Oocyst counts from naturally infected mosquitoes collected in Burkina Faso

The population dynamics of human to mosquito malaria transmission in the field has important implica- tions for the genetics, epidemiology and control of malaria. The number of oocysts in oocyst-positive mosquitoes developing from a single, naturally acquired infectious blood meal (herein referred to as a single-feed infection load) greatly influences the efficacy of transmission blocking interventions but still remains poorly documented. During a year-long analysis of malaria parasite transmission in Burkina Faso we caught and dissected wild malaria vectors to assess Plasmodium oocyst prevalence and load (the number of oocysts counted in mosquitoes with detectable oocysts) and the prevalence of salivary gland sporozoites. This was compared with malaria endemicity in the human population, assessed in cross-sectional surveys. Data were analysed using a novel transmission mathematical model to estimate the per bite transmission probability and the average single-feed infection load for each location. The observed oocyst load and the estimated single-feed infection load in naturally infected mosquitoes were substantially higher than previous estimates (means ranging from 3.2 to 24.5 according to seasons and locations) and indicate a strong positive association between the single-feed infection load and parasite prevalence in humans. This work suggests that highly infected mosquitoes are not rare in the field and might have a greater influence on the epidemiology and genetics of the parasite, and on the efficacy of novel transmission blocking interventions. During a year-long analysis of malaria parasite transmission in Burkina Faso, wild malaria vectors were caught and dissected to assess Plasmodium oocyst prevalence and load (the number of oocysts counted in mosquitoes with detectable oocysts).  The R script attached allows the user to extract the oocyst counts observed in each location (Klesso & Longo). Specifically the counts for 'normal' (rather than 'broken') were used in this analysis. As well as the primary analysis described above, the data were also used in the following, forthcoming study (submitted to Nature Communications): Predicting the public health impact of a malaria transmission-blocking vaccine by Joseph D. Challenger, Daniela Olivera Mesa, Dari F. Da, R. Serge Yerbanga, Thierry Lefèvre, Anna Cohuet, Thomas S. Churcher [2021]. Correspondance relating to this analysis of the data should be addressed to: j.challenger@imperial.ac.uk