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Purpose: Chromosomal instability, as assessed by many techniques, including DNA content aneuploidy, loss of heterozygosity, 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 loss of heterozygosity 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 (CNA) 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 Bacterial Artificial Chromosome (BAC) array comparative genomic hybridization to characterize CNAs at different stages of progression ranging from early BE to advanced EA.Results: CNAs were rare in early stages (less than high-grade dysplasia) but were progressively more frequent and larger in later stages (high-grade dysplasia and EA), including high-level amplifications. The number of CNAs correlated highly with DNA content aneuploidy. Patients whose biopsies contained CNAs involving >70 Mbp were at increased risk of progression to DNA content abnormalities or EA (hazards ratio, 4.9; 95% confidence interval, 1.6-14.8; P = 0.0047), and the risk increased as more of the genome was affected.Conclusions: Genome-wide analysis of CNAs provides a common platform for the evaluation of chromosome instability for cancer risk assessment as well as for the identification of common regions of alteration that can be further studied for biomarker discovery.

Supplementary Data from Chromosomal Instability and Copy Number Alterations in Barrett's Esophagus and Esophageal Adenocarcinoma

Summary Background Child mortality from sickle cell disease in sub-Saharan Africa is presumed to be high but is not well quantified. This uncertainty contributes to the neglect of sickle cell disease and delays the prioritisation of interventions. In this study, we estimated the mortality of children in Nigeria with sickle cell disease, and the proportion of national under-5 mortality attributable to sickle cell disease. Methods We did a model-estimated, population-level analysis of data from Nigeria's 2018 Demographic and Health Survey (DHS) to estimate the prevalence and geographical distribution of HbSS and HbSC genotypes assuming Hardy-Weinberg equilibrium near birth. Interviews for the survey were done between Aug 14 and Dec 29, 2018, and the embedded sickle cell disease survey was done in a randomly selected third of the overall survey's households. We developed an approach for estimating child mortality from sickle cell disease by combining information on tested children and their untested siblings. Tested children were aged 6–59 months at the time of the survey. Untested siblings born 0–14 years before the survey were also included in analyses. Testing as part of the DHS was done without regard to disease status. We analysed mortality differences using the inheritance-derived genotypic distribution of untested siblings older than the tested cohort, enabling us to estimate excess mortality from sickle cell disease for the older-sibling cohort (ie, those born between 2003 and 2013). Findings We analysed test results for 11 186 children aged 6–59 months from 7411 households in Nigeria. The estimated average birth prevalence of HbSS was 1·21% (95% CI 1·09–1·37) and was 0·24% (0·19–0·31) for HbSC. We obtained data for estimating child mortality from 10 195 tested children (who could be matched to the individual mother survey) and 17 205 of their untested siblings. 15 227 of the siblings were in the older-sibling cohort. The group of children with sickle cell disease born between 2003 and 2013 with at least one younger sibling in the survey had about 370 excess under-5 deaths per 1000 livebirths (95% CI 150–580; p=0·0008) than children with HbAA. The estimated national average under-5 mortality for children with sickle cell disease born between 2003 and 2013 was 490 per 1000 livebirths (95% CI 270–700), 4·0 times higher (95% CI 2·1–6·0) than children with HbAA. About 4·2% (95% CI 1·7–6·9) of national under-5 mortality was attributable to excess mortality from sickle cell disease. Interpretation The burden of child mortality from sickle cell disease in Nigeria continues to be disproportionately higher than the burden of mortality of children without sickle cell disease. Most of these deaths could be prevented if adequate resources were allocated and available focused interventions were implemented. The methods developed in this study could be used to estimate the burden of sickle cell disease elsewhere in Africa and south Asia. Funding Sickle Pan African Research Consortium, and the Bill & Melinda Gates Foundation.

Mathematical modeling can be used to project the impact of mass vaccination on cholera transmission. Here, we discuss 2 examples for which indirect protection from mass vaccination needs to be considered. In the first, we show that nonvaccinees can be protected by mass vaccination campaigns. This additional benefit of indirect protection improves the cost-effectiveness of mass vaccination. In the second, we model the use of mass vaccination to eliminate cholera. In this case, a high population level of immunity, including contributions from infection and vaccination, is required to reach the “herd immunity” threshold needed to stop transmission and achieve elimination.

ObjectivesTo estimate the fraction of anaemia attributable to malaria and sickle cell disease (SCD) among children aged 6–59 months in Nigeria.DesignCross-sectional analysis of data from Nigeria’s 2018 Demographic and Health Survey (DHS).SettingNigeria.Participants11 536 children aged 6–59 months from randomly selected households were eligible for participation, of whom 11 142 had complete and valid biomarker data required for this analysis. Maternal education data were available from 10 305 of these children.Primary outcome measureHaemoglobin concentration.ResultsWe found that 70.6% (95% CI: 62.7% to 78.5%) of severe anaemia was attributable to malaria compared with 12.4% (95% CI: 11.1% to 13.7%) of mild-to-severe and 29.6% (95% CI: 29.6% to 31.8%) of moderate-to-severe anaemia and that SCD contributed 0.6% (95% CI: 0.4% to 0.9%), 1.3% (95% CI: 1.0% to 1.7%) and 10.6% (95% CI: 6.7% to 14.9%) mild-to-severe, moderate-to-severe and severe anaemia, respectively. Sickle trait was protective against anaemia and was associated with higher haemoglobin concentration compared with children with normal haemoglobin (HbAA) among malaria-positive but not malaria-negative children.ConclusionsThis approach used offers a new tool to estimate the contribution of malaria to anaemia in many settings using widely available DHS data. The fraction of anaemia among young children in Nigeria attributable to malaria and SCD is higher at more severe levels of anaemia. Prevention of malaria and SCD and timely treatment of affected individuals would reduce cases of severe anaemia.

ImportanceInappropriate use of antibiotics for diarrheal illness can result in adverse effects and increase in antimicrobial resistance.ObjectiveTo determine whether the diarrheal etiology prediction (DEP) algorithm, which uses patient-specific and location-specific features to estimate the probability that diarrhea etiology is exclusively viral, impacts antibiotic prescriptions in patients with acute diarrhea.Design, Setting, and ParticipantsA randomized crossover study was conducted to evaluate the DEP incorporated into a smartphone-based electronic clinical decision-support (eCDS) tool. The DEP calculated the probability of viral etiology of diarrhea, based on dynamic patient-specific and location-specific features. Physicians were randomized in the first 4-week study period to the intervention arm (eCDS with the DEP) or control arm (eCDS without the DEP), followed by a 1-week washout period before a subsequent 4-week crossover period. The study was conducted at 3 sites in Bangladesh from November 17, 2021, to January 21, 2021, and at 4 sites in Mali from January 6, 2021, to March 5, 2021. Eligible physicians were those who treated children with diarrhea. Eligible patients were children between ages 2 and 59 months with acute diarrhea and household access to a cell phone for follow-up.InterventionsUse of the eCDS with the DEP (intervention arm) vs use of the eCDS without the DEP (control arm).Main Outcomes and MeasuresThe primary outcome was the proportion of children prescribed an antibiotic.ResultsA total of 30 physician participants and 941 patient participants (57.1% male; median [IQR] age, 12 [8-18] months) were enrolled. There was no evidence of a difference in the proportion of children prescribed antibiotics by physicians using the DEP (risk difference [RD], −4.2%; 95% CI, −10.7% to 1.0%). In a post hoc analysis that accounted for the predicted probability of a viral-only etiology, there was a statistically significant difference in risk of antibiotic prescription between the DEP and control arms (RD, −0.056; 95% CI, −0.128 to −0.01). No known adverse effects of the DEP were detected at 10-day postdischarge.Conclusions and RelevanceUse of a tool that provides an estimate of etiological likelihood did not result in a significant change in overall antibiotic prescriptions. Post hoc analysis suggests that a higher predicted probability of viral etiology was linked to reductions in antibiotic use.Trial RegistrationClinicaltrials.gov Identifier: NCT04602676

IntroductionAnaemia is a major cause of morbidity and mortality among children in sub-Saharan Africa. Anaemia has many aetiologies best addressed by different treatments, so regional studies of the aetiology of anaemia may be required.MethodsWe analysed data from Nigeria’s 2018 Demographic and Health Survey (DHS) to study predictors of anaemia among children ages 6-59m. We computed the fraction of anaemia at different degrees of severity attributable to malaria and sickle cell disease (SCD) using a regression model adjusting for demographic and socioeconomic risk factors. We also estimated the contribution of the risk factors to haemoglobin concentration.ResultsWe found that 63.7% (95% CI: 58.3-69.4) of semi-severe anaemia (ConclusionThis approach used offers a new tool to estimate the contribution of malaria to anaemia in many settings using widely available DHS data. The fraction of anaemia among young children in Nigeria attributable to malaria and SCD is higher at more severe levels of anaemia. Prevention of malaria and SCD and timely treatment of affected individuals would reduce cases of severe anaemia.

BackgroundDuring the first year of the COVID-19 pandemic, the most effective way to reduce transmission and to protect oneself was to reduce contact with others. However, it is unclear how behavior changed, despite numerous surveys about peoples’ attitudes and actions during the pandemic and public health efforts to influence behavior.MethodsWe used two sources of data to quantify changes in behavior at the county level during the first year of the pandemic in the United States: aggregated mobile device (smartphone) location data to approximate the fraction of people staying at home each day and digital invitation data to capture the number and size of social gatherings.ResultsBetween mid-March to early April 2020, the number of events fell and the fraction of devices staying at home peaked, independently of when states issued emergency orders or stay-at-home recommendations. Activity began to recover in May or June, with later rebounds in counties that suffered an early spring wave of reported COVID-19 cases. Counties with high incidence in the summer had more events, higher mobility, and less stringent state-level COVID-related restrictions the month before than counties with low incidence. Counties with high incidence in early fall stayed at home less and had less stringent state-level COVID-related restrictions in October, when cases began to rise in some parts of the US. During the early months of the pandemic, the number of events was inversely correlated with the fraction of devices staying at home, but after the fall of 2020 mobility appeared to stay constant as the number of events fell. Greater changes in behavior were observed in counties where a larger fraction voted for Biden in the 2020 US Presidential election. The number of people invited per event dropped gradually throughout the first year of the pandemic.ConclusionsThe mobility and events datasets uncovered different kinds of behavioral responses to the pandemic. Our results indicate that people did in fact change their behavior in ways that likely reduced COVID exposure and transmission, though the degree of change appeared to be affected by political views. Though the mobility data captured the initial massive behavior changes in the first months of the pandemic, the digital invitation data, presented here for the first time, continued to show large changes in behavior later in the first year of the pandemic.

How COVID-19 vaccine is distributed within low- and middle-income countries has received little attention outside of equity or logistical concerns but may ultimately affect campaign impact in terms of infections, severe cases, or deaths averted. In this study we examined whether subnational (urban-rural) prioritization may affect the cumulative two-year impact on disease transmission and burden of a vaccination campaign using an agent-based model of COVID-19 in a representative Sub-Saharan Africa country setting. We simulated a range of vaccination strategies that differed by urban-rural prioritization, age group prioritization, timing of introduction, and final coverage level. Urban prioritization averted more infections in only a narrow set of scenarios, when internal migration rates were low and vaccination was started by day 30 of an outbreak. Rural prioritization was the optimal strategy for all other scenarios, e.g., with higher internal migration rates or later start dates, due to the presence of a large immunological naive rural population. Among other factors, timing of the vaccination campaign was important to determining maximum impact, and delays as short as 30 days prevented larger campaigns from having the same impact as smaller campaigns that began earlier. The optimal age group for prioritization depended on choice of metric, as prioritizing older adults consistently averted more deaths across all of the scenarios. While guidelines exist for these latter factors, urban-rural allocation is an orthogonal factor that we predict to affect impact and warrants consideration as countries plan the scale-up of their vaccination campaigns.

Traditional clinical prediction models focus on parameters of the individual patient. For infectious diseases, sources external to the patient, including characteristics of prior patients and seasonal factors, may improve predictive performance. We describe the development of a predictive model that integrates multiple sources of data in a principled statistical framework using a post-test odds formulation. Our method enables electronic real-time updating and flexibility, such that components can be included or excluded according to data availability. We apply this method to the prediction of etiology of pediatric diarrhea, where “pre-test” epidemiologic data may be highly informative. Diarrhea has a high burden in low-resource settings, and antibiotics are often over-prescribed. We demonstrate that our integrative method outperforms traditional prediction in accurately identifying cases with a viral etiology, and show that its clinical application, especially when used with an additional diagnostic test, could result in a 61% reduction in inappropriately prescribed antibiotics.

Background: Sickle cell disease (SCD) child mortality in sub-Saharan Africa (SSA) is presumably high but not well-quantified. This contributes to the neglect of SCD and delays the prioritization of interventions. Nigeria has the world’s largest SCD population. Methods: We used Nigeria’s 2018 Demographic and Health Survey (DHS) data to estimate prevalence and geographic distribution of HbSS and HbSC genotypes. We developed a novel approach for estimating SCD child mortality, combining information on tested children (n=10195) and their untested siblings (n=17205). Mortality differences were decomposed using the inheritance-derived genotypic distribution of untested siblings older than the tested cohort, enabling us to estimate excess SCD mortality for the older-sibling cohort (i.e., born between 2003 and 2013, n=15227). Findings: The average birth prevalence of HbSS and HbSC in Nigeria was 1.20% (95% CI: 1.11-1.32%) and 0.23% (95% CI: 0.18-0.28%), respectively. Children born during 2003-2013 with at least one younger sibling in the survey with SCD suffered about 99 (95% CI: 55,144) excess deaths per 1,000 live births, compared with children whose tested siblings were all HbAA. After decomposition, we estimate that children born in 2003-2013 with SCD suffered 480 (95% CI: 270,700) deaths per 1000 live births, 4.0 times (95%CI: 2.1-6.0) higher than comparable children with HbAA. About 4.2% (95%CI: 1.6%-6.8%) of national U5M was attributable to excess SCD mortality. Interpretation: SCD child mortality burden in Nigeria continues to be disproportionately high. The vast majority of these deaths could be prevented if available focused interventions were implemented. The methodology developed here can be used to estimate the burden elsewhere in Africa and South Asia. Funding Statement: APO and DLC are employees of the Bill & Melinda Gates Foundation; OEN is the Nigeria PI of the Sickle Pan African Research Consortium (NHLBI Grant No 1U24HL135881). Declaration of Interests: FBP declares personal fees from Bluebird Bio, outside of the submitted work. All other authors declare no competing interests.

As COVID-19 cases resurge in the United States, understanding the complex interplay between human behavior, disease transmission, and non-pharmaceutical interventions during the pandemic could provide valuable insights to focus future public health efforts. Cell-phone mobility data offers a modern measurement instrument to investigate human mobility and behavior at an unprecedented scale. We investigate mobility data collected, aggregated, and anonymized by SafeGraph Inc. which measures how populations at the census-block-group geographic scale stayed at home in California, Georgia, Texas, and Washington since the beginning of the pandemic. Using manifold learning techniques, we find patterns of mobility behavior that align with stay-at-home orders, correlate with socioeconomic factors, cluster geographically, and reveal sub-populations that likely migrated out of urban areas. The analysis and approach provides policy makers a framework for interpreting mobility data and behavior to inform actions aimed at curbing the spread of COVID-19.

A fundamental, clinical, and scientific concern is how lytic bacteriophage, as well as antibiotics, impact diagnostic positivity. Cholera was chosen as a model disease to investigate this important question, because cholera outbreaks enable large enrollment, field methods are well established, and the predatory relationship between lytic bacteriophage and the etiologic agent Vibrio cholerae share commonalities across bacterial taxa. Patients with diarrheal disease were enrolled at two remote hospitals in Bangladesh., A fundamental, clinical, and scientific concern is how lytic bacteriophage, as well as antibiotics, impact diagnostic positivity. Cholera was chosen as a model disease to investigate this important question, because cholera outbreaks enable large enrollment, field methods are well established, and the predatory relationship between lytic bacteriophage and the etiologic agent Vibrio cholerae share commonalities across bacterial taxa. Patients with diarrheal disease were enrolled at two remote hospitals in Bangladesh. Diagnostic performance was assessed as a function of lytic bacteriophage detection and exposure to the first-line antibiotic azithromycin, detected in stool samples by mass spectrometry. Among diarrheal samples positive by nanoliter quantitative PCR (qPCR) for V. cholerae (n = 78/849), the odds that a rapid diagnostic test (RDT) or qPCR was positive was reduced by 89% (odds ratio [OR], 0.108; 95% confidence interval [CI], 0.002 to 0.872) and 87% (OR, 0.130; 95% CI, 0.022 to 0.649), respectively, when lytic bacteriophage were detected. The odds that an RDT or qPCR was positive was reduced by more than 99% (OR, 0.00; 95% CI, 0.00 to 0.28) and 89% (OR, 0.11; 95% CI, 0.03 to 0.44), respectively, when azithromycin was detected. Analysis of additional samples from South Sudan found similar phage effects on RDTs; antibiotics were not assayed. Cholera burden estimates may improve by accommodating for the negative effects of lytic bacteriophage and antibiotic exposure on diagnostic positivity. One accommodation is using bacteriophage detection as a proxy for pathogen detection. These findings have relevance for other diagnostic settings where bacterial pathogens are vulnerable to lytic bacteriophage predation.

BackgroundThe novel coronavirus SARS-CoV-2 has rapidly spread across the globe and is poised to cause millions of deaths worldwide. There are currently no proven pharmaceutical treatments, and vaccines are likely over a year away. At present, non-pharmaceutical interventions (NPIs) are the only effective option to reduce transmission of the virus, but it is not clear how to deploy these potentially expensive and disruptive measures. Modeling can be used to understand the potential effectiveness of NPIs for both suppression and mitigation efforts.Methods and FindingsWe developed Corvid, an adaptation of the agent-based influenza model called FluTE to SARS-CoV-2 transmission. To demonstrate features of the model relevant for studying the effects of NPIs, we simulated transmission of SARS-CoV-2 in a synthetic population representing a metropolitan area in the United States. Transmission in the model occurs in several settings, including at home, at work, and in schools. We simulated several combinations of NPIs that targeted transmission in these settings, such as school closures and work-from-home policies. We also simulated three strategies for testing and isolating symptomatic cases. For our demonstration parameters, we show that testing followed by home isolation of ascertained cases reduced transmission by a modest amount. We also show how further reductions may follow by isolating cases in safe facilities away from susceptible family members or by quarantining all family members to prevent transmission from likely infections that have yet to manifest.ConclusionsModels that explicitly include settings where individuals interact such as the home, work, and school are useful for studying the effectiveness of NPIs, as these are more dependent on community structure than pharmaceutical interventions such as vaccination. Corvid can be used to help evaluate complex combinations of interventions, although there is no substitute for real-world observations. Our results on NPI effectiveness summarize the behavior of the model for an assumed set of parameters for demonstration purposes. Model results can be sensitive to the assumptions made about disease transmission and the natural history of the disease, both of which are not yet sufficiently characterized for SARS-CoV-2 for quantitative modeling. Models of SARS-CoV-2 transmission will need to be updated as the pathogen becomes better-understood.

BackgroundA fundamental clinical and scientific concern is how lytic bacteriophage, as well as antibiotics, impact diagnostic positivity.MethodsCholera was chosen as a model disease to investigate this important question. Patients with diarrheal disease were enrolled at two remote hospitals in Bangladesh. Diagnostic performance was assessed as a function of lytic bacteriophage detection, as well as exposure to the first-line antibiotic azithromycin detected by mass spectrometry.ResultsAmong diarrheal samples positive by nanoliter quantitative PCR for Vibrio cholerae (n=78/849), the odds that a rapid diagnostic test (RDT) or qPCR was positive was reduced by 89% (OR 0.108; 95%CI 0.002-0.872) and 87% (OR 0.130; 95%CI 0.022-0.649) when lytic bacteriophage were detected, respectively. The odds that a rapid diagnostic test (RDT) or qPCR was positive was reduced by more than 99% (OR 0.00; 95% CI: 0.00-0.28) and 89% (OR 0.11; 95% CI: 0.03-0.44) when azithromycin was detected, respectively.ConclusionsEstimations of cholera burden may improve by accommodating for the negative effect of antimicrobial exposure on diagnostic positivity. Furthermore, the findings herein challenge our current approach to interpreting and developing bacterial diagnostics given variable rates of lytic bacteriophage and antibiotic exposure.

Summary Background Cholera was introduced into Haiti in 2010. Since then, more than 820 000 cases and nearly 10 000 deaths have been reported. Oral cholera vaccine (OCV) is safe and effective, but has not been seen as a primary tool for cholera elimination due to a limited period of protection and constrained supplies. Regionally, epidemic cholera is contained to the island of Hispaniola, and the lowest numbers of cases since the epidemic began were reported in 2019. Hence, Haiti may represent a unique opportunity to eliminate cholera with OCV. Methods In this modelling study, we assessed the probability of elimination, time to elimination, and percentage of cases averted with OCV campaign scenarios in Haiti through simulations from four modelling teams. For a 10-year period from January 19, 2019, to Jan 13, 2029, we compared a no vaccination scenario with five OCV campaign scenarios that differed in geographical scope, coverage, and rollout duration. Teams used weekly department-level reports of suspected cholera cases from the Haiti Ministry of Public Health and Population to calibrate the models and used common vaccine-related assumptions, but other model features were determined independently. Findings Among campaigns with the same vaccination coverage (70% fully vaccinated), the median probability of elimination after 5 years was 0–18% for no vaccination, 0–33% for 2-year campaigns focused in the two departments with the highest historical incidence, 0–72% for three-department campaigns, and 35–100% for nationwide campaigns. Two-department campaigns averted a median of 12–58% of infections, three-department campaigns averted 29–80% of infections, and national campaigns averted 58–95% of infections. Extending the national campaign to a 5-year rollout (compared to a 2-year rollout), reduced the probability of elimination to 0–95% and the proportion of cases averted to 37–86%. Interpretation Models suggest that the probability of achieving zero transmission of Vibrio cholerae in Haiti with current methods of control is low, and that bolder action is needed to promote elimination of cholera from the region. Large-scale cholera vaccination campaigns in Haiti would offer the opportunity to synchronise nationwide immunity, providing near-term population protection while improvements to water and sanitation promote long-term cholera elimination. Funding Bill & Melinda Gates Foundation, Global Good Fund, Institute for Disease Modeling, Swiss National Science Foundation, and US National Institutes of Health.

SummaryBackgroundCholera was introduced into Haiti in 2010. Since, there have been over 820,000 reported cases and nearly 10,000 deaths. The year 2019 has seen the lowest reported number of cases since the epidemic began. Oral cholera vaccine (OCV) is safe and effective, but has generally not been seen as a primary tool for cholera elimination due to a limited period of protection and constrained supplies. Regionally, epidemic cholera is contained to the island of Hispaniola. Hence, Haiti may represent a unique opportunity to eliminate cholera by use of OCV.MethodsWe assess the probability of elimination and the potential health impact of OCV use in Haiti by leveraging simulations from four independent modeling teams. For a 10-year projection period, we compared the impact of five vaccination campaign scenarios, differing in geographic scope, vaccination coverage, and rollout duration to a status quo scenario without vaccination. Teams used common calibration data and assumptions for vaccine efficacy and vaccination scenarios, but all other model features and assumptions were determined independently.FindingsA two-department OCV campaign proposed in Haiti’s national plan for elimination had less than 50% probability of elimination across models, and only ambitious, nationwide campaigns had a high probability of reaching this goal. Despite their low probability of elimination, two-department campaigns averted a median of 13-58% of infections across models over the five years after the start of vaccination campaigns; a nationwide campaign implemented at the same coverage and rollout duration averted a median of 58-95% of infections across models.InterpretationDespite recent declines in cholera cases in Haiti, bold action is needed to promote elimination of cholera from the region. Large-scale cholera vaccination campaigns in Haiti offer the opportunity to synchronize nationwide immunity, providing near-term protection to the population while improvements to water and sanitation infrastructure create an environment favorable to long-term cholera elimination.Research in ContextEvidence before this studyWe searched PubMed without language or date restrictions on October 4, 2019 for all records matching (“cholera*” AND “Haiti” AND (“vaccin*” OR “elim*”)) in any field and added one known article on the probability of elimination of cholera that was not indexed by PubMed to our review. Of 94 results, four articles were not about the cholera outbreak in Haiti or the use of cholera vaccination, and 34 were not original research articles. Fourteen articles presented research on cholera biology or cholera vaccine biology, either through discussion of Vibrio cholerae genetics, immunogenicity of oral cholera vaccine (OCV), or prospective vaccine candidate antigens. Twenty articles assessed OCV vaccine effectiveness, evaluated OCV campaign implementation or attitudes and knowledge about cholera control, or presented lessons learned on outbreak response and policy as a result of the Haiti cholera outbreak. Seven articles were about general cholera outbreak epidemiology in Haiti, and six articles were related to cholera transmission models outside our research scope.Of the nine remaining articles, five examined the impact of potential OCV campaigns at an early time point when Haiti’s cholera outbreak still exhibited epidemic dynamics, and one other projected the impact of the OCV campaigns planned after Hurricane Matthew in 2016. Two of the articles considered prospects for cholera elimination in Haiti in 2013 and 2014 and found that further targeted interventions were needed. One final study from 2017 modeled the possibility for OCV campaigns to eliminate cholera transmission in the Ouest department within a few years.Added value of this studyPrevious assessments of the impact of OCV use in Haiti occurred during early points of the outbreak when OCV campaigns were unlikely to lead to cholera elimination. Our study projects cholera transmission in Haiti with multiple years of more recent data, and directly examines prospect of cholera elimination in the status quo and under various mass OCV campaign scenarios. In bringing together results from multiple modeling teams, our study provides robust evidence about the current state of cholera transmission across Haiti and the potential impact of multiple mass OCV campaign scenarios.Implications of all of the available evidenceWhile 2019 has seen the lowest number of cholera cases in Haiti since the outbreak began, model simulations suggest that it may be possible for cholera transmission to persist without additional cholera control interventions.While a single two-department vaccination campaign may avert roughly 13-58% of infections with V. cholerae over a five year period, only a nationwide campaign led to a high probability of cholera elimination. Ambitious nationwide vaccination campaigns may break the cycle of endemic cholera transmission in Haiti as long-term improvements to water and sanitation infrastructure, which will limit the effects of potential re-introductions of Vibrio cholerae, are being made.

SUMMARYAn individual's risk of infection from an infectious agent can depend on both the individual's own risk and protective factors and those of individuals in the same community. We hypothesize that an individual's exposure to an infectious agent is associated with the risks of infection of those living nearby, whether their risks are modified by pharmaceutical interventions or by other factors, because of the potential for transmission from them. For example, unvaccinated individuals living in a highly vaccinated community can benefit from indirect protection, or living near more children in a typhoid-endemic region (where children are at highest risk) might result in more exposure to typhoid. We tested this hypothesis using data from a cluster-randomized typhoid vaccine trial. We first estimated each individual's relative risk of confirmed typhoid outcome using their vaccination status and age. We defined a new covariate, potential exposure, to be the sum of the relative risks of all who live within 100 m of each person. We found that potential exposure was significantly associated with an individual's typhoid outcome, and adjusting for potential exposure affected estimates of vaccine efficacy. We suggest that it is useful and feasible to adjust for spatially heterogeneous distributions of individual-level risk factors, but further work is required to develop and test such approaches.

Summary Background The 2014 epidemic of Ebola virus disease in parts of west Africa defines an unprecedented health threat. We developed a model of Ebola virus transmission that integrates detailed geographical and demographic data from Liberia to overcome the limitations of non-spatial approaches in projecting the disease dynamics and assessing non-pharmaceutical control interventions. Methods We modelled the movements of individuals, including patients not infected with Ebola virus, seeking assistance in health-care facilities, the movements of individuals taking care of patients infected with Ebola virus not admitted to hospital, and the attendance of funerals. Individuals were grouped into randomly assigned households (size based on Demographic Health Survey data) that were geographically placed to match population density estimates on a grid of 3157 cells covering the country. The spatial agent-based model was calibrated with a Markov chain Monte Carlo approach. The model was used to estimate Ebola virus transmission parameters and investigate the effectiveness of interventions such as availability of Ebola treatment units, safe burials procedures, and household protection kits. Findings Up to Aug 16, 2014, we estimated that 38·3% of infections (95% CI 17·4–76·4) were acquired in hospitals, 30·7% (14·1–46·4) in households, and 8·6% (3·2–11·8) while participating in funerals. We noted that the movement and mixing, in hospitals at the early stage of the epidemic, of patients infected with Ebola virus and those not infected was a sufficient driver of the reported pattern of spatial spread. The subsequent decrease of incidence at country and county level is attributable to the increasing availability of Ebola treatment units (which in turn contributed to drastically decreased hospital transmission), safe burials, and distribution of household protection kits. Interpretation The model allows assessment of intervention options and the understanding of their role in the decrease in incidence reported since Sept 7, 2014. High-quality data (eg, to estimate household secondary attack rate, contact patterns within hospitals, and effects of ongoing interventions) are needed to reduce uncertainty in model estimates. Funding US Defense Threat Reduction Agency, US National Institutes of Health.

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.

Background Oral cholera vaccine (OCV) is a feasible tool to prevent or mitigate cholera outbreaks. A better understanding of the vaccine’s efficacy among different age groups and how rapidly its protection wanes could help guide vaccination policy. Methods To estimate the level and duration of OCV efficacy, we re-analyzed data from a previously published cluster-randomized, double-blind, placebo controlled trial with five years of follow-up. We used a Cox proportional hazards model and modeled the potentially time-dependent effect of age categories on both vaccine efficacy and risk of infection in the placebo group. In addition, we investigated the impact of an outbreak period on model estimation. Results Vaccine efficacy was 38% (95% CI: -2%,62%) for those vaccinated from ages 1 to under 5 years old, 85% (95% CI: 67%,93%) for those 5 to under 15 years, and 69% (95% CI: 49%,81%) for those vaccinated at ages 15 years and older. Among adult vaccinees, efficacy did not appear to wane during the trial, but there was insufficient data to assess the waning of efficacy among child vaccinees. Conclusions Through this re-analysis we were able to detect a statistically significant difference in OCV efficacy when the vaccine was administered to children under 5 years old vs. children 5 years and older. The estimated efficacies are more similar to the previously published analysis based on the first two years of follow-up than the analysis based on all five years. Trial registration ClinicalTrials.gov identifier NCT00289224 Electronic supplementary material The online version of this article (10.1186/s12879-018-2981-4) contains supplementary material, which is available to authorized users.

Many infectious diseases have seasonal outbreaks, which may be driven by cyclical environmental conditions (e.g., an annual rainy season) or human behavior (e.g., school calendars or seasonal migration). If a pathogen is only transmissible for a limited period of time each year, then seasonal outbreaks could infect fewer individuals than expected given the pathogen's in-season transmissibility. Influenza, with its short serial interval and long season, probably spreads throughout a population until a substantial fraction of susceptible individuals are infected. Dengue, with a long serial interval and shorter season, may be constrained by its short transmission season rather than the depletion of susceptibles. Using mathematical modeling, we show that mass vaccination is most efficient, in terms of infections prevented per vaccine administered, at high levels of coverage for pathogens that have relatively long epidemic seasons, like influenza, and at low levels of coverage for pathogens with short epidemic seasons, like dengue. Therefore, the length of a pathogen's epidemic season may need to be considered when evaluating the costs and benefits of vaccination programs.

Please cite this paper as: Chao. (2012) Modelling the global transmission of antiviral-resistant influenza viruses. Influenza and Other Respiratory Viruses 7(Suppl. 1), 58–62. Background 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. Objectives We used mathematical modeling 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. Methods We developed a mathematical model of the transmission of influenza among 321 cities around the globe. In the model, influenza strains resistant to antiviral agents competed with sensitive strains. Results and Conclusions 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. Because of the complex global patterns of influenza circulation, tracking the emergence and spread of antiviral resistance must be a coordinated global effort.

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 article, 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.

Flu's Tricky Tricks After vaccination against influenza A virus, single-point mutations are selected in hemagglutinin (the virus molecule that binds to sialic acid molecules on the surface of host cells) that escape neutralization by polyclonal antibody responses. Hensley et al. (p. 734 ) have discovered that in mice these mutations increased the virus's avidity for sialic acid. Amino acid substitutions that occur during reiterations of immune escape and avidity modulation can thus drive antigenic variation. This constant evolution of influenza viruses requires us to change vaccine components annually, and, for equine influenza, Park et al. (p. 726 ) show that as the match between virus and vaccine strains drifts apart with time, the probability of becoming infected and the length of the infectious period increase to the point where outbreaks occur. Nevertheless, even imperfect vaccines may be of benefit to a population because increasing the proportion of vaccinated individuals can supply enough herd immunity to offset a poor antigenic match, especially if used in conjunction with antiviral drugs. For humans, Yang et al. (p. 729 , published online 10 September) estimate that the rate of transmission within U.S. households puts influenza A 2009 H1N1 (the current pandemic “swine flu”) in the higher range of transmissibility, compared to past seasonal and pandemic strains. Thus, to achieve mitigation this fall, children should be the first recipients of vaccine, followed by adults—aiming overall for 70% coverage of the population.

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 ¼ 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. communicable disease control; influenza, human; influenza vaccines; mass immunization

Clonal expansion of premalignant lesions is an important step in the progression to cancer. This process is commonly considered to be a consequence of sustaining a proliferative mutation. Here, we investigate whether the growth trajectory of clones can be better described by a model in which clone growth does not depend on a proliferative advantage. We developed a simple computer model of clonal expansion in an epithelium in which mutant clones can only colonize space left unoccupied by the death of adjacent normal stem cells. In this model, competition for space occurs along the frontier between mutant and normal territories, and both the shapes and the growth rates of lesions are governed by the differences between mutant and normal cells' replication or apoptosis rates. The behavior of this model of clonal expansion along a mutant clone's frontier, when apoptosis of both normal and mutant cells is included, matches the growth of UVB-induced p53 -mutant clones in mouse dorsal epidermis better than a standard exponential growth model that does not include tissue architecture. The model predicts precancer cell mutation and death rates that agree with biological observations. These results support the hypothesis that clonal expansion of premalignant lesions can be driven by agents, such as ionizing or nonionizing radiation, that cause cell killing but do not directly stimulate cell replication.

International audience; In 1998, a cholera epidemic in east Africa reached the Comoros Islands, an archipelago in the Mozambique Channel that had not reported a cholera case for more than 20 years. In just a little over 1 year (between January 1998 and March 1999), Grande Comore, the largest island in the Union of the Comoros, reported 7,851 cases of cholera, about 3% of the population. Using case reports and field observations during the medical response, we describe the epidemiology of the 1998–1999 cholera epidemic in Grande Comore. Outbreaks of infectious diseases on islands provide a unique opportunity to study transmission dynamics in a nearly closed population, and they may serve as stepping-stones for human pathogens to cross unpopulated expanses of ocean.

The 2014 West African Ebola Outbreak is the largest Ebola virus disease (EVD) epidemic ever recorded, not only in number of cases but also in geographical extent. Unlike previous EVD outbreaks, the large number of cases observed in major cities with international airports raised the concern about the possibility of exportation of the infection in countries around the world. Starting in July 2014, we used the Global Epidemic and Mobility model to provide a real-time assessment of the potential international spread of the EVD epidemic. We modeled the unfolding of the outbreak in the most affected countries, considered different scenarios reflecting changes in the disease dynamic, and provided estimates for the probability of observing imported cases around the world for 220 countries. The model went through successive calibrations as more surveillance data were available, providing projections extending from a few weeks to several months. The results show that along the entire course of the epidemic the probability of observing cases outside of Africa was small, but not negligible, from September to November 2014. The inflection point of the epidemic occurred in late September and early October 2014 with a consistent longitudinal decrease in new cases, thus averting the status quo epidemic growth that could have seen hundreds of exported cases at the global scale in the following months.

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 by only 17% the number of T cells that can detect any given foreign peptide, even though it eliminates over 95% of pre-selection cells; (2) negative selection decreases the average cross-reactivity of the pre-selection repertoire by fivefold; 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.

The concept of an information immune system (IIS) is introduced, in which undesirable information is eliminated before it can reach the user. The IIS is inspired by the natural immune systems that protect us from pathogens. IISs from multiple individuals can be combined to form a group IIS which filters out information undesirable to any of the members. The relationship between our proposed IIS architecture and the natural immune system is outlined, and potential applications, including information filtering, interactive design, and collaborative design, are discussed.

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.

International audience; The quick spread of an Ebola outbreak in West Africa has led a number of countries and airline companies to issue travel bans to the affected areas. Considering data up to 31 Aug 2014, we assess the impact of the resulting traffic reductions with detailed numerical simulations of the international spread of the epidemic. Traffic reductions are shown to delay by only a few weeks the risk that the outbreak extends to new countries.

Colonies of the harvester ant, Pogonomyrmex barbatus, perform a variety of tasks. The behavior of an individual worker appears to depend on its recent history of brief contacts with ants of the same and other task groups. The purpose of this study was to determine whether task groups differ in cuticular hydrocarbon composition. We compared the cuticular hydrocarbon composition of ants collected under natural conditions as they performed one of three tasks: patrolling (locating food sources), foraging, or nest maintenance. Task groups differed significantly in the relative proportions of classes of hydrocarbon compounds, as well as in individual compounds. Relative to nest maintenance workers, foragers and patrollers had a higher proportion of straight-chain alkanes relative to monomethylalkanes, dimethylalkanes, and alkenes. There was no significant difference in the chain length of n-alkanes among the task groups. Foragers did not differ in hydrocarbon composition from patrollers. Colonies differed significantly from one another in hydrocarbon composition, but task groups differed in consistent ways from colony to colony, suggesting that the mechanism responsible for task-related hydrocarbon composition was the same in all colonies. P. barbatus workers switch tasks during their lifetimes, suggesting that cuticular hydrocarbon composition changes during adulthood as well. Nest maintenance workers are probably younger than foragers and patrollers and perform very little of their work outside of the nest. Task-related hydrocarbon differences detected here may be associated with worker age, and/or the abiotic characteristics (temperature, humidity, and ultraviolet light) of the interior and exterior work environments.

Background: The 2014 West African Ebola Outbreak is so far the largest and deadliest recorded in history. The affected countries, Sierra Leone, Guinea, Liberia, and Nigeria, have been struggling to contain and to mitigate the outbreak. The ongoing rise in confirmed and suspected cases, 2615 as of 20 August 2014, is considered to increase the risk of international dissemination, especially because the epidemic is now affecting cities with major commercial airports. Method: We use the Global Epidemic and Mobility Model to generate stochastic, individual based simulations of epidemic spread worldwide, yielding, among other measures, the incidence and seeding events at a daily resolution for 3,362 subpopulations in 220 countries. The mobility model integrates daily airline passenger traffic worldwide and the disease model includes the community, hospital, and burial transmission dynamic. We use a multimodel inference approach calibrated on data from 6 July to the date of 9 August 2014. The estimates obtained were used to generate a 3-month ensemble forecast that provides quantitative estimates of the local transmission of Ebola virus disease in West Africa and the probability of international spread if the containment measures are not successful at curtailing the outbreak. Results: We model the short-term growth rate of the disease in the affected West African countries and estimate the basic reproductive number to be in the range 1.5 "� 2.0 (interval at the 1/10 relative likelihood). We simulated the international spreading of the outbreak and provide the estimate for the probability of Ebola virus disease case importation in countries across the world. Results indicate that the short-term (3 and 6 weeks) probability of international spread outside the African region is small, but not negligible. The extension of the outbreak is more likely occurring in African countries, increasing the risk of international dissemination on a longer time scale. Abstract Background: The 2014 West African Ebola Outbreak is so far the largest and deadliest recorded in history. The affected countries, Sierra Leone, Guinea, Liberia, and Nigeria, have been struggling to contain and to mitigate the outbreak. The ongoing rise in confirmed and suspected cases, 2615 as of 20 August 2014, is considered to increase the risk of international dissemination, especially because the epidemic is now affecting cities with major commercial airports. Method: We use the Global Epidemic and Mobility Model to generate stochastic, individual based simulations of epidemic spread worldwide, yielding, among other measures, the incidence and seeding events at a daily resolution for 3,362 subpopulations in 220 countries. The mobility model integrates daily airline passenger traffic worldwide and the disease model includes the community, hospital, and burial transmission dynamic. We use a multimodel inference approach calibrated on data from 6 July to the date of 9 August 2014. The estimates obtained were used to generate a 3-month ensemble forecast that provides quantitative estimates of the local transmission of Ebola virus disease in West Africa and the probability of international spread if the containment measures are not successful at curtailing the outbreak. Results: We model the short-term growth rate of the disease in the affected West African countries and estimate the basic reproductive number to be in the range 1.5 "� 2.0 (interval at the 1/10 relative likelihood). We simulated the international spreading of the outbreak and provide the estimate for the probability of Ebola virus disease case importation in countries across the world. Results indicate that the short-term (3 and 6 weeks) probability of international spread outside the African region is small, but not negligible. The extension of the outbreak is more likely occurring in African countries, increasing the risk of international dissemination on a longer time scale. Abstract Background: The 2014 West African Ebola Outbreak is so far the largest and deadliest recorded in history. The affected countries, Sierra Leone, Guinea, Liberia, and Nigeria, have been struggling to contain and to mitigate the outbreak. The ongoing rise in confirmed and suspected cases, 2615 as of 20 August 2014, is considered to increase the risk of international dissemination, especially because the epidemic is now affecting cities with major commercial airports. Method: We use the Global Epidemic and Mobility Model to generate stochastic, individual based simulations of epidemic spread worldwide, yielding, among other measures, the incidence and seeding events at a daily resolution for 3,362 subpopulations in 220 countries. The mobility model integrates daily airline passenger traffic worldwide and the disease model includes the community, hospital, and burial transmission dynamic. We use a multimodel inference approach calibrated on data from 6 July to the date of 9 August 2014. The estimates obtained were used to generate a 3-month ensemble forecast that provides quantitative estimates of the local transmission of Ebola virus disease in West Africa and the probability of international spread if the containment measures are not successful at curtailing the outbreak. Results: We model the short-term growth rate of the disease in the affected West African countries and estimate the basic reproductive number to be in the range 1.5 "� 2.0 (interval at the 1/10 relative likelihood). We simulated the international spreading of the outbreak and provide the estimate for the probability of Ebola virus disease case importation in countries across the world. Results indicate that the short-term (3 and 6 weeks) probability of international spread outside the African region is small, but not negligible. The extension of the outbreak is more likely occurring in African countries, increasing the risk of international dissemination on a longer time scale.

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.