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Abstract Background Pregnant women are a vulnerable population to COVID-19 given an increased susceptibility to severe SARS-CoV-2 infection and pregnancy complications. However, few SARS-CoV-2 serological surveys have been performed among this population to assess the extent of the infection in sub-Saharan countries. The objectives of this study were to determine SARS-CoV-2 seroprevalence among Beninese pregnant women, to identify spatial seropositivity clusters and to analyse factors associated with the infection. Methods A cross-sectional study including women in their third trimester of pregnancy attending the antenatal care (ANC) clinics at Allada (south Benin) and Natitingou (north Benin) was conducted. Rapid diagnostic tests (RDT) for detection of IgG/IgM against the SARS-CoV-2 spike protein were performed using capillary blood. Seroprevalence of SARS-CoV-2 antibodies and associations between SARS-CoV-2 serostatus and maternal characteristics were analyzed by multivariate logistic regression. Spatial analyses were performed using the spatial scan statistics to identify spatial clusters of SARS-CoV-2 infection. Results A total of 861 pregnant women were enrolled between May 4 and June 29, 2022. 58/861 (6.7%) participants reported having received COVID-19 vaccine. None of the participants had been diagnosed with COVID-19 during their pregnancy. SARS-CoV-2 antibodies were detected in 607/802 (75.7%; 95% CI 72.56%–78.62%) of unvaccinated participants. Several urban and rural spatial clusters of SARS-CoV-2 cases were identified in Allada and one urban spatial cluster was identified in Natitingou. Unvaccinated participants from Allada with at least one previous morbidity were at a three-times higher risk of presenting SARS-CoV-2 antibodies (OR = 2.89; 95%CI 1.19%-7.00%). Conclusion Three out of four pregnant women had SARS-CoV-2 antibodies, suggesting a high virus circulation among pregnant women in Benin, while COVID-19 vaccination coverage was low. Pregnant women with comorbidities may be at increased risk of SARS-CoV-2 infection. This population should be prioritized for COVID-19 diagnosis and vaccination in order to prevent its deleterious effects. Trial registration NCT06170320 (retrospectively registered on December 21, 2023).

Summary Background In Haiti, reported incidence and mortality rates for COVID-19 were lower than expected. We aimed to analyze factors at communal and individual level that might lead to an underestimation of the true burden of the COVID-19 epidemic in Haiti during its first two years. Methods We analyzed national COVID-19 surveillance data from March 2020 to December 2021, to describe the epidemic using cluster detection, time series, and cartographic approach. We performed multivariate Quasi-Poisson regression models to determine socioeconomic factors associated with incidence and mortality. We performed a mixed-effect logistic regression model to determine individual factors associated with the infection. Results Among the 140 communes of Haiti, 57 (40.7%) had a COVID-19 screening center, and the incidence was six times higher in these than in those without. Only 22 (15.7%) communes had a COVID-19 care center, and the mortality was five times higher in these than in those without. All the richest communes had a COVID-19 screening center while only 30.8% of the poorest had one. And 75% of the richest communes had a COVID-19 care center while only 15.4% of the poorest had one. Having more than three healthcare workers per 1000 population in the commune was positively associated with the incidence (SIR: 3.31; IC95%: 2.50, 3.93) and the mortality (SMR: 2.73; IC95%: 2.03, 3.66). At the individual level, male gender (adjusted OR: 1.11; IC95%: 1.01, 1.22), age with a progressive increase of the risk compared to youngers, and having Haitian nationality only (adjusted OR:2.07; IC95%: 1.53, 2.82) were associated with the infection. Conclusions This study highlights the weakness of SARS-CoV-2 screening and care system in Haiti, particularly in the poorest communes, suggesting that the number of COVID-19 cases and deaths were probably greatly underestimated.

Across 133 confirmed mpox zoonotic index cases reported during 1970–2021 in Africa, cases occurred year-round near the equator, where climate is consistent. However, in tropical regions of the northern hemisphere under a dry/wet season cycle, cases occurred seasonally. Our findings further support the seasonality of mpox zoonotic transmission risk.

IntroductionIn 2020, during France’s COVID-19 response, healthcare professionals from a hospital and an association initiated health mediation interventions in Marseille’s vulnerable neighbourhoods, funded by the regional health authorities. This mixed method research evaluates the CORHESAN program that lasted until June 2022.MethodsWe examined CORHESAN documents and reports, conducted interviews, and analysed activity data, comparing it to the COVID-19 hotspots identified on a weekly basis at the neighbourhood level, using generalised linear mixed models (GLMMs).ResultsCORHESAN was implemented by a team of up to nine health mediators, six private nurses hired on an ad hoc basis, supervised by a general coordinator and two part-time medical and nursing coordinators. Multiple partnerships were established with shelters, associations, social-housing landlords and local institutions. The team accompanied 6,253 people affected by COVID-19 or contact in the practical implementation of their isolation and contact tracing. Of the 5,180 nasopharyngeal samples for RT-PCR and 1,875 for antigenic testing: 12% were taken at home and 27% in partner facilities in the targeted neighbourhoods; 32% were taken from symptomatic patients and 30% in the context of contact tracing; and 40% were positive. Multiple awareness sessions on prevention methods and distributions of personal protection kits and self-diagnostic tests were conducted in the streets, in shelters, in associations or at home. A total of 5,929 doses of COVID-19 vaccine were administered in a walk-in vaccination centre, at temporary street vaccination posts, during operations at partner facilities, or during home-visits to patients with limited autonomy. GLMMs showed that the intervention significantly targeted its testing interventions in neighbourhoods with socioeconomic disadvantage and/or past under-testing (adjusted odds ratio (aOR), 2.75 [1.50–5.00]) and those with high hotspot level (aOR for level-3 versus level-0, 1.83 [1.24–2.71]).DiscussionThe pandemic emphasised the potential of health mediation interventions to address health disparities. Building on this, a new program began in July 2022, aiming at enhancing cancer screening and vaccinations in deprived areas of Marseille. Evaluations are ongoing to assess its activities and impact, and provide evidence to future implementation initiatives.

Abstract Background In the Greater Mekong Subregion, case–control studies and national-level analyses have shown an association between malaria transmission and forest activities. The term ‘forest malaria’ hides the diversity of ecosystems in the GMS, which likely do not share a uniform malaria risk. To reach malaria elimination goals, it is crucial to document accurately (both spatially and temporally) the influence of environmental factors on malaria to improve resource allocation and policy planning within given areas. The aim of this ecological study is to characterize the association between malaria dynamics and detailed ecological environments determined at village level over a period of several years in Kayin State, Myanmar. Methods We characterized malaria incidence profiles at village scale based on intra- and inter-annual variations in amplitude, seasonality, and trend over 4 years (2016–2020). Environment was described independently of village localization by overlaying a 2-km hexagonal grid over the region. Specifically, hierarchical classification on principal components, using remote sensing data of high spatial resolution, was used to assign a landscape and a climate type to each grid cell. We used conditional inference trees and random forests to study the association between the malaria incidence profile of each village, climate and landscape. Finally, we constructed eco-epidemiological zones to stratify and map malaria risk in the region by summarizing incidence and environment association information. Results We identified a high diversity of landscapes (n = 19) corresponding to a gradient from pristine to highly anthropogenically modified landscapes. Within this diversity of landscapes, only three were associated with malaria-affected profiles. These landscapes were composed of a mosaic of dense and sparse forest fragmented by small agricultural patches. A single climate with moderate rainfall and a temperature range suitable for mosquito presence was also associated with malaria-affected profiles. Based on these environmental associations, we identified three eco-epidemiological zones marked by later persistence of Plasmodium falciparum, high Plasmodium vivax incidence after 2018, or a seasonality pattern in the rainy season. Conclusions The term forest malaria covers a multitude of contexts of malaria persistence, dynamics and populations at risk. Intervention planning and surveillance could benefit from consideration of the diversity of landscapes to focus on those specifically associated with malaria transmission. Graphical Abstract

Abstract In South Africa, the population at risk of malaria is 10% (around six million inhabitants) and concern only three provinces of which Limpopo Province is the most affected, particularly in Vhembe District. As the elimination approaches, a finer scale analysis is needed to accelerate the results. Therefore, in the process of refining local malaria control and elimination strategies, the aim of this study was to identify and describe malaria incidence patterns at the locality scale in the Vhembe District, Limpopo Province, South Africa. The study area comprised 474 localities in Vhembe District for which smoothed malaria incidence curve were fitted with functional data method based on their weekly observed malaria incidence from July 2015 to June 2018. Then, hierarchical clustering algorithm was carried out considering different distances to classify the 474 smoothed malaria incidence curves. Thereafter, validity indices were used to determine the number of malaria incidence patterns. The cumulative malaria incidence of the study area was 4.1 cases/1000 person-years. Four distinct patterns of malaria incidence were identified: high, intermediate, low and very low with varying characteristics. Malaria incidence increased across transmission seasons and patterns. The localities in the two highest incidence patterns were mainly located around farms, and along the rivers. Some unusual malaria phenomena in Vhembe District were also highlighted as resurgence. Four distinct malaria incidence patterns were found in Vhembe District with varying characteristics. Findings show also unusual malaria phenomena in Vhembe District that hinder malaria elimination in South Africa. Assessing the factors associated with these unusual malaria phenome would be helpful on building innovative strategies that lead South Africa on malaria elimination.

Abstract Background Over the past decade, implementation of multiple malaria control strategies in most countries has largely contributed to advance the global malaria elimination agenda. Nevertheless, in some regions, seasonal epidemics may adversely affect the health of local populations. In South Africa, Plasmodium falciparum malaria is still present, with the Vhembe District experiencing an incidence rate of 3.79 cases/1000 person-years in 2018, particularly in the Limpopo River Valley, bordering Zimbabwe. To elucidate the complexity of the mechanisms involved in local regular malaria outbreaks, a community-based survey was implemented in 2020 that focused on the relationship between housing conditions and malaria risky behaviours. Methods The community-based cross-sectional survey was conducted among the population of three study sites in the Vhembe District, which were selected based on malaria incidence rate, social and health characteristics of inhabitants. The household survey used a random sampling strategy, where data were collected through face-to-face questionnaires and field notes; to described the housing conditions (housing questionnaire), and focus on individual behaviours of household members. Statistical analyses were performed combining hierarchical classifications and logistic regressions. Results In this study, 398 households were described, covering a population of 1681 inhabitants of all ages, and 439 adults who participated in community-based survey. The analysis of situations at risk of malaria showed that the influence of contextual factors, particularly those defined by the type of habitat, was significant. Housing conditions and poor living environments were factors of malaria exposure and history, regardless of site of investigation, individual preventive behaviours and personal characteristics of inhabitants. Multivariate models showed that, considering all personal characteristics or behaviours of inhabitants, housing conditions such as overcrowding pressures were significantly associated with individual malaria risk. Conclusions The results showed the overwhelming weight of social and contextual factors on risk situations. Considering the Fundamental Causes Theory, malaria control policies based on health behaviour prevention, should reinforce access to care or promoting health education actions. Overarching economic development interventions in targeted geographical areas and populations have to be implemented, so that malaria control and elimination strategies can be efficiently and effectively managed.

Abstract Background In malaria endemic countries, seasonal malaria chemoprevention (SMC) interventions are performed during the high malaria transmission in accordance with epidemiological surveillance data. In this study we propose a predictive approach for tailoring the timing and number of cycles of SMC in all health districts of Mali based on sub-national epidemiological surveillance and rainfall data. Our primary objective was to select the best of two approaches for predicting the onset of the high transmission season at the operational scale. Our secondary objective was to evaluate the number of malaria cases, hospitalisations and deaths in children under 5 years of age that would be prevented annually and the additional cost that would be incurred using the best approach. Methods For each of the 75 health districts of Mali over the study period (2014–2019), we determined (1) the onset of the rainy season period based on weekly rainfall data; (ii) the onset and duration of the high transmission season using change point analysis of weekly incidence data; and (iii) the lag between the onset of the rainy season and the onset of the high transmission. Two approaches for predicting the onset of the high transmission season in 2019 were evaluated. Results In the study period (2014–2019), the onset of the rainy season ranged from week (W) 17 (W17; April) to W34 (August). The onset of the high transmission season ranged from W25 (June) to W40 (September). The lag between these two events ranged from 5 to 12 weeks. The duration of the high transmission season ranged from 3 to 6 months. The best of the two approaches predicted the onset of the high transmission season in 2019 to be in June in two districts, in July in 46 districts, in August in 21 districts and in September in six districts. Using our proposed approach would prevent 43,819 cases, 1943 hospitalisations and 70 deaths in children under 5 years of age annually for a minimal additional cost. Our analysis shows that the number of cycles of SMC should be changed in 36 health districts. Conclusion Adapting the timing of SMC interventions using our proposed approach could improve the prevention of malaria cases and decrease hospitalisations and deaths. Future studies should be conducted to validate this approach. Graphical Abstract

Background: Targeting interventions where most needed and effective is crucial for public health. Malaria control and elimination strategies increasingly rely on stratification to guide surveillance, to allocate vector control campaigns, and to prioritize access to community-based early diagnosis and treatment (EDT). We developed an original approach of dynamic clustering to improve local discrimination between heterogeneous malaria transmission settings. Methods: We analysed weekly malaria incidence records obtained from community-based EDT (malaria posts) in Karen/Kayin state, Myanmar. We smoothed longitudinal incidence series over multiple seasons using functional transformation. We regrouped village incidence series into clusters using a dynamic time warping clustering and compared them to the standard, 5-category annual incidence standard stratification. Results: We included 1115 villages from 2016 to 2020. We identified eleven P. falciparum and P. vivax incidence clusters which differed by amplitude, trends and seasonality. Specifically the 124 villages classified as “high transmission area” in the standard P. falciparum stratification belonged to the 11 distinct groups when accounting to inter-annual trends and intra-annual variations. Likewise for P. vivax, 399 “high transmission” villages actually corresponded to the 11 distinct dynamics. Conclusion: Our temporal dynamic clustering methodology is easy to implement and extracts more information than standard malaria stratification. Our method exploits longitudinal surveillance data to distinguish local dynamics, such as increasing inter-annual trends or seasonal differences, providing key information for decision-making. It is relevant to malaria strategies in other settings and to other diseases, especially when many countries deploy health information systems and collect increasing amounts of health outcome data. Funding: The Bill & Melinda Gates Foundation, The Global Fund against AIDS, Tuberculosis and Malaria (the Regional Artemisinin Initiative) and the Wellcome Trust funded the METF program.

BackgroundTesting was the cornerstone of the COVID-19 epidemic response in most countries until vaccination became available for the general population. Social inequalities generally affect access to healthcare and health behaviors, and COVID-19 was rapidly shown to impact deprived population more drastically. In support of the regional health agency in Provence-Alpes-Côte d’Azur (PACA) in South-Eastern France, we analyzed the relationship between testing rate and socio-demographic characteristics of the population, to identify gaps in testing coverage and improve targeting of response strategies.MethodsWe conducted an ecological analysis of SARS-CoV-2/COVID-19 testing rate in the PACA region, based on data aggregated at the finest spatial resolution available in France (IRIS) and by periods defined by public health implemented measures and major epidemiological changes. Using general census data, population density, and specific deprivation indices, we used principal component analysis followed by hierarchical clustering to define profiles describing local socio-demographic characteristics. We analyzed the association between these profiles and testing rates in a generalized additive multilevel model, adjusting for access to healthcare, presence of a retirement home, and the age profile of the population.ResultsWe identified 6 socio-demographic profiles across the 2,306 analyzed IRIS spatial units: privileged, remote, intermediate, downtown, deprived, and very deprived (ordered by increasing social deprivation index). Profiles also ranged from rural (remote) to high density urban areas (downtown, very deprived). From July 2020 to December 2021, we analyzed SARS-CoV-2/COVID-19 testing rate over 10 periods. Testing rates fluctuated strongly but were highest in privileged and downtown areas, and lowest in very deprived ones. The lowest adjusted testing rate ratios (aTRR) between privileged (reference) and other profiles occurred after implementation of a mandatory healthpass for many leisure activities in July 2021. Periods of contextual testing near Christmas displayed the largest aTRR, especially during the last periods of 2021 after the end of free convenience testing for unvaccinated individuals.ConclusionWe characterized in-depth local heterogeneity and temporal trends in testing rates and identified areas and circumstances associated with low testing rates, which the regional health agency targeted specifically for the deployment of health mediation activities.

Objectives In low-income settings with limited access to diagnosis, COVID-19 information is scarce. In September 2020, after the first COVID-19 wave, Mali reported 3086 confirmed cases and 130 deaths. Most reports originated from Bamako, with 1532 cases and 81 deaths (2.42 million inhabitants). This observed prevalence of 0.06% appeared very low. Our objective was to estimate SARS-CoV-2 infection among inhabitants of Bamako, after the first epidemic wave. We assessed demographic, social and living conditions, health behaviours and knowledges associated with SARS-CoV-2 seropositivity.Settings We conducted a cross-sectional multistage household survey during September 2020, in three neighbourhoods of the commune VI (Bamako), where 30% of the cases were reported.Participants We recruited 1526 inhabitants in 3 areas, that is, 306 households, and 1327 serological results (≥1 years), 220 household questionnaires and collected answers for 962 participants (≥12 years).Primary and secondary outcome measures We measured serological status, detecting SARS-CoV-2 spike protein antibodies in blood sampled. We documented housing conditions and individual health behaviours through questionnaires among participants. We estimated the number of SARS-CoV-2 infections and deaths in the population of Bamako using the age and sex distributions.Results The prevalence of SARS-CoV-2 seropositivity was 16.4% (95% CI 15.1% to 19.1%) after adjusting on the population structure. This suggested that ~400 000 cases and ~2000 deaths could have occurred of which only 0.4% of cases and 5% of deaths were officially reported. Questionnaires analyses suggested strong agreement with washing hands but lower acceptability of movement restrictions (lockdown/curfew), and mask wearing.Conclusions The first wave of SARS-CoV-2 spread broadly in Bamako. Expected fatalities remained limited largely due to the population age structure and the low prevalence of comorbidities. Improving diagnostic capacities to encourage testing and preventive behaviours, and avoiding the spread of false information remain key pillars, regardless of the developed or developing setting.Ethics This study was registered in the registry of the ethics committee of the Faculty of Medicine and Odonto-Stomatology and the Faculty of Pharmacy, Bamako, Mali, under the number: 2020/162/CA/FMOS/FAPH.

Abstract Malaria is the leading cause of morbidity and mortality in Mali. Between 2017 and 2020, the number of cases increased in the country, with 2,884,827 confirmed cases and 1454 reported deaths in 2020. We performed a malaria risk stratification at the health district level in Mali with a view to proposing targeted control interventions. Data on confirmed malaria cases were obtained from the District Health Information Software 2, data on malaria prevalence and mortality in children aged 6–59 months from the 2018 Demographic and Health Survey, entomological data from Malian research institutions working on malaria in the sentinel sites of the National Malaria Control Program (NMCP), and environmental data from the National Aeronautics and Space Administration. A stratification of malaria risk was performed. Targeted malaria control interventions were selected based on spatial heterogeneity of malaria incidence, malaria prevalence in children, vector resistance distribution, health facility usage, child mortality, and seasonality of transmission. These interventions were discussed with the NMCP and the different funding partners. In 2017–2019, median incidence across the 75 health districts was 129.34 cases per 1000 person-years (standard deviation = 86.48). Risk stratification identified 12 health districts in very low transmission areas, 19 in low transmission areas, 20 in moderate transmission areas, and 24 in high transmission areas. Low health facility usage and increased vector resistance were observed in high transmission areas. Eight intervention combinations were selected for implementation. Our work provides an updated risk stratification using advanced statistical methods to inform the targeting of malaria control interventions in Mali. This stratification can serve as a template for continuous malaria risk stratifications in Mali and other countries.

BackgroundThe number of malaria cases worldwide has increased, with over 241 million cases and 69,000 more deaths in 2020 compared to 2019. Burkina Faso recorded over 11 million malaria cases in 2020, resulting in nearly 4,000 deaths. The overall incidence of malaria in Burkina Faso has been steadily increasing since 2016. This study investigates the spatiotemporal pattern and environmental and meteorological determinants of malaria incidence in Burkina Faso.MethodsWe described the temporal dynamics of malaria cases by detecting the transmission periods and the evolution trend from 2013 to 2018. We detected hotspots using spatial scan statistics. We assessed different environmental zones through a hierarchical clustering and analyzed the environmental and climatic data to identify their association with malaria incidence at the national and at the district's levels through generalized additive models. We also assessed the time lag between malaria peaks onset and the rainfall at the district level. The environmental and climatic data were synthetized into indicators.ResultsThe study found that malaria incidence had a seasonal pattern, with high transmission occurring during the rainy seasons. We also found an increasing trend in the incidence. The highest-risk districts for malaria incidence were identified, with a significant expansion of high-risk areas from less than half of the districts in 2013-2014 to nearly 90% of the districts in 2017-2018. We identified three classes of health districts based on environmental and climatic data, with the northern, south-western, and western districts forming separate clusters. Additionally, we found that the time lag between malaria peaks onset and the rainfall at the district level varied from 7 weeks to 17 weeks with a median at 10 weeks. Environmental and climatic factors have been found to be associated with the number of cases both at global and districts levels.ConclusionThe study provides important insights into the environmental and spatiotemporal patterns of malaria in Burkina Faso by assessing the spatio temporal dynamics of Malaria cases but also linking those dynamics to the environmental and climatic factors. The findings highlight the importance of targeted control strategies to reduce the burden of malaria in high-risk areas as we found that Malaria epidemiology is complex and linked to many factors that make some regions more at risk than others.

Abstract Malaria control and prevention programs are more efficient and cost-effective when they target hotspots or select the best periods of year to implement interventions. This study aimed to identify the spatial distribution of malaria hotspots at the village level in Diébougou health district, Burkina Faso, and to model the temporal dynamics of malaria cases as a function of meteorological conditions and of the distance between villages and health centres (HCs). Case data for 27 villages were collected in 13 HCs. Meteorological data were obtained through remote sensing. Two synthetic meteorological indicators (SMIs) were created to summarize meteorological variables. Spatial hotspots were detected using the Kulldorf scanning method. A General Additive Model was used to determine the time lag between cases and SMIs and to evaluate the effect of SMIs and distance to HC on the temporal evolution of malaria cases. The multivariate model was fitted with data from the epidemic year to predict the number of cases in the following outbreak. Overall, the incidence rate in the area was 429.13 cases per 1000 person-year with important spatial and temporal heterogeneities. Four spatial hotspots, involving 7 of the 27 villages, were detected, for an incidence rate of 854.02 cases per 1000 person-year. The hotspot with the highest risk (relative risk = 4.06) consisted of a single village, with an incidence rate of 1750.75 cases per 1000 person-years. The multivariate analysis found greater variability in incidence between HCs than between villages linked to the same HC. The time lag that generated the better predictions of cases was 9 weeks for SMI1 (positively correlated with precipitation variables) and 16 weeks for SMI2 (positively correlated with temperature variables. The prediction followed the overall pattern of the time series of reported cases and predicted the onset of the following outbreak with a precision of less than 3 weeks. This analysis of malaria cases in Diébougou health district, Burkina Faso, provides a powerful prospective method for identifying and predicting high-risk areas and high-transmission periods that could be targeted in future malaria control and prevention campaigns.

IntroductionThe SARS-CoV-2 pandemic led to the implementation of several non-pharmaceutical interventions (NPIs), from closings of bars and restaurants to curfews and lockdowns. Vaccination campaigns started hoping it could efficiently alleviate NPI. The primary objective of the “Indoor Transmission of COVID-19” (ITOC) study is to determine among a fully vaccinated population the relative risk of SARS-CoV-2 transmission during one indoor clubbing event. Secondary objectives are to assess the transmission of other respiratory viruses, risk exposure, and attitudes toward COVID-19 vaccination, health pass, and psychological impact of indoor club closing.Methods and analysisFour thousand four hundred healthy volunteers aged 18–49 years and fully vaccinated will be included in Paris region. The intervention is an 8-hour indoor clubbing event with no masks, no social distance, maximum room capacity, and ventilation. A reservation group of up to 10 people will recruit participants, who will be randomized 1:1 to either the experimental group (2,200 volunteers in two venues with capacities of 1,000 people each) or the control group (2,200 volunteers asked not to go to the club). All participants will provide a salivary sample on the day of the experiment and 7 days later. They also will answer several questionnaires. Virological analyses include polymerase chain reaction (PCR) of salivary samples and air of the venue, investigating SARS-CoV-2 and 18 respiratory viruses.Ethics and disseminationEthical clearance was first obtained in France from the institutional review board (Comité de Protection des Personnes Ile de France VII - CPP), and the trial received clearance from the French National Agency for Medicines and Health Products (Agence National de Sécurité du Médicament - ANSM). The trial is supported and approved by The Agence Nationale Recherche sur le SIDA, les hépatites et maladies émergences (ANRS-MIE). Positive, negative, and inconclusive results will be published in peer-reviewed scientific journals.Trial registration numberIDR-CB 2021-A01473-38. Clinicaltrial.gov, identifier: NCT05311865.

Abstract Higher transmissibility of SARS-CoV-2 in cold and dry weather conditions has been hypothesized since the onset of the COVID-19 pandemic but the level of epidemiological evidence remains low. During the first wave of the pandemic, Spain, Italy, France, Portugal, Canada and USA presented an early spread, a heavy COVID-19 burden, and low initial public health response until lockdowns. In a context when testing was limited, we calculated the basic reproduction number (R0) in 63 regions from the growth in regional death counts. After adjusting for population density, early spread of the epidemic, and age structure, temperature and humidity were negatively associated with SARS-CoV-2 transmissibility. A reduction of mean absolute humidity by 1 g/m3 was associated with a 0.15-unit increase of R0. Below 10 °C, a temperature reduction of 1 °C was associated with a 0.16-unit increase of R0. Our results confirm a dependency of SARS-CoV-2 transmissibility to weather conditions in the absence of control measures during the first wave. The transition from summer to winter, corresponding to drop in temperature associated with an overall decrease in absolute humidity, likely contributed to the intensification of the second wave in north-west hemisphere countries. Non-pharmaceutical interventions must be adjusted to account for increased transmissibility in winter conditions.

Centre Department, Haiti, was the origin of a major cholera epidemic during 2010–2019. Although no fine-scale spatial delineation is officially available, we aimed to analyze determinants of cholera at the local level and identify priority localities in need of interventions. After estimating the likely boundaries of 1,730 localities by using Voronoi polygons, we mapped 5,322 suspected cholera cases reported during January 2015–September 2016 by locality alongside environmental and socioeconomic variables. A hierarchical clustering on principal components highlighted 2 classes with high cholera risk: localities close to rivers and unimproved water sources (standardized incidence ratio 1.71, 95% CI 1.02–2.87; p = 0.04) and urban localities with markets (standardized incidence ratio 1.69, 95% CI 1.25–2.29; p = 0.0006). Our analyses helped identify and characterize areas where efforts should be focused to reduce vulnerability to cholera and other waterborne diseases; these methods could be used in other contexts.

Abstract Background In malaria endemic areas, identifying spatio-temporal hotspots is becoming an important element of innovative control strategies targeting transmission bottlenecks. The aim of this work was to describe the spatio-temporal variation of malaria hotspots in central Senegal and to identify the meteorological, environmental, and preventive factors that influence this variation. Methods This study analysed the weekly incidence of malaria cases recorded from 2008 to 2012 in 575 villages of central Senegal (total population approximately 500,000) as part of a trial of seasonal malaria chemoprevention (SMC). Data on weekly rainfall and annual vegetation types were obtained for each village through remote sensing. The time series of weekly malaria incidence for the entire study area was divided into periods of high and low transmission using change-point analysis. Malaria hotspots were detected during each transmission period with the SaTScan method. The effects of rainfall, vegetation type, and SMC intervention on the spatio-temporal variation of malaria hotspots were assessed using a General Additive Mixed Model. Results The malaria incidence for the entire area varied between 0 and 115.34 cases/100,000 person weeks during the study period. During high transmission periods, the cumulative malaria incidence rate varied between 7.53 and 38.1 cases/100,000 person-weeks, and the number of hotspot villages varied between 62 and 147. During low transmission periods, the cumulative malaria incidence rate varied between 0.83 and 2.73 cases/100,000 person-weeks, and the number of hotspot villages varied between 10 and 43. Villages with SMC were less likely to be hotspots (OR = 0.48, IC95%: 0.33–0.68). The association between rainfall and hotspot status was non-linear and depended on both vegetation type and amount of rainfall. The association between village location in the study area and hotspot status was also shown. Conclusion In our study, malaria hotspots varied over space and time according to a combination of meteorological, environmental, and preventive factors. By taking into consideration the environmental and meteorological characteristics common to all hotspots, monitoring of these factors could lead targeted public health interventions at the local level. Moreover, spatial hotspots and foci of malaria persisting during LTPs need to be further addressed. Trial registration The data used in this work were obtained from a clinical trial registered on July 10, 2008 at www.clinicaltrials.gov under NCT00712374 .

Background During the last decade, many studies have assessed the performance of malaria tests on non-invasively collected specimens, but no systematic review has hitherto estimated the overall performance of these tests. We report here the first meta-analysis estimating the diagnostic performance of malaria diagnostic tests performed on saliva, urine, faeces, skin odour (‘sniff and tell’) and hair, using either microscopy or PCR on blood sample as reference test.Methods We searched on PubMed, EMBASE, African Journals Online and Cochrane Infectious Diseases from inception until 19 January 2021 for relevant primary studies. A random effects model was used to estimate the overall performance of various diagnostic methods on different types of specimen.Results Eighteen studies providing 30 data sets were included in the meta-analysis. The overall sensitivity, specificity and diagnostic OR (DOR) of PCR were 84.5% (95% CI 79.3% to 88.6%), 97.3% (95% CI 95.3% to 98.5%) and 184.9 (95% CI 95.8 to 356.9) in saliva, respectively; 57.4% (95% CI 41.4% to 72.1%), 98.6% (95% CI 97.3% to 99.3%) and 47.2 (95% CI 22.1 to 101.1) in urine, respectively. The overall sensitivity, specificity and DOR of rapid diagnostic test for malaria in urine was 59.8% (95% CI 40.0% to 76.9%), 96.9% (95% CI 91.0% to 99.0%) and 30.8 (95% CI:23.5 to 40.4).Conclusion In settings where PCR is available, saliva and urine samples should be considered for PCR-based malaria diagnosis only if blood samples cannot be collected. The performance of rapid diagnostic testing in the urine is limited, especially its sensitivity. Malaria testing on non-invasively collected specimen still needs substantial improvement.

Summary: Background: The objective of this study was to better understand the factors associated with the heterogeneity of in-hospital COVID-19 morbidity and mortality across France, one of the countries most affected by COVID-19 in the early months of the pandemic. Methods: This geo-epidemiological analysis was based on data publicly available on government and administration websites for the 96 administrative departments of metropolitan France between March 19 and May 11, 2020, including Public Health France, the Regional Health Agencies, the French national statistics institute, and the Ministry of Health. Using hierarchical ascendant classification on principal component analysis of multidimensional variables, and multivariate analyses with generalised additive models, we assessed the associations between several factors (spatiotemporal spread of the epidemic between Feb 7 and March 17, 2020, the national lockdown, demographic population structure, baseline intensive care capacities, baseline population health and health-care services, new chloroquine and hydroxychloroquine dispensations, economic indicators, degree of urbanisation, and climate profile) and in-hospital COVID-19 incidence, mortality, and case fatality rates. Incidence rate was defined as the cumulative number of in-hospital COVID-19 cases per 100 000 inhabitants, mortality rate as the cumulative number of in-hospital COVID-19 deaths per 100 000, and case fatality rate as the cumulative number of in-hospital COVID-19 deaths per cumulative number of in-hospital COVID-19 cases. Findings: From March 19 to May 11, 2020, hospitals in metropolitan France notified a total of 100 988 COVID-19 cases, including 16 597 people who were admitted to intensive care and 17 062 deaths. There was an overall cumulative in-hospital incidence rate of 155·6 cases per 100 000 inhabitants (range 19·4–489·5), in-hospital mortality rate of 26·3 deaths per 100 000 (1·1–119·2), and in-hospital case fatality rate of 16·9% (4·8–26·2). We found clear spatial heterogeneity of in-hospital COVID-19 incidence and mortality rates, following the spread of the epidemic. After multivariate adjustment, the delay between the first COVID-19-associated death and the onset of the national lockdown was positively associated with in-hospital incidence (adjusted standardised incidence ratio 1·02, 95% CI 1·01–1·04), mortality (adjusted standardised mortality ratio 1·04, 1·02–1·06), and case fatality rates (adjusted standardised fatality ratio 1·01, 1·01–1·02). Mortality and case fatality rates were higher in departments with older populations (adjusted standardised ratio for populations with a high proportion older than aged >85 years 2·17 [95% CI 1·20–3·90] for mortality and 1·43 [1·08–1·88] for case fatality rate). Mortality rate was also associated with incidence rate (1·0004, 1·0002–1·001), but mortality and case fatality rates did not appear to be associated with baseline intensive care capacities. We found no association between climate and in-hospital COVID-19 incidence, or between economic indicators and in-hospital COVID-19 incidence or mortality rates. Interpretation: This ecological study highlights the impact of the epidemic spread, national lockdown, and reactive adaptation of intensive care capacities on the spatial distribution of COVID-19 morbidity and mortality. It provides information for future geo-epidemiological analyses and has implications for preparedness and response policies to current and future epidemic waves in France and elsewhere. Funding: None.

Abstract Background In the context of environmentally influenced communicable diseases, proximity to environmental sources results in spatial heterogeneity of risk, which is sometimes difficult to measure in the field. Most prevention trials use randomization to achieve comparability between groups, thus failing to account for heterogeneity. This study aimed to determine under what conditions spatial heterogeneity biases the results of randomized prevention trials, and to compare different approaches to modeling this heterogeneity. Methods Using the example of a malaria prevention trial, simulations were performed to quantify the impact of spatial heterogeneity and to compare different models. Simulated scenarios combined variation in baseline risk, a continuous protective factor (age), a non-related factor (sex), and a binary protective factor (preventive treatment). Simulated spatial heterogeneity scenarios combined variation in breeding site density and effect, location, and population density. The performances of the following five statistical models were assessed: a non-spatial Cox Proportional Hazard (Cox-PH) model and four models accounting for spatial heterogeneity—i.e., a Data-Generating Model, a Generalized Additive Model (GAM), and two Stochastic Partial Differential Equation (SPDE) models, one modeling survival time and the other the number of events. Using a Bayesian approach, we estimated the SPDE models with an Integrated Nested Laplace Approximation algorithm. For each factor (age, sex, treatment), model performances were assessed by quantifying parameter estimation biases, mean square errors, confidence interval coverage rates (CRs), and significance rates. The four models were applied to data from a malaria transmission blocking vaccine candidate. Results The level of baseline risk did not affect our estimates. However, with a high breeding site density and a strong breeding site effect, the Cox-PH and GAM models underestimated the age and treatment effects (but not the sex effect) with a low CR. When population density was low, the Cox-SPDE model slightly overestimated the effect of related factors (age, treatment). The two SPDE models corrected the impact of spatial heterogeneity, thus providing the best estimates. Conclusion Our results show that when spatial heterogeneity is important but not measured, randomization alone cannot achieve comparability between groups. In such cases, prevention trials should model spatial heterogeneity with an adapted method. Trial registration The dataset used for the application example was extracted from Vaccine Trial #NCT02334462 (ClinicalTrials.gov registry).

Abstract Background With limited resources and spatio-temporal heterogeneity of malaria in developing countries, it is still difficult to assess the real impact of socioeconomic and environmental factors in order to set up targeted campaigns against malaria at an accurate scale. Our goal was to detect malaria hotspots in rural area and assess the extent to which household socioeconomic status and meteorological recordings may explain the occurrence and evolution of these hotspots. Methods Data on malaria cases from 2010 to 2014 and on socioeconomic and meteorological factors were acquired from four health facilities within the Nanoro demographic surveillance area. Statistical cross correlation was used to quantify the temporal association between weekly malaria incidence and meteorological factors. Local spatial autocorrelation analysis was performed and restricted to each transmission period using Kulldorff’s elliptic spatial scan statistic. Univariate and multivariable analysis were used to assess the principal socioeconomic and meteorological determinants of malaria hotspots using a Generalized Estimating Equation (GEE) approach. Results Rainfall and temperature were positively and significantly associated with malaria incidence, with a lag time of 9 and 14 weeks, respectively. Spatial analysis showed a spatial autocorrelation of malaria incidence and significant hotspots which was relatively stable throughout the study period. Furthermore, low socioeconomic status households were strongly associated with malaria hotspots (aOR = 1.21, 95% confidence interval: 1.03–1.40). Conclusion These fine-scale findings highlight a relatively stable spatio-temporal pattern of malaria risk and indicate that social and environmental factors play an important role in malaria incidence. Integrating data on these factors into existing malaria struggle tools would help in the development of sustainable bottleneck strategies adapted to the local context for malaria control.

ABSTRACTWe conducted this study to describe the dynamics of the acquisition of respiratory pathogens, their potential interactions and risk factors for possible lower respiratory tract infection symptoms (LRTI) among French pilgrims during the 2018 Hajj. Each participant underwent four successive systematic nasopharyngeal swabs before and during their stay in Saudi Arabia. Carriage of the main respiratory pathogens was assessed by PCR. 121 pilgrims were included and 93.4% reported respiratory symptoms during the study period. The acquisition of rhinovirus, coronaviruses and Staphylococcus aureus occurred soon after arrival in Saudi Arabia and rates decreased gradually after days 5 and 6. In contrast, Streptococcus pneumoniae and Klebsiella pneumoniae carriage increased progressively until the end of the stay in Saudi Arabia. Haemophilus influenzae and Moraxella catarrhalis carriage increased starting around days 12 and 13, following an initial clearance. Influenza viruses were rarely isolated. We observed an independent positive mutual association between S. aureus and rhinovirus carriage and between H. influenzae and M. catarrhalis carriage. Dual carriage of H. influenzae and M. catarrhalis was strongly associated with S. pneumoniae carriage (OR = 6.22). Finally, our model showed that M. catarrhalis carriage was negatively associated with K. pneumoniae carriage. Chronic respiratory disease was associated with symptoms of LRTI. K. pneumoniae, M. catarrhalis-S. aureus and H. influenzae-rhinovirus dual carriage was associated with LRTI symptoms. Our data suggest that RTIs at the Hajj are a result of complex interactions between a number of respiratory viruses and bacteria.

Abstract Malaria transmission is highly heterogeneous through time and space, and mapping of this heterogeneity is necessary to better understand local dynamics. New targeted policies are needed as numerous countries have placed malaria elimination on their public health agenda for 2030. In this context, developing national health information systems and collecting information at sufficiently precise scales (at least at the ‘week’ and ‘village’ scales), is of strategic importance. In a recent study, Macharia et al. relied on extensive prevalence survey data to develop malaria risk maps for Kenya, including uncertainty assessments specifically designed to support decision-making by the National Malaria Control Program. Targeting local persistent transmission or epidemiologic changes is necessary to maintain efficient control, but also to deploy sustainable elimination strategies against identified transmission bottlenecks such as the reservoir of subpatent infections. Such decision-making tools are paramount to allocate resources based on sound scientific evidence and public health priorities. Please see related article: https://malariajournal.biomedcentral.com/articles/10.1186/s12936-018-2489-9.

Abstract Background The widespread use of artemisinin-based combination therapy (ACT) and long-lasting insecticide-treated nets (LLINs) has led to an impressive decrease of malaria burden these recent years in Africa. However, some new challenges about the future of malaria control and elimination efforts have appeared. Among these challenges, the loss and—or—the only partial acquisition of anti-Plasmodium immunity among exposed populations lead to an increase of the age at risk of malaria. Indeed, older children and adults may become more vulnerable to malaria. Studies about malaria among adults seemed, therefore, important. This study investigated the evolution of malaria morbidity in adults of Dielmo (Senegal) before and after the implementation of LLINs. Methods From August 2007 to July 2015, a longitudinal study involving adults above 15 years old was carried out in Dielmo, where ACT was introduced in June 2006 and LLINs in July 2008. In July 2011 and August 2014, all LLINs were renewed. The presence of each person in the village was monitored daily. Thick smears associated lately with rapid diagnosis test (RDT) and quantitative polymerase chain reaction methods were performed for all cases of fever. To assess malaria prevalence, thick smears and RDT were performed quarterly in all individuals. Malaria risks factors were assessed using negative binomial regression mixed-model based on person-trimester observations. Results Malaria morbidity among adults has decreased significantly since the implementation of LLINs in Dielmo. However, malaria resurgences have occurred twice during the 7 years of LLINs use. During these malaria resurgences, the overall incidence of malaria among adults was similar to the incidence during the year before the implementation of LLINs (adjusted incidence rate ratio [95% CI] aIRR = 1.04 [0.66–1.64], p = 0.88 and aIRR = 1.16 [0.74–1.80], p = 0.52 during the first and the second malaria resurgence period, respectively). Younger adults were most vulnerable during these malaria upsurges as the incidence of malaria increased significantly among them (χ2 = 5.2; p = 0.02). Conclusion Malaria among adults especially younger adults should deserve more attention in the areas where malaria was previously endemic as they became vulnerable probably because of the partial acquisition and—or—the loss of anti-Plasmodium relative immunity and the non regular use of LLINs.

Case-area targeted interventions (CATIs) against cholera are conducted by rapid response teams, and may include various activities like water, sanitation, hygiene measures. However, their real-world effectiveness has never been established. We conducted a retrospective observational study in 2015–2017 in the Centre department of Haiti. Using cholera cases, stool cultures and CATI records, we identified 238 outbreaks that were responded to. After adjusting for potential confounders, we found that a prompt response could reduce the number of accumulated cases by 76% (95% confidence interval, 59 to 86) and the outbreak duration by 61% (41 to 75) when compared to a delayed response. An intense response could reduce the number of accumulated cases by 59% (11 to 81) and the outbreak duration by 73% (49 to 86) when compared to a weaker response. These results suggest that prompt and repeated CATIs were significantly effective at mitigating and shortening cholera outbreaks in Haiti.

Abstract Background Given the scarcity of resources in developing countries, malaria treatment requires new strategies that target specific populations, time periods and geographical areas. While the spatial pattern of malaria transmission is known to vary depending on local conditions, its temporal evolution has yet to be evaluated. The aim of this study was to determine the spatio-temporal dynamic of malaria in the central region of Burkina Faso, taking into account meteorological factors. Methods Drawing on national databases, 101 health areas were studied from 2011 to 2015, together with weekly meteorological data (temperature, number of rain events, rainfall, humidity, wind speed). Meteorological factors were investigated using a principal component analysis (PCA) to reduce dimensions and avoid collinearities. The Box–Jenkins ARIMA model was used to test the stationarity of the time series. The impact of meteorological factors on malaria incidence was measured with a general additive model. A change-point analysis was performed to detect malaria transmission periods. For each transmission period, malaria incidence was mapped and hotspots were identified using spatial cluster detection. Results Malaria incidence never went below 13.7 cases/10,000 person-weeks. The first and second PCA components (constituted by rain/humidity and temperatures, respectively) were correlated with malaria incidence with a lag of 2 weeks. The impact of temperature was significantly non-linear: malaria incidence increased with temperature but declined sharply with high temperature. A significant positive linear trend was found for the entire time period. Three transmission periods were detected: low (16.8–29.9 cases/10,000 person-weeks), high (51.7–84.8 cases/10,000 person-weeks), and intermediate (26.7–32.2 cases/10,000 person-weeks). The location of clusters identified as high risk varied little across transmission periods. Conclusion This study highlighted the spatial variability and relative temporal stability of malaria incidence around the capital Ouagadougou, in the central region of Burkina Faso. Despite increasing efforts in fighting the disease, malaria incidence remained high and increased over the period of study. Hotspots, particularly those detected for low transmission periods, should be investigated further to uncover the local environmental and behavioural factors of transmission, and hence to allow for the development of better targeted control strategies.

During 2004–2014, the Democratic Republic of the Congo (DRC) declared 54% of plague cases worldwide. Using national data, we characterized the epidemiology of human plague in DRC for this period. All 4,630 suspected human plague cases and 349 deaths recorded in DRC came from Orientale Province. Pneumonic plague cases (8.8% of total) occurred during 2 major outbreaks in mining camps in the equatorial forest, and some limited outbreaks occurred in the Ituri highlands. Epidemics originated in 5 health zones clustered in Ituri, where sporadic bubonic cases were recorded throughout every year. Classification and regression tree characterized this cluster by the dominance of ecosystem 40 (mountain tropical climate). In conclusion, a small, stable, endemic focus of plague in the highlands of the Ituri tropical region persisted, acting as a source of outbreaks in DRC.

Abstract Background Mathematical models of human mobility have demonstrated a great potential for infectious disease epidemiology in contexts of data scarcity. While the commonly used gravity model involves parameter tuning and is thus difficult to implement without reference data, the more recent radiation model based on population densities is parameter-free, but biased. In this study we introduce the new impedance model, by analogy with electricity. Previous research has compared models on the basis of a few specific available spatial patterns. In this study, we use a systematic simulation-based approach to assess the performances. Methods Five hundred spatial patterns were generated using various area sizes and location coordinates. Model performances were evaluated based on these patterns. For simulated data, comparison measures were average root mean square error (aRMSE) and bias criteria. Modeling of the 2010 Haiti cholera epidemic with a basic susceptible–infected–recovered (SIR) framework allowed an empirical evaluation through assessing the goodness-of-fit of the observed epidemic curve. Results The new, parameter-free impedance model outperformed previous models on simulated data according to average aRMSE and bias criteria. The impedance model achieved better performances with heterogeneous population densities and small destination populations. As a proof of concept, the basic compartmental SIR framework was used to confirm the results obtained with the impedance model in predicting the spread of cholera in Haiti in 2010. Conclusions The proposed new impedance model provides accurate estimations of human mobility, especially when the population distribution is highly heterogeneous. This model can therefore help to achieve more accurate predictions of disease spread in the context of an epidemic.

Abstract Background Even if rainfall and temperature are factors classically associated to malaria, little is known about other meteorological factors, their variability and combinations related to malaria, in association with river height variations. Furthermore, in suburban area, urbanization and growing population density should be assessed in relation to these environmental factors. The aim of this study was to assess the impact of combined environmental, meteorological and hydrological factors on malaria incidence through time in the context of urbanization. Methods Population observational data were prospectively collected. Clinical malaria was defined as the presence of parasites in addition to clinical symptoms. Meteorological and hydrological factors were measured daily. For each factors variation indices were estimated. Urbanization was yearly estimated assessing satellite imaging and field investigations. Principal component analysis was used for dimension reduction and factors combination. Lags between malaria incidences and the main components were assessed by cross-correlation functions. Generalized additive model was used to assess relative impact of different environmental components, taking into account lags, and modelling non-linear relationships. Change-point analysis was used to determine transmission periods within years. Results Malaria incidences were dominated by annual periodicity and varied through time without modification of the dynamic, with no impact of the urbanization. The main meteorological factor associated with malaria was a combination of evaporation, humidity and rainfall, with a lag of 3 months. The relationship between combined temperature factors showed a linear impact until reaching high temperatures limiting malaria incidence, with a lag 3.25 months. Height and variation of the river were related to malaria incidence (respectively 6 week lag and no lag). Conclusions The study emphasizes no decreasing trend of malaria incidence despite accurate access to care and control strategies in accordance to international recommendations. Furthermore, no decreasing trend was showed despite the urbanization of the area. Malaria transmission remain increase 3 months after the beginning of the dry season. Addition to evaporation versus humidity/rainfall, nonlinear relationship for temperature and river height and variations have to be taken into account when implementing malaria control programmes.

Abstract Background The reliability of spatial statistics is often put into question because real spatial variations may not be found, especially in heterogeneous areas. Our objective was to compare empirically different cluster detection methods. We assessed their ability to find spatial clusters of cancer cases and evaluated the impact of the socioeconomic status (e.g., the Townsend index) on cancer incidence. Methods Moran’s I, the empirical Bayes index (EBI), and Potthoff-Whittinghill test were used to investigate the general clustering. The local cluster detection methods were: i) the spatial oblique decision tree (SpODT); ii) the spatial scan statistic of Kulldorff (SaTScan); and, iii) the hierarchical Bayesian spatial modeling (HBSM) in a univariate and multivariate setting. These methods were used with and without introducing the Townsend index of socioeconomic deprivation known to be related to the distribution of cancer incidence. Incidence data stemmed from the Cancer Registry of Isère and were limited to prostate, lung, colon-rectum, and bladder cancers diagnosed between 1999 and 2007 in men only. Results The study found a spatial heterogeneity (p 1.2). The multivariate HBSM found a spatial correlation between lung and bladder cancers (r = 0.6). Conclusions In spatial analysis of cancer incidence, SpODT and HBSM may be used not only for cluster detection but also for searching for confounding or etiological factors in small areas. Moreover, the multivariate HBSM offers a flexible and meaningful modeling of spatial variations; it shows plausible previously unknown associations between various cancers.

BACKGROUND:In October 2010, Haiti was struck by a large-scale cholera epidemic. The Haitian government, UNICEF and other international partners launched an unprecedented nationwide alert-response strategy in July 2013. Coordinated NGOs recruited local rapid response mobile teams to conduct case-area targeted interventions (CATIs), including education sessions, household decontamination by chlorine spraying, and distribution of chlorine tablets. An innovative red-orange-green alert system was also established to monitor the epidemic at the communal scale on a weekly basis. Our study aimed to describe and evaluate the exhaustiveness, intensity and quality of the CATIs in response to cholera alerts in Haiti between July 2013 and June 2017. METHODOLOGY/PRINCIPAL FINDINGS:We analyzed the response to 7,856 weekly cholera alerts using routine surveillance data and severity criteria, which was based on the details of 31,306 notified CATIs. The odds of CATI response during the same week (exhaustiveness) and the number of complete CATIs in responded alerts (intensity and quality) were estimated using multivariate generalized linear mixed models and several covariates. CATIs were carried out significantly more often in response to red alerts (adjusted odds ratio (aOR) [95%-confidence interval, 95%-CI], 2.52 [2.22-2.87]) compared with orange alerts. Significantly more complete CATIs were carried out in response to red alerts compared with orange alerts (adjusted incidence ratio (aIR), 1.85 [1.73-1.99]). Over the course of the eight-semester study, we observed a significant improvement in the exhaustiveness (aOR, 1.43 [1.38-1.48] per semester) as well as the intensity and quality (aIR, 1.23 [1.2-1.25] per semester) of CATI responses, independently of funds available for the strategy. The odds of launching a CATI response significantly decreased with increased rainfall (aOR, 0.99 [0.97-1] per each accumulated cm). Response interventions were significantly heterogeneous between NGOs, communes and departments. CONCLUSIONS/SIGNIFICANCE:The implementation of a nationwide case-area targeted rapid response strategy to control cholera in Haiti was feasible albeit with certain obstacles. Such feedback from the field and ongoing impact studies will be very informative for actors and international donors involved in cholera control and elimination in Haiti and in other affected countries.

To date, there is no effective treatment to cure dengue fever, a mosquito-borne disease which has a major impact on human populations in tropical and sub-tropical regions. Although the characteristics of dengue infection are well known, factors associated with landscape are highly scale dependent in time and space, and therefore difficult to monitor. We propose here a mapping review based on 78 articles that study the relationships between landscape factors and urban dengue cases considering household, neighborhood and administrative levels. Landscape factors were retrieved from survey questionnaires, Geographic Information Systems (GIS), and remote sensing (RS) techniques. We structured these into groups composed of land cover, land use, and housing type and characteristics, as well as subgroups referring to construction material, urban typology, and infrastructure level. We mapped the co-occurrence networks associated with these factors, and analyzed their relevance according to a three-valued interpretation (positive, negative, non significant). From a methodological perspective, coupling RS and GIS techniques with field surveys including entomological observations should be systematically considered, as none digital land use or land cover variables appears to be an univocal determinant of dengue occurrences. Remote sensing urban mapping is however of interest to provide a geographical frame to distribute human population and movement in relation to their activities in the city, and as spatialized input variables for epidemiological and entomological models.

To determine when risk for Buruli ulcer is highest, we examined seasonal patterns in a highly disease-endemic area of Cameroon during 2002–2012. Cases peaked in March, suggesting that risk is highest during the high rainy season. During and after this season, populations should increase protective behaviors, and case detection efforts should be intensified.

Abstract Background Although the burden of malaria has significantly declined in recent years in sub-Saharan Africa through the widespread use of long-lasting insecticide treated bed-nets (LLINs) and artemisinin-based combination therapy, resurgence of malaria is observed in some settings after several years of LLINs use. This study aimed to assess if LLINs use remains protective against malaria during a period of resurgence of malaria morbidity in Dielmo, a rural village of Senegal. Methods In July 2008, LLINs were offered to all villagers and lately in July 2011, LLINs were renewed. A longitudinal study was conducted between July, 2010 and December, 2011 among inhabitants of the village of Dielmo to identify all episodes of fever. Thick smears stained with Giemsa were done for every febrile villager and malaria attacks were treated with combination of Artesunate plus Amodiaquine. Cross-sectional surveys were also conducted at the end of the rainy season (October 2010 and November 2011) to assess asymptomatic carriage. A survey on LLINs use was done every quarter of the year. A random-effect logistic regression was used to assess the effect of LLINs use on the risk of having a malaria attack after adjusting for the main risk factors. Results The study population included 449 individuals corresponding to a total of 2140 observations. One hundred and fifteen (115) clinical malaria attacks attributed to P. falciparum (cases) have been recorded over the study period. Most of the malaria cases occurred in October-December 2010 (49/115 i.e. 43%) and among adults aged 15 years and over (50/115, i.e. 43%). During the study period, the use of LLINs was 61% among non-malaria cases and only 42% among malaria clinical cases but differenced according to age group. After adjusting on gender, age, rainfall and LLINs replacement, we found that LLINs use (AOR [95%CI] = 0.40 [0.25; 0.62], p

Spatial cluster detection is a classical question in epidemiology: Are cases located near other cases? In order to classify a study area into zones of different risks and determine their boundaries, we have developed a spatial partitioning method based on oblique decision trees, which is called spatial oblique decision tree (SpODT). This non-parametric method is based on the classification and regression tree (CART) approach introduced by Leo Breiman. Applied to epidemiological spatial data, the algorithm recursively searches among the coordinates for a threshold or a boundary between zones, so that the risks estimated in these zones are as different as possible. While the CART algorithm leads to rectangular zones, providing perpendicular splits of longitudes and latitudes, the SpODT algorithm provides oblique splitting of the study area, which is more appropriate and accurate for spatial epidemiology. Oblique decision trees can be considered as non-parametric regression models. Beyond the basic function, we have developed a set of functions that enable extended analyses of spatial data, providing: inference, graphical representations, spatio-temporal analysis, adjustments on covariates, spatial weighted partition, and the gathering of similar adjacent final classes. In this paper, we propose a new R package, SPODT, which provides an extensible set of functions for partitioning spatial and spatio-temporal data. The implementation and extensions of the algorithm are described. Function usage examples are proposed, looking for clustering malaria episodes in Bandiagara, Mali, and samples showing three different cluster shapes.

Molecular-based allergy diagnosis yields multiple biomarker datasets. The classical diagnostic score for allergic bronchopulmonary aspergillosis (ABPA), a severe disease usually occurring in asthmatic patients and people with cystic fibrosis, comprises succinct immunological criteria formulated in 1977: total IgE, anti-Aspergillus fumigatus (Af) IgE, anti-Af “precipitins,” and anti-Af IgG. Progress achieved over the last four decades led to multiple IgE and IgG(4) Af biomarkers available with quantitative, standardized, molecular-level reports. These newly available biomarkers have not been included in the current diagnostic criteria, either individually or in algorithms, despite persistent underdiagnosis of ABPA. Large numbers of individual biomarkers may hinder their use in clinical practice. Conversely, multivariate analysis using new tools may bring about a better chance of less diagnostic mistakes. We report here a proof-of-concept work consisting of a three-step multivariate analysis of Af IgE, IgG, and IgG4 biomarkers through a combination of principal component analysis, hierarchical ascendant classification, and classification and regression tree multivariate analysis. The resulting diagnostic algorithms might show the way for novel criteria and improved diagnostic efficiency in Af-sensitized patients at risk for ABPA.

The malaria burden has decreased significantly in recent years in Africa through the widespread use of artemisinin-based combination therapy (ACT) and long-lasting insecticide-treated nets (LLINs). However, the occurrence of malaria resurgences, the loss of immunity of exposed populations constitute among other factors, serious concerns about the future of malaria elimination efforts. This study investigated the evolution of malaria morbidity in Dielmo (Senegal) before and after the implementation of LLINs.A longitudinal study was carried out in Dielmo over eight years, from July 2007 to July 2015. In July 2008, LLINs were offered to all villagers, and in July 2011 and August 2014 the LLINs were renewed. A survey on LLINs use was done each quarter of the year. Thick smears stained with Giemsa, a rapid diagnostic test (RDT) and quantitative polymerase chain reaction (PCR) methods were performed for all cases of fever to assess malaria clinical attacks. Malaria cases were treated with ACT since June 2006.Malaria morbidity has decreased significantly since the implementation of LLINs in Dielmo, together with ACT. However, malaria resurgences have occurred twice during the seven years of LLINs use. These resurgences occurred the first time during the third year after the introduction of LLINs (aIRR (adjusted incidence-rate ratio) [95%CI] = 5.90 [3.53; 9.88] p< 0.001) and a second time during the third year after the renewal of LLINs (aIRR [95%CI] = 5.60 [3.34; 9.39] p< 0.001). Sixty-nine percent (69%) of the nets tested for their long-lasting insecticidal activity remained effective after 3 years of use.Good management of malaria cases by the use of ACT as first-line treatment against malaria in addition to the use of LLINs has significantly reduced malaria in Dielmo and allowed to reach the phase of pre-elimination of the disease. However, the occurrence of malaria resurgences raised serious concerns about malaria elimination, which would require additional tools in this village.

Data collected by the GeoSentinel Surveillance Network for 1,415 ill travelers returning from Indian Ocean islands during 1997–2010 were analyzed. Malaria (from Comoros and Madagascar), acute nonparasitic diarrhea, and parasitoses were the most frequently diagnosed infectious diseases. An increase in arboviral diseases reflected the 2005 outbreak of chikungunya fever.

During 1982–2007, alveolar echinococcosis (AE) was diagnosed in 407 patients in France, a country previously known to register half of all European patients. To better define high-risk groups in France, we conducted a national registry-based study to identify areas where persons were at risk and spatial clusters of cases. We interviewed 180 AE patients about their way of life and compared responses to those of 517 controls. We found that almost all AE patients lived in 22 départements in eastern and central France (relative risk 78.63, 95% CI 52.84–117.02). Classification and regression tree analysis showed that the main risk factor was living in AE-endemic areas. There, most at-risk populations lived in rural settings (odds ratio [OR] 66.67, 95% CI 6.21–464.51 for farmers and OR 6.98, 95% CI 2.88–18.25 for other persons) or gardened in nonrural settings (OR 4.30, 95% CI 1.82–10.91). These findings can help sensitization campaigns focus on specific groups.

Dermatophytosis, and particularly the subtype tinea capitis, is common among African children; however, the risk factors associated with this condition are poorly understood. To describe the epidemiology of dermatophytosis in distinct eco-climatic zones, three cross-sectional surveys were conducted in public primary schools located in the Sahelian, Sudanian and Sudano-Guinean eco-climatic zones in Mali.Among 590 children (average age 9.7 years) the overall clinical prevalence of tinea capitis was 39.3%. Tinea capitis prevalence was 59.5% in the Sudano-Guinean zone, 41.6% in the Sudanian zone and 17% in the Sahelian eco-climatic zone. Microsporum audouinii was isolated primarily from large and/or microsporic lesions. Trichophyton soudanense was primarily isolated from trichophytic lesions. Based on the multivariate analysis, tinea capitis was independently associated with male gender (OR = 2.51, 95%CI [1.74-3.61], P

After onset of a cholera epidemic in Haiti in mid-October 2010, a team of researchers from France and Haiti implemented field investigations and built a database of daily cases to facilitate identification of communes most affected. Several models were used to identify spatiotemporal clusters, assess relative risk associated with the epidemic’s spread, and investigate causes of its rapid expansion in Artibonite Department. Spatiotemporal analyses highlighted 5 significant clusters (p

We analyzed prospective data on 17,228 European patients who sought treatment at GeoSentinel sites from 1997 to 2007. Gastrointestinal illness (particularly in tourists), fever (those visiting friends and relatives [VFRs]), and skin disorders (in tourists) were the most common reasons for seeking medical care. Diagnoses varied by country of origin, region visited, or categories of travelers. VFRs who returned from sub-Saharan Africa and Indian Ocean islands were more likely to experience falciparum malaria than any other group. Multiple correspondence analysis identified Italian, French, and Swiss VFRs and expatriate travelers to sub-Saharan Africa and Indian Ocean Islands as most likely to exhibit febrile illnesses. German tourists to Southeast and south-central Asia were most likely to seek treatment for acute diarrhea. Non-European travelers (12,663 patients from other industrialized countries) were less likely to acquire certain travel-associated infectious diseases. These results should be considered in the practice of travel medicine and development of health recommendations for European travelers.

The human landing catches is the gold standard method used to study the vectors of malaria and to estimate their aggressiveness. However, this method has raised safety concerns due to a possible increased risk of malaria or other mosquito-borne diseases among the mosquito collectors. The aim of this study was to evaluate the incidence of malaria attacks among mosquito collectors and to compare these results with those of non-collectors in a Senegalese village.From July 1990 to December 2011, a longitudinal malaria study involving mosquito collectors and non-collectors was performed in Dielmo village, Senegal. During the study period, 4 drugs were successively used to treat clinical malaria, and long-lasting insecticide-treated nets were offered to all villagers in July 2008. No malaria chemoprophylaxis was given to mosquito collectors. Incidence of uncomplicated clinical malaria and asymptomatic malaria infection were analyzed among these two groups while controlling for confounding factors associated with malaria risk in random effects negative binomial and logistic regression models, respectively.A total of 3,812 person-trimester observations of 199 adults at least 15 years of age were analyzed. Clinical malaria attacks accounted for 6.3% both in collectors and non-collectors, and asymptomatic malaria infections accounted for 21% and 20% in collectors and non-collectors, respectively. A non-significant lower risk of malaria was observed in the collector group in comparison with the non-collector group after adjusting for other risk factors of malaria and endemicity level (Clinical malaria: adjusted incidence rate ratio = 0.89; 95% confidence interval = 0.65-1.22; p= 0.47).Being a mosquito collector in Dielmo was not significantly associated with an increased risk of malaria both under holoendemic, mesoendemic and hypoendemic conditions of malaria epidemiology. This result supports the view that HLC, the most accurate method for evaluating malaria transmission, may be used without health concerns in Dielmo.

In cluster detection of disease, the use of local cluster detection tests (CDTs) is current. These methods aim both at locating likely clusters and testing for their statistical significance. New or improved CDTs are regularly proposed to epidemiologists and must be subjected to performance assessment. Because location accuracy has to be considered, performance assessment goes beyond the raw estimation of type I or II errors. As no consensus exists for performance evaluations, heterogeneous methods are used, and therefore studies are rarely comparable. A global indicator of performance, which assesses both spatial accuracy and usual power, would facilitate the exploration of CDTs behaviour and help between-studies comparisons. The Tanimoto coefficient (TC) is a well-known measure of similarity that can assess location accuracy but only for one detected cluster. In a simulation study, performance is measured for many tests. From the TC, we here propose two statistics, the averaged TC and the cumulated TC, as indicators able to provide a global overview of CDTs performance for both usual power and location accuracy. We evidence the properties of these two indicators and the superiority of the cumulated TC to assess performance. We tested these indicators to conduct a systematic spatial assessment displayed through performance maps.

BACKGROUND:Buruli ulcer (BU) is an extensively damaging skin infection caused by Mycobacterium ulcerans, whose transmission mode is still unknown. The focal distribution of BU and the absence of interpersonal transmission suggest a major role of environmental factors, which remain unidentified. This study provides the first description of the spatio-temporal variations of BU in an endemic African region, in Akonolinga, Cameroon. We quantify landscape-associated risk of BU, and reveal local patterns of endemicity. METHODOLOGY/PRINCIPAL FINDINGS:From January 2002 to May 2012, 787 new BU cases were recorded in 154 villages of the district of Akonolinga. Incidence per village ranged from 0 (n = 59 villages) to 10.4 cases/1000 person.years (py); median incidence was 0.4 cases/1,000 py. Villages neighbouring the Nyong River flood plain near Akonolinga town were identified as the highest risk zone using the SPODT algorithm. We found a decreasing risk with increasing distance to the Nyong and identified 4 time phases with changes in spatial distribution. We classified the villages into 8 groups according to landscape characteristics using principal component analysis and hierarchical clustering. We estimated the incidence ratio (IR) associated with each landscape using a generalised linear model. BU risk was highest in landscapes with abundant wetlands, especially cultivated ones (IR = 15.7, 95% confidence interval [95%CI] = 15.7[4.2-59.2]), and lowest in reference landscape where primary and secondary forest cover was abundant. In intermediate-risk landscapes, risk decreased with agriculture pressure (from IR[95%CI] = 7.9[2.2-28.8] to 2.0[0.6-6.6]). We identified landscapes where endemicity was stable and landscapes where incidence increased with time. CONCLUSION/SIGNIFICANCE:Our study on the largest series of BU cases recorded in a single endemic region illustrates the local evolution of BU and identifies the Nyong River as the major driver of BU incidence. Local differences along the river are explained by wetland abundance and human modification of the environment.

Prevalence of healthcare associated infections remains high in patients in intensive care units (ICU), estimated at 23.4% in 2011. It is important to reduce the overall risk while minimizing the cost and disruption to service provision by targeted infection control interventions. The aim of this study was to develop a monitoring tool to analyze the spatial variability of bacteriological contamination within the healthcare environment to assist in the planning of interventions. Within three cross-sectional surveys, in two ICU wards, air and surface samples from different heights and locations were analyzed. Surface sampling was carried out with tryptic Soy Agar contact plates and Total Viable Counts (TVC) were calculated at 48 hrs (incubation at 37 °C). TVCs were analyzed using Poisson Generalized Additive Mixed Model for surface type analysis, and for spatial analysis. Through three cross-sectional survey, 370 samples were collected. Contamination varied from place-to-place, height-to-height, and by surface type. Hard-to-reach surfaces, such as bed wheels and floor area under beds, were generally more contaminated, but the height level at which maximal TVCs were found changed between cross-sectional surveys. Bedside locations and bed occupation were risk factors for contamination. Air sampling identified clusters of contamination around the nursing station and surface sampling identified contamination clusters at numerous bed locations. By investigating dynamic hospital wards, the methodology employed in this study will be useful to monitor contamination variability within the healthcare environment and should help to assist in the planning of interventions.

BACKGROUND: In October 2010, cholera importation in Haiti triggered an epidemic that rapidly proved to be the world's largest epidemic of the seventh cholera pandemic. To establish effective control and elimination policies, strategies rely on the analysis of cholera dynamics. In this report, we describe the spatio-temporal dynamics of cholera and the associated environmental factors. METHODOLOGY/PRINCIPAL FINDINGS: Cholera-associated morbidity and mortality data were prospectively collected at the commune level according to the World Health Organization standard definition. Attack and mortality rates were estimated and mapped to assess epidemic clusters and trends. The relationships between environmental factors were assessed at the commune level using multivariate analysis. The global attack and mortality rates were 488.9 cases/10,000 inhabitants and 6.24 deaths/10,000 inhabitants, respectively. Attack rates displayed a significantly high level of spatial heterogeneity (varying from 64.7 to 3070.9 per 10,000 inhabitants), thereby suggesting disparate outbreak processes. The epidemic course exhibited two principal outbreaks. The first outbreak (October 16, 2010-January 30, 2011) displayed a centrifugal spread of a damping wave that suddenly emerged from Mirebalais. The second outbreak began at the end of May 2011, concomitant with the onset of the rainy season, and displayed a highly fragmented epidemic pattern. Environmental factors (river and rice fields: p