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Reply to Zhang et al.: Linear regression does not encapsulate the effect of non-pharmaceutical interventions on the number of COVID-19 cases

Year: 2020

Type: letter

Abstract: Zhang et al. (2020) used linear regression to quantify the effect of lockdowns on the number of cases of COVID-19. We show using differential equations from the susceptible-exposed-infected-recovered (SEIR) model and with an example from another location not previously considered that the Zhang et al. analysis should not be considered sound evidence that mask mandates are sufficient to control or the primary factor controlling the spread of COVID-19.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2007.00268

Authors: Pendergrass, Angeline G., Ebi, Kristie L., Hahn, Micah B.

Institutions: NSF National Center for Atmospheric Research, ETH Zurich, NSF NCAR Climate and Global Dynamics Laboratory, Institute for Atmospheric and Climate Science, University of Washington, University of Alaska Anchorage

Language: English

Topic: COVID-19 epidemiological studies

Subfield: Modeling and Simulation

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W4394645736

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