Global co-hotspots of dengue, malaria, and yellow fever: a comparative spatiotemporal analysis across 142 countries

Shared respiratory infectious disease hotspots identify priority countries for pandemic preparedness: a Bayesian spatiotemporal analysis with COVID-19 external validation

Data-Driven Geospatial Modeling and Forecasting of Malaria Burden to Support Control and Elimination in Africa

Malaria elimination is shaped by complex interactions among climatic, environmental, socioeconomic, demographic, health-system, and intervention-related factors. However most studies examine only subsets of these drivers, limiting understanding of their combined influence on epidemiological risks. In this study, we integrated 25 years of data from 44 African countries on malaria burden and control, climate, environmental and land-use conditions, socioeconomic and demographic characteristics, and health-system capacity within a unified geospatial, explainable machine-learning, and forecasting framework to characterize spatiotemporal patterns of malaria, quantify the relative contributions of key determinants, and generate 10-year Africa-wide and country-specific forecasts of malaria incidence and mortality rates per 1,000 people at risk. We identified and mapped malaria incidence and mortality hotspots using the Getis-Ord Gi* statistic. Our analyses showed that both incidence and mortality burden remained highly heterogeneous across Africa, with persistent hotspots concentrated in the West and Central Africa. The explainable machine-learning model, that achieved high predictive performance (i.e., XGBoost for incidence, holdout R$^2$ = 0.92; Random Forest for mortality, holdout R$^2$ = 0.91), identified lower availability of hospital beds (per 1,000 people), higher mortality rate attributed to unsafe WASH (per 100,000), and lower percentage (%) of people using handwashing facilities as the top three most influential determinants of higher risk of infection across Africa whereas higher mortality rate attributed to unsafe WASH (per 100,000), lower % of people using at least basic sanitation services, and access to electricity (%) were associated with worse mortality outcomes. Forecasting models also demonstrated strong predictive accuracy (Naive persistence and Elastic Net, holdout R$^2$ = 0.98 for incidence and 0.97 for mortality). Assuming current intervention and structural conditions persist, Africa-wide malaria incidence was projected to remain broadly stable, with a modest upward trend by 2035, whereas mortality was projected to decline initially and subsequently remain relatively unchanged. However, substantial country-level heterogeneity showed both emerging transmission hotspots and persistently high-burden countries. this suggests there is a need for sustained control and accelerated elimination efforts. Overall, this study demonstrates that integrating geospatial analysis, explainable machine learning, and forecasting provides a robust framework for understanding malaria dynamics, identifying the key determinants of burden, anticipating future trends, and supporting geographically targeted malaria control across Africa. Beyond malaria, our analyses can be applied as a generalizable approach for infectious disease surveillance, early-warning systems, hotspot detection, resource prioritization, and precision public health using large-scale longitudinal health data.

Nonlinear and lagged climatic drivers of malaria incidence in sub-Saharan Africa using multi-country panel analysis 2015-2024.

The results reveal significant nonlinear and temporally lagged effects of rainfall and temperature on malaria incidence, with evidence of threshold behaviour across climatic ranges and evidence to support climate-informed early warning systems and adaptive malaria control strategies across SSA.

Geographic expansion, not viral intensification, leads to human dengue outbreaks in Mexico: a 40-year integrated remote sensing and machine learning analysis

Background Dengue fever poses a pervasive, yet escalating public health burden in Mexico and abroad. Methods We conducted a 41-year spatiotemporal analysis of dengue fever across Mexico (1985–2025), integrating monthly case surveillance with climate, land cover, vegetation, and novel disaster severity covariates derived from the Emergency Events Database. Four supervised regression models were trained on 1990–2021 data, with models evaluated on a 2022–2023 temporal holdout and against observed 2024–2025 surveillance totals. Five supplementary hazard analyses examined temporal correlation, disaster type breakdown, spatial co-occurrence, pre/post event trajectories, and sensitivity to scoring weight assumptions. Results Mann–Kendall trend analysis identified statistically significant increasing dengue incidence in 15 of 32 states (9 inland), evidencing geographic expansion over four decades. Z-score analysis confirmed 2024 as a profound anomaly across both endemic and emerging states. Hazard features were significantly associated with national monthly dengue counts at lags 0–2 months across the full 1985–2025 series. A + 613% case increase following the June 2024 tropical storm. Sensitivity analyses confirmed the project’s developed ‘Severity Score’ performed comparably to four theoretically motivated differential weighting schemes. Conclusion This study provides the first structured integration of disaster severity covariates in a dengue forecasting framework for Mexico. Geographic expansion into inland states, the 41-year trend analysis, and the hazard adjustment results collectively support a differentiated public health response that targets both endemic coastal states and newly affected inland regions. The analytical framework is directly transferable to emerging dengue risk contexts in the United States and Central America, geographic neighbors also experiencing increased dengue virus transmission.

A global assessment of dengue seasonality: Applying a novel, proportion-based method to case time series from 1990 to 2024

Spatial patterns of rodent-borne pathogens and ecological suitability of rodent-borne diseases in China.

BACKGROUND Rodent-borne pathogens pose important global health risks, yet national-scale assessments linking host-pathogen ecology with disease dynamics remain limited, particularly in China. METHODS We compiled global (>29,000 records) and China-specific (2,235 records; 1950-2023) rodent-pathogen datasets to characterize host-pathogen networks, identify hyperreservoirs, and quantify prevalence heterogeneity using hierarchical meta-regression. We applied a two-layer framework integrating 1-km host suitability surfaces (stacking ensemble of boosted regression trees and random forest models) with province-level disease inference using generalized linear models for hemorrhagic fever with renal syndrome (HFRS), leptospirosis, and plague. Human exposure was estimated by overlaying suitability with gridded population data. RESULTS Globally, 116 pathogens of concern were identified across 206 host species, including 30 spillover-risk viruses and 34 hyperreservoirs. Meta-regression identified sample source as the only consistently robust moderator of prevalence heterogeneity; other moderators (e.g., rodent family, region, and habitat type) showed inconsistent or non-robust associations. Host suitability was positively associated with HFRS incidence (incidence rate ratio = 1.36, P < 0.01) but negatively or not significantly associated with leptospirosis and plague. In China, the three diseases showed contrasting suitability patterns, with approximately 404 million people (29.3%) exposed to at least one high-suitability area. CONCLUSIONS Our analysis elucidates disease-specific ecological drivers and identifies spatial priorities to inform targeted One Health surveillance and integrated interventions.