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

Abstract

Background 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. Objective 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. Methods 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. Results 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. Conclusions 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.

Impacts of the COVID-19 pandemic on animal behaviour and welfare researchers

“…Projects that depend on specific timing, for example seasonal data collection such as trials on newborn animals in spring, may have been suspended for a whole year or cancelled completely. At the time of writing, there is still great uncertainty about the feasibility of planning research projects, as waves of COVID-19 infections, and thus related restrictions, are foreseen ( Kaxiras & Neofotistos, 2020 ). This will affect nearly all researchers in animal behaviour sciences, and especially those who rely heavily on data collection in a limited time frame, such as PhD students.…”

Estimation of parameters for a humidity-dependent compartmental model of the COVID-19 outbreak

“…Another model evaluates regions based on a classification of the population for being susceptible in the first wave, or later waves. Findings of the research are, that geolocalization and traveling of individuals combined with delayed immune reactions can provide the basis of multiple waves Kaxiras & Neofotistos (2020) . Our model can’t handle multi-wave pandemics, indeed.…”

A Serious Game Designed to Promote Safe Behaviors Among Health Care Workers During the COVID-19 Pandemic: Development of “Escape COVID-19”

“…As the COVID-19 death toll continues to rise [1] and many countries fear or are experiencing the emergence of a second wave [2][3][4], avoiding disease transmission is becoming increasingly important. Health care workers (HCWs) and other hospital employees, including medical, nursing, and administrative and support staff, are at high risk of contamination [5][6][7].…”