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Cholera remains a major threat in sub-Saharan Africa (SSA), where some of the highest case-fatality rates are reported. Knowing in what months and where cholera tends to occur across the continent could aid in improving efforts to eliminate cholera as a public health concern. However, largely due to the absence of unified large-scale datasets, no continent-wide estimates exist. In this study, we aimed to estimate cholera seasonality across SSA and explore the correlation between hydroclimatic variables and cholera seasonality.Using the global cholera database of the Global Task Force on Cholera Control, we developed statistical models to synthesise data across spatial and temporal scales to infer the seasonality of excess (defined as incidence higher than the 2010-16 mean incidence rate) suspected cholera occurrence in SSA. We developed a Bayesian statistical model to infer the monthly risk of excess cholera at the first and second administrative levels. Seasonality patterns were then grouped into spatial clusters. Finally, we studied the association between seasonality estimates and hydroclimatic variables (mean monthly fraction of area flooded, mean monthly air temperature, and cumulative monthly precipitation).24 (71%) of the 34 countries studied had seasonal patterns of excess cholera risk, corresponding to approximately 86% of the SSA population. 12 (50%) of these 24 countries also had subnational differences in seasonality patterns, with strong differences in seasonality strength between regions. Seasonality patterns clustered into two macroregions (west Africa and the Sahel vs eastern and southern Africa), which were composed of subregional clusters with varying degrees of seasonality. Exploratory association analysis found most consistent and positive correlations between cholera seasonality and precipitation and, to a lesser extent, between cholera seasonality and temperature and flooding.Widespread cholera seasonality in SSA offers opportunities for intervention planning. Further studies are needed to study the association between cholera and climate.US National Aeronautics and Space Administration Applied Sciences Program and the BillMelinda Gates Foundation.

Binding antibody levels against SARS-CoV-2 have shown to be correlates of protection against infection with pre-Omicron lineages. This has been challenged by the emergence of immune-evasive variants, notably the Omicron sublineages, in an evolving immune landscape with high levels of cumulative incidence and vaccination coverage. This in turn limits the use of commercially available high-throughput methods to quantify binding antibodies as a tool to monitor protection at the population-level. In this work, we leverage repeated serological measurements between April 2020 and December 2021 on 1’083 participants of a population-based cohort in Geneva, Switzerland, to evaluate anti-Spike RBD antibody levels as a correlate of protection against Omicron BA.1/BA.2 infections during the December 2021-March 2022 epidemic wave. We do so by first modeling antibody dynamics in time with kinetic models. We then use these models to predict antibody trajectories into the time period where Omicron BA.1/BA.2 were the predominant circulating sub-lineages and use survival analyses to compare the hazard of having a positive SARS-CoV-2 test by antibody level, vaccination status and infection history. We find that antibody kinetics in our sample are mainly determined by infection and vaccination history, and to a lesser extent by demographics. After controlling for age and previous infections (based on anti-nucleocapsid serology), survival analyses reveal a significant reduction in the hazard of having a documented positive SARS-CoV-2 infection during the Omicron BA.1/BA.2 wave with increasing antibody levels, reaching up to a three-fold reduction for anti-S antibody levels above 800 IU/mL (HR 0.30, 95% CI 0.22-0.41). However, we did not detect a reduction in hazard among uninfected participants. Taken together these results indicate that anti-Spike RBD antibody levels, as quantified by the immunoassay used in this study, are an indirect correlate of protection against Omicron BA.1/BA.2 for individuals with a history of previous SARS-CoV-2 infection. Despite the uncertainty in what SARS-COV-2 variant will come next, these results provide reassuring insights into the continued interpretation of SARS-CoV-2 binding antibody measurements as an independent marker of protection at both the individual and population levels.

Background: More than two years into the COVID-19 pandemic, it is generally assumed that most of the population has developed anti-SARS-CoV-2 antibodies from infection and/or vaccination. However, public health decision-making is hindered by the lack of up-to-date and precise characterization of the immune landscape in the population. We thus aimed to estimate anti-SARS-CoV-2 antibodies seroprevalence and cross-variant neutralization capacity after Omicron became dominant in Geneva, Switzerland. Methods: We conducted a population-based serosurvey between April 29th and June 9th, 2022, recruiting children and adults of all ages from age-stratified random samples of the Geneva general population. Anti-SARS-CoV-2 antibody presence was assessed using commercial immunoassays targeting either the spike (S) or nucleocapsid (N) protein. Antibodies neutralization capacity against different SARS-CoV-2 variants was evaluated using a cell-free Spike trimer-ACE2 binding-based surrogate neutralization assay. Seroprevalence of anti-SARS-CoV-2 antibodies and neutralization capacity were estimated using a Bayesian modeling framework accounting for the demographics, vaccination, and infection statuses of the Geneva population. Results: Among the 2521 individuals included in the analysis (55.2% women; 21.4% aged

After a period of rapidly declining U.S. COVID-19 incidence during January-March 2021, increases occurred in several jurisdictions (1,2) despite the rapid rollout of a large-scale vaccination program. This increase coincided with the spread of more transmissible variants of SARS-CoV-2, the virus that causes COVID-19, including B.1.1.7 (1,3) and relaxation of COVID-19 prevention strategies such as those for businesses, large-scale gatherings, and educational activities. To provide long-term projections of potential trends in COVID-19 cases, hospitalizations, and deaths, COVID-19 Scenario Modeling Hub teams used a multiple-model approach comprising six models to assess the potential course of COVID-19 in the United States across four scenarios with different vaccination coverage rates and effectiveness estimates and strength and implementation of nonpharmaceutical interventions (NPIs) (public health policies, such as physical distancing and masking) over a 6-month period (April-September 2021) using data available through March 27, 2021 (4). Among the four scenarios, an accelerated decline in NPI adherence (which encapsulates NPI mandates and population behavior) was shown to undermine vaccination-related gains over the subsequent 2-3 months and, in combination with increased transmissibility of new variants, could lead to surges in cases, hospitalizations, and deaths. A sharp decline in cases was projected by July 2021, with a faster decline in the high-vaccination scenarios. High vaccination rates and compliance with public health prevention measures are essential to control the COVID-19 pandemic and to prevent surges in hospitalizations and deaths in the coming months.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub ( https://covid19forecasthub.org/ ) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.

More than two years into the COVID-19 pandemic, most of the population has developed anti-SARS-CoV-2 antibodies from infection and/or vaccination. However, public health decision-making is hindered by the lack of up-to-date and precise characterization of the immune landscape in the population. Here, we estimated anti-SARS-CoV-2 antibodies seroprevalence and cross-variant neutralization capacity after Omicron became dominant in Geneva, Switzerland.We conducted a population-based serosurvey between April 29 and June 9, 2022, recruiting children and adults of all ages from age-stratified random samples of the general population of Geneva, Switzerland. We tested for anti-SARS-CoV-2 antibodies using commercial immunoassays targeting either the spike (S) or nucleocapsid (N) protein, and for antibody neutralization capacity against different SARS-CoV-2 variants using a cell-free Spike trimer-ACE2 binding-based surrogate neutralization assay. We estimated seroprevalence and neutralization capacity using a Bayesian modeling framework accounting for the demographics, vaccination, and infection statuses of the Geneva population.Among the 2521 individuals included in the analysis, the estimated total antibodies seroprevalence was 93.8% (95% CrI 93.1-94.5), including 72.4% (70.0-74.7) for infection-induced antibodies. Estimates of neutralizing antibodies in a representative subsample (N = 1160) ranged from 79.5% (77.1-81.8) against the Alpha variant to 46.7% (43.0-50.4) against the Omicron BA.4/BA.5 subvariants. Despite having high seroprevalence of infection-induced antibodies (76.7% [69.7-83.0] for ages 0-5 years, 90.5% [86.5-94.1] for ages 6-11 years), children aged12 years had substantially lower neutralizing activity than older participants, particularly against Omicron subvariants. Overall, vaccination was associated with higher neutralizing activity against pre-Omicron variants. Vaccine booster alongside recent infection was associated with higher neutralizing activity against Omicron subvariants.While most of the Geneva population has developed anti-SARS-CoV-2 antibodies through vaccination and/or infection, less than half has neutralizing activity against the currently circulating Omicron BA.5 subvariant. Hybrid immunity obtained through booster vaccination and infection confers the greatest neutralization capacity, including against Omicron.General Directorate of Health in Geneva canton, Private Foundation of the Geneva University Hospitals, European Commission ("CoVICIS" grant), and a private foundation advised by CARIGEST SA.

BackgroundCholera remains a major threat in Sub-Saharan Africa (SSA) where some of the highest case fatality risks are reported. Knowing in what months and where cholera tends to occur across the continent can aid in improving efforts to eliminate cholera as a public health concern; though largely due to lack of unified large-scale datasets, no continent-wide estimates exist. In this study we aim to estimate cholera seasonality across SSA.MethodsWe leverage the Global Task Force on Cholera Control (GTFCC) global cholera database with statistical models to synthesize data across spatial and temporal scale in order to infer the seasonality of excess suspected cholera occurrence in SSA. We developed a Bayesian statistical model to infer the monthly risk of excess cholera at the first and/or second administrative levels. Seasonality patterns were then grouped into spatial clusters. Finally, we studied the association between seasonality estimates and hydro-climatic variables.FindingsThe majority of studied countries (24/34) have seasonal patterns in excess cholera, corresponding to approximately 85% of the SSA population. Most countries (19/24) also had sub-national differences in seasonality patterns, with strong differences in seasonality strength between regions. Seasonality patterns clustered into two macro-regions (West Africa and the Sahel vs. Eastern and Southern Africa), which were composed of sub-regional clusters with varying degrees of seasonality. Exploratory association analysis found most consistent and positive correlations between cholera seasonality and precipitation, and to a lesser extent with temperature and flooding.InterpretationWidespread cholera seasonality in SSA offers opportunities for intervention planning. Further studies are needed to study the association between cholera and climate.FundingThe NASA Applied Sciences Program and the Bill and Melinda Gates Foundation.Research in ContextEvidence before this studyPrevious studies were searched using PubMed on Nov 15, 2021, without language or date restrictions using the search terms [“cholera*” AND “season*” AND (“Africa*” OR “Angola” OR “Burundi” OR “Benin” OR “Burkina Faso” OR “Botswana” OR “Central African Republic” OR “Côte d’Ivoire” OR “Cameroon” OR “Democratic Republic of the Congo” OR “Republic of Congo” OR “Djibouti” OR “Eritrea” OR “Ethiopia” OR “Gabon” OR “Ghana” OR “Guinea” OR “Gambia” OR “Guinea-Bissau” OR “Equatorial Guinea” OR “Kenya” OR “Liberia” OR “Madagascar” OR “Mali” OR “Mozambique” OR “Mauritania” OR “Malawi” OR “Namibia” OR “Niger” OR “Nigeria” OR “Rwanda” OR “Sudan” OR “Senegal” OR “Sierra Leone” OR “Somalia” OR “South Sudan” OR “São Tomé and Príncipe” OR “Swaziland” OR “Chad” OR “Togo” OR “Tanzania” OR “Uganda” OR “South Africa” OR “Zambia” OR “Zimbabwe”)]. Two additional known articles not identified by our PubMed search were added, one on the seasonality of cholera in Kenya and one on the epidemiology of cholera in West Africa. Studies were included if they focused on the epidemiology of Vibrio cholerae O1 or O139, covering one or multiple countries in sub-Saharan Africa (SSA) for at least two years with reported cases. We identified a total of 140 studies, of which 36 met our inclusion criteria. Of these 4 were reviews, 3 were regional or global studies, and 29 focused on specific countries. Local-level seasonality studies were identified in Burundi (1), Côte d’Ivoire (1), Cameroon (2), Democratic Republic of the Congo (3), Ghana (1), Guinea-Bissau (1), Kenya (1), Mozambique (3), Nigeria (1), Senegal (1), Somalia (1), South Sudan (1), Togo (1), Tanzania (4), Uganda (2), South Africa (1), and Zambia (4). These local studies mainly consisted of epidemiological descriptions of cholera incidence at either the national or first administrative unit scale, covering between 2 and 31 years of data. Most of these studies found seasonality in cholera with different patterns between countries. Two regional studies in West Africa found seasonal patterns in cholera incidence in the second part of the calendar year, with evidence for synchrony with rainfall patterns. Finally one global study between 1975 and 2005 found evidence for a latitudinal gradient of seasonality strength with weaker seasonality around the equator.Added value of this studyAlthough most local-level and regional studies have found evidence for cholera seasonal patterns in SSA, gaps remain for certain countries and there currently lacks a systematic and continental-scale investigation. By leveraging a large database of cholera incidence we evaluate and characterize cholera seasonality at sub-national scale in 34 countries of SSA for which sufficient data was available. We show that cholera is seasonal in the majority of countries (24/34), with sub-national heterogeneity in most of them (19/24). Results enable the description of cholera seasonality at the continental level and the exploration of associations with hydro-climatic variables.Implications of all the available evidenceThis work establishes the extent and strength of cholera seasonality in SSA, providing a basis on which to ground strategic decision on large-scale intervention allocation as well as future work on the climatic and non-climatic drivers of cholera seasonality in SSA.

BackgroundCholera remains a major threat in Sub-Saharan Africa (SSA) where some of the highest case fatality risks are reported. Knowing in what months and where cholera tends to occur across the continent can aid in improving efforts to eliminate cholera as a public health concern; though largely due to lack of unified large-scale datasets, no continent-wide estimates exist. In this study we aim to estimate cholera seasonality across SSA. MethodsWe leverage the Global Task Force on Cholera Control (GTFCC) global cholera database with statistical models to synthesize data across spatial and temporal scale in order to infer the seasonality of excess suspected cholera occurrence in SSA. We developed a Bayesian statistical model to infer the monthly risk of excess cholera at the first and/or second administrative levels. Seasonality patterns were then grouped into spatial clusters. Finally, we studied the association between seasonality estimates and hydro-climatic variables.FindingsThe majority of studied countries (24/34) have seasonal patterns in excess cholera, corresponding to approximately 85% of the SSA population. Most countries (19/24) also had sub-national differences in seasonality patterns, with strong differences in seasonality strength between regions. Seasonality patterns clustered into two macro-regions (West Africa and the Sahel vs. Eastern and Southern Africa), which were composed of sub-regional clusters with varying degrees of seasonality. Exploratory association analysis found most consistent and positive correlations between cholera seasonality and precipitation, and to a lesser extent with temperature and flooding.InterpretationWidespread cholera seasonality in SSA offers opportunities for intervention planning. Further studies are needed to study the association between cholera and climate. FundingThe NASA Applied Sciences Program and the Bill and Melinda Gates Foundation.

We report a prospective epidemiological, virological and serological investigation of a SARS-CoV-2 outbreak in a primary school, as part of a longitudinal, prospective, primary school-based surveillance study. It involved repeated testing of pupils and teachers and household members of participants who tested positive, with rapid antigen tests and/or RT-PCR (Day 0-2 and Day 5-7), serologies on dried capillary blood samples (Day 0-2 and Day 30), contact tracing interviews and SARS-CoV-2 whole genome sequencing. This SARS-CoV-2 outbreak caused by the Alpha variant involved 20 children aged 4 to 6 years from 4 classes, 2 teachers and a total of 4 household members. Infection attack rates were between 11.8 and 62.0% among pupils from the 4 classes, 22.2% among teachers and 0% among non-teaching staff. Secondary attack rate among household members was 15.4%. Symptoms were reported by 63% of infected children, 100% of teachers and 50% of household members. All analysed sequences but one showed 100% identity. Serological tests detected 8 seroconversions unidentified by SARS-CoV-2 virological tests. This study confirmed child-to-child and child-to-adult transmission of the infection. Effective measures to limit transmission in schools have the potential to reduce the overall community circulation.

BackgroundPopulation-based serological surveys provide a means for assessing the immunologic landscape of a community, without the biases related to health-seeking behaviors and testing practices typically associated with rt-PCR testing. This study assesses SARS-CoV-2 seroprevalence over the first epidemic wave in Canton Geneva, Switzerland, as well as biological and socio-economic risk factors for infection and symptoms associated with IgG seropositivity.Methods and findingsBetween April 6 and June 30, 2020, former participants of a yearly representative cross-sectional survey of the 20-75-year-old population of the canton of Geneva were invited to participate in a seroprevalence study, along with household members five years and older. We collected blood and tested it for anti-SARS-CoV-2 immunoglobulins G (IgG). Questionnaires were self-administered. We estimated seroprevalence with a Bayesian model accounting for test performance and sampling design. We included 8344 participants (53.5% women, mean age 46.9 years). The population-level seroprevalence over the 12-week study period was 7.8 % (95% Credible Interval (CrI) 6.8-8.9), accounting for sex, age and household random effects. Seroprevalence was highest among 18-49 year olds (9.5%, 95%CrI 8.1-10.9), with young children (5-9 years) and those >65 years having significantly lower seroprevalence (4.3% and 4.7-5.4% respectively). Men were more likely to be seropositive than women (relative risk 1.2, 95%CrI 1.1-1.4). Odds of seropositivity were reduced for female retirees (0.46, 95%CI 0.23-0.93) and unemployed men (0.35, 95%CI 0.13-1.0) compared to employed individuals, and for current smokers (0.36, 95%CI 0.23-0.55) compared to never-smokers. We found no significant association between occupation, level of education, neighborhood income and the risk of being seropositive. Symptoms most strongly associated with seropositivity were anosmia/dysgeusia, loss of appetite, fever, fatigue and myalgia and/or arthralgia. Thirteen percent of seropositive participants reported no symptoms.ConclusionsOur results confirm a low population seroprevalence of anti-SARS-CoV-2 antibodies after the first wave in Geneva, a region hard hit by the COVID-19 pandemic. Socioeconomic factors were not associated with seropositivity in this sample. The elderly and young children were less frequently seropositive, though it is not clear how biology and behaviors shape these differences. These specificities should be considered when assessing the need for targeted public health measures.

SummaryWhat is already known about this topic?The highly transmissible SARS-CoV-2 Delta variant has begun to cause increases in cases, hospitalizations, and deaths in parts of the United States. With slowed vaccination uptake, this novel variant is expected to increase the risk of pandemic resurgence in the US in July—December 2021.What is added by this report?Data from nine mechanistic models project substantial resurgences of COVID-19 across the US resulting from the more transmissible Delta variant. These resurgences, which have now been observed in most states, were projected to occur across most of the US, coinciding with school and business reopening. Reaching higher vaccine coverage in July—December 2021 reduces the size and duration of the projected resurgence substantially. The expected impact of the outbreak is largely concentrated in a subset of states with lower vaccination coverage.What are the implications for public health practice?Renewed efforts to increase vaccination uptake are critical to limiting transmission and disease, particularly in states with lower current vaccination coverage. Reaching higher vaccination goals in the coming months can potentially avert 1.5 million cases and 21,000 deaths and improve the ability to safely resume social contacts, and educational and business activities. Continued or renewed non-pharmaceutical interventions, including masking, can also help limit transmission, particularly as schools and businesses reopen.

BackgroundUp-to-date seroprevalence estimates are critical to describe the SARS-CoV-2 immune landscape in the population and guide public health measures. We aimed to estimate the seroprevalence of anti-SARS-CoV-2 antibodies 15 months into the COVID-19 pandemic and six months into the vaccination campaign.MethodsWe conducted a population-based cross-sectional serosurvey between June 1 and July 7, 2021, recruiting participants from age- and sex-stratified random samples of the general population. We tested participants for anti-SARS-CoV-2 antibodies targeting the spike (S) or nucleocapsid (N) proteins (Roche Elecsys immunoassays). We estimated the anti-SARS-CoV-2 antibodies seroprevalence following vaccination and/or infection (anti-S antibodies), or infection only (anti-N antibodies).ResultsWe included 3355 individuals, of which 1814 (54.1%) were women, 697 (20.8%) were aged ConclusionsMost adults have developed anti-SARS-CoV-2 antibodies, while most teenagers and children remain vulnerable to infection. As the SARS-CoV-2 Delta variant spreads and vaccination rates stagnate, efforts are needed to address vaccine hesitancy, particularly among younger individuals and socioeconomically disadvantaged groups, and to minimize spread among children.

Limited data exist on SARS-CoV-2 infection rates across sectors and occupations, hindering our ability to make rational policy, including vaccination prioritization, to protect workers and limit SARS-CoV-2 spread. Here, we present results from our SEROCoV-WORK + study, a serosurvey of workers recruited after the first wave of the COVID-19 pandemic in Geneva, Switzerland. We tested workers (May 18—September 18, 2020) from 16 sectors and 32 occupations for anti-SARS-CoV-2 IgG antibodies. Of 10,513 participants, 1026 (9.8%) tested positive. The seropositivity rate ranged from 4.2% in the media sector to 14.3% in the nursing home sector. We found considerable within-sector variability: nursing home (0%–31.4%), homecare (3.9%–12.6%), healthcare (0%–23.5%), public administration (2.6%–24.6%), and public security (0%–16.7%). Seropositivity rates also varied across occupations, from 15.0% among kitchen staff and 14.4% among nurses, to 5.4% among domestic care workers and 2.8% among journalists. Our findings show that seropositivity rates varied widely across sectors, between facilities within sectors, and across occupations, reflecting a higher exposure in certain sectors and occupations., Many job sectors classified as ‘essential’ have continued operating with limited restrictions during the COVID-19 pandemic, potentially placing workers at higher risk of infection. Here, the authors show that seropositivity rates in workers vary widely across and between job sectors in Geneva, Switzerland.

Serologic studies have been critical in tracking the evolution of the COVID-19 pandemic. The reliability of serologic studies for quantifying the proportion of the population that have been infected depends on the extent of antibody decay as well as on assay performance in detecting both recent and older infections. Data on anti-SARS-CoV-2 antibodies persistence remain sparse, especially from infected individuals with few to no symptoms. In a cohort of mostly mild/asymptomatic SARS-CoV-2-infected individuals tested with three widely-used immunoassays, antibodies persisted for at least 8 months after infection, although detection depended on immunoassay choice, with one of them missing up to 40% of past infections. Simulations reveal that without appropriate adjustment for time-varying assay sensitivity, seroprevalence surveys may underestimate infection rates. As the immune landscape becomes more complex with naturally-infected and vaccinated individuals, assay choice and appropriate assay-performance-adjustment will become even more important for the interpretation of serologic studies.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multi-model ensemble forecast that combined predictions from dozens of different research groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-week horizon 3-5 times larger than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks. Significance Statement 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 US. 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.

Background Up-to-date seroprevalence estimates are critical to describe the SARS-CoV-2 immune landscape and to guide public health decisions. Aim We estimate seroprevalence of anti-SARS-CoV-2 antibodies 15 months into the COVID-19 pandemic and 6 months into the vaccination campaign. Methods We conducted a population-based cross-sectional serosurvey between 1 June and 7 July 2021, recruiting participants from age- and sex-stratified random samples of the general population. We tested participants for anti-SARS-CoV-2 antibodies targeting the spike (S) or nucleocapsid (N) proteins using the Roche Elecsys immunoassays. We estimated the anti-SARS-CoV-2 antibodies seroprevalence following vaccination and/or infection (anti-S antibodies), or infection only (anti-N antibodies). Results Among 3,355 individuals (54.1% women; 20.8% aged Conclusion Most of the population has developed anti-SARS-CoV-2 antibodies, despite most teenagers and children remaining vulnerable to infection. As the SARS-CoV-2 Delta variant spreads and vaccination rates stagnate, efforts are needed to address vaccine hesitancy, particularly among younger individuals and to minimise spread among children.

Human mobility is a core component of human behavior and its quantification is critical for understanding its impact on infectious disease transmission, traffic forecasting, access to resources and care, intervention strategies, and migratory flows. When mobility data are limited, spatial interaction models have been widely used to estimate human travel, but have not been extensively validated in low- and middle-income settings. Geographic, sociodemographic, and infrastructure differences may impact the ability for models to capture these patterns, particularly in rural settings. Here, we analyzed mobility patterns inferred from mobile phone data in four Sub-Saharan African countries to investigate the ability for variants on gravity and radiation models to estimate travel. Adjusting the gravity model such that parameters were fit to different trip types, including travel between more or less populated areas and/or different regions, improved model fit in all four countries. This suggests that alternative models may be more useful in these settings and better able to capture the range of mobility patterns observed.

Recent evidence suggests that snail predators may aid efforts to control the human parasitic disease schistosomiasis by eating aquatic snail species that serve as intermediate hosts of the parasite. Here, potential synergies between schistosomiasis control and aquaculture of giant prawns are evaluated using an integrated bioeconomic–epidemiological model. Combinations of stocking density and aquaculture cycle length that maximize cumulative, discounted profit are identified for two prawn species in sub-Saharan Africa: the endemic, non-domesticated Macrobrachium vollenhovenii and the non-native, domesticated Macrobrachium rosenbergii. At profit-maximizing densities, both M. rosenbergii and M. vollenhovenii may substantially reduce intermediate host snail populations and aid schistosomiasis control efforts. Control strategies drawing on both prawn aquaculture to reduce intermediate host snail populations and mass drug administration to treat infected individuals are found to be superior to either strategy alone. Integrated aquaculture-based interventions can be a win–win strategy in terms of health and sustainable development in schistosomiasis endemic regions of the world. Predators, including prawns, can suppress schistosomiasis by eating snail hosts. This modelling study finds that two prawn species in sub-Saharan Africa can reduce snail hosts and help control schistosomiasis at densities that maximize profits of associated aquaculture—a potential win–win.

Coronavirus disease 2019 (COVID-19) has caused strain on health systems worldwide due to its high mortality rate and the large portion of cases requiring critical care and mechanical ventilation. During these uncertain times, public health decision makers, from city health departments to federal agencies, sought the use of epidemiological models for decision support in allocating resources, developing non-pharmaceutical interventions, and characterizing the dynamics of COVID-19 in their jurisdictions. In response, we developed a flexible scenario modeling pipeline that could quickly tailor models for decision makers seeking to compare projections of epidemic trajectories and healthcare impacts from multiple intervention scenarios in different locations. Here, we present the components and configurable features of the COVID Scenario Pipeline, with a vignette detailing its current use. We also present model limitations and active areas of development to meet ever-changing decision maker needs.

Following the rapid dissemination of COVID-19 cases in Switzerland, large-scale non-pharmaceutical interventions (NPIs) were implemented by the cantons and the federal government between February 28 and March 20. Estimates of the impact of these interventions on SARS-CoV-2 transmission are critical for decision making in this and future outbreaks. We here aim to assess the impact of these NPIs on disease transmission by estimating changes in the basic reproduction number (R0) at national and cantonal levels in relation to the timing of these NPIs. We estimate the time-varying R0 nationally and in twelve cantons by fitting a stochastic transmission model explicitly simulating within hospital dynamics. We use individual-level data of >1,000 hospitalized patients in Switzerland and public daily reports of hospitalizations and deaths. We estimate the national R0 was 3.15 (95% CI: 2.13-3.76) at the start of the epidemic. Starting from around March 6, we find a strong reduction in R0 with a 85% median decrease (95% quantile range, QR: 83%-90%) to a value of 0.44 (95% QR: 0.27-0.65) in the period of March 29-April 5. At the cantonal-level R0 decreased over the course of the epidemic between 71% and 94%. We found that reductions in R0 were synchronous with changes in mobility patterns as estimated through smartphone activity, which started before the official implementation of NPIs. We found that most of the reduction of transmission is due to behavioural changes as opposed to natural immunity, the latter accounting for only about 3% of the total reduction in effective transmission. As Switzerland considers relaxing some of the restrictions of social mixing, current estimates of R0 well below one are promising. However most of inferred transmission reduction was due to behaviour change (

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.

Background Despite their importance for designing and evaluating schistosomiasis control trials, little attention in the literature has been dedicated to sampling protocols for the parasite’s snail intermediate hosts since their first development. We propose a comparative analysis of time-based and quadrat sampling protocols to quantify the seasonal variations in the abundance of these aquatic snail species of medical importance. Methodology/Principal findings Snail populations were monitored during 42 consecutive months in three types of habitats (ephemeral pond, ephemeral river and permanent stream) in two sites covering different climatic zones in Burkina Faso. We employed both a widely used time-based protocol of 30min of systematic collection at a weekly interval, and a quadrat protocol of 8 replicates per sample at a monthly interval. The correspondence between the two protocols was evaluated using an ensemble of statistical models including linear and saturating-type functional forms as well as allowing for count zero-inflation. The quadrat protocol yielded on average a relative standard error of 40%, for a mean snail density of 16.7 snails/m2 and index of dispersion of 1.51. Both protocols yielded similar seasonal patterns in snail abundance, confirming the asynchrony between permanent and ephemeral habitats with respect to the country’s seasonal rainfall patterns. Formal model comparison of the link between time vs. quadrat counts showed strong support of saturation for the latter and measurement zero-inflation, providing important evidence for the presence of density feedbacks in the snail’s population dynamics, as well as for spatial clustering. Conclusions/Significance In addition to the agreement with the time-based method, quadrat sampling provided insight into snail population dynamics and comparable density estimates across sites. The re-evaluation of these “traditional” sampling protocols, as well as the correspondence between their outputs, is of practical importance for the design and evaluation of schistosomiasis control trials., Author summary The ongoing paradigm shift from schistosomiasis morbidity control to elimination will require addressing multiple facets of the disease’s transmission cycle, including control of the snail obligate intermediate hosts of the parasite. In this context the choice of sampling protocol to monitor and understand population dynamics of aquatic snails has practical implications, in particular for studying the strong seasonal fluctuations in their abundance. However, comparative analyses of alternative methods have seldom been carried out since their development in the 1960s-1970s. Here we compare a widely used time-based protocol with a less commonly employed quadrat protocol. Data collected during 3.5 years in three different habitat types in Burkina Faso (West Africa) suitable for the quadrat method show that both protocols agree in the detection of abundance seasonality. By using statistical models to link the outputs of the two methods we also highlight a saturation of quadrat absolute density estimates as well as zero-inflation in the counts. These results provide insight into the snail’s population dynamics with respect to time-based sampling by supporting the presence of density feedbacks as well as small-scale spatial clustering. This work fosters the use of quadrat sampling in other contexts and the comparison of results with other sampling protocols.

The transmission of waterborne diseases hinges on the interactions between hydrology and ecology of hosts, vectors and parasites, with the long-term absence of water constituting a strict lower bound. However, the link between spatio-temporal patterns of hydrological ephemerality and waterborne disease transmission is poorly understood and difficult to account for. The use of limited biophysical and hydroclimate information from otherwise data scarce regions is therefore needed to characterize, classify, and predict river network ephemerality in a spatially explicit framework. Here, we develop a novel large-scale ephemerality classification and prediction methodology based on monthly discharge data, water and energy availability, and remote-sensing measures of vegetation, that is relevant to epidemiology, and maintains a mechanistic link to catchment hydrologic processes. Specifically, with reference to the context of Burkina Faso in sub-Saharan Africa, we extract a relevant set of catchment covariates that include the aridity index, annual runoff estimation using the Budyko framework, and hysteretical relations between precipitation and vegetation. Five ephemerality classes, from permanent to strongly ephemeral, are defined from the duration of 0-flow periods that also accounts for the sensitivity of river discharge to the long-lasting drought of the 70’s-80’s in West Africa. Using such classes, a gradient-boosted tree-based prediction yielded three distinct geographic regions of ephemerality. Importantly, we observe a strong epidemiological association between our predictions of hydrologic ephemerality and the known spatial patterns of schistosomiasis, an endemic parasitic waterborne disease in which infection occurs with human-water contact, and requires aquatic snails as an intermediate host. The general nature of our approach and its relevance for predicting the hydrologic controls on schistosomiasis occurrence provides a pathway for the explicit inclusion of hydrologic drivers within epidemiological models of waterborne disease transmission.

The infection fatality risk (IFR) is the average number of deaths per infection by a pathogen and is key to characterizing the severity of infection across the population and for specific demographic groups. To date, there are few empirical estimates of IFR published due to challenges in measuring infection rates.1,2 Outside of closed, closely surveilled populations where infection rates can be monitored through viral surveillance, we must rely on indirect measures of infection, like specific antibodies. Representative seroprevalence studies provide an important avenue for estimating the number of infections in a community, and when combined with death counts can lead to robust estimates of the IFR. We estimated overall and age-specific IFR for the canton of Geneva, Switzerland using age-stratified daily case and death incidence reports combined with five weekly population-based seroprevalence estimates.3 From February 24th to June 2nd there were 5’039 confirmed cases and 286 reported deaths within Geneva (population of 506’765). We inferred age-stratified (5-9, 10-19, 20-49, 50-65 and 65+) IFRs by linking the observed number of deaths to the estimated number of infected individuals from each serosurvey. We account for the delays between infection and seroconversion as well as between infection and death.4 Inference is drawn in a Bayesian framework that incorporates uncertainty in seroprevalence estimates (supplement).Of the 286 reported deaths caused by SARS-CoV-2, the youngest person to die was 31 years old. Infected individuals younger than 50 years experienced statistically similar IFRs (range 0.00032-0.0016%), which increases to 0.14% (95% CrI 0.096-0.19) for those 50-64 years old to 5.6% (95% CrI 4.3-7.4) for those 65 years and older (supplement). After accounting for demography and age-specific seroprevalence, we estimate a population-wide IFR of 0.64% (95% CrI 0.38-0.98).Our results are subject to two notable limitations. Among the 65+ age group that died of COVID-19 within Geneva, 50% were reported among residents of assisted care facilities, where around 0.8% of the Geneva population resides. While the serosurvey protocol did not explicitly exclude these individuals, they are likely to have been under-represented. This would lead to an overestimation of the IFR in the 65+ age group if seroprevalence in this institutionalized population was higher than in the general population (supplement). Further, our IFR estimates are based on current evidence regarding post-infection antibody kinetics, which may differ between severe and mild infections. If mild infections have significantly lower and short-lived antibody responses, our estimates of IFR may be biased upwards.5Estimates of IFR are key for understanding the true pandemic burden and for weighing different risk reduction strategies. The IFR is not solely determined by host and pathogen biology, but also by the capacity of health systems to treat severe cases. Despite having among the highest per capita incidence in Switzerland, Geneva’s health system accommodated the influx of cases needing intensive care (peak of 80/110 ICU-beds including surge capacity) while maintaining care quality standards. As such, our IFR estimates can be seen as a best-case scenario with respect to health system capacity. Our results reveal that population-wide estimates of IFR mask great heterogeneity by age and point towards the importance of age-targeted interventions to reduce exposures among those at highest risk of death.

Objectives Serological studies have been critical in tracking the evolution of the COVID-19 pandemic. Data on anti-SARS-CoV-2 antibodies persistence remain sparse, especially from infected individuals with few to no symptoms. The objective of the study was to quantify the sensitivity for detecting historic SARS-CoV-2 infections as a function of time since infection for three commercially available SARS-CoV-2 immunoassays and to explore the implications of decaying immunoassay sensitivity in estimating seroprevalence. Methods We followed a cohort of mostly mild/asymptomatic SARS-CoV-2-infected individuals (n = 354) at least 8 months after their presumed infection date and tested their serum for anti-SARS-CoV-2 antibodies with three commercially available assays: Roche-N, Roche-RBD and EuroImmun-S1. We developed a latent class statistical model to infer the specificity and time-varying sensitivity of each assay and show through simulations how inappropriately accounting for test performance can lead to biased serosurvey estimates. Results Antibodies were detected at follow-up in 74–100% of participants, depending on immunoassays. Both Roche assays maintain high sensitivity, with the EuroImmun assay missing 40% of infections after 9 months. Simulations reveal that without appropriate adjustment for time-varying assay sensitivity, seroprevalence surveys may underestimate infection rates. Discussion Antibodies persist for at least 8 months after infection in a cohort of mildly infected individuals with detection depending on assay choice. Appropriate assay performance adjustment is important for the interpretation of serological studies in the case of diminishing sensitivity after infection.