Back

Masks in a Post COVID-19 World: A Better Alternative to Curtailing Influenza?

Froese, H.; A. Prempeh, A. G.

2021-07-07 epidemiology
10.1101/2021.07.03.21259943 medRxiv
Show abstract

Over the course of the coronavirus pandemic, it has become apparent that non-pharmaceutical interventions such as masks and social distancing are of great help in mitigating the transmission of airborne infectious diseases. Additionally, data from respiratory specimen analysis from the past year show that current mask mandates established for COVID-19 have inadvertently reduced the rates of other respiratory diseases, including influenza. Thus, the question arises as to whether comparatively mild measures should be kept in place after the pandemic to reduce the impact of influenza. In this study, we employed a series of differential equations to simulate past influenza seasons, assuming people wore face masks. This was achieved by introducing a variable to account for the efficacy and prevalence of masks and then analyzing its impact on influenza transmission rate in an SEIR model fit to the actual past seasons. We then compared influenza rates in this hypothetical scenario with the actual rates over the seasons. Our results show that several combinations of mask efficacy and prevalence can significantly reduce the burden of seasonal influenza. Particularly, our simulations suggest that a minority of individuals wearing masks greatly reduce the number of influenza infections. Considering the efficacy rates of masks and the relatively insignificant monetary cost, we highlight that it may be a viable alternative or complement to influenza vaccinations. We conclude with a brief discussion of our results and other practical aspects

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.