Updating estimates of Plasmodium knowlesi malaria risk in response to changing land use patterns across Southeast Asia
PLOS Neglected Tropical Diseases·
- DOI
- 10.1371/journal.pntd.0011570
- PMID
- —
- PMCID
- —
- OpenAlex
- W4391094223
- Study type
- Journal article
- Publisher
- Public Library of Science (PLoS)
- Article type
- journal-article
- Integrity
- current
Why this research matters now
The findings may help inform geographic prioritization for P. knowlesi diagnosis, treatment planning, surveillance and sampling, as described by the authors. The abstract also places the work in the context of malaria elimination planning.
Structured evidence summary
Research question
How do updated environmental risk estimates for Plasmodium knowlesi malaria vary across Southeast Asia after incorporating newer occurrence records and land-use-related covariates?
Study design
The study updated and extended an environmental niche modelling framework previously used to map P. knowlesi transmission suitability. It incorporated literature records identified from October 2015 through March 2020 and added covariates related to deforestation and urbanisation.
Population and setting
The analysis concerned P. knowlesi occurrence and estimated transmission suitability across Southeast Asia, with reported geographic coverage including Malaysia, Indonesia, the Greater Mekong Subregion, the Philippines and Northeast India. The dataset contained 524 consolidated occurrence records.
Main findings
The model estimated the highest relative transmission suitability in Malaysia and Indonesia, with additional localized high-risk areas in the Greater Mekong Subregion, the Philippines and Northeast India. The authors identified these areas as priorities for surveillance and prospective sampling in relation to malaria elimination planning.
Public-health relevance
The findings may help inform geographic prioritization for P. knowlesi diagnosis, treatment planning, surveillance and sampling, as described by the authors. The abstract also places the work in the context of malaria elimination planning.
Important limitations
The authors state that available occurrence data are biased toward areas with greater surveillance and sampling effort, and the modelling framework was designed to account for spatial detection bias. This summary is limited to the supplied single-article abstract and metadata; the original paper is required for decision-grade assessment of model assumptions, validation, uncertainty and other limitations.
GIDS interpretation
The article is discoverable in the supplied evidence as a malaria, environmental and transmission-dynamics study with relevance to surveillance and One Health topics. Its geographic context includes Indonesia, India, Malaysia and the Philippines; this classification does not establish or confirm a current surveillance signal.
Related GIDS surveillance
Literature context does not validate, explain, or change a surveillance signal. Exact and contextual relationships are shown separately.
Evidence relationships
This article has 9 auditable classifier relationships to diseases, places, topics, and study design.