Hotspots and super-spreaders: Modelling fine-scale malaria parasite transmission using mosquito flight behaviour

Abstract

Malaria hotspots have been the focus of public health managers for several years due to the potential elimination gains that can be obtained from targeting them. The identification of hotspots must be accompanied by the description of the overall network of stable and unstable hotspots of malaria, especially in medium and low transmission settings where malaria elimination is targeted. Targeting hotspots with malaria control interventions has, so far, not produced expected benefits. In this work we have employed a mechanistic-stochastic algorithm to identify clusters of super-spreader houses and their related stable hotspots by accounting for mosquito flight capabilities and the spatial configuration of malaria infections at the house level. Our results show that the number of super-spreading houses and hotspots is dependent on the spatial configuration of the villages. In addition, super-spreaders are also associated to house characteristics such as livestock and family composition. We found that most of the transmission is associated with winds between 6pm and 10pm although later hours are also important. Mixed mosquito flight (downwind and upwind both with random components) were the most likely movements causing the spread of malaria in two out of the three study areas. Finally, our algorithm (named MALSWOTS) provided an estimate of the speed of malaria infection progression from house to house which was around 200–400 meters per day, a figure coherent with mark-release-recapture studies of Anopheles dispersion. Cross validation using an out-of-sample procedure showed accurate identification of hotspots. Our findings provide a significant contribution towards the identification and development of optimal tools for efficient and effective spatio-temporal targeted malaria interventions over potential hotspot areas.

Joint spatial modelling of malaria incidence and vector's abundance shows heterogeneity in malaria‐vector geographical relationships

“…These areas are likely to be the results of model‐correlated uncertainties or predictors not considered in this study and that act at both vector and malaria level but also to factors indirectly related to the distribution of the mosquitoes (human behaviour, human mobility, house construction, etc. ; Guerra et al., 2019; Sedda et al., 2022). Investigating the areas with large vector‐malaria‐correlated residuals is necessary not only to target additional surveillance (in order to improve distribution maps) but also to identify hidden predictors of malaria and mosquito suitability that can improve knowledge in malaria transmission risk factors (Handique et al., 2016; Zhou et al., 2007) and inform the future direction for malaria prevention, control and elimination.…”

“…These areas are likely to be the results of model-correlated uncertainties or predictors not considered in this study and that act at both vector and malaria level but also to factors indirectly related to the distribution of the mosquitoes (human behaviour, human mobility, house construction, etc. ; Guerra et al, 2019;Sedda et al, 2022).…”

“…However, spatial analyses have the disadvantage that their scales are influenced by local factors (human behaviour and movements, landscape fragmentation, public health interventions, to name a few) limiting their generalizability beyond areas with similar ecological and epidemiological conditions. A likely indicator of the presence of these local factors not included in this study is the range of 14 km, a spatial scale much larger than common mosquito flight distances (Sedda et al., 2022).…”

“…However, spatial analyses have the disadvantage that their scales are influenced by local factors (human behaviour and movements, landscape fragmentation, public health interventions, to name a few) limiting their generalizability beyond areas with similar ecological and epidemiological conditions. A likely indicator of the presence of these local factors not included in this study is the range of 14 km, a spatial scale much larger than common mosquito flight distances(Sedda et al, 2022).Finally, we used only a restricted set of environmental variables which may cause over-estimation of the random effects. Based on these limitations, follow-up studies with longer repeated measurements over time and with larger spatial scales are needed.…”

Small dams driveAnophelesabundance during the dry season in a high malaria burden area of Malawi

“…funestus emerging as adults from the small dam impoundment flew a short distance (<500m) to households in Malangano. Mosquito dispersal is influenced by many factors including odour plume 34 , host availability and density 35 , wind patterns and the spatial configuration of households and villages 36 . The sharp decline in An.…”