Bayesian Spatio-Temporal modelling for the implementation of a malaria early warning based on the impact of climate change in DR Congo
1
West Africa Regional Office, African Population and Health Center, Dakar, Senegal
2
1Laboratoire de Biomathématiques et d'Estimations Forestières, Université d'Abomey-Calavi, 04 BP 1525, Cotonou, Benin
3
3Department of Epidemiology and Biostatistics, Western University, London Ontario, Canada
4
2Département de la Santé Communautaire,, Institut Supérieur des Techniques Médicales de Kinshasa,, Kinsh, Congo, Democratic Republic of the
5
4Department of Biology, Faculty of Science,, Western University, Western University,, London, Canada
6
Department of Geography and Environment,, The University of Western Ontario, Canada, London, Canada
7
6School of Public Health, Faculty of Health Sciences,, University of the Witwatersrand,, Johannesburg, South Africa
Popul. Med. 2026;8(Supplement Supplement 1):A3822
ABSTRACT
ABSTRACT:
Background. Malaria remains a major public health burden in DRC, where transmission dynamics are shaped by both climatic variability and structural factors. Quantifying joint effects of climate and spatial heterogeneity is essential to strengthen early-warning systems and guide targeted interventions. We assess non-linear climatic effects and residual-spatial patterns of malaria risk across health zones in DRC. Methods. Routine malaria surveillance data from 2014-2024 aggregated at health-zone level, linked with climate variables. Bayesian geo-additive spatio-temporal models were fitted, including structured-neighborhood and unstructured-local spatial random-effects, and non-linear covariate effects modeled using penalized splines. Inference was via MCMC and convergence assessed using trace plots, autocorrelation functions, and cumulative mean diagnostics. Short-term projections to 2029 and a weekly health-zone–level early-warning classification were generated from best-performing models. Results. Strong spatial heterogeneity observed, with unadjusted relative risks exceeding six times the national average in southern, south-eastern and central regions, while northern and western zones consistently showed lower risk. After adjustment for climate, residual-spatial disparities increased, indicating that non-climatic structural determinants dominate spatial distribution of malaria. Climatic effects were markedly non-linear and biologically plausible: malaria risk increased sharply above 22°C, rose with humidity above 60%, showed a peak at moderate cloud cover, and decreased under strong zonal winds and near-zero meridional winds. MCMC diagnostics indicated good convergence and efficient mixing. Forecasts suggested a substantial rise in weekly-malaria incidence by 2029 with persistent seasonality, and the early warning system classified most health-zones as being under moderate-to-high alert.
CONCLUSIONS:
Climate modulates seasonal malaria transmission in DRC and persistent spatial hotspots are primarily driven by structural and contextual factors beyond weather variability. High-risk regions, particularly in Grand-South-eastern corridors, require sustained, non-seasonal interventions alongside climate-informed early warning systems. Bayesian spatio-temporal modeling provides a robust framework for integrating surveillance and climate data to support anticipatory malaria control strategies under climate change.
Background. Malaria remains a major public health burden in DRC, where transmission dynamics are shaped by both climatic variability and structural factors. Quantifying joint effects of climate and spatial heterogeneity is essential to strengthen early-warning systems and guide targeted interventions. We assess non-linear climatic effects and residual-spatial patterns of malaria risk across health zones in DRC. Methods. Routine malaria surveillance data from 2014-2024 aggregated at health-zone level, linked with climate variables. Bayesian geo-additive spatio-temporal models were fitted, including structured-neighborhood and unstructured-local spatial random-effects, and non-linear covariate effects modeled using penalized splines. Inference was via MCMC and convergence assessed using trace plots, autocorrelation functions, and cumulative mean diagnostics. Short-term projections to 2029 and a weekly health-zone–level early-warning classification were generated from best-performing models. Results. Strong spatial heterogeneity observed, with unadjusted relative risks exceeding six times the national average in southern, south-eastern and central regions, while northern and western zones consistently showed lower risk. After adjustment for climate, residual-spatial disparities increased, indicating that non-climatic structural determinants dominate spatial distribution of malaria. Climatic effects were markedly non-linear and biologically plausible: malaria risk increased sharply above 22°C, rose with humidity above 60%, showed a peak at moderate cloud cover, and decreased under strong zonal winds and near-zero meridional winds. MCMC diagnostics indicated good convergence and efficient mixing. Forecasts suggested a substantial rise in weekly-malaria incidence by 2029 with persistent seasonality, and the early warning system classified most health-zones as being under moderate-to-high alert.
CONCLUSIONS:
Climate modulates seasonal malaria transmission in DRC and persistent spatial hotspots are primarily driven by structural and contextual factors beyond weather variability. High-risk regions, particularly in Grand-South-eastern corridors, require sustained, non-seasonal interventions alongside climate-informed early warning systems. Bayesian spatio-temporal modeling provides a robust framework for integrating surveillance and climate data to support anticipatory malaria control strategies under climate change.
| eISSN: | 2654-1459 |
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