Quantifying the effects of quarantine using an IBM SEIR model on scalefree networks

TL;DR

This study uses an individual-based IBM SEIR model on scale-free networks to analyze how quarantine duration, timing, and intensity influence COVID-19 infection peaks, highlighting stochastic effects and the effectiveness of various quarantine strategies.

Contribution

It introduces a stochastic IBM SEIR model on scale-free networks to evaluate quarantine effects, emphasizing the importance of probability analysis for different scenarios.

Findings

Short, intense quarantines can effectively flatten the curve.

Long, low-intensity quarantines delay peaks and reduce size with over 50% probability.

Stochastic effects cause outcome variability, requiring probability-based assessments.

Abstract

The COVID-19 pandemic led several countries to resort to social distancing, the only known way to slow down the spread of the virus and keep the health system under control. Here we use an individual based model (IBM) to study how the duration, start date and intensity of quarantine affect the height and position of the peak of the infection curve. We show that stochastic effects, inherent to the model dynamics, lead to variable outcomes for the same set of parameters, making it crucial to compute the probability of each result. To simplify the analysis we divide the outcomes in only two categories, that we call {best and worst scenarios. Although long and intense quarantine is the best way to end the epidemic, it is very hard to implement in practice. Here we show that relatively short and intense quarantine periods can also be very effective in flattening the infection curve and even killing the virus, but the likelihood of such outcomes are low. Long quarantines of relatively low intensity, on the other hand, can delay the infection peak and reduce its size considerably with more than 50% probability, being a more effective policy than complete lockdown for short periods.