Anopheles sampling collections in the health districts of Korhogo (Côte d'Ivoire) and Diébougou (Burkina Faso) between 2016 and 2018.

Characterizing the entomological profile of malaria transmission at fine spatiotemporal scales is essential for developing and implementing effective vector control strategies. Here, we present a fine-grained dataset of Anopheles mosquitoes (Diptera: Culicidae) collected in 55 villages of the rural districts of Korhogo (Northern Côte d'Ivoire) and Diébougou (South-West Burkina Faso) between 2016 and 2018. In the framework of a randomized controlled trial, Anopheles mosquitoes were periodically collected by Human Landing Catches experts inside and outside households, and analyzed individually to identify the genus and, for a subsample, species, insecticide resistance genetic mutations, Plasmodium falciparum infection, and parity status. More than 3,000 collection sessions were carried out, achieving about 45,000 h of sampling efforts. Over 60,000 Anopheles were collected (mainly A. gambiae s.s., A. coluzzii, and A. funestus). The dataset is published as a Darwin Core archive in the Global Biodiversity Information Facility, comprising four files: events, occurrences, mosquito characterizations, and environmental data.

Influence of the sickle cell trait on Plasmodium falciparum infectivity from naturally infected gametocyte carriers.

BACKGROUND: Sickle cell trait (SCT) refers to the carriage of one abnormal copy of the ß-globin gene, the HbS allele. SCT offers protection against malaria, controlling parasite density and preventing progression to symptomatic malaria. However, it remains unclear whether SCT also affects transmission stages and mosquito infection parameters. Deciphering the impact of the SCT on human to mosquito malaria transmission is key to understanding mechanisms that maintain the trait in malaria endemic areas. METHODS: The study was conducted from June to July 2017 among asymptomatic children living in the locality of Mfou, Cameroon. Blood samples were collected from asymptomatic children to perform malaria diagnosis by microscopy, Plasmodium species by PCR and hemoglobin typing by RFLP. Infectiousness of gametocytes to mosquitoes was assessed by membrane feeding assays using blood from gametocyte carriers of HbAA and HbAS genotypes. A zero-inflated model was fitted to predict distribution of oocysts in mosquitoes according to hemoglobin genotype of the gametocyte source. RESULTS: Among the 1557 children enrolled in the study, 314 (20.16%) were of the HbAS genotype. The prevalence of children with P. falciparum gametocytes was 18.47% in HbAS individuals and 13.57% in HbAA, and the difference is significant (χ2 = 4.61, P = 0.032). Multiplicity of infection was lower in HbAS gametocyte carriers (median = 2 genotypes/carrier in HbAS versus 3.5 genotypes/carrier in HbAA, Wilcoxon sum rank test = 188, P = 0.032). Gametocyte densities in the blood donor significantly influenced mosquito infection prevalence in both HbAS and HbAA individuals. The HbAS genotype had no significant effect on mosquito infection outcomes when using immune or naïve serum in feeding assays. In AB replacement feeding experiments, the odds ratio of mosquito infection for HbAA blood as compared to HbAS was 0.56 (95% CI 0.29-1.10), indicating a twice higher risk of infection in mosquitoes fed on gametocyte-containing blood of HbAS genotype. CONCLUSION: Plasmodium transmission stages were more prevalent in SCT individuals. This may reflect the parasite's enhanced investment in the sexual stage to increase their survival rate when asexual replication is impeded. The public health impact of our results points the need for intensive malaria control interventions in areas with high prevalence of HbAS. The similar infection parameters in feeding experiments where mosquitoes received the original serum from the blood donor indicated that immune responses to gametocyte surface proteins occur in both HbAS and HbAA individuals. The higher risk of infection in mosquitoes fed on HbAS blood depleted of immune factors suggests that changes in the membrane properties in HbAS erythrocytes may impact on the maturation process of gametocytes within circulating red blood cells.

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

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.

Data-driven and interpretable machine-learning modeling to explore the fine-scale environmental determinants of malaria vectors biting rates in rural Burkina Faso.

BACKGROUND: Improving the knowledge and understanding of the environmental determinants of malaria vector abundance at fine spatiotemporal scales is essential to design locally tailored vector control intervention. This work is aimed at exploring the environmental tenets of human-biting activity in the main malaria vectors (Anopheles gambiae s.s., Anopheles coluzzii and Anopheles funestus) in the health district of Diébougou, rural Burkina Faso. METHODS: Anopheles human-biting activity was monitored in 27 villages during 15 months (in 2017-2018), and environmental variables (meteorological and landscape) were extracted from high-resolution satellite imagery. A two-step data-driven modeling study was then carried out. Correlation coefficients between the biting rates of each vector species and the environmental variables taken at various temporal lags and spatial distances from the biting events were first calculated. Then, multivariate machine-learning models were generated and interpreted to (i) pinpoint primary and secondary environmental drivers of variation in the biting rates of each species and (ii) identify complex associations between the environmental conditions and the biting rates. RESULTS: Meteorological and landscape variables were often significantly correlated with the vectors' biting rates. Many nonlinear associations and thresholds were unveiled by the multivariate models, for both meteorological and landscape variables. From these results, several aspects of the bio-ecology of the main malaria vectors were identified or hypothesized for the Diébougou area, including breeding site typologies, development and survival rates in relation to weather, flight ranges from breeding sites and dispersal related to landscape openness. CONCLUSIONS: Using high-resolution data in an interpretable machine-learning modeling framework proved to be an efficient way to enhance the knowledge of the complex links between the environment and the malaria vectors at a local scale. More broadly, the emerging field of interpretable machine learning has significant potential to help improve our understanding of the complex processes leading to malaria transmission, and to aid in developing operational tools to support the fight against the disease (e.g. vector control intervention plans, seasonal maps of predicted biting rates, early warning systems).

Determinants of Plasmodium falciparum multiplicity of infection and genetic diversity in Burkina Faso.

BACKGROUND: Investigating malaria transmission dynamics is essential to inform policy decision making. Whether multiplicity of infection (MOI) dynamic from individual infections could be a reliable malaria metric in high transmission settings with marked variation in seasons of malaria transmission has been poorly assessed. This study aimed at investigating factors driving Plasmodium falciparum MOI and genetic diversity in a hyperendemic area of Burkina Faso. METHODS: Blood samples collected from a pharmacovigilance trial were used for polymerase chain reaction genotyping of the merozoite surface proteins 1 and 2. MOI was defined as the number of distinct parasite genotypes co-existing within a particular infection. Monthly rainfall data were obtained from satellite data of the Global Precipitation Measurement Database while monthly malaria incidence aggregated data were extracted from District Health Information Software 2 medical data of the Center-West health regional direction. RESULTS: In the study area, infected people harboured an average of 2.732 (± 0.056) different parasite genotypes. A significant correlation between the monthly MOI and the monthly malaria incidence was observed, suggesting that MOI could be a good predictor of transmission intensity. A strong effect of season on MOI was observed, with infected patients harbouring higher number of parasite genotypes during the rainy season as compared to the dry season. There was a negative relationship between MOI and host age. In addition, MOI decreased with increasing parasite densities, suggesting that there was a within-host competition among co-infecting genetically distinct P. falciparum variants. Each allelic family of the msp1 and msp2 genes was present all year round with no significant monthly fluctuation. CONCLUSIONS: In high malaria endemic settings with marked variation in seasons of malaria transmission, MOI represents an appropriate malaria metric which provides useful information about the longitudinal changes in malaria transmission in a given area. Besides transmission season, patient age and parasite density are important factors to consider for better understanding of variations in MOI. All allelic families of msp1 and msp2 genes were found in both dry and rainy season. The approach offers the opportunity of translating genotyping data into relevant epidemiological information for malaria control.