Computational modeling of epidemiological count data using Non-Homogeneous Poisson Processes and functional data
In this work, we introduce a novel methodology for modeling discrete count variables within the framework of stochastic processes. Our approach integrates two statistical areas: Non-Homogeneous Poisson Processes for the estimation and prediction of intensity functions based on explanatory variables...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universidad de Antioquia
2025-03-01
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| Series: | Revista Facultad de Ingeniería Universidad de Antioquia |
| Subjects: | |
| Online Access: | https://revistas.udea.edu.co/index.php/ingenieria/article/view/357499 |
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| Summary: | In this work, we introduce a novel methodology for modeling discrete count variables within the framework of stochastic processes. Our approach integrates two statistical areas: Non-Homogeneous Poisson Processes for the estimation and prediction of intensity functions based on explanatory variables and functional data estimation techniques. Through a comprehensive case study focusing on an infectious disease with viral characteristics, we demonstrate the potential of our methodology. We provide empirical evidence that our methodology offers a robust alternative for modeling count variables. Our findings support the utility of our approach in capturing the complex dynamics inherent in count data in infectious disease epidemiological phenomena.
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| ISSN: | 0120-6230 2422-2844 |