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1.  Evaluation of Targeted Mass Cholera Vaccination Strategies in Bangladesh: A Demonstration of a New Cost-Effectiveness Calculator 
Growing interest in mass vaccination with oral cholera vaccine in endemic and epidemic settings will require policymakers to evaluate how to allocate these vaccines in the most efficient manner. Because cholera, when treated properly, has a low case fatality rate, it may not be economically feasible to vaccinate an entire population. Using a new publicly available calculator for estimating the cost-effectiveness of mass vaccination, we show how targeting high-risk subpopulations for vaccination could be cost-effective in Bangladesh. The approach described here is general enough to adapt to different settings or to other vaccine-preventable diseases.
doi:10.4269/ajtmh.14-0159
PMCID: PMC4257645  PMID: 25294614
2.  School opening dates predict pandemic influenza A (H1N1) epidemics in the USA 
The Journal of infectious diseases  2010;202(6):877-880.
The opening of schools in late summer of 2009 may have triggered the fall wave of pandemic influenza A(H1N1) in the United States. We found that elevated percent of outpatient visits for influenza-like illness (ILI%) occurred an average of 14 days after schools opened in a state in the fall of 2009. The timing of these events was highly correlated (Spearman’s correlation coefficient=0.62, p < 1.0 × 10−5). This result provides evidence that transmission in schools catalyzes community-wide transmission. School opening dates can be useful for future pandemic planning, and influenza mitigation strategies should be targeted at school populations before the influenza season.
doi:10.1086/655810
PMCID: PMC2939723  PMID: 20704486
Children; Epidemics; Human Influenza; Pandemics
3.  Comparative Effectiveness of Different Strategies of Oral Cholera Vaccination in Bangladesh: A Modeling Study 
Background
Killed, oral cholera vaccines have proven safe and effective, and several large-scale mass cholera vaccination efforts have demonstrated the feasibility of widespread deployment. This study uses a mathematical model of cholera transmission in Bangladesh to examine the effectiveness of potential vaccination strategies.
Methods & Findings
We developed an age-structured mathematical model of cholera transmission and calibrated it to reproduce the dynamics of cholera in Matlab, Bangladesh. We used the model to predict the effectiveness of different cholera vaccination strategies over a period of 20 years. We explored vaccination programs that targeted one of three increasingly focused age groups (the entire vaccine-eligible population of age one year and older, children of ages 1 to 14 years, or preschoolers of ages 1 to 4 years) and that could occur either as campaigns recurring every five years or as continuous ongoing vaccination efforts. Our modeling results suggest that vaccinating 70% of the population would avert 90% of cholera cases in the first year but that campaign and continuous vaccination strategies differ in effectiveness over 20 years. Maintaining 70% coverage of the population would be sufficient to prevent sustained transmission of endemic cholera in Matlab, while vaccinating periodically every five years is less effective. Selectively vaccinating children 1–14 years old would prevent the most cholera cases per vaccine administered in both campaign and continuous strategies.
Conclusions
We conclude that continuous mass vaccination would be more effective against endemic cholera than periodic campaigns. Vaccinating children averts more cases per dose than vaccinating all age groups, although vaccinating only children is unlikely to control endemic cholera in Bangladesh. Careful consideration must be made before generalizing these results to other regions.
Author Summary
Bangladesh has a high burden of cholera and may become the first country to use cholera vaccine on a large scale. Mass cholera vaccination may be hard to justify to international funding agencies because of the modest efficacy of existing vaccines and their limited duration of protection. However, mass cholera vaccination can induce high levels of indirect protection in a population, i.e., protecting even unvaccinated individuals by lowering cholera incidence, and a case for cost-effective cholera vaccination could be made. Mathematical modeling is one way to predict the magnitude of indirect protection conferred by a proposed vaccination program. Here, we predict the effectiveness of various mass cholera vaccination strategies in Bangladesh using a mathematical model. We found that maintaining high levels of vaccination coverage in children could be very effective in reducing the burden of cholera, and secondary transmission of cholera would virtually stop when 70% of the population is vaccinated. Mathematical modeling may play a key role in planning widespread cholera vaccination efforts in Bangladesh and other countries.
doi:10.1371/journal.pntd.0003343
PMCID: PMC4256212  PMID: 25473851
4.  Modeling cholera outbreaks 
Mathematical modeling can be a valuable tool for studying infectious disease outbreak dynamics and simulating the effects of possible interventions. Here, we describe approaches to modeling cholera outbreaks and how models have been applied to explore intervention strategies, particularly in Haiti. Mathematical models can play an important role in formulating and evaluating complex cholera outbreak response options. Major challenges to cholera modeling are insufficient data for calibrating models and the need to tailor models for different outbreak scenarios.
doi:10.1007/82_2013_307
PMCID: PMC4238032  PMID: 23412687
5.  Spatial Transmission of 2009 Pandemic Influenza in the US 
PLoS Computational Biology  2014;10(6):e1003635.
The 2009 H1N1 influenza pandemic provides a unique opportunity for detailed examination of the spatial dynamics of an emerging pathogen. In the US, the pandemic was characterized by substantial geographical heterogeneity: the 2009 spring wave was limited mainly to northeastern cities while the larger fall wave affected the whole country. Here we use finely resolved spatial and temporal influenza disease data based on electronic medical claims to explore the spread of the fall pandemic wave across 271 US cities and associated suburban areas. We document a clear spatial pattern in the timing of onset of the fall wave, starting in southeastern cities and spreading outwards over a period of three months. We use mechanistic models to tease apart the external factors associated with the timing of the fall wave arrival: differential seeding events linked to demographic factors, school opening dates, absolute humidity, prior immunity from the spring wave, spatial diffusion, and their interactions. Although the onset of the fall wave was correlated with school openings as previously reported, models including spatial spread alone resulted in better fit. The best model had a combination of the two. Absolute humidity or prior exposure during the spring wave did not improve the fit and population size only played a weak role. In conclusion, the protracted spread of pandemic influenza in fall 2009 in the US was dominated by short-distance spatial spread partially catalysed by school openings rather than long-distance transmission events. This is in contrast to the rapid hierarchical transmission patterns previously described for seasonal influenza. The findings underline the critical role that school-age children play in facilitating the geographic spread of pandemic influenza and highlight the need for further information on the movement and mixing patterns of this age group.
Author Summary
The determinants of influenza spatial spread are not fully understood, in part due to the insufficient geographic resolution of incidence data. We address this using a fine-grained private sector electronic health database of insurance claims data from health encounters in the US during 2009. We used physician diagnoses codes to generate a dataset of the weekly number of office visits with diagnosed influenza-like illness for 271 US locations. Applying statistical and mathematical models to these disease data, we find that the main autumn wave of the 2009 pandemic in the US was remarkably spatially structured. Its onset in the South Eastern US precipitated a slow radial spread that took 3 months to diffuse across the country. These patterns were replicated by models that included short-distance spatial transmission between nearby locations and increased transmission rates when school was in session. Our results contrast with previous modelling studies that indicated that environmental factors, population sizes, and long-distance transmission events (air traffic) are major determinants in disease spread. We conclude that the 2009 pandemic autumn wave spread slowly because transmissibility of the influenza virus was relatively low and children (who travel long distance far less than adults) were the predominant sources of infection.
doi:10.1371/journal.pcbi.1003635
PMCID: PMC4055284  PMID: 24921923
6.  Modelling the global transmission of antiviral-resistant influenza viruses 
The mutations that confer resistance to antiviral agents are thought to be detrimental, or at best neutral, to influenza virus fitness. The fact that resistant influenza strains can circulate and sometimes replace sensitive strains is of great public health concern. We used mathematical modelling to understand how antiviral-resistant influenza viruses can spread across the globe. We developed a model to simulate the transmission of influenza among 321 cities. We used the model to test various hypotheses about the transmission of antiviral-resistant influenza viruses by comparing the model’s output with the observed rise in antiviral resistance of seasonal A(H1N1) influenza viruses between 2006 and 2009. We found that a resistant strain of influenza could not displace the sensitive strain as rapidly as has been observed unless it was more transmissible than the sensitive strain in the general population. We believe that an antiviral-resistant strain displaced the antiviral-sensitive seasonal A(H1N1) virus by hitchhiking on an escape mutation.
doi:10.1111/irv.12043
PMCID: PMC3747505  PMID: 23279898
7.  The Effects of Vector Movement and Distribution in a Mathematical Model of Dengue Transmission 
PLoS ONE  2013;8(10):e76044.
Background
Mathematical models have been used to study the dynamics of infectious disease outbreaks and predict the effectiveness of potential mass vaccination campaigns. However, models depend on simplifying assumptions to be tractable, and the consequences of making such assumptions need to be studied. Two assumptions usually incorporated by mathematical models of vector-borne disease transmission is homogeneous mixing among the hosts and vectors and homogeneous distribution of the vectors.
Methodology/Principal Findings
We explored the effects of mosquito movement and distribution in an individual-based model of dengue transmission in which humans and mosquitoes are explicitly represented in a spatial environment. We found that the limited flight range of the vector in the model greatly reduced its ability to transmit dengue among humans. A model that does not assume a limited flight range could yield similar attack rates when transmissibility of dengue was reduced by 39%. A model in which mosquitoes are distributed uniformly across locations behaves similarly to one in which the number of mosquitoes per location is drawn from an exponential distribution with a slightly higher mean number of mosquitoes per location. When the models with different assumptions were calibrated to have similar human infection attack rates, mass vaccination had nearly identical effects.
Conclusions/Significance
Small changes in assumptions in a mathematical model of dengue transmission can greatly change its behavior, but estimates of the effectiveness of mass dengue vaccination are robust to some simplifying assumptions typically made in mathematical models of vector-borne disease.
doi:10.1371/journal.pone.0076044
PMCID: PMC3804532  PMID: 24204590
8.  The global spread of drug-resistant influenza 
Resistance to oseltamivir, the most widely used influenza antiviral drug, spread to fixation in seasonal influenza A(H1N1) between 2006 and 2009. This sudden rise in resistance seemed puzzling given the low overall level of the oseltamivir usage and the lack of a correlation between local rates of resistance and oseltamivir usage. We used a stochastic simulation model and deterministic approximations to examine how such events can occur, and in particular to determine how the rate of fixation of the resistant strain depends both on its fitness in untreated hosts as well as the frequency of antiviral treatment. We found that, for the levels of antiviral usage in the population, the resistant strain will eventually spread to fixation, if it is not attenuated in transmissibility relative to the drug-sensitive strain, but not at the speed observed in seasonal H1N1. The extreme speed with which the resistance spread in seasonal H1N1 suggests that the resistant strain had a transmission advantage in untreated hosts, and this could have arisen from genetic hitchhiking, or from the mutations responsible for resistance and compensation. Importantly, our model also shows that resistant virus will fail to spread if it is even slightly less transmissible than its sensitive counterpart—a finding of relevance given that resistant pandemic influenza (H1N1) 2009 may currently suffer from a small, but nonetheless experimentally perceptible reduction in transmissibility.
doi:10.1098/rsif.2011.0427
PMCID: PMC3284134  PMID: 21865253
influenza; antiviral agents; mathematical model
9.  A Nice Day for an Infection? Weather Conditions and Social Contact Patterns Relevant to Influenza Transmission 
PLoS ONE  2012;7(11):e48695.
Although there is no doubt that significant morbidity and mortality occur during annual influenza epidemics, the role of contextual circumstances, which catalyze seasonal influenza transmission, remains unclear. Weather conditions are believed to affect virus survival, efficiency of transmission and host immunity, but seasonality may also be driven by a tendency of people to congregate indoors during periods of bad weather. To test this hypothesis, we combined data from a social contact survey in Belgium with local weather data. In the absence of a previous in-depth weather impact analysis of social contact patterns, we explored the possibilities and identified pitfalls. We found general dominance of day-type (weekend, holiday, working day) over weather conditions, but nonetheless observed an increase in long duration contacts (1 hour) on regular workdays with low temperatures, almost no precipitation and low absolute humidity of the air. Interestingly, these conditions are often assumed to be beneficial for virus survival and transmission. Further research is needed to establish the impact of the weather on social contacts. We recommend that future studies sample over a broad spectrum of weather conditions and day types and include a sufficiently large proportion of holiday periods and weekends.
doi:10.1371/journal.pone.0048695
PMCID: PMC3498265  PMID: 23155399
10.  Controlling Dengue with Vaccines in Thailand 
Background
Dengue is a mosquito-borne infectious disease that constitutes a growing global threat with the habitat expansion of its vectors Aedes aegyti and A. albopictus and increasing urbanization. With no effective treatment and limited success of vector control, dengue vaccines constitute the best control measure for the foreseeable future. With four interacting dengue serotypes, the development of an effective vaccine has been a challenge. Several dengue vaccine candidates are currently being tested in clinical trials. Before the widespread introduction of a new dengue vaccine, one needs to consider how best to use limited supplies of vaccine given the complex dengue transmission dynamics and the immunological interaction among the four dengue serotypes.
Methodology/Principal Findings
We developed an individual-level (including both humans and mosquitoes), stochastic simulation model for dengue transmission and control in a semi-rural area in Thailand. We calibrated the model to dengue serotype-specific infection, illness and hospitalization data from Thailand. Our simulations show that a realistic roll-out plan, starting with young children then covering progressively older individuals in following seasons, could reduce local transmission of dengue to low levels. Simulations indicate that this strategy could avert about 7,700 uncomplicated dengue fever cases and 220 dengue hospitalizations per 100,000 people at risk over a ten-year period.
Conclusions/Significance
Vaccination will have an important role in controlling dengue. According to our modeling results, children should be prioritized to receive vaccine, but adults will also need to be vaccinated if one wants to reduce community-wide dengue transmission to low levels.
Author Summary
An estimated 40% of the world's population is at risk of infection with dengue, a mosquito-borne disease that can lead to hospitalization or death. Dengue vaccines are currently being tested in clinical trials and at least one product will likely be available within a couple of years. Before widespread deployment, one should plan how best to use limited supplies of vaccine. We developed a mathematical model of dengue transmission in semi-rural Thailand to help evaluate different vaccination strategies. Our modeling results indicate that children should be prioritized to receive vaccine to reduce dengue-related morbidity, but adults will also need to be vaccinated if one wants to eliminate local dengue transmission. Dengue is a challenging disease to study because of its four interacting serotypes, seasonality of its transmission, and pre-existing immunity in a population. Models such as this one are useful coherent framework for synthesizing these complex issues and evaluating potential public health interventions such as mass vaccination.
doi:10.1371/journal.pntd.0001876
PMCID: PMC3493390  PMID: 23145197
11.  Planning for the Control of Pandemic Influenza A (H1N1) in Los Angeles County and the United States 
American Journal of Epidemiology  2011;173(10):1121-1130.
Mathematical and computer models can provide guidance to public health officials by projecting the course of an epidemic and evaluating control measures. The authors built upon an existing collaboration between an academic research group and the Los Angeles County, California, Department of Public Health to plan for and respond to the first and subsequent years of pandemic influenza A (H1N1) circulation. The use of models allowed the authors to 1) project the timing and magnitude of the epidemic in Los Angeles County and the continental United States; 2) predict the effect of the influenza mass vaccination campaign that began in October 2009 on the spread of pandemic H1N1 in Los Angeles County and the continental United States; and 3) predict that a third wave of pandemic influenza in the winter or spring of 2010 was unlikely to occur. The close collaboration between modelers and public health officials during pandemic H1N1 spread in the fall of 2009 helped Los Angeles County officials develop a measured and appropriate response to the unfolding pandemic and establish reasonable goals for mitigation of pandemic H1N1.
doi:10.1093/aje/kwq497
PMCID: PMC3121321  PMID: 21427173
communicable disease control; influenza, human; influenza vaccines; mass vaccination
12.  Learning oncogenic pathways from binary genomic instability data 
Biometrics  2011;67(1):164-173.
Summary
Genomic instability, the propensity of aberrations in chromosomes, plays a critical role in the development of many diseases. High throughput genotyping experiments have been performed to study genomic instability in diseases. The output of such experiments can be summarized as high dimensional binary vectors, where each binary variable records aberration status at one marker locus. It is of keen interest to understand how aberrations may interact with each other, as it provides insight into the process of the disease development. In this paper, we propose a novel method, LogitNet, to infer such interactions among these aberration events. The method is based on penalized logistic regression with an extension to account for spatial correlation in the genomic instability data. We conduct extensive simulation studies and show that the proposed method performs well in the situations considered. Finally, we illustrate the method using genomic instability data from breast cancer samples.
doi:10.1111/j.1541-0420.2010.01417.x
PMCID: PMC3020238  PMID: 20377578
Conditional Dependence; Graphical Model; Lasso; Loss-of-Heterozygosity; Regularized Logistic Regression
13.  The Global Transmission and Control of Influenza 
PLoS ONE  2011;6(5):e19515.
New strains of influenza spread around the globe via the movement of infected individuals. The global dynamics of influenza are complicated by different patterns of influenza seasonality in different regions of the world. We have released an open-source stochastic mathematical model of the spread of influenza across 321 major, strategically located cities of the world. Influenza is transmitted between cities via infected airline passengers. Seasonality is simulated by increasing the transmissibility in each city at the times of the year when influenza has been observed to be most prevalent. The spatiotemporal spread of pandemic influenza can be understood through clusters of global transmission and links between them, which we identify using the epidemic percolation network (EPN) of the model. We use the model to explain the observed global pattern of spread for pandemic influenza A(H1N1) 2009–2010 (pandemic H1N1 2009) and to examine possible global patterns of spread for future pandemics depending on the origin of pandemic spread, time of year of emergence, and basic reproductive number (). We also use the model to investigate the effectiveness of a plausible global distribution of vaccine for various pandemic scenarios. For pandemic H1N1 2009, we show that the biggest impact of vaccination was in the temperate northern hemisphere. For pandemics starting in the temperate northern hemisphere in May or April, vaccination would have little effect in the temperate southern hemisphere and a small effect in the tropics. With the increasing interconnectedness of the world's population, we must take a global view of infectious disease transmission. Our open-source, computationally simple model can help public health officials plan for the next pandemic as well as deal with interpandemic influenza.
doi:10.1371/journal.pone.0019515
PMCID: PMC3089626  PMID: 21573121
14.  The Transmissibility and Control of Pandemic Influenza A (H1N1) Virus 
Science (New York, N.Y.)  2009;326(5953):729-733.
Pandemic influenza A (H1N1) 2009 (pandemic H1N1) is spreading throughout the planet. It has become the dominant strain in the southern hemisphere, where the influenza season is underway. Here, based on reported case clusters in the USA, we estimate the household secondary attack rate for pandemic H1N1 to be 27.3% (95% CI: 12.2%–50.5%). From a school outbreak, we estimate a school child infects 2.4 (95% CI: 1.8–3.2) other children within the school. We estimate the basic reproductive number, R0, to range from 1.3–1.7 and the generation interval to range from 2.6–3.2 days. We use a simulation model to evaluate the effectiveness of vaccination strategies in the USA for the Fall, 2009. If vaccine were available soon enough, vaccination of children, followed by adults, reaching 70% overall coverage, in addition to high risk and essential workforce groups, could mitigate a severe epidemic.
doi:10.1126/science.1177373
PMCID: PMC2880578  PMID: 19745114
15.  Chromosomal instability and copy number alterations in Barrett's esophagus and esophageal adenocarcinoma 
Purpose
Chromosomal instability, as assessed by many techniques, including DNA content aneuploidy, LOH, and comparative genomic hybridization, has consistently been reported to be common in cancer and rare in normal tissues. Recently, a panel of chromosome instability biomarkers, including LOH and DNA content, has been reported to identify patients at high and low risk of progression from Barrett's esophagus (BE) to esophageal adenocarcinoma (EA), but required multiple platforms for implementation. Although chromosomal instability involving amplifications and deletions of chromosome regions have been observed in nearly all cancers, copy number alterations (CNAs) in premalignant tissues have not been well characterized or evaluated in cohort studies as biomarkers of cancer risk.
Experimental Design
We examined CNAs in 98 patients having either BE or EA using BAC array CGH to characterize CNAs at different stages of progression ranging from early BE to advanced EA.
Results
CNAs were rare in early stages (
Conclusions
Genome wide analysis of CNAs provides a common platform for evaluation of chromosome instability for cancer risk assessment as well as identification of common regions of alteration that can be further studied for biomarker discovery.
doi:10.1158/1078-0432.CCR-08-2494
PMCID: PMC2684570  PMID: 19417022
copy number alteration; Barrett's esophagus; esophageal adenocarcinoma; array CGH; premalignant; aneuploidy; chromosomal instability
PLoS Computational Biology  2010;6(1):e1000656.
Mathematical and computer models of epidemics have contributed to our understanding of the spread of infectious disease and the measures needed to contain or mitigate them. To help prepare for future influenza seasonal epidemics or pandemics, we developed a new stochastic model of the spread of influenza across a large population. Individuals in this model have realistic social contact networks, and transmission and infections are based on the current state of knowledge of the natural history of influenza. The model has been calibrated so that outcomes are consistent with the 1957/1958 Asian A(H2N2) and 2009 pandemic A(H1N1) influenza viruses. We present examples of how this model can be used to study the dynamics of influenza epidemics in the United States and simulate how to mitigate or delay them using pharmaceutical interventions and social distancing measures. Computer simulation models play an essential role in informing public policy and evaluating pandemic preparedness plans. We have made the source code of this model publicly available to encourage its use and further development.
Author Summary
Computer simulations can provide valuable information to communities preparing for epidemics. These simulations can be used to investigate the effectiveness of various intervention strategies in reducing or delaying the peak of an epidemic. We have made a detailed influenza epidemic simulator for the United States publicly available so that others may use the software to inform public policy or adapt it to suit their needs.
doi:10.1371/journal.pcbi.1000656
PMCID: PMC2813259  PMID: 20126529
Purpose
Elevated cellular proliferation and cell cycle abnormalities, which have been associated with premalignant lesions, may be caused by inactivation of tumor suppressor genes. We measured proliferative and cell cycle fractions of biopsies from a cohort of patients with Barrett’s esophagus to better understand the role of proliferation in early neoplastic progression and the association between cell cycle dysregulation and tumor suppressor gene inactivation.
Experimental Design
Cell proliferative fractions (determined by Ki67/DNA multiparameter flow cytometry) and cell cycle fractions (DNA content flow cytometry) were measured in 853 diploid biopsies from 362 patients with Barrett’s esophagus. The inactivation status of CDKN2A and TP53 was assessed in a subset of these biopsies in a cross-sectional study. A prospective study followed 276 of the patients without detectable aneuploidy for an average of 6.3 years with esophageal adenocarcinoma as an endpoint.
Results
Diploid S and 4N (G2/tetraploid) fractions were significantly higher in biopsies with TP53 mutation and LOH. CDKN2A inactivation was not associated with higher Ki67-positive, diploid S, G1, or 4N fractions. High Ki67-positive and G1 phase fractions were not associated with the future development of esophageal adenocarcinoma (p=0.13 and p=0.15, respectively), while high diploid S phase and 4N fractions were (p=0.03 and p<0.0001, respectively).
Conclusions
High Ki67-positive proliferative fractions were not associated with inactivation of CDKN2A and TP53 or future development of cancer in our cohort of patients with Barrett’s esophagus. Bi-allelic inactivation of TP53 was associated with elevated 4N fractions, which have been associated with the future development of esophageal adenocarcinoma.
doi:10.1158/1078-0432.CCR-07-5063
PMCID: PMC2587072  PMID: 18980994
Barrett’s esophagus; esophageal adenocarcinoma; cell cycle; p16; p53
American Journal of Epidemiology  2009;170(6):679-686.
Vaccinating school-aged children against influenza can reduce age-specific and population-level illness attack rates. Using a stochastic simulation model of influenza transmission, the authors assessed strategies for vaccinating children in the United States, varying the vaccine type, coverage level, and reproductive number R (average number of secondary cases produced by a typical primary case). Results indicated that vaccinating children can substantially reduce population-level illness attack rates over a wide range of scenarios. The greatest absolute reduction in influenza illness cases per season occurred at R values ranging from 1.2 to 1.6 for a given vaccine coverage level. The indirect, total, and overall effects of vaccinating children were strong when transmission intensity was low to intermediate. The indirect effects declined rapidly as transmission intensity increased. In a mild influenza season (R = 1.1), approximately 19 million influenza cases could be prevented by vaccinating 70% of children. At most, nearly 100 million cases of influenza illness could be prevented, depending on the proportion of children vaccinated and the transmission intensity. Given the current worldwide threat of novel influenza A (H1N1), with an estimated R of 1.4–1.6, health officials should consider strategies for vaccinating children against novel influenza A (H1N1) as well as seasonal influenza.
doi:10.1093/aje/kwp237
PMCID: PMC2737588  PMID: 19679750
communicable disease control; influenza, human; influenza vaccines; mass immunization
European journal of immunology  2005;35(12):3452-3459.
Based on the results of a computational model of thymic selection, we propose a mechanism that produces the observed wide range of T cell cross-reactivity. The model suggests that the cross-reactivity of a T cell that survives thymic selection is correlated with its affinity for self peptides. In order to survive thymic selection, a T cell with low affinity for all self peptides expressed in the thymus must have high affinity for major histocompatibility complex (MHC), which makes it highly cross-reactive. A T cell with high affinity for any self peptide must have low MHC affinity to survive selection, which makes it highly specific for its cognate peptide. Our model predicts that 1) positive selection reduces the number of T cells that can detect any given foreign peptide by only 17% even though it eliminates over 95% of pre-selection cells, 2) negative selection decreases the average cross-reactivity of the pre-selection repertoire by 5-fold, and 3) T cells responding to foreign peptides similar to self peptides will have a lower average cross-reactivity than cells responding to epitopes dissimilar to self.
doi:10.1002/eji.200535098
PMCID: PMC1857316  PMID: 16285012
T cells; TCR; thymic selection; cross-reactivity; computational modeling
Journal of Virology  2004;78(20):11340-11351.
Studies of human immunodeficiency virus (HIV) vaccines in animal models suggest that it is difficult to induce complete protection from infection (sterilizing immunity) but that it is possible to reduce the viral load and to slow or prevent disease progression following infection. We have developed an age-structured epidemiological model of the effects of a disease-modifying HIV vaccine that incorporates the intrahost dynamics of infection, a transmission rate and host mortality that depend on the viral load, the possible evolution and transmission of vaccine escape mutant viruses, a finite duration of vaccine protection, and possible changes in sexual behavior. Using this model, we investigated the long-term outcome of a disease-modifying vaccine and utilized uncertainty analysis to quantify the effects of our lack of precise knowledge of various parameters. Our results suggest that the extent of viral load reduction in vaccinated infected individuals (compared to unvaccinated individuals) is the key predictor of vaccine efficacy. Reductions in viral load of about 1 log10 copies ml−1 would be sufficient to significantly reduce HIV-associated mortality in the first 20 years after the introduction of vaccination. Changes in sexual risk behavior also had a strong impact on the epidemic outcome. The impact of vaccination is dependent on the population in which it is used, with disease-modifying vaccines predicted to have the most impact in areas of low prevalence and rapid epidemic growth. Surprisingly, the extent to which vaccination alters disease progression, the rate of generation of escape mutants, and the transmission of escape mutants are predicted to have only a weak impact on the epidemic outcome over the first 25 years after the introduction of a vaccine.
doi:10.1128/JVI.78.20.11340-11351.2004
PMCID: PMC521856  PMID: 15452255

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