Journal Article

Multiple Epidemic Wave Model of the COVID-19 Pandemic: Modeling Study

Kaxiras, Efthimios, Neofotistos, Georgios

2020

Overview

Intervention measures have been implemented around the world to mitigate the spread of the coronavirus disease (COVID-19) pandemic. Understanding the dynamics of the disease spread and the effectiveness of the interventions is essential in predicting its future evolution. The aim of this study is to simulate the effect of different social distancing interventions and investigate whether their timing and stringency can lead to multiple waves (subepidemics), which can provide a better fit to the wavy behavior observed in the infected population curve in the majority of countries. We have designed and run agent-based simulations and a multiple wave model to fit the infected population data for many countries. We have also developed a novel Pandemic Response Index to provide a quantitative and objective way of ranking countries according to their COVID-19 response performance. We have analyzed data from 18 countries based on the multiple wave (subepidemics) hypothesis and present the relevant parameters. Multiple waves have been identified and were found to describe the data better. The effectiveness of intervention measures can be inferred by the peak intensities of the waves. Countries imposing fast and stringent interventions exhibit multiple waves with declining peak intensities. This result strongly corroborated with agent-based simulations outcomes. We also provided an estimate of how much lower the number of infections could have been if early and strict intervention measures had been taken to stop the spread at the first wave, as actually happened for a handful of countries. A novel index, the Pandemic Response Index, was constructed, and based on the model's results, an index value was assigned to each country, quantifying in an objective manner the country's response to the pandemic. Our results support the hypothesis that the COVID-19 pandemic can be successfully modeled as a series of epidemic waves (subepidemics) and that it is possible to infer to what extent the imposition of early intervention measures can slow the spread of the disease.

• Publisher: Gunther Eysenbach MD MPH, Associate Professor,JMIR Publications
• Subject: Algorithms / Betacoronavirus / Communicable Disease Control / Computer Simulation / Coronavirus Infections - epidemiology / Coronaviruses / COVID-19 / Data / Disease / Disease transmission / Early intervention / Epidemics / Forecasting / Global Health / Humans / Imposition / Indexes / Infections / Intervention / Objectives / Original Paper / Pandemics / Pneumonia, Viral - epidemiology / Population / Population Dynamics / Public Health Informatics - methods / Quarantine / SARS-CoV-2 / Simulation