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State-by-State prediction of likely COVID-19 scenarios in the United States and assessment of the role of testing and control measures
Zheng-Meng Zhai; Yong-Shang Long; Jie Kang; Yi-Lin Li; Lang Zeng; Li-Lei Han; Zhao-Hua Lin; Yin-Qi Zeng; Da-Yu Wu; Ming Tang; Di Xu; Zonghua Liu; Ying-Cheng Lai.
Affiliation
  • Zheng-Meng Zhai; East China Normal University
  • Yong-Shang Long; East China Normal University
  • Jie Kang; East China Normal University
  • Yi-Lin Li; East China Normal University
  • Lang Zeng; East China Normal University
  • Li-Lei Han; East China Normal University
  • Zhao-Hua Lin; East China Normal University
  • Yin-Qi Zeng; East China Normal University
  • Da-Yu Wu; East China Normal University
  • Ming Tang; East China Normal University
  • Di Xu; Fudan University
  • Zonghua Liu; East China Normal University
  • Ying-Cheng Lai; Arizona State University - Tempe Campus
Preprint in English | medRxiv | ID: ppmedrxiv-20078774
ABSTRACT
Due to the heterogeneity among the States in the US, predicting COVID-19 trends and quantitatively assessing the effects of government testing capability and control measures need to be done via a State-by-State approach. We develop a comprehensive model for COVID-19 incorporating time delays and population movements. With key parameter values determined by empirical data, the model enables the most likely epidemic scenarios to be predicted for each State, which are indicative of whether testing services and control measures are vigorous enough to contain the disease. We find that government control measures play a more important role than testing in suppressing the epidemic. The vast disparities in the epidemic trends among the States imply the need for long-term placement of control measures to fully contain COVID-19.
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Full text: Available Collection: Preprints Database: medRxiv Type of study: Prognostic study Language: English Year: 2020 Document type: Preprint
Full text: Available Collection: Preprints Database: medRxiv Type of study: Prognostic study Language: English Year: 2020 Document type: Preprint
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