Selected article for: "control prevention and propose model"
Author: Bei, Chengcheng; Liu, Shiping; Liao, Yin; Tian, Gaoliang; Tian, Zichen
Title: Predicting new cases of COVID‐19 and the application to population sustainability analysis
  • Cord-id: x3h48pal
  • Document date: 2021_4_1
    • ID: x3h48pal
    Snippet: We propose a new spatio‐temporal point process model to predict infectious cases of COVID‐19. We illustrate its practical use with data from six key cities in China, and we analyse the effects of natural and social factors on the occurrence and spread of COVID‐19. We show that large‐scale testing and strict containment are key factors for the successful suppression of the COVID‐19 contagion. This study provides an effective tool to develop early warning systems for major infectious dis
    Document: We propose a new spatio‐temporal point process model to predict infectious cases of COVID‐19. We illustrate its practical use with data from six key cities in China, and we analyse the effects of natural and social factors on the occurrence and spread of COVID‐19. We show that large‐scale testing and strict containment are key factors for the successful suppression of the COVID‐19 contagion. This study provides an effective tool to develop early warning systems for major infectious diseases, offering insights on how to develop prevention and control strategies to reduce the impact of disease and maintain population sustainability.

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