ArticleMalaria journal2024
Assessing receptivity to malaria using case surveillance and forest data in a near-elimination setting in northeast Thailand.
Article in Malaria journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Sustaining community-based malaria services through stakeholder engagement: lessons from co-creation in northeastern Thailand.Malaria journal · 2026Article
- Mapping malaria transmission foci in Northeast Thailand from 2011 to 2021: approaching elimination in a hypoendemic area.Malaria journal · 2024Article
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Authors and funding
9 authors.
Funding
Abstract
backgroundThailand aimed to eliminate malaria by 2024, and as such is planning for future prevention of re-establishment in malaria free provinces. Understanding the receptivity of local areas to malaria allows the appropriate targeting of interventions. Current approaches to assessing receptivity involve collecting entomological data. Forest coverage is known to be associated with malaria risk, as an environment conducive to both vector breeding and high-risk human behaviours.
methodsGeolocated, anonymized, individual-level surveillance data from 2011 to 2021 from the Thai Division of Vector-Borne Disease (DVBD) was used to calculate incidence and estimated R
resultsThere was a non-linear decrease in annual cases by 96.6% (1061 to 36) across the two provinces from 2011 to 2021. Indigenous Annual Parasite Index (API) and approximated R
conclusionsThere was a positive association between forest coverage around a village and indigenous malaria cases. Most local transmission was observed in the heavily forested subdistricts on the international borders with Laos and Cambodia, which are where the most receptive villages are located. These areas are at greater risk of importation of malaria due to population mobility and forest-going activities. Combining forest cover and recent case surveillance data with measures of vulnerability may be useful for prediction of malaria recurrence risk.
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