Modelling COVID-19 in the North American region with a metapopulation network and Kalman filter
Background: Understanding the dynamics of infectious disease spread and predicting clinical outcomes are critical for managing large-scale epidemics and pandemics, such as COVID-19. Effective modeling of disease transmission in interconnected populations helps inform public health responses and inte...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Epidemics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1755436525000064 |
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| author | Matteo Perini Teresa K. Yamana Marta Galanti Jiyeon Suh Roselyn Kaondera-Shava Jeffrey Shaman |
| author_facet | Matteo Perini Teresa K. Yamana Marta Galanti Jiyeon Suh Roselyn Kaondera-Shava Jeffrey Shaman |
| author_sort | Matteo Perini |
| collection | DOAJ |
| description | Background: Understanding the dynamics of infectious disease spread and predicting clinical outcomes are critical for managing large-scale epidemics and pandemics, such as COVID-19. Effective modeling of disease transmission in interconnected populations helps inform public health responses and interventions across regions. Methods: We developed a novel metapopulation model for simulating respiratory virus transmission in the North America region, specifically for the 96 states, provinces, and territories of Canada, Mexico, and the United States. The model is informed by COVID-19 case data, which are assimilated using the Ensemble Adjustment Kalman filter (EAKF), a Bayesian inference algorithm. Additionally, commuting and mobility data are used to build and adjust the network and movement across locations on a daily basis. Results: This model-inference system provides estimates of transmission dynamics, infection rates, and ascertainment rates for each of the 96 locations from January 2020 to March 2021. The results highlight differences in disease dynamics and ascertainment among the three countries. Conclusions: The metapopulation structure enables rapid simulation at a large scale, and the data assimilation method makes the system responsive to changes in system dynamics. This model can serve as a versatile platform for modeling other infectious diseases across the North American region. |
| format | Article |
| id | doaj-art-5ac523db2eae435daa3f89486b6d857b |
| institution | Kabale University |
| issn | 1755-4365 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Epidemics |
| spelling | doaj-art-5ac523db2eae435daa3f89486b6d857b2025-02-02T05:26:59ZengElsevierEpidemics1755-43652025-03-0150100818Modelling COVID-19 in the North American region with a metapopulation network and Kalman filterMatteo Perini0Teresa K. Yamana1Marta Galanti2Jiyeon Suh3Roselyn Kaondera-Shava4Jeffrey Shaman5Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, 722 W 168th St, New York, NY 10032, United States; Corresponding author.Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, 722 W 168th St, New York, NY 10032, United StatesDepartment of Environmental Health Sciences, Mailman School of Public Health, Columbia University, 722 W 168th St, New York, NY 10032, United StatesDepartment of Environmental Health Sciences, Mailman School of Public Health, Columbia University, 722 W 168th St, New York, NY 10032, United StatesDepartment of Environmental Health Sciences, Mailman School of Public Health, Columbia University, 722 W 168th St, New York, NY 10032, United StatesDepartment of Environmental Health Sciences, Mailman School of Public Health, Columbia University, 722 W 168th St, New York, NY 10032, United States; Columbia Climate School, Columbia University, Level A Hogan, 2910 Broadway, New York, NY 10025, United StatesBackground: Understanding the dynamics of infectious disease spread and predicting clinical outcomes are critical for managing large-scale epidemics and pandemics, such as COVID-19. Effective modeling of disease transmission in interconnected populations helps inform public health responses and interventions across regions. Methods: We developed a novel metapopulation model for simulating respiratory virus transmission in the North America region, specifically for the 96 states, provinces, and territories of Canada, Mexico, and the United States. The model is informed by COVID-19 case data, which are assimilated using the Ensemble Adjustment Kalman filter (EAKF), a Bayesian inference algorithm. Additionally, commuting and mobility data are used to build and adjust the network and movement across locations on a daily basis. Results: This model-inference system provides estimates of transmission dynamics, infection rates, and ascertainment rates for each of the 96 locations from January 2020 to March 2021. The results highlight differences in disease dynamics and ascertainment among the three countries. Conclusions: The metapopulation structure enables rapid simulation at a large scale, and the data assimilation method makes the system responsive to changes in system dynamics. This model can serve as a versatile platform for modeling other infectious diseases across the North American region.http://www.sciencedirect.com/science/article/pii/S1755436525000064COVID-19Metapopulation modelNorth AmericaTransmission dynamicsBayesian inference |
| spellingShingle | Matteo Perini Teresa K. Yamana Marta Galanti Jiyeon Suh Roselyn Kaondera-Shava Jeffrey Shaman Modelling COVID-19 in the North American region with a metapopulation network and Kalman filter Epidemics COVID-19 Metapopulation model North America Transmission dynamics Bayesian inference |
| title | Modelling COVID-19 in the North American region with a metapopulation network and Kalman filter |
| title_full | Modelling COVID-19 in the North American region with a metapopulation network and Kalman filter |
| title_fullStr | Modelling COVID-19 in the North American region with a metapopulation network and Kalman filter |
| title_full_unstemmed | Modelling COVID-19 in the North American region with a metapopulation network and Kalman filter |
| title_short | Modelling COVID-19 in the North American region with a metapopulation network and Kalman filter |
| title_sort | modelling covid 19 in the north american region with a metapopulation network and kalman filter |
| topic | COVID-19 Metapopulation model North America Transmission dynamics Bayesian inference |
| url | http://www.sciencedirect.com/science/article/pii/S1755436525000064 |
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