TY - JOUR
T1 - TW-SIR: time-window based SIR for COVID-19 forecasts
AU - Liao, Zhifang
AU - Lan, Peng
AU - Liao, Zhining
AU - Zhang, Yan
AU - Liu, Shengzong
N1 - Funding Information:
The works that are described in this paper are supported by NSF 61802120, Hunan Provincial Key Laboratory of Finance & Economics Big Data Science and Technology (Hunan University of Finance and Economics) 2017TP1025 and HNNSF 2019JJ50018, The scientific research project of Hunan Provincial Education Department No.: 18B480.
PY - 2020/12/31
Y1 - 2020/12/31
N2 - Since the outbreak of COVID-19, many COVID-19 research studies have proposed different models for predicting the trend of COVID-19. Among them, the prediction model based on mathematical epidemiology (SIR) is the most widely used, but most of these models are adapted in special situations based on various assumptions. In this study, a general adapted time-window based SIR prediction model is proposed, which is characterized by introducing a time window mechanism for dynamic data analysis and using machine learning method predicts the basic reproduction number and the exponential growth rate of the epidemic. We analyzed COVID-19 data from February to July 2020 in seven countries–––China, South Korea, Italy, Spain, Brazil, Germany and France, and the numerical results showed that the framework can effectively measure the real-time changes of the parameters during the epidemic, and error rate of predicting the number of COVID-19 infections in a single day is within 5%.
AB - Since the outbreak of COVID-19, many COVID-19 research studies have proposed different models for predicting the trend of COVID-19. Among them, the prediction model based on mathematical epidemiology (SIR) is the most widely used, but most of these models are adapted in special situations based on various assumptions. In this study, a general adapted time-window based SIR prediction model is proposed, which is characterized by introducing a time window mechanism for dynamic data analysis and using machine learning method predicts the basic reproduction number and the exponential growth rate of the epidemic. We analyzed COVID-19 data from February to July 2020 in seven countries–––China, South Korea, Italy, Spain, Brazil, Germany and France, and the numerical results showed that the framework can effectively measure the real-time changes of the parameters during the epidemic, and error rate of predicting the number of COVID-19 infections in a single day is within 5%.
U2 - 10.1038/s41598-020-80007-8
DO - 10.1038/s41598-020-80007-8
M3 - Article
C2 - 33384444
AN - SCOPUS:85098494249
SN - 2045-2322
VL - 10
JO - Scientific Reports
JF - Scientific Reports
IS - 1
M1 - 22454
ER -