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Larval source management (LSM) could reduce malaria transmission when executed alongside core vector control strategies. Involving communities in LSM could increase intervention coverage, reduce operational costs, and promote sustainability via community buy-in. We assessed the effectiveness of community-led LSM to reduce anopheline larval densities in 26 villages along the perimeter of Majete Wildlife Reserve in southern Malawi. The communities formed LSM committees which coordinated LSM activities in their villages following specialized training. Effectiveness of larviciding by LSM committees was assessed via pre- and post-spray larval sampling. The effect of community-led LSM on anopheline larval densities in intervention villages was assessed via comparisons with densities in non-LSM villages over a period of 14 months. Surveys involving 502 respondents were undertaken in intervention villages to explore community motivation and participation, and factors influencing these outcomes. Larviciding by LSM committees reduced anopheline larval densities in post-spray sampling compared with pre-spray sampling (P < 0.0001). No differences were observed between anopheline larval densities during pre-spray sampling in LSM villages and those in non-LSM villages (P = 0.282). Knowledge about vector biology and control, and someone’s role in LSM motivated community participation in the vector control program. Despite reducing anopheline larval densities in LSM villages, the impact of the community-led LSM could not be detected in our study setting because of low mosquito densities after scale-up of core malaria control interventions. Still, the contributions of the intervention in increasing a community’s knowledge of malaria, its risk factors, and its control methods highlight potential benefits of the approach.

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.

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.

Background: Monitoring malaria transmission is a critical component of efforts to achieve targets for elimination and eradication. Two commonly monitored metrics of transmission intensity are parasite prevalence (PR) and the entomological inoculation rate (EIR). Comparing the spatial and temporal variations in the PR and EIR of a given geographical region and modelling the relationship between the two metrics may provide a fuller picture of the malaria epidemiology of the region to inform control activities. Methods: Using geostatistical methods, we compare the spatial and temporal patterns of Plasmodium falciparum EIR and PR using data collected over 38 months in a rural area of Malawi. We then quantify the relationship between EIR and PR by using empirical and mechanistic statistical models. Results: Hotspots identified through the EIR and PR partly overlapped during high transmission seasons but not during low transmission seasons. The estimated relationship showed a 1-month delayed effect of EIR on PR such that at lower levels of EIR, increases in EIR are associated with rapid rise in PR, whereas at higher levels of EIR, changes in EIR do not translate into notable changes in PR. Conclusions: Our study emphasises the need for integrated malaria control strategies that combine vector and human host managements monitored by both entomological and parasitaemia indices.