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

Autor: Sedda L; Lancaster Ecology and Epidemiology Group, Lancaster Medical School, Lancaster University, United Kingdom., McCann RS; Laboratory of Entomology, Wageningen University & Research, Wageningen, The Netherlands.; School of Global and Public Health, Kamuzu University of Health Sciences, Blantyre, Malawi.; Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, Maryland, United States of America., Kabaghe AN; School of Global and Public Health, Kamuzu University of Health Sciences, Blantyre, Malawi., Gowelo S; Laboratory of Entomology, Wageningen University & Research, Wageningen, The Netherlands.; School of Global and Public Health, Kamuzu University of Health Sciences, Blantyre, Malawi.; MAC Communicable Diseases Action Centre, Kamuzu University of Health Sciences, Blantyre, Malawi., Mburu MM; Laboratory of Entomology, Wageningen University & Research, Wageningen, The Netherlands.; School of Global and Public Health, Kamuzu University of Health Sciences, Blantyre, Malawi., Tizifa TA; School of Global and Public Health, Kamuzu University of Health Sciences, Blantyre, Malawi.; Center for Tropical Medicine and Travel Medicine, University of Amsterdam, The Netherlands., Chipeta MG; School of Global and Public Health, Kamuzu University of Health Sciences, Blantyre, Malawi.; Malawi-Liverpool-Wellcome Trust Clinical Research Programme, Blantyre, Malawi., van den Berg H; Laboratory of Entomology, Wageningen University & Research, Wageningen, The Netherlands., Takken W; Laboratory of Entomology, Wageningen University & Research, Wageningen, The Netherlands., van Vugt M; Center for Tropical Medicine and Travel Medicine, University of Amsterdam, The Netherlands., Phiri KS; School of Global and Public Health, Kamuzu University of Health Sciences, Blantyre, Malawi., Cain R; Lancaster Ecology and Epidemiology Group, Lancaster Medical School, Lancaster University, United Kingdom., Tangena JA; Vector Biology Department, Liverpool School of Tropical Medicine, Liverpool, United Kingdom., Jones CM; Malawi-Liverpool-Wellcome Trust Clinical Research Programme, Blantyre, Malawi.; Vector Biology Department, Liverpool School of Tropical Medicine, Liverpool, United Kingdom.
Jazyk: angličtina
Zdroj: PLoS pathogens [PLoS Pathog] 2022 Jul 06; Vol. 18 (7), pp. e1010622. Date of Electronic Publication: 2022 Jul 06 (Print Publication: 2022).
DOI: 10.1371/journal.ppat.1010622
Abstrakt: 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.
Competing Interests: The authors have declared that no competing interests exist.
Databáze: MEDLINE
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