Evidence map›Paper›PMID 42518761›Full record

ArticleParasite epidemiology and control2026

Bayesian modelling of spatio-temporal dynamics for early-warning and control of severe malaria in Cameroon.

Akindeh Mbuh Nji, Taiwo M Adegoke, Sheetal P Silal, Ali Innocent, Pascal F Madeleine, Masso T Sylvanie, Calvino F Tah, Andrillene L D Wondeu, Christophe Antinio-Nkondjio, Jude D Bigoga and 4 more

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

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0citing papers in PubMed
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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

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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

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5 · Who and what money

Authors and funding

14 authors.

Akindeh Mbuh NjiMARCAD Plus Program, The Biotechnology Centre, University of Yaounde I, BP 8094, Yaounde, Centre Region, Cameroon.
Taiwo M AdegokeMARCAD Plus Program, The Biotechnology Centre, University of Yaounde I, BP 8094, Yaounde, Centre Region, Cameroon.
Sheetal P SilalModelling and Simulation Hub, Africa, Department of Statistical Sciences, University of Cape Town, Cape Town, South Africa.
Ali InnocentICEMR for Central Africa, Fobang's Institute for Innovation in Science and Technology, Yaounde, Cameroon.
Pascal F MadeleineMARCAD Plus Program, The Biotechnology Centre, University of Yaounde I, BP 8094, Yaounde, Centre Region, Cameroon.
Masso T SylvanieMARCAD Plus Program, The Biotechnology Centre, University of Yaounde I, BP 8094, Yaounde, Centre Region, Cameroon.
Calvino F TahMARCAD Plus Program, The Biotechnology Centre, University of Yaounde I, BP 8094, Yaounde, Centre Region, Cameroon.
Andrillene L D WondeuMARCAD Plus Program, The Biotechnology Centre, University of Yaounde I, BP 8094, Yaounde, Centre Region, Cameroon.
Christophe Antinio-NkondjioMalaria Research Laboratory, OCEAC, Yaounde, Centre, PO Box 288, Cameroon.
Jude D BigogaDepartment of Biochemistry, Faculty of Science, University of Yaounde I, BP 8094, Yaounde, Centre Region, Cameroon.
Rose F G LekeICEMR for Central Africa, Fobang's Institute for Innovation in Science and Technology, Yaounde, Cameroon.
Gillian StresmanICEMR for Central Africa, University of South Florida, Tampa, Florida, USA.
Liwang CuiICEMR for Central Africa, University of South Florida, Tampa, Florida, USA.
Wilfred F MbachamMARCAD Plus Program, The Biotechnology Centre, University of Yaounde I, BP 8094, Yaounde, Centre Region, Cameroon.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Malaria is a significant public health problem in Cameroon where spatial and temporal heterogeneities in transmission make early-warning and control complicated. The traditional forecasting methods have the limitation that they are not always able to model both the spatial dependency and the uncertainty; therefore, restricting their functionality. Methods: We used a spatio-temporal bayesian model that incorporates a Stochastic Partial Differential Equation (SPDE) that models the spatial correlations and an autoregressive time prior AR(1), solved by Integrated Nested Laplace Approximation (INLA). The analysis of severe malaria cases in 2015-2024 was done separately for children <5 years, adults and pregnant women, and rainfall as a climatic covariate and population adjusted offsets for at-risk individuals. Exceedance probabilities were calculated considering historical risk to identify hotspots. Model performance was evaluated through DIC, WAIC and calibration diagnostics. Results: Severe malaria risk was positively related to rainfall and this relationship was observed for all demographic groups. The Far North had long-term high risk foci; secondary risks were noted in the North, Centre and Littoral. The most intense hotspots were observed in pregnant women and children. Conclusions: The SPDE-AR(1) model offers a strong and uncertainty-based early warning system for severe malaria in Cameroon, which allows interventions to be targeted in areas and populations at high risk.

Indexed as

Bayesian geostatisticsEarly-warning systemsINLAMalaria forecastingSPDE–AR(1)

Identifiers

PMID42518761
PMCPMC13382336

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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.