BMJ Open

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. 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. 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). 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. 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. 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. 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

Malaria in Burkina Faso: A comprehensive analysis of spatiotemporal distribution of incidence and environmental drivers, and implications for control strategies

International audienceBackground The 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. Methods We 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. Results The 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. Conclusion The 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

Kady

Popular Malian Mandinga music accompanied by Mandinga percussion, kora, and n'goni traditional instruments fused with Western brass, keyboard and guita

Kya

Popular Malian Mandinga music accompanied by Mandinga percussion, kora, and n'goni traditional instruments fused with Western brass, keyboard and guita

Sarama

Popular Malian Mandinga music accompanied by Mandinga percussion, kora, and n'goni traditional instruments fused with Western brass, keyboard and guita

Bebe

Popular Malian Mandinga music accompanied by Mandinga percussion, kora, and n'goni traditional instruments fused with Western brass, keyboard and guita

Spatio-temporal analysis and prediction of malaria cases using remote sensing meteorological data in Diébougou health district, Burkina Faso, 2016–2017

International audienceMalaria 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

Siraba

Popular Malian Mandinga music accompanied by Mandinga percussion, kora, and n'goni traditional instruments fused with Western brass, keyboard and guita

Kalo 9

Popular Malian Mandinga music accompanied by Mandinga percussion, kora, and n'goni traditional instruments fused with Western brass, keyboard and guita