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Simulating and Forecasting the COVID-19 Spread in a U.S. Metropolitan Region with a Spatial SEIR Model

Author

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  • Faizeh Hatami

    (Department of Geography and Earth Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA)

  • Shi Chen

    (Department of Public Health Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
    School of Data Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA)

  • Rajib Paul

    (Department of Public Health Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
    School of Data Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA)

  • Jean-Claude Thill

    (Department of Geography and Earth Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
    School of Data Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA)

Abstract

The global COVID-19 pandemic has taken a heavy toll on health, social, and economic costs since the end of 2019. Predicting the spread of a pandemic is essential to developing effective intervention policies. Since the beginning of this pandemic, many models have been developed to predict its pathways. However, the majority of these models assume homogeneous dynamics over the geographic space, while the pandemic exhibits substantial spatial heterogeneity. In addition, spatial interaction among territorial entities and variations in their magnitude impact the pandemic dynamics. In this study, we used a spatial extension of the SEIR-type epidemiological model to simulate and predict the 4-week number of COVID-19 cases in the Charlotte–Concord–Gastonia Metropolitan Statistical Area (MSA), USA. We incorporated a variety of covariates, including mobility, pharmaceutical, and non-pharmaceutical interventions, demographics, and weather data to improve the model’s predictive performance. We predicted the number of COVID-19 cases for up to four weeks in the 10 counties of the studied MSA simultaneously over the time period 29 March 2020 to 13 March 2021, and compared the results with the reported number of cases using the root-mean-squared error (RMSE) metric. Our results highlight the importance of spatial heterogeneity and spatial interactions among locations in COVID-19 pandemic modeling.

Suggested Citation

  • Faizeh Hatami & Shi Chen & Rajib Paul & Jean-Claude Thill, 2022. "Simulating and Forecasting the COVID-19 Spread in a U.S. Metropolitan Region with a Spatial SEIR Model," IJERPH, MDPI, vol. 19(23), pages 1-16, November.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:23:p:15771-:d:985483
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    References listed on IDEAS

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    1. Laura F. Boehm Vock & Brian J. Reich & Montserrat Fuentes & Francesca Dominici, 2015. "Spatial variable selection methods for investigating acute health effects of fine particulate matter components," Biometrics, The International Biometric Society, vol. 71(1), pages 167-177, March.
    2. Zeroual, Abdelhafid & Harrou, Fouzi & Dairi, Abdelkader & Sun, Ying, 2020. "Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    3. Smith, Michael & Fahrmeir, Ludwig, 2007. "Spatial Bayesian Variable Selection With Application to Functional Magnetic Resonance Imaging," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 417-431, June.
    4. Grant D. Brown & Jacob J. Oleson & Aaron T. Porter, 2016. "An empirically adjusted approach to reproductive number estimation for stochastic compartmental models: A case study of two Ebola outbreaks," Biometrics, The International Biometric Society, vol. 72(2), pages 335-343, June.
    5. Lalmuanawma, Samuel & Hussain, Jamal & Chhakchhuak, Lalrinfela, 2020. "Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    6. Chiang, Wen-Hao & Liu, Xueying & Mohler, George, 2022. "Hawkes process modeling of COVID-19 with mobility leading indicators and spatial covariates," International Journal of Forecasting, Elsevier, vol. 38(2), pages 505-520.
    7. Elizabeth C. Delmelle & Yuhong Zhou & Jean-Claude Thill, 2014. "Densification without Growth Management? Evidence from Local Land Development and Housing Trends in Charlotte, North Carolina, USA," Sustainability, MDPI, vol. 6(6), pages 1-16, June.
    8. Md. Mokhlesur Rahman & Jean-Claude Thill, 2022. "Associations between COVID-19 Pandemic, Lockdown Measures and Human Mobility: Longitudinal Evidence from 86 Countries," IJERPH, MDPI, vol. 19(12), pages 1-31, June.
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    Cited by:

    1. Thiago Christiano Silva & Leandro Anghinoni & Cassia Pereira das Chagas & Liang Zhao & Benjamin Miranda Tabak, 2023. "Analysis of the Effectiveness of Public Health Measures on COVID-19 Transmission," IJERPH, MDPI, vol. 20(18), pages 1-19, September.
    2. Mingdong Lyu & Kuofu Liu & Randolph W. Hall, 2024. "Spatial Interaction Analysis of Infectious Disease Import and Export between Regions," IJERPH, MDPI, vol. 21(5), pages 1-19, May.
    3. Yaming Zhang & Jiaqi Zhang & Yaya Hamadou Koura & Changyuan Feng & Yanyuan Su & Wenjie Song & Linghao Kong, 2023. "Multiple Concurrent Causal Relationships and Multiple Governance Pathways for Non-Pharmaceutical Intervention Policies in Pandemics: A Fuzzy Set Qualitative Comparative Analysis Based on 102 Countries," IJERPH, MDPI, vol. 20(2), pages 1-16, January.
    4. Lei Zhang & Guang-Hui She & Yu-Rong She & Rong Li & Zhen-Su She, 2022. "Quantifying Social Interventions for Combating COVID-19 via a Symmetry-Based Model," IJERPH, MDPI, vol. 20(1), pages 1-15, December.

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