This paper is in the following e-collection/theme issue:

Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1517) JMIR Theme Issue: COVID-19 Special Issue (2452) Machine Learning (1465) Infectious Diseases (non-STD/STI) (1575) Outbreak and Pandemic Preparedness and Management (1727) Statistical Methods for Surveillance and Population Health (156)

Published on in Vol 6, No 3 (2020): Jul-Sep

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/19446, first published .
Early Stage Machine Learning–Based Prediction of US County Vulnerability to the COVID-19 Pandemic: Machine Learning Approach

Early Stage Machine Learning–Based Prediction of US County Vulnerability to the COVID-19 Pandemic: Machine Learning Approach

Early Stage Machine Learning–Based Prediction of US County Vulnerability to the COVID-19 Pandemic: Machine Learning Approach

Authors of this article:

Mihir Mehta1 Author Orcid Image ;   Juxihong Julaiti1 Author Orcid Image ;   Paul Griffin2 Author Orcid Image ;   Soundar Kumara1 Author Orcid Image
Article Authors Cited by (28) Tweetations (1) Metrics

Journals

  1. Senthil Prakash P, Hariharan B, Kaliraj S, Siva R, Vivek D. WITHDRAWN: The impact of various policy factors implemented for controlling the spread of COVID-19. Materials Today: Proceedings 2021 View
  2. Shapiro M, Karim F, Muscioni G, Augustine A. Adaptive Susceptible-Infectious-Removed Model for Continuous Estimation of the COVID-19 Infection Rate and Reproduction Number in the United States: Modeling Study. Journal of Medical Internet Research 2021;23(4):e24389 View
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  5. Guo Y, Zhang Y, Lyu T, Prosperi M, Wang F, Xu H, Bian J. The application of artificial intelligence and data integration in COVID-19 studies: a scoping review. Journal of the American Medical Informatics Association 2021;28(9):2050 View
  6. Snider B, McBean E, Yawney J, Gadsden S, Patel B. Identification of Variable Importance for Predictions of Mortality From COVID-19 Using AI Models for Ontario, Canada. Frontiers in Public Health 2021;9 View
  7. Nguyen H, Turk P, McWilliams A. Forecasting COVID-19 Hospital Census: A Multivariate Time-Series Model Based on Local Infection Incidence. JMIR Public Health and Surveillance 2021;7(8):e28195 View
  8. Hernández-Pereira E, Fontenla-Romero O, Bolón-Canedo V, Cancela-Barizo B, Guijarro-Berdiñas B, Alonso-Betanzos A. Machine learning techniques to predict different levels of hospital care of CoVid-19. Applied Intelligence 2022;52(6):6413 View
  9. Li M, Pham A, Kuo T. Predicting COVID-19 county-level case number trend by combining demographic characteristics and social distancing policies. JAMIA Open 2022;5(3) View
  10. Deonarine A, Lyons G, Lakhani C, De Brouwer W. Identifying Communities at Risk for COVID-19–Related Burden Across 500 US Cities and Within New York City: Unsupervised Learning of the Coprevalence of Health Indicators. JMIR Public Health and Surveillance 2021;7(8):e26604 View
  11. Nazirun N, Omar N, Selvaganeson K, Abdul Wahab A. A Review on Machine Learning Approaches in COVID-19 Pandemic Prediction and Forecasting. Malaysian Journal of Medicine and Health Sciences 2022:78 View
  12. Chen Y, Zheng L, Song J, Huang L, Zheng J. Revealing the Impact of Urban Form on COVID-19 Based on Machine Learning: Taking Macau as an Example. Sustainability 2022;14(21):14341 View
  13. Botz J, Wang D, Lambert N, Wagner N, Génin M, Thommes E, Madan S, Coudeville L, Fröhlich H. Modeling approaches for early warning and monitoring of pandemic situations as well as decision support. Frontiers in Public Health 2022;10 View
  14. Grima S, Rupeika-Apoga R, Kizilkaya M, Romānova I, Dalli Gonzi R, Jakovljevic M. A Proactive Approach to Identify the Exposure Risk to COVID-19: Validation of the Pandemic Risk Exposure Measurement (PREM) Model Using Real-World Data. Risk Management and Healthcare Policy 2021;Volume 14:4775 View
  15. Wang X, Washington D, Weber G. Complex systems analysis informs on the spread of COVID-19. Epidemiologic Methods 2021;10(s1) View
  16. Asada K, Komatsu M, Shimoyama R, Takasawa K, Shinkai N, Sakai A, Bolatkan A, Yamada M, Takahashi S, Machino H, Kobayashi K, Kaneko S, Hamamoto R. Application of Artificial Intelligence in COVID-19 Diagnosis and Therapeutics. Journal of Personalized Medicine 2021;11(9):886 View
  17. Liu C, Lu C, Lin L. Pandemic strategies with computational and structural biology against COVID-19: A retrospective. Computational and Structural Biotechnology Journal 2022;20:187 View
  18. Fang Z, Yang S, Lv C, An S, Wu W. Application of a data-driven XGBoost model for the prediction of COVID-19 in the USA: a time-series study. BMJ Open 2022;12(7):e056685 View
  19. Wang B, Zhang H, Zhang X, Wang J, Wang H, Jiang J. Association between urinary concentrations of polycyclic aromatic hydrocarbons and risk of endometriosis in the NHANES 2003–2006. Environmental Science and Pollution Research 2023;30(55):117715 View
  20. Roy S, Ghosh N, Uplavikar N, Ghosh P. Towards a Unified Pandemic Management Architecture: Survey, Challenges, and Future Directions. ACM Computing Surveys 2024;56(2):1 View
  21. Zhang T, Rabhi F, Chen X, Paik H, MacIntyre C. A machine learning-based universal outbreak risk prediction tool. Computers in Biology and Medicine 2024;169:107876 View

Books/Policy Documents

  1. Zehtabian S, Khodadadeh S, Turgut D, Bölöni L. Multimodal AI in Healthcare. View
  2. Hou Z, Fang J, Huang Y, Sun L, Zhan C, Tsang K. International Conference on Neural Computing for Advanced Applications. View
  3. Butt Z. Artificial Intelligence, Big Data, Blockchain and 5G for the Digital Transformation of the Healthcare Industry. View