Your browser doesn't support javascript.
loading
Forecasting the Worldwide Spread of COVID-19 based on Logistic Model and SEIR Model
Xiang Zhou; Xudong Ma; Na Hong; Longxiang Su; Yingying Ma; Jie He; Huizhen Jiang; Chun Liu; Guangliang Shan; Weiguo Zhu; Shuyang Zhang; Yun Long.
Affiliation
  • Xiang Zhou; Peking Union Medical College Hospital
  • Xudong Ma; National Health Commission of the People's Republic of China
  • Na Hong; Digital China Health Technologies Co. Ltd
  • Longxiang Su; Peking Union Medical College Hospital
  • Yingying Ma; Digital China Health Technologies Co. Ltd
  • Jie He; Digital China Health Technologies Co. Ltd
  • Huizhen Jiang; Peking Union Medical College Hospital
  • Chun Liu; Digital China Health Technologies Co. Ltd.
  • Guangliang Shan; Peking Union Medical College
  • Weiguo Zhu; Peking Union Medical College Hospital
  • Shuyang Zhang; Peking union medical college hospital
  • Yun Long; Peking Union Medical College Hospital
Preprint in English | medRxiv | ID: ppmedrxiv-20044289
ABSTRACT
BackgroundWith the outbreak of coronavirus disease 2019 (COVID-19), a sudden case increase in late February 2020 led to deep concern globally. Italy, South Korea, Iran, France, Germany, Spain, the US and Japan are probably the countries with the most severe outbreaks. Collecting epidemiological data and predicting epidemic trends are important for the development and measurement of public intervention strategies. Epidemic prediction results yielded by different mathematical models are inconsistent; therefore, we sought to compare different models and their prediction results to generate objective conclusions. MethodsWe used the number of cases reported from January 23 to March 20, 2020, to estimate the possible spread size and peak time of COVID-19, especially in 8 high-risk countries. The logistic growth model, basic SEIR model and adjusted SEIR model were adopted for prediction. Given that different model inputs may infer different model outputs, we implemented three model predictions with three scenarios of epidemic development. ResultsWhen comparing all 8 countries short-term prediction results and peak predictions, the differences among the models were relatively large. The logistic growth model estimated a smaller epidemic size than the basic SERI model did; however, once we added parameters that considered the effects of public health interventions and control measures, the adjusted SERI model results demonstrated a considerably rapid deceleration of epidemic development. Our results demonstrated that contact rate, quarantine scale, and the initial quarantine time and length are important factors in controlling epidemic size and length. ConclusionsWe demonstrated a comparative assessment of the predictions of the COVID-19 outbreak in eight high-risk countries using multiple methods. By forecasting epidemic size and peak time as well as simulating the effects of public health interventions, the intent of this paper is to help clarify the transmission dynamics of COVID-19 and recommend operation suggestions to slow down the epidemic. It is suggested that the quick detection of cases, sufficient implementation of quarantine and public self-protection behaviors are critical to slow down the epidemic.
License
cc_by_nc_nd
Full text: Available Collection: Preprints Database: medRxiv Type of study: Observational study / Prognostic study Language: English Year: 2020 Document type: Preprint
Full text: Available Collection: Preprints Database: medRxiv Type of study: Observational study / Prognostic study Language: English Year: 2020 Document type: Preprint
...