Volume 2, Issue 1 (2021)
Baolian Cheng*, Yi-Ming Wang
Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States.
- *Corresponding Author:
- Baolian Cheng
Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States.
E-mail: bcheng@lanl.gov
Received date: March 22, 2021; Accepted date: April 19, 2021; Published date: April 26, 2021
Citation: Cheng B, Wang YM. Forecasting the Spreading Trajectory of the COVID-19 Pandemic. Adv Genom Proteom Bioinform. 2021;2(1):1-4.
Copyright: © 2021 Cheng B. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Predictively forecasting future developments for the spread of the COVID-19 pandemic is extremely challenging. A recently published logistic mathematic model has achieved good predictions for infections weeks ahead. In this short communication, we summarize the Logistic spread model, which describes the dynamics of the pandemic evolution and the impacts of people social behavior in fighting against the pandemic. The new pandemic model has two parameters (i.e., transmission rate γ and social distancing d) to be calibrated to the data from the pandemic regions in the early stage of the outbreak while the social distancing is put in place.
The model is capable to make early predictions about the spreading trajectory in the communities of any size (countries, states, counties and cities) including the total infections, the date of peak daily infections and the date of the infections reaching a plateau if the testing is sufficient. The results are in good agreement with data and have important applications for ongoing outbreaks and similar infectious disease pandemics in the future.
Keywords
Epidemiology modeling; COVID-19; Pandemic control; Logistic model; Social distancing.