SummaryBackgroundUnderstanding the extent to which HIV burden differs across communities and the drivers of local disparities is critical for an effective and targeted HIV response. We assessed community-level variations in HIV prevalence, risk factors, and treatment and prevention service uptake in Rakai, Uganda.MethodsThe Rakai Community Cohort Study (RCCS) is an open, population-based cohort surveying persons aged 15–49 in 40 communities. Participants are HIV tested and interviewed to obtain sociodemographic, behavioral, and health information. RCCS data from August 2011 to May 2013 were used to classify communities as agrarian (n=27), trading (n=9), or lakeside fishing sites (n=4). HIV prevalence was mapped using Bayesian methods, and variability across and within community classifications was characterized. Differences in HIV risk factors and uptake of antiretroviral therapy and male circumcision between community types were assessed.Findings17,119 individuals were included; 9215 (54%) were female. 9931 participants resided in agrarian, 3318 in trading, and 3870 in fishing communities. There was large variation in HIV prevalence, ranging from 9% to 43% across communities. Fishing communities had a higher median HIV prevalence (41%, range: 37–43%) compared to trading (17%, range: 11–22%) and agrarian communities (14%, range: 9–26%); ART and male circumcision coverage were significantly lower in fishing communities. Self-reported risk behaviors were significantly higher in men compared to women and in fishing communities compared to other community types.InterpretationThere is substantial heterogeneity in HIV prevalence, risk factors, and service uptake across communities within one region of Uganda. These findings underscore the need for local surveillance and have important implications for the design of targeted HIV responses. In particular, the extremely high HIV burden and risk behaviors, and low use of combination HIV prevention in fishing communities make these areas a priority for intervention.

SummaryBackgroundCholera remains a persistent health problem in sub-Saharan Africa and worldwide. Cholera can be controlled through appropriate water and sanitation, or by oral cholera vaccination, which provides transient (∼3 years) protection, although vaccine supplies remain scarce. We aimed to map cholera burden in sub-Saharan Africa and assess how geographical targeting could lead to more efficient interventions.MethodsWe combined information on cholera incidence in sub-Saharan Africa (excluding Djibouti and Eritrea) from 2010 to 2016 from datasets from WHO, Médecins Sans Frontières, ProMED, ReliefWeb, ministries of health, and the scientific literature. We divided the study region into 20 km × 20 km grid cells and modelled annual cholera incidence in each grid cell assuming a Poisson process adjusted for covariates and spatially correlated random effects. We combined these findings with data on population distribution to estimate the number of people living in areas of high cholera incidence (>1 case per 1000 people per year). We further estimated the reduction in cholera incidence that could be achieved by targeting cholera prevention and control interventions at areas of high cholera incidence.FindingsWe included 279 datasets covering 2283 locations in our analyses. In sub-Saharan Africa (excluding Djibouti and Eritrea), a mean of 141 918 cholera cases (95% credible interval [CrI] 141 538–146 505) were reported per year. 4·0% (95% CrI 1·7–16·8) of districts, home to 87·2 million people (95% CrI 60·3 million to 118·9 million), have high cholera incidence. By focusing on the highest incidence districts first, effective targeted interventions could eliminate 50% of the region's cholera by covering 35·3 million people (95% CrI 26·3 million to 62·0 million), which is less than 4% of the total population.InterpretationAlthough cholera occurs throughout sub-Saharan Africa, its highest incidence is concentrated in a small proportion of the continent. Prioritising high-risk areas could substantially increase the efficiency of cholera control programmes.FundingThe Bill & Melinda Gates Foundation.

SignificanceForecasts routinely provide critical information for dangerous weather events but not yet for epidemics. Researchers develop computational models that can be used for infectious disease forecasting, but forecasts have not been broadly compared or tested. We collaboratively compared forecasts from 16 teams for 8 y of dengue epidemics in Peru and Puerto Rico. The comparison highlighted components that forecasts did well (e.g., situational awareness late in the season) and those that need more work (e.g., early season forecasts). It also identified key facets to improve forecasts, including using multiple model ensemble approaches to improve overall forecast skill. Future infectious disease forecasting work can build on these findings and this framework to improve the skill and utility of forecasts.

Significance This paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the United States. Results show high variation in accuracy between and within stand-alone models and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public-health action.

Summary Background The past two decades have seen expansion of childhood vaccination programmes in low-income and middle-income countries (LMICs). We quantify the health impact of these programmes by estimating the deaths and disability-adjusted life-years (DALYs) averted by vaccination against ten pathogens in 98 LMICs between 2000 and 2030. Methods 16 independent research groups provided model-based disease burden estimates under a range of vaccination coverage scenarios for ten pathogens: hepatitis B virus, Haemophilus influenzae type B, human papillomavirus, Japanese encephalitis, measles, Neisseria meningitidis serogroup A, Streptococcus pneumoniae , rotavirus, rubella, and yellow fever. Using standardised demographic data and vaccine coverage, the impact of vaccination programmes was determined by comparing model estimates from a no-vaccination counterfactual scenario with those from a reported and projected vaccination scenario. We present deaths and DALYs averted between 2000 and 2030 by calendar year and by annual birth cohort. Findings We estimate that vaccination of the ten selected pathogens will have averted 69 million (95% credible interval 52–88) deaths between 2000 and 2030, of which 37 million (30–48) were averted between 2000 and 2019. From 2000 to 2019, this represents a 45% (36–58) reduction in deaths compared with the counterfactual scenario of no vaccination. Most of this impact is concentrated in a reduction in mortality among children younger than 5 years (57% reduction [52–66]), most notably from measles. Over the lifetime of birth cohorts born between 2000 and 2030, we predict that 120 million (93–150) deaths will be averted by vaccination, of which 58 million (39–76) are due to measles vaccination and 38 million (25–52) are due to hepatitis B vaccination. We estimate that increases in vaccine coverage and introductions of additional vaccines will result in a 72% (59–81) reduction in lifetime mortality in the 2019 birth cohort. Interpretation Increases in vaccine coverage and the introduction of new vaccines into LMICs have had a major impact in reducing mortality. These public health gains are predicted to increase in coming decades if progress in increasing coverage is sustained. Funding Gavi, the Vaccine Alliance and the Bill & Melinda Gates Foundation.