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Statistical Modeling and Bayesian Methods for Disease Mapping
Bayesian Spatio-Temporal Analysis of COVID-19 and Tuberculosis.DE
Angleščina
99 b
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This book investigates the spatio-temporal patterns of COVID-19 and tuberculosis using complementary statistical and Bayesian modeling approaches. COVID-19 is analyzed at weekly scale to capture short-term epidemic waves, while TB is studied annually to assess long-term spatial trends. The study applied Generalized Additive Models and Bayesian hierarchical models using INLA to estimate nonlinear effects, spatial dependence, and temporal variation. Results revealed strong seasonality and mobility-related effects for COVID-19, while TB shows stable but uneven geographic distribution across districts. The models produce reliable risk maps and highlight the importance of targeted surveillance, improved monitoring, and resource allocation for effective disease control.
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