Your browser doesn't support javascript.
TW-SIR: time-window based SIR for COVID-19 forecasts.
Liao, Zhifang; Lan, Peng; Liao, Zhining; Zhang, Yan; Liu, Shengzong.
  • Liao Z; School of Computer Science and Engineering, Central South University, Changsha, 410075, China.
  • Lan P; School of Computer Science and Engineering, Central South University, Changsha, 410075, China.
  • Liao Z; Nuffield Health Research Group, Nuffield Health, Ashley Avenue, Epsom, Surrey, KT18 5AL, UK. zhining.liao@nuffieldhealth.com.
  • Zhang Y; Department of Computing, School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow, G4 OBA, UK.
  • Liu S; Department of Information Management, Hunan University of Finance and Economics, Changsha, 410075, China. lshz179@163.com.
Sci Rep ; 10(1): 22454, 2020 12 31.
Article in English | MEDLINE | ID: covidwho-1003317
ABSTRACT
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%.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: Models, Statistical / Epidemiological Monitoring / Forecasting / COVID-19 Type of study: Prognostic study / Risk factors / Screening study Limits: Humans Country/Region as subject: South America / Asia / Brazil / Europa Language: English Journal: Sci Rep Year: 2020 Document Type: Article Affiliation country: S41598-020-80007-8

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Main subject: Models, Statistical / Epidemiological Monitoring / Forecasting / COVID-19 Type of study: Prognostic study / Risk factors / Screening study Limits: Humans Country/Region as subject: South America / Asia / Brazil / Europa Language: English Journal: Sci Rep Year: 2020 Document Type: Article Affiliation country: S41598-020-80007-8