key: cord-0912594-krfg15o9
authors: Daud, Auni Aslah Mat
title: Comment on “Poorly known aspects of flattening the curve of COVID-19”
date: 2021-02-16
journal: Technol Forecast Soc Change
DOI: 10.1016/j.techfore.2021.120674
sha: 9918fd4f1e26711f589adb0a588e243c89af6fe5
doc_id: 912594
cord_uid: krfg15o9

This short research note describes and summarizes several recent peer-reviewed and non peer-reviewed studies on the concept of flattening-the-curve (FTC) in the context of the COVID-19 pandemic. This note also highlights contradictory findings of these studies in terms of the effect of FTC on the total number of infections (the final epidemic size), and poses a research problem for future studies.

In the fight against COVID-19 pandemic, the phrase "Flattening the curve" (FTC) has become a rallying cry and entered our collective consciousness. The aim of FTC is to slow down the outbreak and win us time to improve the healthcare system. However, there is a common misunderstanding on the effect of FTC on the final epidemic size. It is often claimed that the total number of infections (or the final epidemic size) will remain the same, as exemplified in the following two analogies, which can be found in the news outlet or on the social media.

Your workplace bathroom has only so many stalls. If everyone decides to go at the same time, there are problems. If the same number of people need go to the restroom but spread over several hours, it's all ok 1 .

Think of the health care system capacity as a subway car that can only hold so many people at once. During rush hour, that capacity is not enough to handle the demand, so people must wait on the platform for their turn to ride. Staggering work hours diminishes the rush hour and increases the likelihood that you will get on the train and maybe even get a seat 2 .

In a recent article published in this journal, Debecker and Modis claim that FTC results in an increase in the total number of infections and possibly the total number of deaths too 3 . They established correlations between the parameters of the logistic equation, namely the level of the final ceiling (the niche capacity) and the rate of growth (the slope α).

However, other studies (two peer-reviewed and three non peer-reviewed) claimed that, FTC would actually reduce the final epidemic size to a certain degree 4 . Cooper et al, 5 simulated an augmented classic Susceptible-Infected-Removed (SIR) model and showed that the implementation of intervention measures can reduce the final epidemic size. Höhle used a SIR model to show that the drastic reduction of contacts or isolations will produce a 17% decrease in the total number of infections 6 . Bolker and Dushoff used a SIR model with a doubling time of 6 days and a maximum basic reproduction number of 2.5 to illustrate how a reduction in transmission will produce a 11% decrease in the total number of infections 7 .

Feng et al simulated a Susceptible-Exposed-Infected-Recovered-Dead (SEIRD) model and showed that FTC will decrease the final epidemic size 8 . The authors claimed the total number of infections will only remain the same if the mean interval between being infected and infecting others, or generation time is changed. They found that physical distancing produces up to 31% decrease of the final epidemic size. Churches and Jorm 9 used a stochastic individual contact model and showed that the strong public health actions, such as intensive case-finding and strictly enforcing isolation and quarantine, will result in fewer COVID-19 infections and deaths by around 1/3. The details of these studies are summarized in Table   1 . 

Flattening the Coronavirus curve

Poorly known aspects of flattening the curve of COVID-19

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A SIR model assumption for the spread of COVID-19 in different communities

Flatten the COVID-19 curve

Flattening vs shrinking: the math of #FlattenTheCurve

On the benefits of flattening the curve: A perspective

We can "shrink" the COVID-19 curve, rather than just flatten it