SEIR model for COVID-19 dynamics incorporating the environment and social distancing

BMC Res Notes. 2020 Jul 23;13(1):352. doi: 10.1186/s13104-020-05192-1.

Abstract

Objective: Coronavirus disease 2019 (COVID-19) is a pandemic respiratory illness spreading from person-to-person caused by a novel coronavirus and poses a serious public health risk. The goal of this study was to apply a modified susceptible-exposed-infectious-recovered (SEIR) compartmental mathematical model for prediction of COVID-19 epidemic dynamics incorporating pathogen in the environment and interventions. The next generation matrix approach was used to determine the basic reproduction number [Formula: see text]. The model equations are solved numerically using fourth and fifth order Runge-Kutta methods.

Results: We found an [Formula: see text] of 2.03, implying that the pandemic will persist in the human population in the absence of strong control measures. Results after simulating various scenarios indicate that disregarding social distancing and hygiene measures can have devastating effects on the human population. The model shows that quarantine of contacts and isolation of cases can help halt the spread on novel coronavirus.

Keywords: Basic reproduction number; COVID-19 dynamics; Mathematical model; Runge–Kutta method; SEIR model; Social distancing.

MeSH terms

  • Betacoronavirus*
  • COVID-19
  • Contact Tracing
  • Convalescence
  • Coronavirus Infections / epidemiology
  • Coronavirus Infections / prevention & control
  • Coronavirus Infections / transmission*
  • Disease Susceptibility
  • Environmental Exposure*
  • Forecasting
  • Guideline Adherence*
  • Hand Hygiene
  • Humans
  • Infection Control / methods*
  • Infection Control / statistics & numerical data
  • Masks
  • Models, Theoretical*
  • Pandemics* / prevention & control
  • Patient Compliance
  • Patient Isolation
  • Pneumonia, Viral / epidemiology
  • Pneumonia, Viral / prevention & control
  • Pneumonia, Viral / transmission*
  • Quarantine
  • SARS-CoV-2
  • Time Factors
  • Travel