A data-driven model to describe and forecast the dynamics of COVID-19 transmission

PLoS One. 2020 Jul 31;15(7):e0236386. doi: 10.1371/journal.pone.0236386. eCollection 2020.


This paper proposes a dynamic model to describe and forecast the dynamics of the coronavirus disease COVID-19 transmission. The model is based on an approach previously used to describe the Middle East Respiratory Syndrome (MERS) epidemic. This methodology is used to describe the COVID-19 dynamics in six countries where the pandemic is widely spread, namely China, Italy, Spain, France, Germany, and the USA. For this purpose, data from the European Centre for Disease Prevention and Control (ECDC) are adopted. It is shown how the model can be used to forecast new infection cases and new deceased and how the uncertainties associated to this prediction can be quantified. This approach has the advantage of being relatively simple, grouping in few mathematical parameters the many conditions which affect the spreading of the disease. On the other hand, it requires previous data from the disease transmission in the country, being better suited for regions where the epidemic is not at a very early stage. With the estimated parameters at hand, one can use the model to predict the evolution of the disease, which in turn enables authorities to plan their actions. Moreover, one key advantage is the straightforward interpretation of these parameters and their influence over the evolution of the disease, which enables altering some of them, so that one can evaluate the effect of public policy, such as social distancing. The results presented for the selected countries confirm the accuracy to perform predictions.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Asymptomatic Diseases
  • Betacoronavirus*
  • COVID-19
  • China / epidemiology
  • Community-Acquired Infections
  • Coronavirus Infections / epidemiology*
  • Coronavirus Infections / mortality
  • Coronavirus Infections / transmission*
  • Coronavirus Infections / virology
  • Cross Infection
  • Data Accuracy
  • Europe / epidemiology
  • Forecasting / methods*
  • Hospitalization
  • Humans
  • Models, Theoretical*
  • Pandemics
  • Pneumonia, Viral / epidemiology*
  • Pneumonia, Viral / mortality
  • Pneumonia, Viral / transmission*
  • Pneumonia, Viral / virology
  • SARS-CoV-2
  • United States / epidemiology

Grants and funding

Dr Rubens Junqueira Magalhães Afonso received a postdoctoral fellowship (#88881.145490/2017-01) from a joint program between the Brazilian governmental agency CAPES and the Alexander Von Humboldt foundation, from the Federal Ministry for Education and Research of Germany. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. The other authors received no specific funding for this work. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.