Hotspots and super-spreaders: Modelling fine-scale malaria parasite transmission using mosquito flight behaviour.
Journal
PLoS pathogens
ISSN: 1553-7374
Titre abrégé: PLoS Pathog
Pays: United States
ID NLM: 101238921
Informations de publication
Date de publication:
07 2022
07 2022
Historique:
received:
23
01
2022
accepted:
27
05
2022
revised:
18
07
2022
pubmed:
7
7
2022
medline:
22
7
2022
entrez:
6
7
2022
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 35793345
doi: 10.1371/journal.ppat.1010622
pii: PPATHOGENS-D-22-00125
pmc: PMC9292116
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
e1010622Subventions
Organisme : NIAID NIH HHS
ID : T32 AI007524
Pays : United States
Organisme : FIC NIH HHS
ID : K01 TW011770
Pays : United States
Organisme : Wellcome Trust
ID : 212501/Z/18/Z
Pays : United Kingdom
Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/T031743/1
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 220870/Z/20/Z
Pays : United Kingdom
Déclaration de conflit d'intérêts
The authors have declared that no competing interests exist.
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