Evidence map›Paper›PMID 40255344›Full record

ArticleThe Lancet regional health. Western Pacific2025

Mapping residual malaria transmission in Vietnam.

Michael A McPhail, Yalemzewod Assefa Gelaw, Xuan Thang Nguyen, Win Han Oo, Freya J I Fowkes, Duc Thang Ngo, Thi Hong Phuc Nguyen, Tasmin L Symons, Dan J Weiss, Peter W Gething

Abstract read
In one paragraph

Article in The Lancet regional health. Western Pacific, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

10 authors.

Michael A McPhailThe Kids Research Institute Australia, Perth, WA, Australia.
Yalemzewod Assefa GelawThe Kids Research Institute Australia, Perth, WA, Australia.
Xuan Thang NguyenNational Institute of Malariology, Parasitology and Entomology, Ministry of Health, Hanoi, Vietnam.
Win Han OoBurnet Institute, 85 Commercial Rd, Melbourne, VIC, Australia.
Freya J I FowkesBurnet Institute, 85 Commercial Rd, Melbourne, VIC, Australia.
Duc Thang NgoNational Institute of Malariology, Parasitology and Entomology, Ministry of Health, Hanoi, Vietnam.
Thi Hong Phuc NguyenNational Institute of Malariology, Parasitology and Entomology, Ministry of Health, Hanoi, Vietnam.
Tasmin L SymonsThe Kids Research Institute Australia, Perth, WA, Australia.
Dan J WeissThe Kids Research Institute Australia, Perth, WA, Australia.
Peter W GethingThe Kids Research Institute Australia, Perth, WA, Australia.

Funding

Bill & Melinda Gates Foundation INV-009390
6 · The paper itself

Abstract

Background: Vietnam, as one of the countries in the Greater Mekong Subregion, has committed to eliminating all malaria by 2030. Declining case numbers highlight the country's progress, but challenges including imported cases and pockets of residual transmission remain. To successfully eliminate malaria and to prevent reintroduction of malaria transmission, geostatistical modelling of vulnerability (importation rate) and receptivity (quantified by the reproduction number) of malaria is critical. Methods: Case data from 2019 to 2022 was used to train a range of network and geostatistical models, from which strategically useful metrics were computed. These metrics include vulnerability, which was estimated from the abundance of imported cases, and receptivity, which was estimated based on a transmission model linking cases as well as environmental covariate data. Findings: There is considerable spatiotemporal heterogeneity in the computed metrics. Importations are concentrated in the South Central Coast and Central highlands regions. The importation rate of Interpretation: Receptivity and vulnerability to malaria is spatiotemporally heterogeneous in Vietnam. Despite the average reproduction number being less than one, the spatial pockets and temporal windows of elevated reproduction number could prevent timely elimination of malaria or even lead to a reversal of progress. The predictive maps presented in this paper can inform appropriate intervention strategies to advance goals of malaria elimination. Funding: This work was supported, in whole or in part, by the Bill & Melinda Gates Foundation [INV-055192 and INV-009390/OPP1197730]. The conclusions and opinions expressed in this work are those of the author(s) alone and shall not be attributed to the Foundation. Under the grant conditions of the Foundation, a Creative Commons Attribution 4.0 License has already been assigned to the Author Accepted Manuscript version that might arise from this submission. Please note works submitted as a preprint have not undergone a peer review process. This work also includes funding support from the Australian Government, National Health and Medical Research Council (Award No: GNT2025280) and Telethon Trust, Western Australia.

Indexed as

Geospatial modellingMalariaReceptivityRisk mapVulnerability

Identifiers

PMID40255344
PMCPMC12008676

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LicenceCC BY
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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.