Is climate a curse or a bless in the Covid-19 virus fighting?

This article has been Reviewed by the following groups

Read the full article See related articles

Abstract

Faced with the global pandemic of Covid-19, we need to better understand the links between meteorological factors and the virus and investigate the existence of potential seasonal patterns. In the vein of a recent empirical literature, we reassess the impact of weather factors on Covid-19 daily cases in a panel of advanced and emerging countries between January the first and 28th May 2020. We consider 5 different meteorological factors and go further previous studies. In addition, we give a short-run and medium/long-run time perspective of the dramatic outcomes of the pandemic by both considering infected people (short-run) and fatalities (long-run). Our results reveal that the choice of delays and time perspective of the effects of climatic factors on the virus are crucial as well as Covid-19 outcomes can explain the discrepancies in the previous literature. For the first time, we use a dynamic panel model and consider two different kinds of channels between climate and Covid-19 virus: 1) direct/physical factors related to the survivals and durability dynamics of the virus in surfaces and outdoors and 2) an indirect factor through human behaviors and individual mobility – walking or driving outdoors – to capture the impact of climate on social distancing and thus on Covid-19 outcomes. Our model is estimated via two different estimators and persistence, delays in patterns, nonlinearities and numerous specifications changes are taken into account with many robustness checks. Our work highlights that temperatures and, more interestingly, solar radiation – that has been clearly undervalued in previous studies – are significant climatic drivers on Covid-19 outbreak. Indirect effects through human behaviors i.e interrelationships between climatic variables and people mobility are significantly positive and should be considered to correctly assess the effects of climatic factors. Since climate is per se purely exogenous, climate tend to strengthen the effect of mobility on virus spread. The net effect from climate on Covid-19 outbreak will thus result from the direct negative effect of climatic variables and from the indirect effect due to the interaction between mobility and them. Direct negative effects from climatic factors on Covid-19 outcomes – when they are significant – are partly compensated by positive indirect effects through human mobility. Suitable control policies should be implemented to control the mobility and social distancing.

Article activity feed

  1. SciScore for 10.1101/2020.09.04.20182998: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.


    Results from OddPub: Thank you for sharing your code and data.


    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.


    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.


    Results from JetFighter: We did not find any issues relating to colormaps.


    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.