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Artificial Neural Network Modeling of Novel Coronavirus (COVID-19) Incidence Rates across the Continental United States

Author

Listed:
  • Abolfazl Mollalo

    (Department of Public Health and Prevention Sciences, School of Health Sciences, Baldwin Wallace University, Berea, OH 44017, USA)

  • Kiara M. Rivera

    (Department of Public Health and Prevention Sciences, School of Health Sciences, Baldwin Wallace University, Berea, OH 44017, USA)

  • Behzad Vahedi

    (Department of Geography, University of California Santa Barbara (UCSB), Santa Barbara, CA 93106, USA)

Abstract

Prediction of the COVID-19 incidence rate is a matter of global importance, particularly in the United States. As of 4 June 2020, more than 1.8 million confirmed cases and over 108 thousand deaths have been reported in this country. Few studies have examined nationwide modeling of COVID-19 incidence in the United States particularly using machine-learning algorithms. Thus, we collected and prepared a database of 57 candidate explanatory variables to examine the performance of multilayer perceptron (MLP) neural network in predicting the cumulative COVID-19 incidence rates across the continental United States. Our results indicated that a single-hidden-layer MLP could explain almost 65% of the correlation with ground truth for the holdout samples. Sensitivity analysis conducted on this model showed that the age-adjusted mortality rates of ischemic heart disease, pancreatic cancer, and leukemia, together with two socioeconomic and environmental factors (median household income and total precipitation), are among the most substantial factors for predicting COVID-19 incidence rates. Moreover, results of the logistic regression model indicated that these variables could explain the presence/absence of the hotspots of disease incidence that were identified by Getis-Ord Gi* ( p < 0.05) in a geographic information system environment. The findings may provide useful insights for public health decision makers regarding the influence of potential risk factors associated with the COVID-19 incidence at the county level.

Suggested Citation

  • Abolfazl Mollalo & Kiara M. Rivera & Behzad Vahedi, 2020. "Artificial Neural Network Modeling of Novel Coronavirus (COVID-19) Incidence Rates across the Continental United States," IJERPH, MDPI, vol. 17(12), pages 1-13, June.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:12:p:4204-:d:370702
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    References listed on IDEAS

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    1. Kursa, Miron B. & Rudnicki, Witold R., 2010. "Feature Selection with the Boruta Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i11).
    2. Abolfazl Mollalo & Liang Mao & Parisa Rashidi & Gregory E. Glass, 2019. "A GIS-Based Artificial Neural Network Model for Spatial Distribution of Tuberculosis across the Continental United States," IJERPH, MDPI, vol. 16(1), pages 1-17, January.
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    Cited by:

    1. Xin Jing & Jin Seo Cho, 2023. "Forecasting the Confirmed COVID-19 Cases Using Modal Regression," Working papers 2023rwp-217, Yonsei University, Yonsei Economics Research Institute.
    2. Jelena Musulin & Sandi Baressi Šegota & Daniel Štifanić & Ivan Lorencin & Nikola Anđelić & Tijana Šušteršič & Anđela Blagojević & Nenad Filipović & Tomislav Ćabov & Elitza Markova-Car, 2021. "Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review," IJERPH, MDPI, vol. 18(8), pages 1-39, April.
    3. Lorenzo Gianquintieri & Maria Antonia Brovelli & Andrea Pagliosa & Gabriele Dassi & Piero Maria Brambilla & Rodolfo Bonora & Giuseppe Maria Sechi & Enrico Gianluca Caiani, 2022. "Generating High-Granularity COVID-19 Territorial Early Alerts Using Emergency Medical Services and Machine Learning," IJERPH, MDPI, vol. 19(15), pages 1-19, July.
    4. Sandi Baressi Šegota & Ivan Lorencin & Nikola Anđelić & Jelena Musulin & Daniel Štifanić & Matko Glučina & Saša Vlahinić & Zlatan Car, 2022. "Applying Regressive Machine Learning Techniques in Determination of COVID-19 Vaccinated Patients’ Influence on the Number of Confirmed and Deceased Patients," Mathematics, MDPI, vol. 10(16), pages 1-24, August.
    5. Munazza Fatima & Kara J. O’Keefe & Wenjia Wei & Sana Arshad & Oliver Gruebner, 2021. "Geospatial Analysis of COVID-19: A Scoping Review," IJERPH, MDPI, vol. 18(5), pages 1-14, February.
    6. Seung-Hun Lee & Hyeon-Seong Ju & Sang-Hun Lee & Sung-Woo Kim & Hun-Young Park & Seung-Wan Kang & Young-Eun Song & Kiwon Lim & Hoeryong Jung, 2021. "Estimation of Health-Related Physical Fitness (HRPF) Levels of the General Public Using Artificial Neural Network with the National Fitness Award (NFA) Datasets," IJERPH, MDPI, vol. 18(19), pages 1-13, October.
    7. Abolfazl Mollalo & Moosa Tatar, 2021. "Spatial Modeling of COVID-19 Vaccine Hesitancy in the United States," IJERPH, MDPI, vol. 18(18), pages 1-14, September.
    8. Abiodun O. Oluyomi & Sarah M. Gunter & Lauren M. Leining & Kristy O. Murray & Chris Amos, 2021. "COVID-19 Community Incidence and Associated Neighborhood-Level Characteristics in Houston, Texas, USA," IJERPH, MDPI, vol. 18(4), pages 1-15, February.
    9. Anil Babu Payedimarri & Diego Concina & Luigi Portinale & Massimo Canonico & Deborah Seys & Kris Vanhaecht & Massimiliano Panella, 2021. "Prediction Models for Public Health Containment Measures on COVID-19 Using Artificial Intelligence and Machine Learning: A Systematic Review," IJERPH, MDPI, vol. 18(9), pages 1-11, April.
    10. Xin Chen & Liangwen Xu & Zhigeng Pan, 2022. "Design and Preliminary Realization of a Screening and Early Warning Health Management System for Populations at High Risk for Depression," IJERPH, MDPI, vol. 19(6), pages 1-12, March.
    11. Abdallah S. A. Yaseen, 2022. "Impact of social determinants on COVID-19 infections: a comprehensive study from Saudi Arabia governorates," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-9, December.

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