On the inadequacy of species distribution models for modelling the spread of SARS-CoV-2: response to Araújo and Naimi

Author:

Chipperfield Joseph Daniel,Benito Blas M.,O'Hara RobertORCID,Telford Richard James,Carlson Colin J.

Abstract

The ongoing pandemic of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is causing significant damage to public health and economic livelihoods, and is putting significant strains on healthcare services globally. This unfolding emergency has prompted the preparation and dissemination of the article “Spread of SARS-CoV-2 Coronavirus likely to be constrained by climate” by Araújo and Naimi (2020). The authors present the results of an ensemble forecast made from a suite of species distribution models (SDMs), where they attempt to predict the suitability of the climate for the spread of SARS-CoV-2 over the coming months. They argue that climate is likely to be a primary regulator for the spread of the infection and that people in warm-temperate and cold climates are more vulnerable than those in tropical and arid climates. A central finding of their study is that the possibility of a synchronous global pandemic of SARS-CoV-2 is unlikely. Whilst we understand that the motivations behind producing such work are grounded in trying to be helpful, we demonstrate here that there are clear conceptual and methodological deficiencies with their study that render their results and conclusions invalid.What follows is a response to the Araújo and Naimi article centered around three main criticisms:1) Given the fact that SARS-CoV-2 has a primary infection pathway of direct contact, it is in an active spreading phase, and remains largely underreported in the Global South, it represents an inappropriate system for analysis using the SDM framework.2) Even if we were to accept that an SDM framework would be applicable here, the methodology presented in the article strays far from best-practice guidelines for the application of SDMs.3) The dissemination strategy of the authors failed to respect the frameworks of risks adhered to in other academic disciplines pertaining to public health, resulting in erroneous but well-publicised claims with broad policy implications before any scientific oversight could be applied.

Publisher

Center for Open Science

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