Cureus · 2026
Bayesian Hierarchical Spatiotemporal Prediction of Multi-pathogen Outbreaks: Integrating Mobility and Environmental Exposures.
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Adekunle Hakeem, Adewuyi Oladimeji S, Okunola Kayode, Balogun Olalekan T
Аннотация
INTRODUCTION: Infectious disease outbreaks remain a persistent global health burden, particularly as populations experience the concurrent circulation of multiple infectious agents that interact across space and time. Forecasting such complex epidemic systems requires models that can capture shared transmission mechanisms, pathogen-specific dynamics, and uncertainty arising from incomplete surveillance.
METHODS: In this study, we develop a unified Bayesian hierarchical spatiotemporal framework for predicting multi-pathogen outbreak trajectories while integrating human mobility patterns, environmental exposures, and structured reporting uncertainties. We conducted a comprehensive simulation experiment to evaluate the model's ability to recover known parameters, distinguish pathogen-specific transmission effects, and generate calibrated forecasts under varying levels of reporting noise and spatial heterogeneity. We further applied the method to CDC FluView surveillance weekly data from the United States, spanning January 2017 to December 2025.
RESULTS: In the simulation study, the model showed good parameter recovery under different levels of reporting noise and spatial heterogeneity, with stable estimates and satisfactory convergence. The model effectively distinguished pathogen dynamics, with posterior means for baseline incidence ([Formula: see text] and [Formula: see text] at -0.74 and -0.87). Human mobility ([Formula: see text] = 0.35, 95% CI: 0.06-0.72) was a significant driver of spatial transmission, while overdispersion parameters ([Formula: see text] = 11.61, [Formula: see text] = 5.74) accounted for variability beyond the mean structure. Diagnostic Rhat values near 1 confirmed model convergence and robust chain mixing. In the real data application, influenza showed a higher baseline incidence than measles ([Formula: see text] = -0.74, [Formula: see text] = -0.87), temperature had a positive effect on transmission ([Formula: see text] = 0.19), while humidity effects were weaker and more uncertain, and the mobility parameter ([Formula: see text] = 0.35) indicated that human movement contributed to spatial spread; influenza also exhibited greater variability ([Formula: see text] = 11.61 vs [Formula: see text] = 5.74), and the model captured seasonal patterns while closely tracking the observed incidence over time.
CONCLUSIONS: Across all scenarios, the model demonstrated robust parameter recovery, reduced bias in reproduction number estimates, and improved predictive accuracy relative to conventional compartmental and independent-pathogen Bayesian models. This performance was consistent in both simulation and real data settings, where the model distinguished pathogen-specific dynamics, captured the contribution of human mobility to spatial transmission, and accounted for variability in case counts through overdispersion. The results support the use of this approach for stable inference and reliable forecasting in complex multi-pathogen systems.
METHODS: In this study, we develop a unified Bayesian hierarchical spatiotemporal framework for predicting multi-pathogen outbreak trajectories while integrating human mobility patterns, environmental exposures, and structured reporting uncertainties. We conducted a comprehensive simulation experiment to evaluate the model's ability to recover known parameters, distinguish pathogen-specific transmission effects, and generate calibrated forecasts under varying levels of reporting noise and spatial heterogeneity. We further applied the method to CDC FluView surveillance weekly data from the United States, spanning January 2017 to December 2025.
RESULTS: In the simulation study, the model showed good parameter recovery under different levels of reporting noise and spatial heterogeneity, with stable estimates and satisfactory convergence. The model effectively distinguished pathogen dynamics, with posterior means for baseline incidence ([Formula: see text] and [Formula: see text] at -0.74 and -0.87). Human mobility ([Formula: see text] = 0.35, 95% CI: 0.06-0.72) was a significant driver of spatial transmission, while overdispersion parameters ([Formula: see text] = 11.61, [Formula: see text] = 5.74) accounted for variability beyond the mean structure. Diagnostic Rhat values near 1 confirmed model convergence and robust chain mixing. In the real data application, influenza showed a higher baseline incidence than measles ([Formula: see text] = -0.74, [Formula: see text] = -0.87), temperature had a positive effect on transmission ([Formula: see text] = 0.19), while humidity effects were weaker and more uncertain, and the mobility parameter ([Formula: see text] = 0.35) indicated that human movement contributed to spatial spread; influenza also exhibited greater variability ([Formula: see text] = 11.61 vs [Formula: see text] = 5.74), and the model captured seasonal patterns while closely tracking the observed incidence over time.
CONCLUSIONS: Across all scenarios, the model demonstrated robust parameter recovery, reduced bias in reproduction number estimates, and improved predictive accuracy relative to conventional compartmental and independent-pathogen Bayesian models. This performance was consistent in both simulation and real data settings, where the model distinguished pathogen-specific dynamics, captured the contribution of human mobility to spatial transmission, and accounted for variability in case counts through overdispersion. The results support the use of this approach for stable inference and reliable forecasting in complex multi-pathogen systems.