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Significant variations have been observed in viral copies generated during SARS-CoV-2 infections. However, the factors that impact viral copies and infection dynamics are not fully understood, and may be inherently dependent upon different viral and host factors. Here, we conducted virus whole genome sequencing and measured viral copies using RT-qPCR from 9,902 SARS-CoV-2 infections over a 2-year period to examine the impact of virus genetic variation on changes in viral copies adjusted for host age and vaccination status. Using a genome-wide association study (GWAS) approach, we identified multiple single-nucleotide polymorphisms (SNPs) corresponding to amino acid changes in the SARS-CoV-2 genome associated with variations in viral copies. We further applied a marginal epistasis test to detect interactions among SNPs and identified multiple pairs of substitutions located in the spike gene that have non-linear effects on viral copies. We also analyzed the temporal patterns and found that SNPs associated with increased viral copies were predominantly observed in Delta and Omicron BA.2/BA.4/BA.5/XBB infections, whereas those associated with decreased viral copies were only observed in infections with Omicron BA.1 variants. Our work showcases how GWAS can be a useful tool for probing phenotypes related to SNPs in viral genomes that are worth further exploration. We argue that this approach can be used more broadly across pathogens to characterize emerging variants and monitor therapeutic interventions. Author summary: Our study explores why viral load (copies measured by RT-qPCR) varies during SARS-CoV-2 infections by analyzing viral mutations and measuring viral copies in 9,902 individuals over two years. We aimed to understand how genetic differences in SARS-CoV-2 influence viral copies, considering host age and vaccination status. Using a genome-wide association study (GWAS), we identified several single-nucleotide polymorphisms (SNPs) in the virus linked to variations in viral levels. Notably, interactions between certain SNPs in the spike gene had non-linear effects on viral copies. Our analysis revealed that SNPs associated with higher viral copies were common in Delta and Omicron BA.2/BA.4/BA.5/XBB variants, while those linked to lower levels were mainly found in Omicron BA.1. This research highlights GWAS as a powerful tool for exploring virus genetics and suggests it can be broadly applied to monitor new variants of COVID-19 and other infectious diseases. [ABSTRACT FROM AUTHOR]

Background: With more than 1.2 million illnesses and 29,000 deaths in sub-Saharan Africa in 2017, typhoid fever continues to be a major public health problem. Effective control of the disease would benefit from an understanding of the subnational geospatial distribution of the disease incidence. Method: We collated records of the incidence rate of typhoid fever confirmed by culture of blood in Africa from 2000 to 2022. We estimated the typhoid incidence rate for sub-Saharan Africa on 20 km × 20 km grids by exploring the association with geospatial covariates representing access to improved water and sanitation, health conditions of the population, and environmental conditions. Results: We identified six published articles and one pre-print representing incidence rate estimates in 22 sites in 2000–2022. Estimated incidence rates showed geospatial variation at sub-national, national, and regional levels. The incidence rate was high in Western and Eastern African subregions followed by Southern and Middle African subregions. By age, the incidence rate was highest among 5–14 yo followed by 2–4 yo, > 14 yo, and 0–1 yo. When aggregated across all age classes and grids that comprise each country, predicted incidence rates ranged from 43.7 (95% confidence interval: 0.6 to 591.2) in Zimbabwe to 2,957.8 (95% CI: 20.8 to 4,245.2) in South Sudan per 100,000 person-years. Sub-national heterogeneity was evident with the coefficient of variation at the 20 km × 20 km grid-level ranging from 0.7 to 3.3 and was generally lower in high-incidence countries and widely varying in low-incidence countries. Conclusion: Our study provides estimates of 20 km × 20 km incidence rate of typhoid fever across sub-Saharan Africa based on data collected from 2000 through 2020. Increased understanding of the subnational geospatial variation of typhoid fever in Africa may inform more effective intervention programs by better targeting resources to heterogeneously disturbed disease risk. Author summary: Typhoid fever remains a significant health challenge in low- and middle-income nations, especially in sub-Saharan Africa. A comprehensive understanding of the disease's geospatial distribution is pivotal for its control—a gap previous studies overlooked. Addressing this, we undertook a study to chart the incidence of typhoid fever throughout sub-Saharan Africa. Using data from 2000 to 2022, we developed high-resolution maps with a granularity of 20 km by 20 km, detailing the spatial distribution of typhoid incidence. Our findings reveal pronounced disparities in typhoid incidence across different geospatial tiers: from local communities to entire nations and regions. Particularly, Western and Eastern Africa registered the highest incidences, with children aged 5–14 years being the most vulnerable. Distinctively, countries such as South Sudan reported alarmingly high figures, whereas Zimbabwe had notably fewer cases. Such insights are indispensable for health policymakers at local, national, and global levels. Pinpointing the areas hardest hit by typhoid allows for a more strategic allocation of resources and interventions. Armed with this data, we're better positioned to fight typhoid effectively and safeguard lives in sub-Saharan Africa. [ABSTRACT FROM AUTHOR]

PLOS Computational Biology recently celebrated the milestone of publishing its 10,000th article, coinciding with its upcoming 20th anniversary as a journal. The article emphasizes the importance of recognizing the collective efforts of the scientific community in computational biology, rather than solely focusing on individual achievements. It highlights that scientific progress is built upon the work of many contributors, including staff, technicians, students, fellows, and principal investigators. The journal aims to innovate how scientific contributions are acknowledged, moving beyond traditional metrics such as citations and incorporating diverse measures of impact. Overall, the article encourages celebrating the collaborative nature of computational biology and the significant contributions made by the entire community. [Extracted from the article]

Reported COVID-19 cases and deaths provide a delayed and incomplete picture of SARS-CoV-2 infections in the United States (US). Accurate estimates of both the timing and magnitude of infections are needed to characterize viral transmission dynamics and better understand COVID-19 disease burden. We estimated time trends in SARS-CoV-2 transmission and other COVID-19 outcomes for every county in the US, from the first reported COVID-19 case in January 13, 2020 through January 1, 2021. To do so we employed a Bayesian modeling approach that explicitly accounts for reporting delays and variation in case ascertainment, and generates daily estimates of incident SARS-CoV-2 infections on the basis of reported COVID-19 cases and deaths. The model is freely available as the covidestim R package. Nationally, we estimated there had been 49 million symptomatic COVID-19 cases and 404,214 COVID-19 deaths by the end of 2020, and that 28% of the US population had been infected. There was county-level variability in the timing and magnitude of incidence, with local epidemiological trends differing substantially from state or regional averages, leading to large differences in the estimated proportion of the population infected by the end of 2020. Our estimates of true COVID-19 related deaths are consistent with independent estimates of excess mortality, and our estimated trends in cumulative incidence of SARS-CoV-2 infection are consistent with trends in seroprevalence estimates from available antibody testing studies. Reconstructing the underlying incidence of SARS-CoV-2 infections across US counties allows for a more granular understanding of disease trends and the potential impact of epidemiological drivers. Author summary: Because many COVID-19 infections go undetected, reported numbers of COVID-19 cases and deaths underestimate the true size of the epidemic. To address this problem, we built a model to estimate the number of new SARS-CoV-2 infections over time in each U.S. state and county. In this paper, we present time trends of infections and other disease outcomes from the first reported case in the U.S. until January 1, 2021, for each state and county. The time series of infection estimates suggest that the US epidemic is best described a series of related epidemics, varying in their timing and intensity. We estimate that over a quarter of the US population was infected with SARS-CoV-2 in 2020 and by the end of 2020 0.12% of the population had died from COVID-19. State-level results were consistent with external measures of disease burden, including estimates of SARS-CoV-2 seroprevalence and excess mortality. Our findings help better understand the epidemic in the pre-vaccine era and demonstrate the feasibility of estimating SARS-CoV-2 infections at local levels using routinely reported case and death data. [ABSTRACT FROM AUTHOR]

As the percentage of published articles affected by the policy increases (from 17% of 2021 publications ( I n i = 890) to 87% of 2022 publications ( I n i = 202)), so does the sharing rate. This rose to 73% for research articles published in 2021, which includes a high proportion of research articles that were submitted before the policy was implemented (83% of articles published in 2021 were submitted before March 30, 2021). Graph: Fig 1 Code sharing rates (% of research articles that share code) of PLOS Computational Biology articles, based on the time of publication. [Extracted from the article]

Background Introduction of Vibrio cholerae to Haiti during the deployment of United Nations (UN) peacekeepers in 2010 resulted in one of the largest cholera epidemics of the modern era. Following the outbreak, a UN-commissioned independent panel recommended three predeployment intervention strategies to minimize the risk of cholera introduction in future peacekeeping operations: screening for V. cholerae carriage, administering prophylactic antimicrobial chemotherapies, or immunizing with oral cholera vaccines. However, uncertainty regarding the effectiveness of these approaches has forestalled their implementation by the UN. We assessed how the interventions would have impacted the likelihood of the Haiti cholera epidemic. Methods and Findings We developed a stochastic model for cholera importation and transmission, fitted to reported cases during the first weeks of the 2010 outbreak in Haiti. Using this model, we estimated that diagnostic screening reduces the probability of cases occurring by 82% (95% credible interval: 75%, 85%); however, false-positive test outcomes may hamper this approach. Antimicrobial chemoprophylaxis at time of departure and oral cholera vaccination reduce the probability of cases by 50% (41%, 57%) and by up to 61% (58%, 63%), respectively. Chemoprophylaxis beginning 1 wk before departure confers a 91% (78%, 96%) reduction independently, and up to a 98% reduction (94%, 99%) if coupled with vaccination. These results are not sensitive to assumptions about the background cholera incidence rate in the endemic troop-sending country. Further research is needed to (1) validate the sensitivity and specificity of rapid test approaches for detecting asymptomatic carriage, (2) compare prophylactic efficacy across antimicrobial regimens, and (3) quantify the impact of oral cholera vaccine on transmission from asymptomatic carriers. Conclusions Screening, chemoprophylaxis, and vaccination are all effective strategies to prevent cholera introduction during large-scale personnel deployments such as that precipitating the 2010 Haiti outbreak. Antimicrobial chemoprophylaxis was estimated to provide the greatest protection at the lowest cost among the approaches recently evaluated by the UN., Introduction An estimated 1.4 billion people are at risk for cholera in countries across Africa, Asia, and South and Central America where transmission is endemic [1]. In addition, explosive epidemics [...]

Investments in water and sanitation systems are believed to have led to the decline in typhoid fever in developed countries, such that most cases now occur in regions lacking adequate clean water and sanitation. Exploring seasonal and long-term patterns in historical typhoid mortality in the United States can offer deeper understanding of disease drivers. We fit modified Time-series Susceptible-Infectious-Recovered models to city-level weekly mortality counts to estimate seasonal and long-term typhoid transmission. We examined seasonal transmission separately by city and aggregated by water source. Typhoid transmission peaked in late summer/early fall. Seasonality varied by water source, with the greatest variation occurring in cities with reservoirs. We then fit hierarchical regression models to measure associations between long-term transmission and annual financial investments in water and sewer systems. Overall historical $1 per capita ($16.13 in 2017) investments in the water supply were associated with approximately 5% (95% confidence interval: 3–6%) decreases in typhoid transmission, while $1 increases in the overall sewer system investments were associated with estimated 6% (95% confidence interval: 4–9%) decreases. Our findings aid in the understanding of typhoid transmission dynamics and potential impacts of water and sanitation improvements, and can inform cost-effectiveness analyses of interventions to reduce the typhoid burden. Author summary: Typhoid fever remains a major source of morbidity and mortality in low- and middle-income countries. Historical investments in water and sanitation systems are thought to have led to the decline in typhoid fever in developed countries, such that most of the global burden of disease now occurs in regions with poor sanitary conditions and inadequate access to clean water and sanitation. However, there is limited empirical evidence to quantify the impact of investments in water and sanitation on typhoid fever incidence. We developed a mathematical model to examine trends in weekly typhoid mortality data from 1889–1931 in 16 U.S. cities. Through this analysis, we were able to examine how seasonal patterns of typhoid transmission varied geographically and historically depending on the water supply and treatment, and quantify the relationship between investments in water and sanitation infrastructures and long-term typhoid transmission rates. Our findings have important implications for the understanding of typhoid transmission dynamics and potential impact of improvements in water and sanitation infrastructure. Resource-poor countries must prioritize spending on public health issues, weighing the costs and benefits of interventions. Our results can help to inform comparative cost-effectiveness analyses of different interventions to reduce the global burden of typhoid fever. [ABSTRACT FROM AUTHOR]

Background: In surveillance for typhoid fever, under-detection of cases occurs when patients with fever do not seek medical care, or seek medical care but do not receive a blood test. Missing data may result in incorrect estimates of disease incidence. Methods: We used data from an ongoing randomised clinical trial of typhoid conjugate vaccine among children in Nepal to determine if eligible patients attending our fever clinics who did not have blood taken for culture had a lower risk of disease than those who had blood drawn. We assessed clinical and demographic predictors of having blood taken for culture, and predictors of culture-positive results. Missing blood culture data were imputed using multiple imputations. Results: During the first year of surveillance, 2392 fever presentations were recorded and 1615 (68%) of these had blood cultures. Children were more likely to have blood taken for culture if they were older, had fever for longer, a current temperature ≥38 degrees, or if typhoid or a urinary tract infection were suspected. Based on imputation models, those with blood cultures were 1.87 times more likely to have blood culture-positive fever than those with missing data. Conclusion: Clinical opinion on the cause of the fever may play a large part in the decision to offer blood culture, regardless of study protocol. Crude typhoid incidence estimates should be adjusted for the proportion of cases that go undetected due to missing blood cultures while adjusting for the lower likelihood of culture-positivity in the group with missing data. Author summary: Typhoid fever is a serious illness caused by a bacterial infection in the blood. Typhoid spreads through contaminated water sources, poor sanitation or poor hygiene, and occurs most often in low-resource environments, with children being most at risk. It is hard to determine how common typhoid infections are in low-resource setting as there are many pathogens that can cause diseases with similar characteristics, and not all patients present to health-care facilities. The culture test to detect the presence of bacteria in the blood is poorly sensitive, costly to perform, and not available in all facilities. Therefore, true disease cases often go undetected, resulting in an underestimation of the true burden of disease. We examined factors associated with blood culture collection in fever patients attending typhoid surveillance clinics in Kathmandu, Nepal. We aimed to determine whether those who did not have a blood culture were different in terms of clinical and demographic characteristics to those who did have blood taken for culture and, additionally, whether they were more or less likely to test blood culture positive for typhoid fever. We provide estimates of inflation factors that can be used to adjust the crude incidence rate to account for the under-detection of typhoid cases. [ABSTRACT FROM AUTHOR]

Cohort studies, randomized trials, and post-licensure studies have reported reduced natural and vaccine-derived protection against rotavirus gastroenteritis (RVGE) in low- and middle-income countries. While susceptibility of children to rotavirus is known to vary within and between settings, implications for estimation of immune protection are not well understood. We sought to re-estimate naturally-acquired protection against rotavirus infection and RVGE, and to understand how differences in susceptibility among children impacted estimates. We re-analyzed data from studies conducted in Mexico City, Mexico and Vellore, India. Cumulatively, 573 rotavirus-unvaccinated children experienced 1418 rotavirus infections and 371 episodes of RVGE over 17,636 child-months. We developed a model that characterized susceptibility to rotavirus infection and RVGE among children, accounting for aspects of the natural history of rotavirus and differences in transmission rates between settings. We tested whether model-generated susceptibility measurements were associated with demographic and anthropometric factors, and with the severity of RVGE symptoms. We identified greater variation in susceptibility to rotavirus infection and RVGE in Vellore than in Mexico City. In both cohorts, susceptibility to rotavirus infection and RVGE were associated with male sex, lower birth weight, lower maternal education, and having fewer siblings; within Vellore, susceptibility was also associated with lower socioeconomic status. Children who were more susceptible to rotavirus also experienced higher rates of rotavirus-negative diarrhea, and higher risk of moderate-to-severe symptoms when experiencing RVGE. Simulations suggested that discrepant estimates of naturally-acquired immunity against RVGE can be attributed, in part, to between-setting differences in susceptibility of children, but result primarily from the interaction of transmission rates with age-dependent risk for infections to cause RVGE. We found that more children in Vellore than in Mexico City belong to a high-risk group for rotavirus infection and RVGE, and demonstrate that unmeasured individual- and age-dependent susceptibility may influence estimates of naturally-acquired immune protection against RVGE. [ABSTRACT FROM AUTHOR]

The image that appears as Fig 1 should be Fig 2, and the image that appears as Fig 2 should be Fig 1. Correction: Changes in historical typhoid transmission across 16 U.S. cities, 1889-1931: Quantifying the impact of investments in water and sewer infrastructures Graph: Fig 2 Long-term typhoid transmission rate by city estimated from Time-series Susceptible-Infectious-Recovered models. [Extracted from the article]

Background: Planning interventions to respond to cholera epidemics requires an understanding of the major transmission routes. Interrupting short-cycle (household, foodborne) transmission may require different approaches as compared long-cycle (environmentally-mediated/waterborne) transmission. However, differentiating the relative contribution of short- and long-cycle routes has remained difficult, and most cholera outbreak control efforts focus on interrupting long-cycle transmission. Here we use high-resolution epidemiological and municipal infrastructure data from a cholera outbreak in 1853 Copenhagen to explore the relative contribution of short- and long-cycle transmission routes during a major urban epidemic. Methodology/Principal findings: We fit a spatially explicit time-series meta-population model to 6,552 physician-reported cholera cases from Copenhagen in 1853. We estimated the contribution of long-cycle waterborne transmission between neighborhoods using historical municipal water infrastructure data, fitting the force of infection from hydraulic flow, then comparing model performance. We found the epidemic was characterized by considerable transmission heterogeneity. Some neighborhoods acted as localized transmission hotspots, while other neighborhoods were less affected or important in driving the epidemic. We found little evidence to support long-cycle transmission between hydrologically-connected neighborhoods. Collectively, these findings suggest short-cycle transmission was significant. Conclusions/Significance: Spatially targeted cholera interventions, such as reactive vaccination or sanitation/hygiene campaigns in hotspot neighborhoods, would likely have been more effective in this epidemic than control measures aimed at interrupting long-cycle transmission, such as improving municipal water quality. We recommend public health planners consider programs aimed at interrupting short-cycle transmission as essential tools in the cholera control arsenal. [ABSTRACT FROM AUTHOR]

Background: A substantial proportion of the global burden of typhoid fever occurs in South Asia. Kathmandu, Nepal experienced a substantial increase in the number of typhoid fever cases (caused by Salmonella Typhi) between 2000 and 2003, which subsequently declined but to a higher endemic level than in 2000. This epidemic of S. Typhi coincided with an increase in organisms with reduced susceptibility against fluoroquinolones, the emergence of S. Typhi H58, and an increase in the migratory population in Kathmandu. Methods: We devised a mathematical model to investigate the potential epidemic drivers of typhoid in Kathmandu and fit this model to weekly data of S. Typhi cases between April 1997 and June 2011 and the age distribution of S. Typhi cases. We used this model to determine if the typhoid epidemic in Kathmandu was driven by heightened migration, the emergence of organisms with reduced susceptibility against fluoroquinolones or a combination of these factors. Results: Models allowing for the migration of susceptible individuals into Kathmandu alone or in combination with the emergence of S. Typhi with reduced susceptibility against fluoroquinolones provided a good fit for the data. The emergence of organisms with reduced susceptibility against fluoroquinolones organisms alone, either through an increase in disease duration or increased transmission, did not fully explain the pattern of S. Typhi infections. Conclusions: Our analysis is consistent with the hypothesis that the increase in typhoid fever in Kathmandu was associated with the migration of susceptible individuals into the city and aided by the emergence of reduced susceptibility against fluoroquinolones. These data support identifying and targeting migrant populations with typhoid immunization programmes to prevent transmission and disease. [ABSTRACT FROM AUTHOR]

Background: Upcoming vaccination efforts against typhoid fever require an assessment of the baseline burden of disease in countries at risk. There are no typhoid incidence data from most low- and middle-income countries (LMICs), so model-based estimates offer insights for decision-makers in the absence of readily available data. Methods: We developed a mixed-effects model fit to data from 32 population-based studies of typhoid incidence in 22 locations in 14 countries. We tested the contribution of economic and environmental indices for predicting typhoid incidence using a stochastic search variable selection algorithm. We performed out-of-sample validation to assess the predictive performance of the model. Results: We estimated that 17.8 million cases of typhoid fever occur each year in LMICs (95% credible interval: 6.9–48.4 million). Central Africa was predicted to experience the highest incidence of typhoid, followed by select countries in Central, South, and Southeast Asia. Incidence typically peaked in the 2–4 year old age group. Models incorporating widely available economic and environmental indicators were found to describe incidence better than null models. Conclusions: Recent estimates of typhoid burden may under-estimate the number of cases and magnitude of uncertainty in typhoid incidence. Our analysis permits prediction of overall as well as age-specific incidence of typhoid fever in LMICs, and incorporates uncertainty around the model structure and estimates of the predictors. Future studies are needed to further validate and refine model predictions and better understand year-to-year variation in cases. [ABSTRACT FROM AUTHOR]

Epidemics of respiratory syncytial virus (RSV) are known to occur in wintertime in temperate countries including the United States, but there is a limited understanding of the importance of climatic drivers in determining the seasonality of RSV. In the United States, RSV activity is highly spatially structured, with seasonal peaks beginning in Florida in November through December and ending in the upper Midwest in February-March, and prolonged disease activity in the southeastern US. Using data on both age-specific hospitalizations and laboratory reports of RSV in the US, and employing a combination of statistical and mechanistic epidemic modeling, we examined the association between environmental variables and state-specific measures of RSV seasonality. Temperature, vapor pressure, precipitation, and potential evapotranspiration (PET) were significantly associated with the timing of RSV activity across states in univariate exploratory analyses. The amplitude and timing of seasonality in the transmission rate was significantly correlated with seasonal fluctuations in PET, and negatively correlated with mean vapor pressure, minimum temperature, and precipitation. States with low mean vapor pressure and the largest seasonal variation in PET tended to experience biennial patterns of RSV activity, with alternating years of “early-big” and “late-small” epidemics. Our model for the transmission dynamics of RSV was able to replicate these biennial transitions at higher amplitudes of seasonality in the transmission rate. This successfully connects environmental drivers to the epidemic dynamics of RSV; however, it does not fully explain why RSV activity begins in Florida, one of the warmest states, when RSV is a winter-seasonal pathogen. Understanding and predicting the seasonality of RSV is essential in determining the optimal timing of immunoprophylaxis. [ABSTRACT FROM AUTHOR]

Background: Modeling of the transmission dynamics of typhoid allows for an evaluation of the potential direct and indirect effects of vaccination; however, relevant typhoid models rooted in data have rarely been deployed. Methodology/Principal Findings: We developed a parsimonious age-structured model describing the natural history and immunity to typhoid infection. The model was fit to data on culture-confirmed cases of typhoid fever presenting to Christian Medical College hospital in Vellore, India from 2000–2012. The model was then used to evaluate the potential impact of school-based vaccination strategies using live oral, Vi-polysaccharide, and Vi-conjugate vaccines. The model was able to reproduce the incidence and age distribution of typhoid cases in Vellore. The basic reproductive number (R0) of typhoid was estimated to be 2.8 in this setting. Vaccination was predicted to confer substantial indirect protection leading to a decrease in the incidence of typhoid in the short term, but (intuitively) typhoid incidence was predicted to rebound 5–15 years following a one-time campaign. Conclusions/Significance: We found that model predictions for the overall and indirect effects of vaccination depend strongly on the role of chronic carriers in transmission. Carrier transmissibility was tentatively estimated to be low, consistent with recent studies, but was identified as a pivotal area for future research. It is unlikely that typhoid can be eliminated from endemic settings through vaccination alone. Author Summary: We developed a mathematical model for the transmission dynamics of typhoid in order to evaluate the potential direct and indirect (i.e. herd immunity) effects of vaccination. The model was fit to data from Vellore, India and validated against the results of cluster randomized vaccine trials. We evaluated a variety of school-based vaccination strategies and found that typhoid vaccination is expected to lead to short-term indirect protection and a decrease in typhoid incidence, but vaccination alone is unlikely to lead to elimination. The level of indirect protection was found to depend on assumptions about the role of chronic carriers, identifying an important area for future research. [ABSTRACT FROM AUTHOR]

Early observations from countries that have introduced rotavirus vaccination suggest that there may be indirect protection for unvaccinated individuals, but it is unclear whether these benefits will extend to the long term. Transmission dynamic models have attempted to quantify the indirect protection that might be expected from rotavirus vaccination in developed countries, but results have varied. To better understand the magnitude and sources of variability in model projections, we undertook a comparative analysis of transmission dynamic models for rotavirus. We fit five models to reported rotavirus gastroenteritis (RVGE) data from England and Wales, and evaluated outcomes for short- and long-term vaccination effects. All of our models reproduced the important features of rotavirus epidemics in England and Wales. Models predicted that during the initial year after vaccine introduction, incidence of severe RVGE would be reduced 1.8-2.9 times more than expected from the direct effects of the vaccine alone (28-50% at 90% coverage), but over a 5- year period following vaccine introduction severe RVGE would be reduced only by 1.1-1.7 times more than expected from the direct effects (54-90% at 90% coverage). Projections for the long-term reduction of severe RVGE ranged from a 55% reduction at full coverage to elimination with at least 80% coverage. Our models predicted short-term reductions in the incidence of RVGE that exceeded estimates of the direct effects, consistent with observations from the United States and other countries. Some of the models predicted that the short-term indirect benefits may be offset by a partial shifting of the burden of RVGE to older unvaccinated individuals. Nonetheless, even when such a shift occurs, the overall reduction in severe RVGE is considerable. Discrepancies among model predictions reflect uncertainties about age variation in the risk and reporting of RVGE, and the duration of natural and vaccine-induced immunity, highlighting important questions for future research. [ABSTRACT FROM AUTHOR]

Introduction: Rotavirus vaccine efficacy ranges from >90% in high socio- economic settings (SES) to 50% in low SES. With the imminent introduction of rotavirus vaccine in low SES countries, understanding reasons for reduced efficacy in these settings could identify strategies to improve vaccine performance. Methods: We developed a mathematical model to predict rotavirus vaccine efficacy in high, middle and low SES based on data specific for each setting on incidence, protection conferred by natural infection and immune response to vaccination. We then examined factors affecting efficacy. Results: Vaccination was predicted to prevent 93%, 86% and 51% of severe rotavirus gastroenteritis in high, middle and low SES, respectively. Also predicted was that vaccines are most effective against severe disease and efficacy declines with age in low but not high SES. Reduced immunogenicity of vaccination and reduced protection conferred by natural infection are the main factors that compromise efficacy in low SES. Discussion: The continued risk of severe disease in non-primary natural infections in low SES is a key factor underpinning reduced efficacy of rotavirus vaccines. Predicted efficacy was remarkably consistent with observed clinical trial results from different SES, validating the model. The phenomenon of reduced vaccine efficacy can be predicted by intrinsic immunological and epidemiological factors of low SES populations. Modifying aspects of the vaccine (e.g. improving immunogenicity in low SES) and vaccination program (e.g. additional doses) may bring improvements. [ABSTRACT FROM AUTHOR]

Much of the observed wintertime increase of mortality in temperate regions is attributed to seasonal influenza. A recent reanalysis of laboratory experiments indicates that absolute humidity strongly modulates the airborne survival and transmission of the influenza virus. Here, we extend these findings to the human population level, showing that the onset of increased wintertime influenza-related mortality in the United States is associated with anomalously low absolute humidity levels during the prior weeks. We then use an epidemiological model, in which observed absolute humidity conditions temper influenza transmission rates, to successfully simulate the seasonal cycle of observed influenza-related mortality. The model results indicate that direct modulation of influenza transmissibility by absolute humidity alone is sufficient to produce this observed seasonality. These findings provide epidemiological support for the hypothesis that absolute humidity drives seasonal variations of influenza transmission in temperate regions. [ABSTRACT FROM AUTHOR]