Evidence map›Paper›PMID 42470607›Full record

ArticleEcoHealth2026

Landscape Fragmentation Shapes Zoonotic Malaria Spillover Risk During Deforestation.

Jaewoon Jeong

Abstract read
PubMed Publisher
In one paragraph

Article in EcoHealth, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Jaewoon JeongDepartment of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada. jwjeong79@gmail.com.ORCID http://orcid.org/0000-0002-1889-5346

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Zoonotic malaria caused by Plasmodium knowlesi is an emerging threat in Southeast Asia, where deforestation and habitat fragmentation are reshaping human-wildlife-vector interfaces. While deforestation is known to increase ecological overlap among humans, mosquitoes, and macaques, how changes in host communities and landscape structure jointly influence spillover risk remains unclear. Here, deforestation is represented as edge accumulation using the edge ratio-a continuous measure of forest-non-forest boundary-within a process-based simulation model parameterized with previously reported estimates. The model integrates mosquito abundance, host densities, biting preferences, and reservoir competence to evaluate how spillover risk responds to landscape fragmentation. Scenarios explore changes in host populations, and global sensitivity analysis quantifies the relative influence of key parameters. Model simulations suggest that spillover risk may peak at intermediate edge ratios, where mosquito density and host overlap are jointly elevated. Changes in host composition can alter both the magnitude and location of peak risk, and abrupt biodiversity loss may produce threshold-like increases under certain conditions. Notably, spillover risk is not estimated to be highest where human density is greatest, but where both humans and macaques are sufficiently represented in the mosquito biting pool, reflecting a nonlinear trade-off in host availability. Sensitivity analysis indicates that variation in macaque competence is a dominant driver of model outcomes. These results suggest that monitoring macaque infection and mosquito activity in fragmented forest-agriculture edges may help identify areas of elevated zoonotic malaria risk, although empirical validation is needed to assess their applicability in real-world settings.

Indexed as

deforestationedge ratiomultiplicative modelPlasmodium knowlesispillover riskZoonotic malaria

Identifiers

PMID42470607

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.