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1.  The Role of Viral Introductions in Sustaining Community-Based HIV Epidemics in Rural Uganda: Evidence from Spatial Clustering, Phylogenetics, and Egocentric Transmission Models 
PLoS Medicine  2014;11(3):e1001610.
Using different approaches to investigate HIV transmission patterns, Justin Lessler and colleagues find that extra-community HIV introductions are frequent and likely play a role in sustaining the epidemic in the Rakai community.
Please see later in the article for the Editors' Summary
It is often assumed that local sexual networks play a dominant role in HIV spread in sub-Saharan Africa. The aim of this study was to determine the extent to which continued HIV transmission in rural communities—home to two-thirds of the African population—is driven by intra-community sexual networks versus viral introductions from outside of communities.
Methods and Findings
We analyzed the spatial dynamics of HIV transmission in rural Rakai District, Uganda, using data from a cohort of 14,594 individuals within 46 communities. We applied spatial clustering statistics, viral phylogenetics, and probabilistic transmission models to quantify the relative contribution of viral introductions into communities versus community- and household-based transmission to HIV incidence. Individuals living in households with HIV-incident (n = 189) or HIV-prevalent (n = 1,597) persons were 3.2 (95% CI: 2.7–3.7) times more likely to be HIV infected themselves compared to the population in general, but spatial clustering outside of households was relatively weak and was confined to distances <500 m. Phylogenetic analyses of gag and env genes suggest that chains of transmission frequently cross community boundaries. A total of 95 phylogenetic clusters were identified, of which 44% (42/95) were two individuals sharing a household. Among the remaining clusters, 72% (38/53) crossed community boundaries. Using the locations of self-reported sexual partners, we estimate that 39% (95% CI: 34%–42%) of new viral transmissions occur within stable household partnerships, and that among those infected by extra-household sexual partners, 62% (95% CI: 55%–70%) are infected by sexual partners from outside their community. These results rely on the representativeness of the sample and the quality of self-reported partnership data and may not reflect HIV transmission patterns outside of Rakai.
Our findings suggest that HIV introductions into communities are common and account for a significant proportion of new HIV infections acquired outside of households in rural Uganda, though the extent to which this is true elsewhere in Africa remains unknown. Our results also suggest that HIV prevention efforts should be implemented at spatial scales broader than the community and should target key populations likely responsible for introductions into communities.
Please see later in the article for the Editors' Summary
Editors' Summary
About 35 million people (25 million of whom live in sub-Saharan Africa) are currently infected with HIV, the virus that causes AIDS, and about 2.3 million people become newly infected every year. HIV destroys immune system cells, leaving infected individuals susceptible to other infections. HIV infection can be controlled by taking antiretroviral drugs (antiretroviral therapy, or ART) daily throughout life. Although originally available only to people living in wealthy countries, recent political efforts mean that 9.7 million people in low- and middle-income countries now have access to ART. However, ART does not cure HIV infection, so prevention of viral transmission remains extremely important. Because HIV is usually transmitted through unprotected sex with an infected partner, individuals can reduce their risk of infection by abstaining from sex, by having one or a few partners, and by using condoms. Male circumcision also reduces HIV transmission. In addition to reducing illness and death among HIV-positive people, ART also reduces HIV transmission.
Why Was This Study Done?
Effective HIV control requires an understanding of how HIV spreads through sexual networks. These networks include sexual partnerships between individuals in households, between community members in different households, and between individuals from different communities. Local sexual networks (household and intra-community sexual partnerships) are sometimes assumed to be the dominant driving force in HIV spread in sub-Saharan Africa, but are viral introductions from sexual partnerships with individuals outside the community also important? This question needs answering because the effectiveness of interventions such as ART as prevention partly depends on how many new infections in an intervention area are attributable to infection from partners residing in that area and how many are attributable to infection from partners living elsewhere. Here, the researchers use three analytical methods—spatial clustering statistics, viral phylogenetics, and egocentric transmission modeling—to ask whether HIV transmission in rural Uganda is driven predominantly by intra-community sexual networks. Spatial clustering analysis uses the geographical coordinates of households to measure the tendency of HIV-infected people to cluster spatially at scales consistent with community transmission. Viral phylogenetic analysis examines the genetic relatedness of viruses; if transmission is through local networks, viruses in newly infected individuals should more closely resemble viruses in other community members than those in people outside the community. Egocentric transmission modelling uses information on the locations of recent sexual partners to estimate the proportions of new transmissions from household, intra-community, and extra-community partners.
What Did the Researchers Do and Find?
The researchers applied their three analytical methods to data collected from 14,594 individuals living in 46 communities (governmental administrative units) in Rakai District, Uganda. Spatial clustering analysis indicated that individuals who lived in households with individuals with incident HIV (newly diagnosed) or prevalent HIV (previously diagnosed) were 3.2 times more likely than the general population to be HIV-positive themselves. Spatial clustering outside households was relatively weak, however, and was confined to distances of less than half a kilometer. Viral phylogenetic analysis indicated that 44% of phylogenetic clusters (viruses with related genetic sequences found in more than one individual) were within households, but that 40% of clusters crossed community borders. Finally, analysis of the locations of self-reported sexual partners indicated that 39% of new viral transmissions occurred within stable household partnerships, but that among people newly infected by extra-household partners, nearly two-thirds were infected by partners from outside their community.
What Do These Findings Mean?
The results of all three analyses suggest that HIV introductions into communities are frequent and are likely to play an important role in sustaining HIV transmission in the Rakai District. Specifically, within this rural HIV-endemic region (a region where HIV infection is always present), viral introductions combined with intra-household transmission account for the majority of new infections, although community-based sexual networks also play a critical role in HIV transmission. These findings may not be generalizable to the broader Ugandan population or to other regions of Africa, and their accuracy is likely to be limited by the use of self-reported sexual partner data. Nevertheless, these findings indicate that the dynamics of HIV transmission in rural Uganda (and probably elsewhere) are complex. Consequently, to halt the spread of HIV, prevention efforts will need to be implemented at spatial scales broader than individual communities, and key populations that are likely to introduce HIV into communities will need to be targeted.
Additional Information
Please access these websites via the online version of this summary at
Information is available from the US National Institute of Allergy and Infectious Diseases on HIV infection and AIDS
NAM/aidsmap provides basic information about HIV/AIDS, and summaries of recent research findings on HIV care and treatment
Information is available from Avert, an international AIDS charity, on many aspects of HIV/AIDS, including information on HIV and AIDS in Uganda and on HIV prevention strategies (in English and Spanish)
The UNAIDS Report on the Global AIDS Epidemic 2013 provides up-to-date information about the AIDS epidemic and efforts to halt it
The Center for AIDS Prevention Studies (University of California, San Francisco) has a fact sheet about sexual networks and HIV prevention
Wikipedia provides information on spatial clustering analysis (note that Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
A PLOS Computational Biology Topic Page (a review article that is a published copy of record of a dynamic version of the article as found in Wikipedia) about viral phylodynamics is available
Personal stories about living with HIV/AIDS are available through Avert, NAM/aidsmap, and Healthtalkonline
PMCID: PMC3942316  PMID: 24595023
2.  Interior-Point Methods for Estimating Seasonal Parameters in Discrete-Time Infectious Disease Models 
PLoS ONE  2013;8(10):e74208.
Infectious diseases remain a significant health concern around the world. Mathematical modeling of these diseases can help us understand their dynamics and develop more effective control strategies. In this work, we show the capabilities of interior-point methods and nonlinear programming (NLP) formulations to efficiently estimate parameters in multiple discrete-time disease models using measles case count data from three cities. These models include multiplicative measurement noise and incorporate seasonality into multiple model parameters. Our results show that nearly identical patterns are estimated even when assuming seasonality in different model parameters, and that these patterns show strong correlation to school term holidays across very different social settings and holiday schedules. We show that interior-point methods provide a fast and flexible approach to parameterizing models that can be an alternative to more computationally intensive methods.
PMCID: PMC3805536  PMID: 24167542
3.  Incubation periods of viral gastroenteritis: a systematic review 
BMC Infectious Diseases  2013;13:446.
Accurate knowledge of incubation period is important to investigate and to control infectious diseases and their transmission, however statements of incubation period in the literature are often uncited, inconsistent, and/or not evidence based.
In a systematic review of the literature on five enteric viruses of public health importance, we found 256 articles with incubation period estimates, including 33 with data for pooled analysis.
We fit a log-normal distribution to pooled data and found the median incubation period to be 4.5 days (95% CI 3.9-5.2 days) for astrovirus, 1.2 days (95% CI 1.1-1.2 days) for norovirus genogroups I and II, 1.7 days (95% CI 1.5-1.8 days) for sapovirus, and 2.0 days (95% CI 1.4-2.4 days) for rotavirus.
Our estimates combine published data and provide sufficient quantitative detail to allow for these estimates to be used in a wide range of clinical and modeling applications. This can translate into improved prevention and control efforts in settings with transmission or the risk of transmission.
PMCID: PMC3849296  PMID: 24066865
Incubation period; Norovirus; Rotavirus; Caliciviruses; Astrovirus; Systematic review
4.  Interactions between serotypes of dengue highlight epidemiological impact of cross-immunity 
Dengue, a mosquito-borne virus of humans, infects over 50 million people annually. Infection with any of the four dengue serotypes induces protective immunity to that serotype, but does not confer long-term protection against infection by other serotypes. The immunological interactions between serotypes are of central importance in understanding epidemiological dynamics and anticipating the impact of dengue vaccines. We analysed a 38-year time series with 12 197 serotyped dengue infections from a hospital in Bangkok, Thailand. Using novel mechanistic models to represent different hypothesized immune interactions between serotypes, we found strong evidence that infection with dengue provides substantial short-term cross-protection against other serotypes (approx. 1–3 years). This is the first quantitative evidence that short-term cross-protection exists since human experimental infection studies performed in the 1950s. These findings will impact strategies for designing dengue vaccine studies, future multi-strain modelling efforts, and our understanding of evolutionary pressures in multi-strain disease systems.
PMCID: PMC3730691  PMID: 23825116
dengue; infectious disease modelling; cross-protection; time-series models
5.  Disease Persistence in Epidemiological Models: The Interplay between Vaccination and Migration 
Mathematical Biosciences  2012;239(1):91-96.
We consider the interplay of vaccination and migration rates on disease persistence in epidemiological systems. We show that short-term and long-term migration can inhibit disease persistence. As a result, we show how migration changes how vaccination rates should be chosen to maintain herd immunity. In a system of coupled SIR models, we analyze how disease eradication depends explicitly on vaccine distribution and migration connectivity. The analysis suggests potentially novel vaccination policies that underscore the importance of optimal placement of finite resources.
PMCID: PMC3391321  PMID: 22652034
epidemics; migration; vaccination; herd immunity
6.  Location-specific patterns of exposure to recent pre-pandemic strains of influenza A in southern China 
Nature communications  2011;2:423.
Variation in influenza incidence between locations is commonly observed on large spatial scales. It is unclear whether such variation occurs on smaller spatial scales and whether it is the result of heterogeneities in population demographics or more subtle differences in population structure and connectivity. Here we show significant differences in immunity to influenza A viruses among communities in China not explained by differences in population demographics. We randomly selected households from 5 randomly selected locations near Guangzhou, China to answer a questionnaire and provide a blood sample for serological testing against 5 recently circulating influenza viruses. We find a significant reduction in the frequency of detectable neutralization titers with increasing age, leveling off in older age groups. There are significant differences between locations in age, employment status, vaccination history, household size and housing conditions. However, after adjustment, significant variations in the frequency of detectable neutralization titers persists between locations. These results suggest there are characteristics of communities that drive influenza transmission dynamics apart from individual and household level risk factors, and that such factors have effects independent of strain.
PMCID: PMC3757505  PMID: 21829185
epidemiology; influenza; dynamics
8.  Rapid 13C Urea Breath Test to Identify Helicobacter pylori Infection in Emergency Department Patients with Upper Abdominal Pain 
Introduction: In emergency department (ED) patients with upper abdominal pain, management includes ruling out serious diseases and providing symptomatic relief. One of the major causes of upper abdominal pain is an ulcer caused by Helicobacter pylori (H. pylori), which can be treated and cured with antibiotics. We sought to estimate the prevalence of H. pylori infection in symptomatic patients using a convenience sample at a single urban academic ED and demonstrate the feasibility of ED-based testing.
Methods: We prospectively enrolled patients with a chief complaint of pain or discomfort in the upper abdomen for 1 year from February 2011 until February 2012 at a single academic urban ED. Enrolled subjects were tested for H. pylori using a rapid point of care 13C Urea Breath Test (UBT) [Exalenz Bioscience]. We compared patient characteristics between those who tested positive versus negative for the disease.
Results: A total of 205 patients with upper abdominal pain were tested over 12 months, and 24% (95% confidence interval: 19% to 30%) tested positive for H. pylori. Black subjects were more likely to test positive than white subjects (28% v. 6%, P < 0.001). Other factors, such as age and sex, were not different between the 2 groups.
Conclusion: In our ED, H. pylori infection was present in 1 in 4 patients with epigastric pain, and testing with a UBT was feasible. Further study is needed to determine the risk factors associated with infection, the prevalence of H. pylori in other EDs, the effect of the test on ED length of stay and the costeffectiveness of an ED-based test-and-treat strategy.
PMCID: PMC3656711  PMID: 23687549
9.  A systematic review of mathematical models of mosquito-borne pathogen transmission: 1970–2010 
Mathematical models of mosquito-borne pathogen transmission originated in the early twentieth century to provide insights into how to most effectively combat malaria. The foundations of the Ross–Macdonald theory were established by 1970. Since then, there has been a growing interest in reducing the public health burden of mosquito-borne pathogens and an expanding use of models to guide their control. To assess how theory has changed to confront evolving public health challenges, we compiled a bibliography of 325 publications from 1970 through 2010 that included at least one mathematical model of mosquito-borne pathogen transmission and then used a 79-part questionnaire to classify each of 388 associated models according to its biological assumptions. As a composite measure to interpret the multidimensional results of our survey, we assigned a numerical value to each model that measured its similarity to 15 core assumptions of the Ross–Macdonald model. Although the analysis illustrated a growing acknowledgement of geographical, ecological and epidemiological complexities in modelling transmission, most models during the past 40 years closely resemble the Ross–Macdonald model. Modern theory would benefit from an expansion around the concepts of heterogeneous mosquito biting, poorly mixed mosquito-host encounters, spatial heterogeneity and temporal variation in the transmission process.
PMCID: PMC3627099  PMID: 23407571
infectious disease dynamics; vector-borne disease; epidemiology; dengue; West Nile; filariasis
10.  Synchrony of Sylvatic Dengue Isolations: A Multi-Host, Multi-Vector SIR Model of Dengue Virus Transmission in Senegal 
Isolations of sylvatic dengue-2 virus from mosquitoes, humans and non-human primates in Senegal show synchronized multi-annual dynamics over the past 50 years. Host demography has been shown to directly affect the period between epidemics in other pathogen systems, therefore, one might expect unsynchronized multi-annual cycles occurring in hosts with dramatically different birth rates and life spans. However, in Senegal, we observe a single synchronized eight-year cycle across all vector species, suggesting synchronized dynamics in all vertebrate hosts. In the current study, we aim to explore two specific hypotheses: 1) primates with different demographics will experience outbreaks of dengue at different periodicities when observed as isolated systems, and that coupling of these subsystems through mosquito biting will act to synchronize incidence; and 2) the eight-year periodicity of isolations observed across multiple primate species is the result of long-term cycling in population immunity in the host populations. To test these hypotheses, we develop a multi-host, multi-vector Susceptible, Infected, Removed (SIR) model to explore the effects of coupling multiple host-vector systems of dengue virus transmission through cross-species biting rates. We find that under small amounts of coupling, incidence in the host species synchronize. Long-period multi-annual dynamics are observed only when prevalence in troughs reaches vanishingly small levels (), suggesting that these dynamics are inconsistent with sustained transmission in this setting, but are consistent with local dengue virus extinctions followed by reintroductions. Inclusion of a constant introduction of infectious individuals into the system causes the multi-annual periods to shrink, while the effects of coupling remain the same. Inclusion of a stochastic rate of introduction allows for multi-annual periods at a cost of reduced synchrony. Thus, we conclude that the eight-year period separating amplifications of dengue may be explained by cycling in immunity with stochastic introductions.
Author Summary
Dengue virus has been isolated from mosquitoes, non-human primates and humans in Senegalese jungles for the past 50 years. This sylvatic cycle shows unique transmission dynamics that are unexpected given previous theory and observation: First, the isolations appear to be synchronized across several host and vector species each with different natural histories of infection. Second, the periodicity of the isolations (time between outbreaks) is approximately eight years, much longer than the one or two year period observed in human endemic settings (e.g., Thailand, Brazil). In this paper we develop a multi-host, multi-vector differential equation model to test hypotheses that are potentially consistent with these observations. We find that coupling of separate primate-mosquito pairs through mosquito biting induces synchrony that is robust over a wide range of parameters. We also find that the eight year cycle is not robust to the inclusion of a constant introduction of infection, but is to a stochastic rate of introduction, and thus may be due to cycling of immunity among primates with long-period stochastic introductions. An accurate and thorough understanding of the sylvatic cycle of dengue may allow prediction of epidemics and lessen its impact on humans living in surrounding areas. This knowledge is especially important given the potential for these primate species to act as reservoirs for dengue in post-vaccination scenarios.
PMCID: PMC3510077  PMID: 23209867
11.  Reduction in the Incidence of Influenza A but Not Influenza B Associated with Use of Hand Sanitizer and Cough Hygiene in Schools: A Randomized Controlled Trial 
Laboratory-based evidence is lacking regarding the efficacy of non-pharmaceutical interventions such as alcohol-based hand sanitizer and respiratory hygiene to reduce the spread of influenza.
The Pittsburgh Influenza Prevention Project was a cluster-randomized trial conducted in ten Pittsburgh, PA elementary schools during the 2007-2008 influenza season. Children in five intervention schools received training in hand and respiratory hygiene, and were provided and encouraged to use hand sanitizer regularly. Children in five schools acted as controls. Children with influenza-like illness were tested for influenza A and B by RT-PCR.
3360 children participated. Using RT-PCR, 54 cases of influenza A and 50 cases of influenza B were detected. We found no significant effect of the intervention on the primary study outcome of all laboratory confirmed influenza cases (IRR 0.81 95% CI 0.54, 1.23). However, we did find statistically significant differences in protocol-specified ancillary outcomes. Children in intervention schools had significantly fewer laboratory-confirmed influenza A infections than children in control schools, with an adjusted IRR of 0.48 (95% CI 0.26, 0.87). Total absent episodes were also significantly lower among the intervention group than among the control group; adjusted IRR 0.74 (95% CI 0.56, 0.97).
Non-pharmaceutical interventions (respiratory hygiene education and the regular use of hand sanitizer) did not reduce total laboratory confirmed influenza. However the interventions did reduce school total absence episodes by 26% and laboratory-confirmed influenza A infections by 52%. Our results suggest that NPIs can be an important adjunct to influenza vaccination programs to reduce the number of influenza A infections among children.
PMCID: PMC3470868  PMID: 21691245
Influenza; Non-pharmaceutical Interventions; School-aged Children; Randomized Controlled Trial; Hand Sanitizer; Absence Surveillance; Laboratory Testing
12.  Variation in dengue virus plaque reduction neutralization testing: systematic review and pooled analysis 
BMC Infectious Diseases  2012;12:233.
The plaque reduction neutralization test (PRNT) remains the gold standard for the detection of serologic immune responses to dengue virus (DENV). While the basic concept of the PRNT remains constant, this test has evolved in multiple laboratories, introducing variation in materials and methods. Despite the importance of laboratory-to-laboratory comparability in DENV vaccine development, the effects of differing PRNT techniques on assay results, particularly the use of different dengue strains within a serotype, have not been fully characterized.
We conducted a systematic review and pooled analysis of published literature reporting individual-level PRNT titers to identify factors associated with heterogeneity in PRNT results and compared variation between strains within DENV serotypes and between articles using hierarchical models.
The literature search and selection criteria identified 8 vaccine trials and 25 natural exposure studies reporting 4,411 titers from 605 individuals using 4 different neutralization percentages, 3 cell lines, 12 virus concentrations and 51 strains. Of 1,057 titers from primary DENV exposure, titers to the exposure serotype were consistently higher than titers to non-exposure serotypes. In contrast, titers from secondary DENV exposures (n = 628) demonstrated high titers to exposure and non-exposure serotypes. Additionally, PRNT titers from different strains within a serotype varied substantially. A pooled analysis of 1,689 titers demonstrated strain choice accounted for 8.04% (90% credible interval [CrI]: 3.05%, 15.7%) of between-titer variation after adjusting for secondary exposure, time since DENV exposure, vaccination and neutralization percentage. Differences between articles (a proxy for inter-laboratory differences) accounted for 50.7% (90% CrI: 30.8%, 71.6%) of between-titer variance.
As promising vaccine candidates arise, the lack of standardized assays among diagnostic and research laboratories make unbiased inferences about vaccine-induced protection difficult. Clearly defined, widely accessible reference reagents, proficiency testing or algorithms to adjust for protocol differences would be a useful first step in improving dengue PRNT comparability and quality assurance.
PMCID: PMC3519720  PMID: 23020074
13.  Influenza Transmission in Households During the 1918 Pandemic 
American Journal of Epidemiology  2011;174(5):505-514.
Analysis of historical data has strongly shaped our understanding of the epidemiology of pandemic influenza and informs analysis of current and future epidemics. Here, the authors analyzed previously unpublished documents from a large household survey of the “Spanish” H1N1 influenza pandemic, conducted in 1918, for the first time quantifying influenza transmissibility at the person-to-person level during that most lethal of pandemics. The authors estimated a low probability of person-to-person transmission relative to comparable estimates from seasonal influenza and other directly transmitted infections but similar to recent estimates from the 2009 H1N1 pandemic. The authors estimated a very low probability of asymptomatic infection, a previously unknown parameter for this pandemic, consistent with an unusually virulent virus. The authors estimated a high frequency of prior immunity that they attributed to a largely unreported influenza epidemic in the spring of 1918 (or perhaps to cross-reactive immunity). Extrapolating from this finding, the authors hypothesize that prior immunity partially protected some populations from the worst of the fall pandemic and helps explain differences in attack rates between populations. Together, these analyses demonstrate that the 1918 influenza virus, though highly virulent, was only moderately transmissible and thus in a modern context would be considered controllable.
PMCID: PMC3695637  PMID: 21749971
disease transmission, infectious; epidemics; history of medicine; influenza, human; Orthomyxoviridae; pandemics; virulence
14.  Local Variations in Spatial Synchrony of Influenza Epidemics 
PLoS ONE  2012;7(8):e43528.
Understanding the mechanism of influenza spread across multiple geographic scales is not complete. While the mechanism of dissemination across regions and states of the United States has been described, understanding the determinants of dissemination between counties has not been elucidated. The paucity of high resolution spatial-temporal influenza incidence data to evaluate disease structure is often not available.
Methodology and Findings
We report on the underlying relationship between the spread of influenza and human movement between counties of one state. Significant synchrony in the timing of epidemics exists across the entire state and decay with distance (regional correlation = 62%). Synchrony as a function of population size display evidence of hierarchical spread with more synchronized epidemics occurring among the most populated counties. A gravity model describing movement between two populations is a stronger predictor of influenza spread than adult movement to and from workplaces suggesting that non-routine and leisure travel drive local epidemics.
These findings highlight the complex nature of influenza spread across multiple geographic scales.
PMCID: PMC3420894  PMID: 22916274
15.  Decay and Persistence of Maternal Dengue Antibodies among Infants in Bangkok 
Maternal dengue antibodies are important in determining the optimal age of dengue vaccination, but no study has quantified the heterogeneity of antibody decay and persistence in infants. We used longitudinal regression methods and survival analysis to measure decay and persistence times of serotype-specific neutralizing antibodies in 139 infants in Bangkok. A biphasic decay pattern was found with half-life times of 24–29 days between birth and 3 months and 44–150 days after 3 months. Atypical decay rates were found in 17% of infants for dengue virus-1 and -4. Median persistence times of plaque reduction neutralization tests > 10 ranged from 6 to 9 months. Persistence times for individuals could not be predicted based on antibody values at birth. Vaccination against dengue before 12 months of age would be ineffective if maternal antibodies at plaque reduction neutralization test levels below 80 interfere with vaccine uptake. Projections of average antibody persistence based on values at birth should be avoided in studies on dengue pathogenesis in infants.
PMCID: PMC3144837  PMID: 21813859
16.  Evidence for Antigenic Seniority in Influenza A (H3N2) Antibody Responses in Southern China 
PLoS Pathogens  2012;8(7):e1002802.
A key observation about the human immune response to repeated exposure to influenza A is that the first strain infecting an individual apparently produces the strongest adaptive immune response. Although antibody titers measure that response, the interpretation of titers to multiple strains – from the same sera – in terms of infection history is clouded by age effects, cross reactivity and immune waning. From July to September 2009, we collected serum samples from 151 residents of Guangdong Province, China, 7 to 81 years of age. Neutralization tests were performed against strains representing six antigenic clusters of H3N2 influenza circulating between 1968 and 2008, and three recent locally circulating strains. Patterns of neutralization titers were compared based on age at time of testing and age at time of the first isolation of each virus. Neutralization titers were highest for H3N2 strains that circulated in an individual's first decade of life (peaking at 7 years). Further, across strains and ages at testing, statistical models strongly supported a pattern of titers declining smoothly with age at the time a strain was first isolated. Those born 10 or more years after a strain emerged generally had undetectable neutralization titers to that strain (<1∶10). Among those over 60 at time of testing, titers tended to increase with age. The observed pattern in H3N2 neutralization titers can be characterized as one of antigenic seniority: repeated exposure and the immune response combine to produce antibody titers that are higher to more ‘senior’ strains encountered earlier in life.
Author Summary
The human immune response to an influenza infection is not the same for every infection. It has often been observed that we tend to have the highest antibody titer (and presumably our strongest immune response) against strains of influenza that we were exposed to early in life. In this study, we obtained blood samples from 151 people between 7 and 81 years of age and tested the samples for the concentration of antibodies to many different (H3N2) strains. We chose strains according to when they first circulated, starting with a strain isolated just after the 1968 pandemic and going all the way through to very recent strains. We found that a participant's age at the time a strain first circulated was very predictive of the strength of their antibody against that strain. Not just for the first strain they were likely to have seen, but also for the second, third and all subsequent strains circulating during their lifetime. This suggests to us that antibody titers to influenza A H3N2 follow a pattern of antigenic seniority, suggesting that we produce progressively fewer specific antibodies to each subsequent infection as we age.
PMCID: PMC3400560  PMID: 22829765
17.  Local Spatial and Temporal Processes of Influenza in Pennsylvania, USA: 2003–2009 
PLoS ONE  2012;7(3):e34245.
Influenza is a contagious respiratory disease responsible for annual seasonal epidemics in temperate climates. An understanding of how influenza spreads geographically and temporally within regions could result in improved public health prevention programs. The purpose of this study was to summarize the spatial and temporal spread of influenza using data obtained from the Pennsylvania Department of Health's influenza surveillance system.
Methodology and Findings
We evaluated the spatial and temporal patterns of laboratory-confirmed influenza cases in Pennsylvania, United States from six influenza seasons (2003–2009). Using a test of spatial autocorrelation, local clusters of elevated risk were identified in the South Central region of the state. Multivariable logistic regression indicated that lower monthly precipitation levels during the influenza season (OR = 0.52, 95% CI: 0.28, 0.94), fewer residents over age 64 (OR = 0.27, 95% CI: 0.10, 0.73) and fewer residents with more than a high school education (OR = 0.76, 95% CI: 0.61, 0.95) were significantly associated with membership in this cluster. In addition, time series analysis revealed a temporal lag in the peak timing of the influenza B epidemic compared to the influenza A epidemic.
These findings illustrate a distinct spatial cluster of cases in the South Central region of Pennsylvania. Further examination of the regional transmission dynamics within these clusters may be useful in planning public health influenza prevention programs.
PMCID: PMC3314628  PMID: 22470544
18.  A nonlinear programming approach for estimation of transmission parameters in childhood infectious disease using a continuous time model 
Mathematical models can enhance our understanding of childhood infectious disease dynamics, but these models depend on appropriate parameter values that are often unknown and must be estimated from disease case data. In this paper, we develop a framework for efficient estimation of childhood infectious disease models with seasonal transmission parameters using continuous differential equations containing model and measurement noise. The problem is formulated using the simultaneous approach where all state variables are discretized, and the discretized differential equations are included as constraints, giving a large-scale algebraic nonlinear programming problem that is solved using a nonlinear primal–dual interior-point solver. The technique is demonstrated using measles case data from three different locations having different school holiday schedules, and our estimates of the seasonality of the transmission parameter show strong correlation to school term holidays. Our approach gives dramatic efficiency gains, showing a 40–400-fold reduction in solution time over other published methods. While our approach has an increased susceptibility to bias over techniques that integrate over the entire unknown state-space, a detailed simulation study shows no evidence of bias. Furthermore, the computational efficiency of our approach allows for investigation of a large model space compared with more computationally intensive approaches.
PMCID: PMC3385750  PMID: 22337634
nonlinear optimization; measles; infectious diseases; mathematical programming; Gauss–Lobatto collocation
19.  Measuring the Performance of Vaccination Programs Using Cross-Sectional Surveys: A Likelihood Framework and Retrospective Analysis 
PLoS Medicine  2011;8(10):e1001110.
Justin Lessler and colleagues describe a method that estimates the fraction of a population accessible to vaccination activities, and they apply it to measles vaccination in three African countries: Ghana, Madagascar, and Sierra Leone.
The performance of routine and supplemental immunization activities is usually measured by the administrative method: dividing the number of doses distributed by the size of the target population. This method leads to coverage estimates that are sometimes impossible (e.g., vaccination of 102% of the target population), and are generally inconsistent with the proportion found to be vaccinated in Demographic and Health Surveys (DHS). We describe a method that estimates the fraction of the population accessible to vaccination activities, as well as within-campaign inefficiencies, thus providing a consistent estimate of vaccination coverage.
Methods and Findings
We developed a likelihood framework for estimating the effective coverage of vaccination programs using cross-sectional surveys of vaccine coverage combined with administrative data. We applied our method to measles vaccination in three African countries: Ghana, Madagascar, and Sierra Leone, using data from each country's most recent DHS survey and administrative coverage data reported to the World Health Organization. We estimate that 93% (95% CI: 91, 94) of the population in Ghana was ever covered by any measles vaccination activity, 77% (95% CI: 78, 81) in Madagascar, and 69% (95% CI: 67, 70) in Sierra Leone. “Within-activity” inefficiencies were estimated to be low in Ghana, and higher in Sierra Leone and Madagascar. Our model successfully fits age-specific vaccination coverage levels seen in DHS data, which differ markedly from those predicted by naïve extrapolation from country-reported and World Health Organization–adjusted vaccination coverage.
Combining administrative data with survey data substantially improves estimates of vaccination coverage. Estimates of the inefficiency of past vaccination activities and the proportion not covered by any activity allow us to more accurately predict the results of future activities and provide insight into the ways in which vaccination programs are failing to meet their goals.
Please see later in the article for the Editors' Summary
Editors' Summary
Immunization (vaccination) is a proven, cost-effective tool for controlling life-threatening infectious diseases. It provides protection against infectious diseases by priming the human immune system to respond quickly and efficiently to bacteria, viruses, and other disease-causing organisms (pathogens). Whenever the human body is exposed to a pathogen, the immune system—a network of cells, tissues, and organs—mounts an attack against the foreign invader. Importantly, the immune system “learns” from the encounter, and the next time the body is exposed to the same pathogen, the immune system responds much faster to the threat. Immunization exposes the body to a very small amount of a pathogen, thereby safely providing protection against subsequent infection. More than two billion deaths are averted every year through routine childhood immunization and supplemental immunization activities (mass vaccination campaigns designed to increase vaccination coverage where immunization goals have not been reached by routine vaccination). Indeed, these two types of vaccination activities have eliminated smallpox from the world and are close to doing the same for several other infectious diseases.
Why Was This Study Done?
To reduce deaths from infectious diseases even further, it is important to know the proportion of the population reached by vaccination activities. At present, countries report vaccination coverage to the World Health Organization (WHO) that is calculated by dividing the number of vaccine doses delivered during the activity by the size of the target population. However, estimates arrived at through this “administrative method” do not account for vaccine doses that were not actually delivered, and can only reflect a single vaccination activity, which prevents us from identifying populations that may be systematically missed by all vaccination activities (for example, children living in remote areas, or children whose parents refuse vaccination). Moreover, estimates of coverage obtained by the administrative method rarely agree with estimates obtained through cross-sectional surveys such as Demographic and Health Surveys (DHS), which are household surveys of family circumstances and health undertaken at a single time point. In this study, the researchers developed a method for measuring the performance of vaccination activities that estimates the fraction of the population accessible to these activities and within-activity inefficiencies. They then tested their method by applying it to measles vaccination in three African countries; before 1980, measles killed about 2.6 million children worldwide every year, but vaccination activities have reduced this death toll to about 164,000 per year.
What Did the Researchers Do and Find?
The researchers developed a set of formulae (a “likelihood framework”) to estimate the effective coverage of vaccination activities using data on vaccine coverage from cross-sectional surveys and administrative data. They then applied their method to measles vaccination in Ghana, Madagascar, and Sierra Leone using data obtained in each country's most recent DHS survey and administrative data reported to WHO. The researchers estimate that 93%, 77%, and 65% of the target populations in Ghana, Madagascar, and Sierra Leone, respectively, were ever covered by any vaccination activity, and that inefficiencies within vaccination activities were low for Ghana, but higher for Madagascar and Sierra Leone. Consequently, the researchers' estimates of vaccination activity coverage were substantially lower than the administrative estimates for Madagascar and Sierra Leone but only slightly lower than that for Ghana. Finally, the researchers' estimates of routine vaccination coverage were generally lower than WHO-adjusted estimates but broadly agreed with age-specific vaccination coverage levels from DHS surveys.
What Do These Findings Mean?
Although the accuracy of the estimates provided by this likelihood framework depends on the assumptions included in the framework and the quality of the data fed into it, these findings show that, by combining administrative data with survey data, estimates of vaccine coverage can be substantially improved. By providing estimates of both the inefficiency of past vaccination activities and the proportion of the target population inaccessible to any vaccination activity, this method should help public health experts predict the results of future activities and help them understand why some vaccination programs fail to meet their goals. Importantly, knowing both the size of the inaccessible population and the inefficiency level of past programs makes it possible to estimate the effect of providing additional doses of vaccine on vaccination coverage. Finally, the application of this new method might help individual countries understand how susceptibility to specific infectious diseases is building up in their population and enable them to avoid outbreaks similar to the measles outbreaks that have recently occurred in several African countries.
Additional Information
Please access these Web sites via the online version of this summary at
WHO provides information about immunization and details of its Expanded Program on Immunization and its Global Immunization Vision and Strategy; WHO Africa provides details about measles immunization in Africa; a photo story about mass measles vaccination in Côte d’Ivoire is available (some material in several languages)
The UK National Health Service Choices website provides information for members of the public about immunization
The Measles Initiative is a collaborative effort that aims to reduce global measles mortality through mass vaccination campaigns and by strengthening routine immunization; its website includes information on measles and measles vaccination, including photos and videos of vaccination activities
MedlinePlus provides links to additional resources about immunization and about measles (in English and Spanish)
The charity website Healthtalkonline has interviews with UK parents about their experience of immunizing their children
PMCID: PMC3201935  PMID: 22039353
20.  Inferring the serotype of dengue virus infections based on pre- and post-infection neutralizing antibody titers 
The Journal of infectious diseases  2010;202(7):1002-1010.
Currently, the only tests capable of determining the serotype of a dengue virus (DENV) infection require sampling during the acute viremic period. No test can accurately detect the serotype of past DENV infections. The standard assay for detecting serotype-specific antibody against DENV is PRNT though the performance of this test continues to be evaluated.
From a cohort study among schoolchildren in Thailand PRNT were determined in serum samples collected before and after infection. A multinomial logistic regression model was used to infer the serotype of intercurrent DENV infections. Models were validated based on PCR identification of DENV serotypes.
The serotype of infection inferred by the model corresponded with PCR in 67.6% of cases and the kappa statistic was 0.479. A model for 35 cases with primary seroconversion correctly identified serotypes of infection in 77.1% of cases compared to 66.9% using a model for 169 cases with secondary seroconversion. The best model using only post-infection PRNT values correctly inferred the serotype of infection in 60.3% of cases.
A statistical model based on both pre- and post-infection PRNT values can be used for inference on the serotype of DENV infections in prospective studies such as vaccine trials.
PMCID: PMC2943243  PMID: 20738205
dengue; children; serotype; Plaque reduction neutralization; vaccine; antibodies; Thailand
21.  H1N1pdm in the Americas 
Epidemics  2010;2(3):132-138.
In late April 2009 the emergence of 2009 pandemic influenza A (H1N1pdm) virus was detected in humans. From its detection through July 18th, 2009, confirmed cases of H1N1pdm in the Americas were periodically reported to the Pan-American Health Organization (PAHO) by member states. Because the Americas span much of the world’s latitudes, this data provides an excellent opportunity to examine variation in H1N1pdm transmission by season.
Using reports from PAHO member states from April 26th, 2009 through July 18th, 2009, we characterize the early spread of the H1N1 pandemic in the Americas. For a geographically representative sample of member states we estimate the reproductive number (R) of H1N1pdm over the reporting period. The association between these estimates and latitude, temperature, humidity and population age structure was estimated.
Estimates of the peak reproductive number of H1N1pdm ranged from 1.3 (for Panama, Colombia) to 2.1 (for Chile). We found that reproductive number estimates were most associated with latitude in both univariate and multivariate analyses. To the extent that latitude is a proxy for seasonal changes in climate and behavior, this association suggests a strong seasonal component to H1N1pdm transmission. However, the reasons for this seasonality remain unclear.
PMCID: PMC2937282  PMID: 20847900
pandemic H1N1; influenza; seasonality; reproductive number
22.  Analysis of Component Findings in 79 Patients Diagnosed with VACTERL Association 
VACTERL association is a relatively common condition, though the causes remain poorly understood. We present data on 79 patients diagnosed with VACTERL association, and perform statistical analysis on a selected subset of 60 patients with at least three component features, and who, after review, did not meet criteria for a likely alternate diagnosis. Considered individually, no two component features are significantly associated, but several multivariate statistical techniques suggest novel patterns of the co-occurrence of component features, and latent class cluster analysis demonstrates the presence of five major subgroups of patients. These findings have implications for both our understanding of VACTERL association and for the approach to research involving this condition.
PMCID: PMC2930065  PMID: 20683998
VACTERL; VACTERL association; VATER; VATER association
23.  Prediction of Dengue Incidence Using Search Query Surveillance 
The use of internet search data has been demonstrated to be effective at predicting influenza incidence. This approach may be more successful for dengue which has large variation in annual incidence and a more distinctive clinical presentation and mode of transmission.
We gathered freely-available dengue incidence data from Singapore (weekly incidence, 2004–2011) and Bangkok (monthly incidence, 2004–2011). Internet search data for the same period were downloaded from Google Insights for Search. Search terms were chosen to reflect three categories of dengue-related search: nomenclature, signs/symptoms, and treatment. We compared three models to predict incidence: a step-down linear regression, generalized boosted regression, and negative binomial regression. Logistic regression and Support Vector Machine (SVM) models were used to predict a binary outcome defined by whether dengue incidence exceeded a chosen threshold. Incidence prediction models were assessed using and Pearson correlation between predicted and observed dengue incidence. Logistic and SVM model performance were assessed by the area under the receiver operating characteristic curve. Models were validated using multiple cross-validation techniques.
The linear model selected by AIC step-down was found to be superior to other models considered. In Bangkok, the model has an , and a correlation of 0.869 between fitted and observed. In Singapore, the model has an , and a correlation of 0.931. In both Singapore and Bangkok, SVM models outperformed logistic regression in predicting periods of high incidence. The AUC for the SVM models using the 75th percentile cutoff is 0.906 in Singapore and 0.960 in Bangkok.
Internet search terms predict incidence and periods of large incidence of dengue with high accuracy and may prove useful in areas with underdeveloped surveillance systems. The methods presented here use freely available data and analysis tools and can be readily adapted to other settings.
Author Summary
Improvements in surveillance, prediction of outbreaks and the monitoring of the epidemiology of dengue virus in countries with underdeveloped surveillance systems are of great importance to ministries of health and other public health decision makers who are often constrained by budget or man-power. Google Flu Trends has proven successful in providing an early warning system for outbreaks of influenza weeks before case data are reported. We believe that there is greater potential for this technique for dengue, as the incidence of this pathogen can vary by a factor of ten in some settings, making prediction all the more important in public health planning. In this paper, we demonstrate the utility of Google search terms in predicting dengue incidence in Singapore and Bangkok, Thailand using several regression techniques. Incidence data were provided by the Singapore Ministry of Health and the Thailand Bureau of Epidemiology. We find our models predict incident cases well (correlation greater than 0.8) and periods of high incidence equally well (AUC greater than 0.95). All data and analysis code used in our study are available free online and can be adapted to other settings.
PMCID: PMC3149016  PMID: 21829744
24.  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
25.  Evidence for inheritance in patients with VACTERL association 
Human genetics  2010;127(6):731-733.
VACTERL/VATER association is typically a sporadic disorder. We present data on inheritance in 78 probands with VACTERL association, and show that 9% of probands have a primary relative with at least one component feature of VACTERL association. The prevalence of component features in first-degree relatives is significantly higher than expected in the general population, which has implications for counseling of affected families and for research into possible etiologies.
PMCID: PMC2871973  PMID: 20369369

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