Data-Driven Modeling of Seasonal Dengue Dynamics in Bangladesh: A Bayesian-Stochastic Approach
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866913526588637184 |
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| author | Hridoy, Mahmudul Bari Mustaquim, S M |
| author_facet | Hridoy, Mahmudul Bari Mustaquim, S M |
| contents | Bangladesh's worsening dengue crisis, fueled by its tropical climate, poor waste management infrastructure, rapid urbanization, and dense population, has led to increasingly deadly outbreaks, posing a significant public health threat. To address this, we propose a nonlinear, time-nonhomogeneous SEIR model incorporating seasonality through a novel transmission rate function. The model parameters are estimated using Bayesian inference with the Metropolis-Hastings algorithm in a Markov Chain Monte Carlo (MCMC) framework, calibrated with real-life dengue data from Bangladesh. To account for stochasticity and better assess outbreak probabilities, we extend the model to a time-nonhomogeneous continuous-time Markov chain (CTMC) framework. Our model provides new insights that can guide policymakers and offer a robust mathematical framework to better combat this crisis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_00947 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Data-Driven Modeling of Seasonal Dengue Dynamics in Bangladesh: A Bayesian-Stochastic Approach Hridoy, Mahmudul Bari Mustaquim, S M Applications Populations and Evolution Quantitative Methods Bangladesh's worsening dengue crisis, fueled by its tropical climate, poor waste management infrastructure, rapid urbanization, and dense population, has led to increasingly deadly outbreaks, posing a significant public health threat. To address this, we propose a nonlinear, time-nonhomogeneous SEIR model incorporating seasonality through a novel transmission rate function. The model parameters are estimated using Bayesian inference with the Metropolis-Hastings algorithm in a Markov Chain Monte Carlo (MCMC) framework, calibrated with real-life dengue data from Bangladesh. To account for stochasticity and better assess outbreak probabilities, we extend the model to a time-nonhomogeneous continuous-time Markov chain (CTMC) framework. Our model provides new insights that can guide policymakers and offer a robust mathematical framework to better combat this crisis. |
| title | Data-Driven Modeling of Seasonal Dengue Dynamics in Bangladesh: A Bayesian-Stochastic Approach |
| topic | Applications Populations and Evolution Quantitative Methods |
| url | https://arxiv.org/abs/2410.00947 |