3.09.2021

A recent mathematical model has suggested that staying at home did not play a dominant role in reducing COVID-19 transmission.

https://www.nature.com/articles/s41598-021-84092-1

Stay-at-home policy is a case of exception fallacy: an internet-based ecological study

Abstract

A recent mathematical model has suggested that staying at home did not play a dominant role in reducing COVID-19 transmission. 

A sophisticated mathematical model based on a high-dimensional system of partial differential equations to represent disease spread has been proposed42. According to this model, staying at home did not play a dominant role in disease transmission, but the combination of these, together with the use of face masks, hand washing, early-case detection (PCR test), and the use of hand sanitizers for at least 50 days could have reduced the number of new cases. 

Finally, after 2 months, the simulations that drove the world to lockdown have been questioned43. These studies applied relatively complex epidemiological models with unrealistic assumptions or parameters that were either user-chosen or not deemed to work properly. Furthermore, the effects in the death rates were directly inferred from the aftermath of a given intervention without a control group. Finally, the temporal delay between the introduction of a certain intervention and the actual measurable variation in death rates was not properly taken into account44,45.

 

The second wave of cases in Europe, in regions that were considered as COVID-19 controlled, may raise some concerns. Our objective was to assess the association between staying at home (%) and the reduction/increase in the number of deaths due to COVID-19 in several regions in the world. In this ecological study, data from www.google.com/covid19/mobility/, ourworldindata.org and covid.saude.gov.br were combined. Countries with > 100 deaths and with a Healthcare Access and Quality Index of ≥ 67 were included. Data were preprocessed and analyzed using the difference between number of deaths/million between 2 regions and the difference between the percentage of staying at home. The analysis was performed using linear regression with special attention to residual analysis. After preprocessing the data, 87 regions around the world were included, yielding 3741 pairwise comparisons for linear regression analysis. Only 63 (1.6%) comparisons were significant. With our results, we were not able to explain if COVID-19 mortality is reduced by staying at home in ~ 98% of the comparisons after epidemiological weeks 9 to 34.