever was conducted through notifiable infectious diseases in Taiwan, but was removed later from the list of notifiable disease because of improved medical care capacities. In 2011, Hong Kong had encountered an outbreak of scarlet fever (1,2). In response, Taiwan developed an integrated syndromic surveillance system using multiple data sources since July 2011.
Methods
More than 99% of the Taiwan population is covered by National Health Insurance. We first retrospectively evaluated claims data from the Bureau of National Health Insurance (BNHI) by comparing with notifiable diseases reporting data from Taiwan Centers for Disease Control (TCDC). The claims data included information on scarlet fever diagnosis (ICD-9-CM code 034.1), date of visits, location of hospitals and age of patients from outpatient (OPD), emergency room (ER) and hospital admissions. Daily aggregate data of scarlet fever visits or hospitalizations were prospectively collected from BNHI since July 2011. Over 70% of the deaths in Taiwan are reported to the Office of Statistics of Department of Health electronically. We obtained daily data on electronic death certification data and used SAS Enterprise Guide 4.3 (SAS Institute Inc., Cary, NC, USA) for data management and analysis. Deaths associated with scarlet fever or other GAS infections were identified by text mining from causes of death with keywords of traditional Chinese ‘scarlet fever’, ‘group A streptococcus’ or ‘toxic shock syndrome’ (3).
Results
From January 2006 to September 2007, the monthly OPD data with ICD-9-CM code 034.1 from BNHI showed strong correlation with TCDC’s notifiable disease data (r=0.89, p<0.0001). From July 6, 2008 (week 28) through July 28, 2012 (week 30), the average weekly numbers of scarlet fever visits to the OPD, ER and hospital admissions were 37 (range 11–70), 7 (range 0–20) and 3 (range 0–9). Eighty-five percent of the scarlet fever patients were less than 10 years old. In Taiwan, scarlet fever occurred year-round with seasonal peaks between May and July (Fig. 1). From January 2008 to July 2012, we identified 12 potential patients (9 males, age range 0–82 years) who died of GAS infections. No report had listed ‘scarlet fever’ as cause of death during the study period.
Conclusions
Taiwan has established an integrated syndromic surveillance system to timely monitor scarlet fever and GAS infection associated mortalities since July 2011. Syndromic surveillance of scarlet fever through BNHI correlated with number of scarlet fever cases through notifiable disease reporting system. Text mining from cause of death with the used keywords may have low sensitivities to identify patients who died of GAS infection. In Taiwan, syndromic surveillance has also been applied to other diseases such as enterovirus, influenza-like illness, and acute diarrhea. Interagency collaborations add values to existing health data in the government and have strengthened TCDC’s capacity of disease surveillance.
PMCID: PMC3692810
syndromic surveillance; Taiwan; scarlet fever; claims data
21.  Evaluation of Cholera and Other Diarrheal Disease Surveillance System, Niger State, Nigeria-2012 
Objective
To determine how the cholera and other diarrheal disease surveillance system in Niger state is meeting its surveillance objectives, to evaluate its performance and attributes and to describe its operation to make recommendations for improvement.
Introduction
Cholera causes frequent outbreaks in Nigeria, resulting in mortality. In 2010 and 2011, 41,936 cases (case fatality rate [CFR]-4.1%) and 23,366 cases (CFR-3.2%) were reported (1). Reported cases in Nigeria by week 26, 2012 was 309 (CFR-1.29%) involving 20 Local Government Areas in 6 States. In Nigeria, there are currently eleven (11) States including Niger state at high risk for cholera/bloodless diarrhea outbreaks.
In 2011, Niger state had 2472 cholera cases (CFR-2%) and 45,111 other diarrhea diseases cases, recorded in more than half of state Purpose of surveillance system is to ensure early detection of cholera and other diarrheal cases and to monitor trends towards evidence-based decision for management, prevention and control.
Methods
We conducted evaluation in July, 2012. We used CDC guideline on surveillance system evaluation (2001) as guide to assess operation, performance and attributes (2). We conducted key informant/in-depth interviews with stakeholders. We examined cholera action plans for preparedness and response, conducted laboratory assessment, extracted and analyzed cholera surveillance (2005–2012) for frequencies/proportions using Microsoft Excel. Thematic analysis was done for qualitative data. We shared findings with stakeholders at all levels.
Results
Surveillance system was setup for early detection and monitoring towards evidence-based decision. State government funds system. Case definition used is highly sensitive and is any patient aged 5 years or more who develops acute watery diarrhea, with/without vomiting. Though simple case definition, laboratory confirmation makes surveillance complex. A passive system, active during outbreaks; has formal and informal sources of information and part of Integrated Disease Surveillance and Response (IDSR) system and flow(fig.1). It takes 24–48 hours between outbreaks onset, confirmation and response.
Line list showed undefined/poorly labeled outcomes. Of 2472 cases in 2011 1320 (49%) were found in line list. 2011 monthly data completeness was 75%. So far in 2012, 5(0.02%) of all diarrhea cases were cholera. System captures only age as sociodemographics.
Of 11 suspected cholera cases tested during 2011 epidemic, 7 confirmed as cholera (PPV-63%). Of 3 rumours of cholera outbreaks (January 2011-July 2012), one (PPV-33%) was true. Acceptability of system is high among all stakeholders interviewed. Timeliness of monthly reporting was 68.7% (Table 1).
Laboratory can isolate Vibro cholerae isolation but has no Cary Blair transport medium and cholera rapid test kits.
Conclusions
Evaluation revealed that surveillance system is meeting its objectives by early detection and response to cholera outbreaks. System is simple, stable, flexible, sensitive with poor data quality, low PPV, fair laboratory capacity and moderate timeliness. We recommended electronic and internet-based reporting for timeliness and data quality improvement; and provision of laboratory consumables.
PMCID: PMC3692794
Surveillance; Evaluation; Cholera; Nigeria
22.  TRACnet: A National Phone-based and Web-based Tool for the Timely Integrated Disease Surveillance and Response in Rwanda 
Objective
(1) To describe the implementation of the electronic system for integrated disease surveillance in Rwanda. (2) To present the sensitivity and specificity of the electronic reporting system to detect potential outbreaks
Introduction
In Rwanda, communicable diseases are the mostly predominant representing 90% of all reported medical consultations in health centers. The country has often faced epidemics including emerging and re-emerging infectious diseases. To enhance its preparedness to identify and respond to outbreaks and prevent epidemics, the Government of Rwanda has developed and deployed an electronic Integrated Disease Surveillance and Response (eIDSR) working with Voxiva with funding from the U.S. Centers for Disease Control and Prevention(CDC).
Methods
The eIDSR is built on Rwanda’s existing national phone and web-based HIV-reporting system, “TRACnet” that has been operating nationwide since 2004. Data is collected for 23 communicable diseases under surveillance in Rwanda categorized into immediately and weekly reportable. If a lab test is required, the sample is taken and sent to laboratory for testing. Immediate, Weekly, Lab request and lab results forms are completed before submitting data in the system. Data is entered using phone or web based application and is stored in the central database.
Results
The design of eIDSR module was completed in November 2011. As of September 2012, 252 out of 457 health facilities in Rwanda have been trained and are using the electronic system (over 50% of coverage); the national roll out is still going on with complete coverage planned for December 2012. The system sends SMS reminders for due and overdue reports. The timeliness and completeness of reporting are 98% and 100% respectively. Notifications are sent to the concerned personnel when the threshold for outbreak detection is reached. When lab results are available and entered in the system, the results are automatically communicated to the health centers originating samples. Data is automatically summarized in predefined tables, graphs, dashboards and maps.
As of September 3rd, 2012, a total of 5813 reports including 1325 immediate reports and 4488 weekly reports were submitted electronically. Out of 1325 immediate reports submitted, 406 potential outbreaks were detected and immediately notified and 7 of them were confirmed for cholera, rubella, Influenza-like illness (H1N1), measles and food poisoning. From these data, the eIDSR system shows a sensitivity of 100% and a specificity of 70% for outbreak detection. The early notification of probable outbreaks stimulated the early investigations and the quick response to outbreaks within the country and across the borders.
Conclusions
The electronic disease surveillance system has improved timeliness and completeness of reporting and extremely supports early detection and notification of outbreaks for timely response. This system should be a model for the East African region as it has demonstrated advantages in the cross-border disease surveillance.
PMCID: PMC3692857
Disease Surveillance; Informatics; m-Health
23.  Evaluating Biosurveillance System Components using Multi-Criteria Decision Analysis 
Objective
The use of Multi-Criteria Decision Analysis (MCDA) has traditionally been limited to the field of operations research, however many of the tools and methods developed for MCDA can also be applied to biosurveillance. Our project demonstrates the utility of MCDA for this purpose by applying it to the evaluation of data streams for use in an integrated, global biosurveillance system.
Introduction
The evaluation of biosurveillance system components is a complex, multi-objective decision that requires consideration of a variety of factors. Multi-Criteria Decision Analysis provides a methodology to assist in the objective analysis of these types of evaluation by creating a mathematical model that can simulate decisions. This model can utilize many types of data, both quantitative and qualitative, that can accurately describe components. The decision-maker can use this model to determine which of the system components best accomplish the goals being evaluated. Before MCDA can be utilized effectively, an evaluation framework needs to be developed. We built a robust framework that identified unique metrics, surveillance goals, and priorities for metrics. Using this framework, we were able to use MCDA to assist in the evaluation of data streams and to determine which types would be of most use within a global biosurveillance system.
Methods
MCDA was implemented using the Logical Decisions® software. The construction of the evaluation framework was carried out in several steps: identification and definition of data streams, metrics and surveillance goals, and the determination of the relative importance of each metric to the respective surveillance goal being evaluated. Sixteen data streams types were defined and identified for evaluation from a survey we conducted that collected over 200 surveillance products. A subject matter expert (SME) panel was assembled to help identify the biosurveillance goals and metrics in which to evaluate the data streams. To assign values for the metrics, we referenced properties of data streams used in currently operational systems.
Results
Our survey identified sixteen different classes of data streams: Ambulance Records, Clinic/Health Care Provider Records, ED/Hospital Records, Employment/School Records, Established Databases, Financial Records, Help Lines, Internet Search Queries, Laboraotry Records, News Aggregators, Official Reports, Police/Fire Department Records, Personal Communication, Prediction Markets, Sales, and Social Media.
Four biosurveillance goals were identified: Early Warning of Health Threats, Early Detection of Health Events, Situational Awareness, and Consequence Management.
Eleven metrics were identified: Accessibility, Cost, Credibility, Flexibility, Integrability, Geographic/Population Coverage, Granularity, Specificity of Detection, Sustainability, Time to Indication, and Timeliness.
Using the framework, it was possible to use MCDA to rank the utility of each data stream for each goal.
Conclusions
The results suggest that a “one size fits all” approach does not work and that there is no ideal data stream that is most useful for each goal. Data streams that scored more highly for speed tended to rank more highly when the biosurveillance goal is early warning or early detection, whereas data streams that scored more highly for data credibility and geographic/population coverage ranked highly when the goal was situational awareness or consequence management. However, there are several data streams that rank consistently within the top 5 for each goal: Internet Search Queries, News Aggregators, Clinic/Health Care Provider records, ED/Hospital Records, and Laboratory Records and may be considered useful for integrated, global biosurveillance for infectious disease.
PMCID: PMC3692806
evaluation; biosurveillance; multi-criteria decision analysis; data stream; evaluation framework
24.  Increasing Mild Enterovirus Cases Provides An Important Signal of Up-coming Trends in Elevating Severe Enterovirus Cases 
Objective
This study was to elucidate the spatio-temporal correlations between the mild and severe enterovirus cases through integrating enterovirus-related three surveillance systems in Taiwan. With these fully understanding epidemiological characteristics, hopefully, we can develop better measures and indicators from mild cases to provide early warning signals and thus minimizing subsequent numbers of severe cases.
Introduction
In July 2012, the 54 children infected with enterovirus-71(EV-71) were died in Cambodia [1]. The media called it as mystery illness and made Asian parents worried. In fact, the severe epidemics of enterovirus occurred frequently in Asia, including Malaysia, Singapore, Taiwan and China [2]. The clinical severity varied from asymptomatic to mild (hand-foot-mouth disease and herpangina) and severe pulmonary edema/hemorrhage and encephalitis [3]. Up to now, the development of vaccine for EV-71 and the more effective antiviral drug was still ongoing [4]. Therefore, surveillance for monitoring the enterovirus activity and understanding the epidemiological characteristics between mild and severe enterovirus cases was crucial.
Methods
Three main databases including national notifiable diseases surveillance, sentinel physician surveillance and laboratory surveillance from July 1, 1999 to December 31, 2008 were analyzed. The Pearson’s correlation coefficient was applied for measuring the consistency of the trend. The Poisson space-time scan statistic [5] was used for identifying the most likely clusters. We used GIS (ArcMap, version9.0; ESRI Inc.,Redlands, CA, USA) for visualization of detected clusters.
Results
Temporal analysis found that the Pearson’s correlation between mild EV cases and severe EV cases occurring in the same week was 0.553 (p<0.01) in Figure 1. Such a correlation became moderate (data) when mild EV cases happened in 1∼4 weeks before the current severe EV cases. Among the 1,517 severe EV cases notified to Taiwan CDC during the study period, the mean age was 27 months, 61.4% was male and 12% were fatal. These severe EV cases were significantly associated with the positive isolation rate of EV-71, with much higher correlation than the mild cases [ 0.498 p<0.01 vs. 0.278, p<0.01]. Using the space-time cluster method, we identified three possible clusters in June 2008 in six cities/counties (Figure 2).
Conclusions
Taiwan’s surveillance data indicate that local public health professionals can monitor the trends in the numbers of mild EV cases in community to provide early warning signals for local residents to prevent the severity of future waves.
PMCID: PMC3692752
Enterovirus; Suveillance; Space Time Clusters
25.  A Bayesian Approach to Characterize Hong Kong Influenza Surveillance Systems 
Objective
Our goal is to develop a statistical model for characterizing influenza surveillance systems that will be helpful in interpreting multiple streams of influenza surveillance data in future outbreaks.
Introduction
Syndromic surveillance has been widely used in influenza surveillance worldwide. However, despite the potential benefits created by the large volume of data, biases due to the changes in healthcare seeking behavior and physicians’ reporting behavior, as well as the background noise caused by seasonal flu epidemics, contribute to the complexity of the surveillance system and may limit its utility as a tool for early detection [1,2]. Since most current analysis methods are developed for outbreak detection, there are few tools to characterize influenza surveillance data for situational awareness purposes in a quantitative manner.
Hong Kong Centre for Health Protection (CHP) has a comprehensive influenza surveillance system based on healthcare providers, laboratories, schools, daycare centers and residential care homes for the elderly. Hong Kong usually experiences a summer peak in July and August [3], which potentially doubles the data volume and constitutes a natural experiment to assess the effect of school-age children in the influenza transmission dynamics. The richness of the available data and the unique epidemiological characteristics make Hong Kong an ideal study object to develop and evaluate our model.
Methods
We have constructed a Bayesian statistical model for influenza surveillance data by parameterizing factors that describe disease transmission, behavior patterns in health care seeking and provision, and biases and errors embedded in the reporting process (Figure 1). The prior distributions are selected for each of the parameters to reflect knowledge of influenza epidemiology and the likely biases in each data system. Using the Markov Chain Monte-Carlo (MCMC) method in OpenBUGS, a posterior distribution can be generated for every parameter to characterize each data stream. The ratios of specific pairs of data streams are assessed in order to identify patterns in the change of ratios at different stage of the flu season.
Results
Preliminary results, as shown in Figure 2, incorporate confirmed influenza infection (solid line), influenza-like illness (double solid line), fever cases (dashed line), and Google search index (round dashed line). Although most of these data series track together, differences among them suggest reporting bias related to public awareness, which will be addressed in the statistical modeling.
Conclusions
The posterior distribution for parameters and ratios between individual data streams can be used to characterize influenza surveillance systems in terms of tendency in peak early or late, or to over or under represent actual influenza cases. To better interpret syndromic surveillance data for situational awareness purposes, behavioral data related to healthcare resource utilization, such as the percentage of intended GP visit among people with ILI, need to be collected together with the flu activity surveillance.
Conceptual model for influenza surveillance statistical model
Blue circles: unobservable true value; white boxes: observation; orange boxes: factors
Hong Kong flu activity in 2009 pH1N1 outbreak
PMCID: PMC3692823
situational awareness; modeling; epidemiology; influenza surveillance; Bayesian

Results 1-25 (1112629)