Comparative Performance of Mechanistic, Statistical, and Hybrid Models of Forecasting Dengue Fever Incidence in Somalia. A Retrospective Time Series Analysis

Association between Rainfall, Temperature, and Dengue Fever Incidence in Selected Regions of Guyana, 2020–2022: A Hydrometeorological Correlation Analysis

Characterizing the meteorological determinants of dengue transmission is critical for developing anticipatory surveillance frameworks in tropical settings like Guyana, where precipitation and temperature fluctuations are intrinsically linked to the reproductive ecology of Aedes aegypti. This study examined the association between monthly and annual hydrometeorological parameters, rainfall (mm), daytime temperature (℃)

Performance Analysis of SIR Model for Comparing to Classical Logistic Model

Dengue fever has emerged as one of the most devastating vector-borne diseases in Bangladesh. Understanding the mechanisms driving transmission is therefore a scientific and public-health priority. This study presents a comprehensive and methodologically robust analysis of the 2023 dengue epidemic in Bangladesh by integrating epidemiological data with advanced numerical modeling. It formulates the transmission dynamics using the classical SIR (Susceptible–Infected–Recovered) framework and benchmark its performance against the logistic growth model, thereby revealing the fundamental differences between mechanistic and phenomenological approaches. Recognizing that the SIR system lacks a closed-form analytical solution, it employs a suite of high-accuracy numerical solvers—including Taylor’s Series Method, RK2, and RK4 to faithfully capture the nonlinear transmission process. Using real-world infection and mortality data from IEDCR (2023), it simulates reproduce the full epidemic arc with high fidelity, identifying the critical peak and the subsequent downturn induced by susceptible depletion and rising immunity. Comparative evaluation demonstrates that the logistic model, while useful for approximating cumulative trends, is structurally incapable of capturing core epidemic mechanisms. In contrast, the numerically solved SIR model delivers superior predictive realism, mechanistic transparency, and epidemiological interpretability. This work underscores the indispensable role of rigorous mathematical modeling in guiding dengue preparedness, optimizing control strategies, and strengthening epidemic response capacity in resource-limited settings. Jagannath University Journal of Science, Volume 12, Number 1, Jun. 2025, pp. 29−38

Time Series Methods in Epidemiological Studies of Dengue in Brazil: A Scoping Review

Geostatistical Modelling and Web‐Based Mapping of Malaria Risks Among Children Under Five Years in Nigeria: Evidence From the 2021 Nigeria Malaria Indicator Survey

Malaria is a critical public health concern in Nigeria with the country bearing an uneven burden of the disease. In Nigeria, malaria is one of the main causes of child mortality and despite all efforts to reduce malaria mortality rates, the disease remains a major concern, notably among children under‐fives. There is a paucity of data on more localized predictive malaria risk geospatial maps to inform control and elimination strategies amidst limited public health resources in this setting. This modelling study therefore sought to understand, predict and map malaria risk in the presence of environmental factors in Nigeria.

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.

Tactical vs. strategic: An adaptable framework for horizon-dependent dengue forecasting using data-driven approaches with serotype and climate covariates in Bangladesh

Dengue remains a major public health problem in endemic regions, including Bangladesh. Forecasting algorithms relying on climatic variables may not capture epidemiological information such as dengue serotype patterns. This study proposes a horizon-dependent dengue forecasting framework and applies it to Bangladesh. This pipeline included temporal, meteorological, demographic, and epidemiological covariates in a district-panel pipeline and compares classical, machine-learning, and deep-learning algorithms using aggregate, horizon-wise, regime-wise, outbreak-detection, and uncertainty metrics. Coefficient-based interpretation, SHAP, permutation importance, and nonlinear causal-dependence analysis were used to examine predictor impacts across horizons. SARIMAX was used as the baseline model. TFT produced quantile forecasts but showed poor interval calibration during outbreak periods. Results show that no single model performed best across all forecasting horizons: SARIMAX ranked highest for one-step outbreak alerting (precision = 0.886, recall = 0.824, F1 score = 0.854, and ROC-AUC = 0.950), whereas Prophet performed best for pooled magnitude forecasting at horizons 2–6. MLR showed competitive performance in regime-wise evaluation. These findings show that dengue forecasting algorithms should be selected according to the intended decision objective and evaluated using task-relevant protocols.