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

Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1518) JMIR Theme Issue: COVID-19 Special Issue (2453) Infectious Diseases (non-STD/STI) (1576) Outbreak and Pandemic Preparedness and Management (1728) 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/18880, first published .
General Model for COVID-19 Spreading With Consideration of Intercity Migration, Insufficient Testing, and Active Intervention: Modeling Study of Pandemic Progression in Japan and the United States

General Model for COVID-19 Spreading With Consideration of Intercity Migration, Insufficient Testing, and Active Intervention: Modeling Study of Pandemic Progression in Japan and the United States

General Model for COVID-19 Spreading With Consideration of Intercity Migration, Insufficient Testing, and Active Intervention: Modeling Study of Pandemic Progression in Japan and the United States

Authors of this article:

Choujun Zhan1 Author Orcid Image ;   Chi Kong Tse2 Author Orcid Image ;   Zhikang Lai3 Author Orcid Image ;   Xiaoyun Chen3 Author Orcid Image ;   Mingshen Mo3 Author Orcid Image
Article Authors Cited by (9) Tweetations (3) Metrics

Journals

  1. Mbwogge M. Mass Testing With Contact Tracing Compared to Test and Trace for the Effective Suppression of COVID-19 in the United Kingdom: Systematic Review. JMIRx Med 2021;2(2):e27254 View
  2. Kim Y, Koo P. Effectiveness of Testing and Contact-Tracing to Counter COVID-19 Pandemic: Designed Experiments of Agent-Based Simulation. Healthcare 2021;9(6):625 View
  3. Niu R, Chan Y, Wong E, van Wyk M, Chen G. A stochastic SEIHR model for COVID-19 data fluctuations. Nonlinear Dynamics 2021;106(2):1311 View
  4. Zhan C, Tse C, Gao Y, Hao T. Comparative Study of COVID-19 Pandemic Progressions in 175 Regions in Australia, Canada, Italy, Japan, Spain, U.K. and USA Using a Novel Model That Considers Testing Capacity and Deficiency in Confirming Infected Cases. IEEE Journal of Biomedical and Health Informatics 2021;25(8):2836 View
  5. Zhan C, Zheng Y, Zhang H, Wen Q. Random-Forest-Bagging Broad Learning System With Applications for COVID-19 Pandemic. IEEE Internet of Things Journal 2021;8(21):15906 View
  6. Zhan C, Shao L, Zhang X, Yin Z, Gao Y, Tse C, Yang D, Wu D, Zhang H. Estimating unconfirmed COVID-19 infection cases and multiple waves of pandemic progression with consideration of testing capacity and non-pharmaceutical interventions: A dynamic spreading model. Information Sciences 2022;607:418 View
  7. McGregor G, Tippett J, Wan A, Wang M, Wong S. Comparing regional and provincial-wide COVID-19 models with physical distancing in British Columbia. AIMS Mathematics 2022;7(4):6743 View
  8. Zhang Y, Guo X, Su Y, Koura H Y, Wang N, Song W. Changes in spatiotemporal pattern and network characteristics in population migration of China’s cities before and after COVID-19. Humanities and Social Sciences Communications 2023;10(1) View