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

Environmental Drivers and Spatial Patterns of Lassa Fever Cases in Nigeria: A GIS-Based Approach to Dynamic Susceptibility Mapping

Spatiotemporal co-circulation of four dengue serotypes across Mexico, 2020–2025: A space-time scan analysis

Background Mexico has experienced escalating dengue transmission driven by the co‑circulation of four antigenically distinct serotypes (DENV‑1 through DENV‑4). Multi‑serotype transmission is epidemiologically relevant, yet its spatiotemporal patterns at sub‑national resolution remain poorly characterized. Methods We analyzed 103,426 PCR‑confirmed, serotyped dengue cases across 1,547 of 2,471 Mexican municipalities from January 2020 to December 2025. Kulldorff’s space‑time scan statistic under a discrete Poisson model was applied independently to each serotype and to all serotypes combined. Co‑circulation was defined as the spatiotemporal overlap of significant clusters from at least two serotypes within the same municipality for ≥ 1 epidemiological week. Results We identified 159 statistically significant clusters across all serotypes combined. DENV‑3 was dominant (64.4% of cases; annual incidence 59.9/100,000), consistent with the reemergence of a long‑absent serotype. The 2024–2025 season produced a nationally synchronized epidemic across geographically distant regions. Co‑circulation of at least two serotypes occurred in 1,545 municipalities; 217 experienced simultaneous clustering of all four serotypes. Active co‑circulation was present in 275 of 311 study weeks. Mean pairwise temporal overlap ranged from 8.0 to 12.0 weeks, with maximum overlaps of 29–30 weeks. Conclusions Four‑serotype co‑circulation was documented at municipal resolution, concentrated in the Gulf coast, the Yucatán Peninsula, and northeastern Mexico. The 217 municipalities with simultaneous clustering of all four serotypes may represent areas of epidemiological interest for further investigation. This municipality‑level spatiotemporal framework offers operationally relevant resolution for tracking multi‑serotype activity and supporting serotype‑aware dengue monitoring at sub‑national scale.

Spatio-temporal analysis of dengue virus infection in the Bobonaro Municipality (2022-2024): insight from demographics and risk

Purpose: This study aimed to analyze the spatio-temporal patterns of dengue virus infection via kernel density estimation to assess the relative risk distribution and identify transmission hotspots in the Bobonaro municipality. Methods: A retrospective analysis was conducted on confirmed dengue cases (n=311) reported from seven community health centers and one referral hospital in Bobonaro from January 2022 to December 2024. Kernel density estimation with optimal bandwidth selection was employed to map the relative risk distributions and identify spatial clusters. Demographic patterns across age categories were analyzed using negative binomial regression with a quadratic age-rank term, and sex distribution was analyzed using an exact two-proportion binomial test. Results: Annual dengue cases increased by 41.48% over the study period, with significant seasonal patterns observed during the dry and rainy seasons. Case counts increased from the infancy category toward a peak in the youth category (5–14 years, 158 cases, 50.8% of all cases) before declining in older age groups, a pattern confirmed by negative binomial regression with a quadratic age-rank term (incidence rate ratio [IRR] 8.45, 95% CI 2.70–25.37, p<0.001 for the linear term and 0.74, 95% CI 0.65–0.85, p<0.001 for the quadratic term). Females accounted for a slightly higher proportion of cases (51.8%, n=161) than males (48.2%, n=150), a difference that was not statistically significant (p= 0.571). Spatial analysis revealed persistent hotspots in southeastern Bobonaro, with new clusters emerging in the northern regions by 2023 and transmission typically contained within an 80 m radius. Conclusion: This study identified clear demographic vulnerabilities and dynamic spatial patterns of dengue transmission in Bobonaro, demonstrating the utility of GIS-based spatial analysis for strengthening surveillance and guiding targeted vector control in resource-constrained settings.

A mechanistic statistical model of dengue dynamics in an endemic region

Background Dengue is a major public health challenge, and predictive models are crucial for early warning systems. However, many current modeling practices rely exclusively on climatic factors or employ complex algorithms that lack the interpretability needed for informed public health decision-making. To address these shortcomings, we developed and validated a multidimensional, interpretable statistical model to predict monthly dengue incidence. Methodology/Principal Findings We used a Generalized Linear Mixed Model (GLMM) with a Negative Binomial distribution to analyze 14 years (2010-2023) of spatiotemporal data from 37 municipalities in Huila, Colombia, an endemic region. The model integrates non-linear and lagged effects of climatic, demographic, and socioeconomic factors. The final model underwent rigorous external validation on an independent test set (2021-2023). Our model demonstrated high predictive discrimination (R2 = 0.743, Spearman's {rho} = 0.657), accurately capturing the timing of epidemic outbreaks. Key findings include the identification of an optimal thermal window for transmission at 27-28{degrees}C, a threshold effect for precipitation above 800 mm, and a saturation dynamic in outbreak autocorrelation. Conclusions/Significance This mechanistically-informed statistical approach provides a robust and transparent tool for epidemiological surveillance, successfully balancing high predictive performance with the explanatory power needed for effective, data-driven public health interventions.

Predicting Dengue Clinical Severity in Eastern Sudan's 2023 Outbreak: A Comparative Analysis of Statistical and Machine Learning Models Using Routine Surveillance Data

Objectives: This study characterized clinical severity patterns during the 2023 dengue outbreak in Al Qadarif State, Eastern Sudan; identified associated factors; compared statistical and machine-learning models; and explored patterns of clinical manifestations using routine surveillance data. Methods: This retrospective observational study included 2,552 dengue case records. The primary outcome was a pragmatic, study-specific composite clinical severity indicator, defined as the documented presence of bleeding, severe bleeding, loss of consciousness, convulsions, or thrombocytopenia.Analyses included descriptive statistics, multivariable logistic regression, random forest, XGBoost, multiple correspondence analysis, and hierarchical clustering. Model performance was assessed using a stratified hold-out test set, with uncertainty estimated from 1,000 bootstrap resamples. Results: Overall, 182 patients (7.13%) met the study-specific composite clinical severity indicator.Thirty-two deaths were recorded, giving a case-fatality rate of 1.25%. Mortality was higher among patients with severity markers than among those without them (3.85% vs. 1.05%; relative risk=3.65). Skin rash showed the strongest adjusted association with the composite severity indicator (adjusted OR=6.26, 95% CI: 3.15–12.45; P<0.001), followed by symptom-onset timing (adjusted OR=4.65, 95% CI: 2.41–8.98; P<0.001). XGBoost achieved the numerically highest AUROC (0.765, 95% CI: 0.691–0.837) and the lowest Brier score (0.161, 95% CI: 0.145–0.176). Multiple correspondence analysis suggested hemorrhagic and neurological/systemic symptom patterns. Conclusion: The study-specific composite clinical severity indicator was uncommon but was associated with a higher risk of death. Skin rash and symptom-onset timing showed the strongest adjusted associations with the study-specific composite clinical severity indicator. Predictive performance was moderate, and the findings require confirmation using independent datasets with more detailed clinical and laboratory information.

Spatio-temporal Modelling of H5N1 Avian Influenza Outbreaks in Europe (2021-2024)

A data-driven spatio-temporal framework that integrates geospatial, ecological and climatic datasets to explain and forecast the dynamics of H5N1 outbreaks between 2021 and 2024 indicates that H5N1 transmission is structured by ecological drivers and local persistence mechanisms rather than purely seasonal effects.