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1.  Situational Awareness of Influenza Activity Based on Multiple Streams of Surveillance Data Using Multivariate Dynamic Linear Model 
PLoS ONE  2012;7(5):e38346.
Multiple sources of influenza surveillance data are becoming more available; however integration of these data streams for situational awareness of influenza activity is less explored.
Methods and Results
We applied multivariate time-series methods to sentinel outpatient and school absenteeism surveillance data in Hong Kong during 2004–2009. School absenteeism data and outpatient surveillance data experienced interruptions due to school holidays and changes in public health guidelines during the pandemic, including school closures and the establishment of special designated flu clinics, which in turn provided ‘drop-in’ fever counts surveillance data. A multivariate dynamic linear model was used to monitor influenza activity throughout epidemics based on all available data. The inferred level followed influenza activity closely at different times, while the inferred trend was less competent with low influenza activity. Correlations between inferred level and trend from the multivariate model and reference influenza activity, measured by the product of weekly laboratory influenza detection rates and weekly general practitioner influenza-like illness consultation rates, were calculated and compared with those from univariate models. Over the whole study period, there was a significantly higher correlation (ρ = 0.82, p≤0.02) for the inferred trend based on the multivariate model compared to other univariate models, while the inferred trend from the multivariate model performed as well as the best univariate model in the pre-pandemic and the pandemic period. The inferred trend and level from the multivariate model was able to match, if not outperform, the best univariate model albeit with missing data plus drop-in and drop-out of different surveillance data streams. An overall influenza index combining level and trend was constructed to demonstrate another potential use of the method.
Our results demonstrate the potential use of multiple streams of influenza surveillance data to promote situational awareness about the level and trend of seasonal and pandemic influenza activity.
PMCID: PMC3364986  PMID: 22675456
2.  Evaluation of school absenteeism data for early outbreak detection, New York City 
BMC Public Health  2005;5:105.
School absenteeism data may have utility as an early indicator of disease outbreaks, however their value should be critically examined. This paper describes an evaluation of the utility of school absenteeism data for early outbreak detection in New York City (NYC).
To assess citywide temporal trends in absenteeism, we downloaded three years (2001–02, 2002–03, 2003–04) of daily school attendance data from the NYC Department of Education (DOE) website. We applied the CuSum method to identify aberrations in the adjusted daily percent absent. A spatial scan statistic was used to assess geographic clustering in absenteeism for the 2001–02 academic year.
Moderate increases in absenteeism were observed among children during peak influenza season. Spatial analysis detected 790 significant clusters of absenteeism among elementary school children (p < 0.01), two of which occurred during a previously reported outbreak.
Monitoring school absenteeism may be moderately useful for detecting large citywide epidemics, however, school-level data were noisy and we were unable to demonstrate any practical value in using cluster analysis to detect localized outbreaks. Based on these results, we will not implement prospective monitoring of school absenteeism data, but are evaluating the utility of more specific school-based data for outbreak detection.
PMCID: PMC1260024  PMID: 16212669
3.  Potential use of multiple surveillance data in the forecast of hospital admissions 
This paper describes the potential use of multiple influenza surveillance data to forecast hospital admissions for respiratory diseases.
A sudden surge in hospital admissions in public hospital during influenza peak season has been a challenge to healthcare and manpower planning. In Hong Kong, the timing of influenza peak seasons are variable and early short-term indication of possible surge may facilitate preparedness which could be translated into strategies such as early discharge or reallocation of extra hospital beds. In this study we explore the potential use of multiple routinely collected syndromic data in the forecast of hospital admissions.
A multivariate dynamic linear time series model was fitted to multiple syndromic data including influenza-like illness (ILI) rates among networks of public and private general practitioners (GP), and school absenteeism rates, plus drop-in fever count data from designated flu clinics (DFC) that were created during the pandemic. The latent process derived from the model has been used as a measure of the influenza activity [1]. We compare the cross-correlations between estimated influenza level based on multiple surveillance data and GP ILI data, versus accident and emergency hospital admissions with principal diagnoses of respiratory diseases and pneumonia & influenza (P&I).
The estimated influenza activity has higher cross-correlation with respiratory and P&I admissions (ρ=0.66 and 0.73 respectively) compared to that of GP ILI rates (Table 1). Cross correlations drop distinctly after lag 2 for both estimated influenza activity and GP ILI rates.
The use of a multivariate method to integrate information from multiple sources of influenza surveillance data may have the potential to improve forecasting of admission surge of respiratory diseases.
PMCID: PMC3692818
influenza; surveillance; admission; respiratory
4.  The New School Absentees Reporting System for Pandemic Influenza A/H1N1 2009 Infection in Japan 
PLoS ONE  2012;7(2):e30639.
To evaluate the new Japanese School Absentees Reporting System for Infectious Disease (SARSID) for pandemic influenza A/H1N1 2009 infection in comparison with the National epidemiological Surveillance of Infectious Disease (NESID).
We used data of 53,223 students (97.7%) in Takamatsu city Japan. Data regarding school absentees in SARSID was compared with that in NESID from Oct 13, 2009 to Jan 12, 2010.
Similar trends were observed both in SARSID and NESID. However, the epidemic trend for influenza in SARSID was thought to be more sensitive than that in NESID.
The epidemic trend for influenza among school-aged children could be easily and rapidly assessed by SARSID compared to NESID. SARSID might be useful for detecting the epidemic trend of influenza.
PMCID: PMC3281859  PMID: 22363458
5.  Statistical estimates of absenteeism attributable to seasonal and pandemic influenza from the Canadian Labour Force Survey 
As many respiratory viruses are responsible for influenza like symptoms, accurate measures of the disease burden are not available and estimates are generally based on statistical methods. The objective of this study was to estimate absenteeism rates and hours lost due to seasonal influenza and compare these estimates with estimates of absenteeism attributable to the two H1N1 pandemic waves that occurred in 2009.
Key absenteeism variables were extracted from Statistics Canada's monthly labour force survey (LFS). Absenteeism and the proportion of hours lost due to own illness or disability were modelled as a function of trend, seasonality and proxy variables for influenza activity from 1998 to 2009.
Hours lost due to the H1N1/09 pandemic strain were elevated compared to seasonal influenza, accounting for a loss of 0.2% of potential hours worked annually. In comparison, an estimated 0.08% of hours worked annually were lost due to seasonal influenza illnesses. Absenteeism rates due to influenza were estimated at 12% per year for seasonal influenza over the 1997/98 to 2008/09 seasons, and 13% for the two H1N1/09 pandemic waves. Employees who took time off due to a seasonal influenza infection took an average of 14 hours off. For the pandemic strain, the average absence was 25 hours.
This study confirms that absenteeism due to seasonal influenza has typically ranged from 5% to 20%, with higher rates associated with multiple circulating strains. Absenteeism rates for the 2009 pandemic were similar to those occurring for seasonal influenza. Employees took more time off due to the pandemic strain than was typical for seasonal influenza.
PMCID: PMC3103439  PMID: 21486453
6.  School Absenteeism As an Adjunct Surveillance Indicator: Experience during the Second Wave of the 2009 H1N1 Pandemic in Quebec, Canada 
PLoS ONE  2012;7(3):e34084.
A school absenteeism surveillance system was implemented in the province of Quebec, Canada during the second wave of the 2009 H1N1pandemic. This paper compares this surveillance approach with other available indicators.
All (3432) elementary and high schools from Quebec were included. Each school was required to report through a web-based system any day where the proportion of students absent for influenza-like illness (ILI) exceeded 10% of current school enrolment.
Between October 18 and December 12 2009, 35.6% of all schools met the 10% absenteeism threshold. This proportion was greater in elementary compared to high schools (40% vs 19%) and in smaller compared to larger schools (44% vs 22%). The maximum absenteeism rate was reached the first day of reporting or within the next two days in 55% and 31% of schools respectively. The first reports and subsequent peak in school absenteeism provincially preceded the peak in paediatric hospitalization by two and one weeks, respectively. Trends in school surveillance otherwise mirrored other indicators.
During a pandemic, school outbreak surveillance based on a 10% threshold appears insufficient to trigger timely intervention within a given affected school. However, school surveillance appears well-correlated and slightly anticipatory compared to other population indicators. As such, school absenteeism warrants further evaluation as an adjunct surveillance indicator whose overall utility will depend upon specified objectives, and other existing capacity for monitoring and response.
PMCID: PMC3316605  PMID: 22479531
7.  Enhanced Influenza Surveillance using Telephone Triage Data in the VA ESSENCE Biosurveillance System 
To evaluate the utility and timeliness of telephone triage (TT) for influenza surveillance in the Department of Veterans Affairs (VA).
Telephone triage is a relatively new data source available to biosurveillance systems.1–2 Because early detection and warning is a high priority, many biosurveillance systems have begun to collect and analyze data from non-traditional sources [absenteeism records, over-the-counter drug sales, electronic laboratory reporting, internet searches (e.g. Google Flu Trends) and TT]. These sources may provide disease activity alerts earlier than conventional sources. Little is known about whether VA telephone program influenza data correlates with established influenza biosurveillance.
Veterans phoning VA’s TT system, and those admitted or seen at a VA facility with influenza or influenza-like-illness (ILI) diagnosis were included in this analysis. Influenza-specific ICD-9-CM coded emergency department (ED) and urgent care (UC) visits, hospitalizations, TT calls, and ILI outpatient visits were analyzed covering 2010–2011 and 2011–2012 influenza seasons (July 11, 2010–April 14, 2012). Data came from 80 VA Medical Centers and over 500 outpatient clinics with complete reporting data for the time period of interest. We calculated Spearman rank-order coefficients, 95% confidence intervals and p-values using Fisher’s z transformation to describe correlation between TT data and other influenza healthcare measures. For comparison of time trends, we plotted data for hospitalizations, ED/UC visits and outpatient ILI syndrome visits against TT encounters. We applied ESSENCE detection algorithms to identify high-level alerts for influenza activity. ESSENCE aberration detection was restricted to the 2011–2012 season because limited historical TT and outpatient data from 2009–2010 was available to accurately predict aberrancy in the 2010–2011 season. We then calculated the peak measure of healthcare utilization during both influenza seasons (2010–2011 and 2011–2012) for each data source and compared timing of peaks and alerts between TT and other healthcare encounters to assess maximum healthcare system usage and timeliness of surveillance.
There were 7,044 influenza-coded calls, 564 hospitalizations, 1,849 emergency/urgent visits, and 416,613 ILI-coded outpatient visits. Spearman rank correlation coefficients were calculated for influenza-coded calls with hospitalizations (0.77); ED/UC visits (0.85); and ILI-outpatient visits (0.88), respectively (P< 0.0001 for all correlations). Peak influenza activity occurred on the same week or within 1 week across all settings for both seasons. For the 2011–2012 season, TT alerted with increased influenza activity before all other settings.
Data from VA telephone care correlates well with other VA data sources for influenza activity. TT may serve to augment these existing clinical data sources and provide earlier alerts of influenza activity. As a national health care system with a large patient population, VA could provide a robust early-warning system for influenza if ongoing biosurveillance activities are combined with TT data. Additional analyses are needed to understand and correlate TT with healthcare utilization and severity of illness.
PMCID: PMC3692747
Surveillance; Influenza; Telephone triage; Veterans
8.  Estimating the costs of school closure for mitigating an influenza pandemic 
BMC Public Health  2008;8:135.
School closure is a key component of many countries' plans to mitigate the effect of an influenza pandemic. Although a number of studies have suggested that such a policy might reduce the incidence, there are no published studies of the cost of such policies. This study attempts to fill this knowledge gap
School closure is expected to lead to significant work absenteeism of working parents who are likely to be the main care givers to their dependent children at home. The cost of absenteeism due to school closure is calculated as the paid productivity loss of parental absenteeism during the period of school closure. The cost is estimated from societal perspective using a nationally representative survey.
The results show that overall about 16% of the workforce is likely to be the main caregiver for dependent children and therefore likely to take absenteeism. This rises to 30% in the health and social care sector, as a large proportion of the workforce are women. The estimated costs of school closure are significant, at £0.2 bn – £1.2 bn per week. School closure is likely to significantly exacerbate the pressures on the health system through staff absenteeism.
The estimates of school closure associated absenteeism and the projected cost would be useful for pandemic planning for business continuity, and for cost effectiveness evaluation of different pandemic influenza mitigation strategies.
PMCID: PMC2377259  PMID: 18435855
9.  Estimation of Influenza Incidence by Age in the 2011/12 Seasons in Japan using SASSy 
So far, it is difficult to show the incidence rate of influenza in the official sentinel surveillance in Japan. Hence we construct the system which record infectious diseases at schools, kindergartens, and nursery schools, and then can show the accurate incidence rate of influenza in children by age/grade.
So as to develop more effective countermeasures against influenza, timely and precise information about influenza activity at schools, kindergartens, and nursery schools may be helpful. At the Infectious Diseases Surveillance Center of the National Institute of Infectious Diseases, a School Absenteeism Surveillance System (SASSy) has been in operation since 2009. SASSy monitors the activity of varicella, mumps, mycoplasma pneumonia, pharyngoconjunctival fever, hand-foot-mouth disease, influenza, and many other infectious diseases in schools. In 2010, SASSy was extended to the Nursery School Absenteeism Surveillance System (NSASSy). These systems record the number of absentees due to infectious diseases in each class of all grades of schools every day. As a powerful countermeasure to the pandemic flu of 2009, SASSy was activated in 9 prefectures, in which included more than 6000 schools, and it is gradually being adopted in other prefectures. As of February 2012, 18 prefectures and 4 big cities, which together comprised 15,700 schools (about 35% of all schools in Japan), utilized SASSy. NSASSy is used in more than 4100 nursery schools, which is about 18% of all nursery schools in Japan. Some studies of similar systems were performed in the UK (1), Hong Kong (2), and the USA (3,4), examined surveillance systems for monitoring infectious disease incidence, but the systems to construct in those studies do not operate nationwide like SASSy or NSASSy, and they cannot provide influenza incidence rates in children.
All schools, kindergartens, and nursery schools in the community, enter data of the absentees due to infectious diseases into the system every day, thereby providing real-time data regarding infectious diseases prevalent in schools, to the schools around, school boards, public health centers, local governments, and medical professionals. It analyzed data for the 2011/2012 season (from September 1, 2011 to March 31, 2012) mainly, but also two seasons (2010/2011 and 2011/2012) were compared in some prefectures. In total, 12 prefectures, which comprised 2,352,839 children, were participated in 2011/2012 season. In the 2010/2011 season, 1,795,766 children of 9 prefectures were analyzed.
The incidence rate in the first grade of elementary schools is the highest both in the two seasons. The highest incidence rate in this grade distributes from 17.8% to 40.3% in 2011/2012 season, and from 11.0% to 30.7% in 2010/2011 season.
This study proved SASSy and NSASSy are quite useful for monitoring of influenza outbreak in schools and it will be gold standard of surveillance for school children in Japan. The present study also showed incidence rate of influenza in children at schools, kindergartens, and nursery schools, and proved the highest incidence was in the first grade of the elementary school. This is the first finding using such the huge number of subjects, which is more than 2 million. The intervention targeting to the weak age/grade is necessary for effective countermeasure and control of influenza and other infectious diseases.
PMCID: PMC3692790
Surveillance; Influenza; School Absenteeism
10.  Effective Detection of the 2009 H1N1 Influenza Pandemic in U.S. Veterans Affairs Medical Centers Using a National Electronic Biosurveillance System 
PLoS ONE  2010;5(3):e9533.
The 2008–09 influenza season was the time in which the Department of Veterans Affairs (VA) utilized an electronic biosurveillance system for tracking and monitoring of influenza trends. The system, known as ESSENCE or Electronic Surveillance System for the Early Notification of Community-based Epidemics, was monitored for the influenza season as well as for a rise in influenza cases at the start of the H1N1 2009 influenza pandemic. We also describe trends noted in influenza-like illness (ILI) outpatient encounter data in VA medical centers during the 2008–09 influenza season, before and after the recognition of pandemic H1N1 2009 influenza virus.
Methodology/Principal Findings
We determined prevalence of ILI coded visits using VA's ESSENCE for 2008–09 seasonal influenza (Sept. 28, 2008–April 25, 2009 corresponding to CDC 2008–2009 flu season weeks 40–16) and the early period of pandemic H1N1 2009 (April 26, 2009–July 31, 2009 corresponding to CDC 2008–2009 flu season weeks 17–30). Differences in diagnostic ICD-9-CM code frequencies were analyzed using Chi-square and odds ratios. There were 649,574 ILI encounters captured representing 633,893 patients. The prevalence of VA ILI visits mirrored the CDC's Outpatient ILI Surveillance Network (ILINet) data with peaks in late December, early February, and late April/early May, mirroring the ILINet data; however, the peaks seen in the VA were smaller. Of 31 ILI codes, 6 decreased and 11 increased significantly during the early period of pandemic H1N1 2009. The ILI codes that significantly increased were more likely to be symptom codes. Although influenza with respiratory manifestation (487.1) was the most common code used among 150 confirmed pandemic H1N1 2009 cases, overall it significantly decreased since the start of the pandemic.
VA ESSENCE effectively detected and tracked changing ILI trends during pandemic H1N1 2009 and represents an important temporal alerting system for monitoring health events in VA facilities.
PMCID: PMC2832014  PMID: 20209055
11.  Applying Zero-inflated Mixed Model to School Absenteeism Surveillance in Rural China 
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.
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).
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.
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.
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.  Forecasting Influenza Epidemics from Multi-Stream Surveillance Data in a Subtropical City of China 
PLoS ONE  2014;9(3):e92945.
Influenza has been associated with heavy burden of mortality and morbidity in subtropical regions. However, timely forecast of influenza epidemic in these regions has been hindered by unclear seasonality of influenza viruses. In this study, we developed a forecasting model by integrating multiple sentinel surveillance data to predict influenza epidemics in a subtropical city Shenzhen, China.
Dynamic linear models with the predictors of single or multiple surveillance data for influenza-like illness (ILI) were adopted to forecast influenza epidemics from 2006 to 2012 in Shenzhen. Temporal coherence of these surveillance data with laboratory-confirmed influenza cases was evaluated by wavelet analysis and only the coherent data streams were entered into the model. Timeliness, sensitivity and specificity of these models were also evaluated to compare their performance.
Both influenza virology data and ILI consultation rates in Shenzhen demonstrated a significant annual seasonal cycle (p<0.05) during the entire study period, with occasional deviations observed in some data streams. The forecasting models that combined multi-stream ILI surveillance data generally outperformed the models with single-stream ILI data, by providing more timely, sensitive and specific alerts.
Forecasting models that combine multiple sentinel surveillance data can be considered to generate timely alerts for influenza epidemics in subtropical regions like Shenzhen.
PMCID: PMC3968046  PMID: 24676091
13.  Early Detection of Influenza Activity Using Syndromic Surveillance in Missouri 
To assess how weekly percent of influenza-like illness (ILI) reported via Early Notification of Community-based Epidemics (ESSENCE) tracked weekly counts of laboratory confirmed influenza cases in five influenza seasons in order to evaluate the early warning potential of ILI in ESSENCE and improve ongoing influenza surveillance efforts in Missouri.
Syndromic surveillance is used routinely to detect outbreaks of disease earlier than traditional methods due to its ability to automatically acquire data in near real-time. Missouri has used emergency department (ED) visits to monitor and track seasonal influenza activity since 2006.
The Missouri ESSENCE system utilizes data from 84 hospitals, which represents up to 90 percent of all ED visits occurring in Missouri statewide each day. The influenza season is defined as starting during Centers for Disease Control and Prevention (CDC) week number 40 (around the first of October) and ending on CDC week 20 of the following year, which is usually at the end of May.
A confirmed influenza case is laboratory confirmed by viral culture, rapid diagnostic tests, or a four-fold rise in antibody titer between acute and convalescent serum samples. Laboratory results are reported on a weekly basis. To assess the severity of influenza activity, all flu seasons were compared with the 2008–09 season, which experienced the lowest influenza activity based on laboratory data. Analysis of variance (ANOVA) was applied for this analysis using Statistical Analysis Software (SAS) (version 9.2).
The standard ESSENCE ILI subsyndrome includes ED chief complaints that contain keywords such as “flu”, “flulike”, “influenza” or “fever plus cough” or “fever plus sore throat”. The ESSENCE ILI weekly percent is the number of ILI visits divided by total ED visits.
Time series of weekly percent of ILI in ESSENCE were compared to weekly counts of laboratory confirmed influenza cases. Spearman correlation coefficients were calculated using SAS. The baseline refers to the mean of three flu seasons with low influenza activity (2006–07, 2008–09 and 2010–11 seasons). The threshold was calculated as this baseline plus three standard deviations.
The early warning potential of the ESSENCE weekly ILI percent was evaluated for five consecutive influenza seasons, beginning in 2006. This was accomplished by calculating the time lag between the first ESSENCE ILI warning versus the first lab confirmed influenza warning. A warning was identified if either lab confirmed case counts or weekly percent of ILI crossed over their respective baselines.
For each influenza season evaluated, weekly ILI rates reported via ESSENCE were significantly correlated with weekly counts of laboratory-confirmed influenza cases (Table 1). The baseline of ILI activity in ESSENCE was 1.8 ILI /100 ED visits/week and the threshold was set at 4.1 ILI visits per 100 ED visits/week. The ESSENCE ILI baseline provided, on average, two weeks of advanced warning for seasonal influenza activity. Figure 1 shows that two influenza seasons (2007–08 and 2009–10) were more severe than others examined based on the ESSENCE percent ILI threshold analysis, this result is consistent with the examination of severity of influenza activity based on lab confirmed influenza data (p<0.05).
The significant correlation between ILI surveillance in ESSENCE and laboratory confirmed influenza cases justifies the use of weekly ILI percent in ESSENCE to describe seasonal influenza activity. The ESSENCE ILI baseline and threshold provided advanced warning of influenza and allowed for the classification of influenza severity in the community.
PMCID: PMC3692881
ESSENCE; syndromic surveillance; influenza-like illness (ILI); baseline; threshold
14.  Computerized general practice based networks yield comparable performance with sentinel data in monitoring epidemiological time-course of influenza-like illness and acute respiratory illness 
BMC Family Practice  2010;11:24.
Computerized morbidity registration networks might serve as early warning systems in a time where natural epidemics such as the H1N1 flu can easily spread from one region to another.
In this contribution we examine whether general practice based broad-spectrum computerized morbidity registration networks have the potential to act as a valid surveillance instrument of frequently occurring diseases. We compare general practice based computerized data assessing the frequency of influenza-like illness (ILI) and acute respiratory infections (ARI) with data from a well established case-specific sentinel network, the European Influenza Surveillance Scheme (EISS). The overall frequency and trends of weekly ILI and ARI data are compared using both networks.
Detection of influenza-like illness and acute respiratory illness occurs equally fast in EISS and the computerized network. The overall frequency data for ARI are the same for both networks, the overall trends are similar, but the increases and decreases in frequency do not occur in exactly the same weeks. For ILI, the overall rate was slightly higher for the computerized network population, especially before the increase of ILI, the overall trend was almost identical and the increases and decreases occur in the same weeks for both networks.
Computerized morbidity registration networks are a valid tool for monitoring frequent occurring respiratory diseases and the detection of sudden outbreaks.
PMCID: PMC2856540  PMID: 20307266
15.  Comparison of five influenza surveillance systems during the 2009 pandemic and their association with media attention 
BMC Public Health  2013;13:881.
During the 2009 influenza pandemic period, routine surveillance of influenza-like-illness (ILI) was conducted in The Netherlands by a network of sentinel general practitioners (GPs). In addition during the pandemic period, four other ILI/influenza surveillance systems existed. For pandemic preparedness, we evaluated the performance of the sentinel system and the others to assess which of the four could be useful additions in the future. We also assessed whether performance of the five systems was influenced by media reports during the pandemic period.
The trends in ILI consultation rates reported by sentinel GPs from 20 April 2009 through 3 January 2010 were compared with trends in data from the other systems: ILI cases self-reported through the web-based Great Influenza Survey (GIS); influenza-related web searches through Google Flu Trends (GFT); patients admitted to hospital with laboratory-confirmed pandemic influenza, and detections of influenza virus by laboratories. In addition, correlations were determined between ILI consultation rates of the sentinel GPs and data from the four other systems. We also compared the trends of the five surveillance systems with trends in pandemic-related newspaper and television coverage and determined correlation coefficients with and without time lags.
The four other systems showed similar trends and had strong correlations with the ILI consultation rates reported by sentinel GPs. The number of influenza virus detections was the only system to register a summer peak. Increases in the number of newspaper articles and television broadcasts did not precede increases in activity among the five surveillance systems.
The sentinel general practice network should remain the basis of influenza surveillance, as it integrates epidemiological and virological information and was able to maintain stability and continuity under pandemic pressure. Hospital and virological data are important during a pandemic, tracking the severity, molecular and phenotypic characterization of the viruses and confirming whether ILI incidence is truly related to influenza virus infections. GIS showed that web-based, self-reported ILI can be a useful addition, especially if virological self-sampling is added and an epidemic threshold could be determined. GFT showed negligible added value.
PMCID: PMC3849360  PMID: 24063523
Influenza virus; Pandemic; Surveillance; Influenza-like illness; Media attention
16.  Who’s Not Coming to Dinner? Evaluating Trends in Online Restaurant Reservations for Outbreak Surveillance 
The objective of this study is to evaluate whether trends in online restaurant table reservations can be used as an early indicator for a disease outbreak.
Epidemiologists, public health agencies and scientists increasingly augment traditional surveillance systems with alternative data sources such as, digital surveillance systems utilizing news reports and social media, over-the-counter medication sales, and school absenteeism. Similar to school absenteeism, an increase in reservation cancellations could serve as an early indicator of social disruption including a major public health event. In this study, we evaluated whether a rise in restaurant table availabilities could be associated with an increase in disease incidence.
We monitored table availability using OpenTable; an online restaurant table reservation site for cities in the USA and Mexico. Our analysis can be summarized as follows. First, using the OpenTable site, we searched for the number of restaurants with available tables for two persons at lunch and dinner. Since different regions and individuals have different eating habits, we defined the lunch period between 12–3:30pm and dinner between 6–10:30pm. We searched for available tables every hour and half past the hour for every day of the week. Next, we investigated any occurrences of social unrest and natural disasters, which might have affected the trend in the time series. Lastly, using moving averages, cross-correlations and regression models, we elucidated and compared the time-trend in the data of table availabilities to data collected for various disease outbreaks. In the USA, we examined table availability for restaurants in Boston, Atlanta, Baltimore and Miami. For Mexico, we studied table availabilities in Cancun, Mexico City, Puebla, Monterrey, and Guadalajara.
Preliminary results indicated differences in mean table availabilities observed during weekdays and weekends. However, these differences were statistically significant only for Boston and Miami (p < 0.01). Statistical significant differences were also observed for mean table availabilities at lunch and dinner for all the cities (p < 0.001).
The unavailability of reasons for cancellations introduces limitations to this data source. However, monitoring increases in cancellation of restaurant table reservations may be moderately useful for detecting epidemics especially in developing countries with limited public health infrastructures and resources. We therefore present a framework for future surveillance efforts.
PMCID: PMC3692821
developing countries; infectious diseases; alternative data sources; reservation sites
17.  Biosurveillance applying scan statistics with multiple, disparate data sources 
Researchers working on the Department of Defense Global Emerging Infections System (DoD-GEIS) pilot system, the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE), have applied scan statistics for early outbreak detection using hoth traditional and nontraditional data sources. These sources include medical data indexed byInternational Classification of Disease, 9th Revision (ICD-9) diagnosis codes, as well as less-specific, but potentially timelier, indicators such as records of over-the-counter remedy sales and of school absenteeism. Early efforts employed the Kulldorff scan statistic as implemented in the SaTScan software of the National Cancer Institute. A key obstacle to this application is that the input data streams are typically based on time-varying factors, such as consumer behavior, rather than simply on the populations of the component subregions. We have used both modeling and recent historical data distributions to obtain background spatial distributions. Data analyses have provided guidance on how to condition and model input data to avoid excessive clustering. We have used this methodology in combining data sources for both retrospective studies of known outbreaks and surveillance of high-profile events of concern to local public health authorities. We have integrated the scan statistic capability into a Microsoft Access-based system in which we may include or exclude data sources, vary time windows separately for different data sources, censor data from subsets of individual providers or subregions, adjust the background computation method, and run retrospective or simulated studies.
PMCID: PMC3456540  PMID: 12791780
Biosurveillance; Clustering; Kulldorff; Scan statistics
18.  Age Distribution of Influenza Like Illness Cases during Post-Pandemic A(H3N2): Comparison with the Twelve Previous Seasons, in France 
PLoS ONE  2013;8(6):e65919.
In France, the 2011–2012 influenza epidemic was characterized by the circulation of antigenically drifted influenza A(H3N2) viruses and by an increased disease severity and mortality among the elderly, with respect to the A(H1N1)pdm09 pandemic and post-pandemic outbreaks. Whether the epidemiology of influenza in France differed between the 2011–2012 epidemic and the previous outbreaks is unclear. Here, we analyse the age distribution of influenza like illness (ILI) cases attended in general practice during the 2011–2012 epidemic, and compare it with that of the twelve previous epidemic seasons. Influenza like illness data were obtained through a nationwide surveillance system based on sentinel general practitioners. Vaccine effectiveness was also estimated. The estimated number of ILI cases attended in general practice during the 2011–2012 was lower than that of the past twelve epidemics. The age distribution was characteristic of previous A(H3N2)-dominated outbreaks: school-age children were relatively spared compared to epidemics (co-)dominated by A(H1N1) and/or B viruses (including the 2009 pandemic and post-pandemic outbreaks), while the proportion of adults over 30 year-old was higher. The estimated vaccine effectiveness (54%, 95% CI (48, 60)) was in the lower range for A(H3N2) epidemics. In conclusion, the age distribution of ILI cases attended in general practice seems to be not different between the A(H3N2) pre-pandemic and post-pandemic epidemics. Future researches including a more important number of ILI epidemics and confirmed virological data of influenza and other respiratory pathogens are necessary to confirm these results.
PMCID: PMC3673950  PMID: 23755294
19.  Teacher led school-based surveillance can allow accurate tracking of emerging infectious diseases - evidence from serial cross-sectional surveys of febrile respiratory illness during the H1N1 2009 influenza pandemic in Singapore 
BMC Infectious Diseases  2012;12:336.
Schools are important foci of influenza transmission and potential targets for surveillance and interventions. We compared several school-based influenza monitoring systems with clinic-based influenza-like illness (ILI) surveillance, and assessed the variation in illness rates between and within schools.
During the initial wave of pandemic H1N1 (pdmH1N1) infections from June to Sept 2009 in Singapore, we collected data on nation-wide laboratory confirmed cases (Sch-LCC) and daily temperature monitoring (Sch-DTM), and teacher-led febrile respiratory illness reporting in 6 sentinel schools (Sch-FRI). Comparisons were made against age-stratified clinic-based influenza-like illness (ILI) data from 23 primary care clinics (GP-ILI) and proportions of ILI testing positive for pdmH1N1 (Lab-ILI) by computing the fraction of cumulative incidence occurring by epidemiological week 30 (when GP-ILI incidence peaked); and cumulative incidence rates between school-based indicators and sero-epidemiological pdmH1N1 incidence (estimated from changes in prevalence of A/California/7/2009 H1N1 hemagglutination inhibition titers ≥ 40 between pre-epidemic and post-epidemic sera). Variation in Sch-FRI rates in the 6 schools was also investigated through a Bayesian hierarchical model.
By week 30, for primary and secondary school children respectively, 63% and 79% of incidence for Sch-LCC had occurred, compared with 50% and 52% for GP-ILI data, and 48% and 53% for Sch-FRI. There were 1,187 notified cases and 7,588 episodes in the Sch-LCC and Sch-DTM systems; given school enrollment of 485,723 children, this represented 0.24 cases and 1.6 episodes per 100 children respectively. Mean Sch-FRI rate was 28.8 per 100 children (95% CI: 27.7 to 29.9) in the 6 schools. We estimate from serology that 41.8% (95% CI: 30.2% to 55.9%) of primary and 43.2% (95% CI: 28.2% to 60.8%) of secondary school-aged children were infected. Sch-FRI rates were similar across the 6 schools (23 to 34 episodes per 100 children), but there was widespread variation by classrooms; in the hierarchical model, omitting age and school effects was inconsequential but neglecting classroom level effects led to highly significant reductions in goodness of fit.
Epidemic curves from Sch-FRI were comparable to GP-ILI data, and Sch-FRI detected substantially more infections than Sch-LCC and Sch-DTM. Variability in classroom attack rates suggests localized class-room transmission.
PMCID: PMC3544582  PMID: 23206689
Respiratory tract infections; Vaccination; Serology
20.  Timeliness of Data Sources Used for Influenza Surveillance 
In recent years, influenza surveillance data has expanded to include alternative sources such as emergency department data, absenteeism reports, pharmaceutical sales, website access and health advice calls. This study presents a review of alternative data sources for influenza surveillance, summarizes the time advantage or timeliness of each source relative to traditional reporting and discusses the strengths and weaknesses of competing approaches.
A literature search was conducted on Medline to identify relevant articles published after 1990. A total of 15 articles were obtained that reported the timeliness of an influenza surveillance system. Timeliness was described by peak comparison, aberration detection comparison and correlation.
Overall, the data sources were highly correlated with traditional sources and had variable timeliness. Over-the-counter pharmaceutical sales, emergency visits, absenteeism and health calls appear to be more timely than physician diagnoses, sentinel influenza-like-illness surveillance and virological confirmation.
The methods used to describe timeliness vary greatly between studies and hence no strong conclusions regarding the most timely source/s of data can be reached. Future studies should apply the aberration detection method to determine data source timeliness in preference to the peak comparison method and correlation.
PMCID: PMC1975801  PMID: 17600101
21.  Influenza epidemiology in Italy two years after the 2009–2010 pandemic 
Since 2000, a sentinel surveillance of influenza, INFLUNET, exists in Italy. It is coordinated by the Ministry of Health and is divided into two parts; one of these is coordinated by the National Institute of Health (NIH), the other by the Inter-University Centre for Research on Influenza and other Transmissible Infections (CIRI-IT). The influenza surveillance system performs its activity from the 42nd week of each year (mid-October) to the 17th week of the following year (late April). Only during the pandemic season (2009/2010) did surveillance continue uninterruptedly. Sentinel physicians – about 1,200 general practitioners and independent pediatricians – send in weekly reports of cases of influenza-like illness (ILI) among their patients (over 2% of the population of Italy) to these centers.
In order to estimate the burden of pandemic and seasonal influenza, we examined the epidemiological data collected over the last 3 seasons (2009–2012). On the basis of the incidences of ILIs at different ages, we estimated that: 4,882,415; 5,519,917; and 4,660,601 cases occurred in Italy in 2009–2010, 2010–2011 and 2011–2012, respectively.
Considering the ILIs, the most part of cases occurred in < 14 y old subjects and especially in 5–14 y old individuals, about 30% and 21% of cases respectively during 2009–2010 and 2010–2011 influenza seasons. In 2011–2012, our evaluation was of about 4.7 million of cases, and as in the previous season, the peak of cases regarded subjects < 14 y (about 29%).
A/California/07/09 predominated in 2009–2010 and continued to circulate in 2010–2011. During 2010–2011 B/Brisbane/60/08 like viruses circulated and A/H3N2 influenza type was sporadically present. H3N2 (A/Perth/16/2009 and A/Victoria/361/2011) was the predominant influenza type-A virus that caused illness in the 2011–2012 season. Many strains of influenza viruses were present in the epidemiological scenario in 2009–2012.
In the period 2009–2012, overall vaccination coverage was low, never exceeding 20% of the Italian population. Among the elderly, coverage rates grew from 40% in 1999 to almost 70% in 2005–2006, but subsequently decreased, in spite of the pandemic; this trend reveals a slight, though constant, decline in compliance with vaccination.
Our data confirm that 2009 pandemics had had a spread particularly important in infants and schoolchildren, and this fact supports the strategy to vaccinate schoolchildren at least until 14 y of age. Furthermore, the low levels of vaccination coverage in Italy reveal the need to improve the catch-up of at-risk subjects during annual influenza vaccination campaigns, and, if possible, to extend free vaccination to at least all 50–64-y-old subjects.
Virologic and epidemiological surveillance remains critical for detection of evolving influenza viruses and to monitor the health and economic burden in all age class annually.
PMCID: PMC3891712  PMID: 23292210
coverage vaccination; epidemiology; influenza; influenza vaccine; influenza-like illness; pandemics
22.  Effects of Hand Hygiene Campaigns on Incidence of Laboratory-confirmed Influenza and Absenteeism in Schoolchildren, Cairo, Egypt 
Emerging Infectious Diseases  2011;17(4):619-625.
To evaluate the effectiveness of an intensive hand hygiene campaign on reducing absenteeism caused by influenza-like illness (ILI), diarrhea, conjunctivitis, and laboratory-confirmed influenza, we conducted a randomized control trial in 60 elementary schools in Cairo, Egypt. Children in the intervention schools were required to wash hands twice each day, and health messages were provided through entertainment activities. Data were collected on student absenteeism and reasons for illness. School nurses collected nasal swabs from students with ILI, which were tested by using a qualitative diagnostic test for influenza A and B. Compared with results for the control group, in the intervention group, overall absences caused by ILI, diarrhea, conjunctivitis, and laboratory-confirmed influenza were reduced by 40%, 30%, 67%, and 50%, respectively (p<0.0001 for each illness). An intensive hand hygiene campaign was effective in reducing absenteeism caused by these illnesses.
PMCID: PMC3377412  PMID: 21470450
Hand hygiene; campaigns; influenza; viruses; schoolchildren; Egypt; absenteeism; expedited; research
23.  Absenteeism among hospital staff during an influenza epidemic: implications for immunoprophylaxis. 
The 1980-81 epidemic of influenza A/Bangkok 79 was responsible for increased absenteeism (1.7 times the rate for the corresponding period of the subsequent nonepidemic year) among selected hospital staff in Winnipeg's Health Sciences Centre. Retrospective study of employment records for 25 of the centre's largest departments showed excess sick-leave costs of about $24 500 during the 2-week period of peak absenteeism that included the epidemic. Although the centre was sampling prospectively for the virus the first positive results became available too late for chemoprophylactic measures to have been effective. The greater increase in absenteeism among nursing staff caring for patients with chronic respiratory disease and nurses working on general medical or pediatric acute infection/isolation wards suggested that these groups be targeted for influenza vaccination in hospitals.
PMCID: PMC1483462  PMID: 6467117
24.  Rapid detection of pandemic influenza in the presence of seasonal influenza 
BMC Public Health  2010;10:726.
Key to the control of pandemic influenza are surveillance systems that raise alarms rapidly and sensitively. In addition, they must minimise false alarms during a normal influenza season. We develop a method that uses historical syndromic influenza data from the existing surveillance system 'SERVIS' (Scottish Enhanced Respiratory Virus Infection Surveillance) for influenza-like illness (ILI) in Scotland.
We develop an algorithm based on the weekly case ratio (WCR) of reported ILI cases to generate an alarm for pandemic influenza. From the seasonal influenza data from 13 Scottish health boards, we estimate the joint probability distribution of the country-level WCR and the number of health boards showing synchronous increases in reported influenza cases over the previous week. Pandemic cases are sampled with various case reporting rates from simulated pandemic influenza infections and overlaid with seasonal SERVIS data from 2001 to 2007. Using this combined time series we test our method for speed of detection, sensitivity and specificity. Also, the 2008-09 SERVIS ILI cases are used for testing detection performances of the three methods with a real pandemic data.
We compare our method, based on our simulation study, to the moving-average Cumulative Sums (Mov-Avg Cusum) and ILI rate threshold methods and find it to be more sensitive and rapid. For 1% case reporting and detection specificity of 95%, our method is 100% sensitive and has median detection time (MDT) of 4 weeks while the Mov-Avg Cusum and ILI rate threshold methods are, respectively, 97% and 100% sensitive with MDT of 5 weeks. At 99% specificity, our method remains 100% sensitive with MDT of 5 weeks. Although the threshold method maintains its sensitivity of 100% with MDT of 5 weeks, sensitivity of Mov-Avg Cusum declines to 92% with increased MDT of 6 weeks. For a two-fold decrease in the case reporting rate (0.5%) and 99% specificity, the WCR and threshold methods, respectively, have MDT of 5 and 6 weeks with both having sensitivity close to 100% while the Mov-Avg Cusum method can only manage sensitivity of 77% with MDT of 6 weeks. However, the WCR and Mov-Avg Cusum methods outperform the ILI threshold method by 1 week in retrospective detection of the 2009 pandemic in Scotland.
While computationally and statistically simple to implement, the WCR algorithm is capable of raising alarms, rapidly and sensitively, for influenza pandemics against a background of seasonal influenza. Although the algorithm was developed using the SERVIS data, it has the capacity to be used at other geographic scales and for different disease systems where buying some early extra time is critical.
PMCID: PMC3001734  PMID: 21106071
25.  Predicting the Epidemic Sizes of Influenza A/H1N1, A/H3N2, and B: A Statistical Method 
PLoS Medicine  2011;8(7):e1001051.
Using weekly influenza surveillance data from the US CDC, Edward Goldstein and colleagues develop a statistical method to predict the sizes of epidemics caused by seasonal influenza strains. This method could inform decisions about the most appropriate vaccines or drugs needed early in the influenza season.
The epidemic sizes of influenza A/H3N2, A/H1N1, and B infections vary from year to year in the United States. We use publicly available US Centers for Disease Control (CDC) influenza surveillance data between 1997 and 2009 to study the temporal dynamics of influenza over this period.
Methods and Findings
Regional outpatient surveillance data on influenza-like illness (ILI) and virologic surveillance data were combined to define a weekly proxy for the incidence of each strain in the United States. All strains exhibited a negative association between their cumulative incidence proxy (CIP) for the whole season (from calendar week 40 of each year to calendar week 20 of the next year) and the CIP of the other two strains (the complementary CIP) from the start of the season up to calendar week 2 (or 3, 4, or 5) of the next year. We introduce a method to predict a particular strain's CIP for the whole season by following the incidence of each strain from the start of the season until either the CIP of the chosen strain or its complementary CIP exceed certain thresholds. The method yielded accurate predictions, which generally occurred within a few weeks of the peak of incidence of the chosen strain, sometimes after that peak. For the largest seasons in the data, which were dominated by A/H3N2, prediction of A/H3N2 incidence always occurred at least several weeks in advance of the peak.
Early circulation of one influenza strain is associated with a reduced total incidence of the other strains, consistent with the presence of interference between subtypes. Routine ILI and virologic surveillance data can be combined using this new method to predict the relative size of each influenza strain's epidemic by following the change in incidence of a given strain in the context of the incidence of cocirculating strains.
Please see later in the article for the Editors' Summary
Editors' Summary
Every winter in temperate countries, millions of people catch influenza, a viral infection of the nose, throat, and airways. Most infected individuals recover quickly but seasonal influenza outbreaks (epidemics) kill about half a million people annually. Epidemics of influenza occur because small but frequent changes in the viral proteins (antigens) to which the immune system responds mean that an immune response produced one year provides only partial protection against influenza the next year. Annual immunization with a vaccine that contains killed influenza viruses of the major circulating strains boosts this natural immunity and greatly reduces a person's chances of catching influenza. Influenza epidemics in temperate latitudes are usually caused by an influenza B virus or one of two influenza A subtypes called A/H3N2 and A/H1N1. The names of the influenza A viruses indicate the types of two major influenza antigens—hemagglutinin (H3 or H1) and neuraminidase (N2 or N1)—present in the viruses.
Why Was This Study Done?
At present, there is no way to predict whether influenza B or an influenza A subtype will be dominant (responsible for the majority of infections) in any given influenza season. There is also no way to predict the size of the epidemic that will be caused by each viral strain. Public health officials would like to be able to make predictions of this sort early in the winter to help them determine which measures to recommend to minimize the illness and death caused by influenza. In this study, the researchers use weekly influenza surveillance data collected by the US Centers for Disease Control and Prevention (CDC) to study the temporal dynamics of seasonal influenza in the United States between 1997 and 2009 and to develop a statistical method to predict the sizes of epidemics caused by influenza A/H1N1, A/H3N2, and B.
What Did the Researchers Do and Find?
The CDC influenza surveillance system collects information on the proportion of patients attending US outpatient facilities who have an influenza-like illness (fever and a cough and/or a sore throat in the absence of any known cause other than influenza) and on the proportion of respiratory viral isolates testing positive for specific influenza strains at US viral surveillance laboratories. The researchers combined these data to define a weekly “proxy” incidence of each influenza strain across the United States (an estimate of the number of new cases per week in the US population) and a cumulative incidence proxy (CIP) for each influenza season. For each strain, there was a negative association between its whole-season CIP and the early-season CIP of the other two strains (the complementary CIP). That is, high infection rates with one strain appeared to interfere with the transmission of other strains. Given this relationship, the researchers then developed a statistical algorithm (a step-by-step problem solving method) that accurately predicted the whole-season CIP for a particular strain by following the incidence of each strain from the start of the season until either its CIP or the complementary CIP had exceeded a specific threshold. So, for example, for influenza B, the algorithm provided an accurate prediction of the whole-season CIP before the peak of influenza B incidence for each season included in the study. Similarly, prediction of whole-season A/H3N2 incidence always occurred several weeks in advance of its weekly incidence peak.
What Do These Findings Mean?
These findings suggest that early circulation of one influenza strain is associated with a reduced total incidence of other strains, possibly because of cross-subtype immunity. Importantly, they also suggest that routine early-season surveillance data can be used to predict the relative size of the epidemics caused by each influenza strain in the United States and in other countries where sufficient surveillance data are available. Because the algorithm makes many assumptions and simplifies the behavior of influenza epidemics, its predictions may not always be accurate. Moreover, it needs to be tested with data collected over more influenza seasons. Nevertheless, the algorithm's ability to predict the relative epidemic size of A/H3N2, the influenza strain with the highest death rates, several weeks before its peak in seasons in which it was the dominant strain suggests that this predictive method could help public-health officials introduce relevant preventative and/or treatment measures early in each influenza season.
Additional Information
Please access these Web sites via the online version of this summary at
The US Centers for Disease Control and Prevention provides information for patients and health professionals on all aspects of seasonal influenza, including information about the US influenza surveillance system
The UK National Health Service Choices Web site also provides information for patients about seasonal influenza; the UK Health Protection Agency provides information on influenza surveillance in the UK
MedlinePlus has links to further information about influenza l (in English and Spanish)
PMCID: PMC3130020  PMID: 21750666

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