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URL: https://dx.doi.org/DOI:10.17504/protocols.io.bd88i9zw

Authors: Gianpaolo Coro

Group: Coronavirus Method Development Community

Summary: This image reports a Maximum Entropy model that estimates suitable locations for COVID-19 spread, i.e. places that could favour the spread of the virus just in terms of environmental parameters.The model was trained just on locations in Italy that have reported a rate of new infections higher than the geometric mean of all Italian infection rates. The following environmental parameters were used, which are correlated to those used by other studies:Average Annual Surface Air Temperature in 2018 (NASA)Average Annual Precipitation in 2018 (NASA)CO2 emission (natural+artificial) averaged between January 1979 and December 2013 (Copernicus Atmosphere Monitoring Service)Elevation (NOAA ETOPO2)Population per 0.5° cell (NASA Gridded Population of the World)The model file (in ASC format) and all parameters used are attached.A higher resolution map and also the model file (in ASC format) and all parameters are available at the external link (Zenodo).The model indicates highest correlation with infection rate for CO2 around 0.03 gCm^−2day^−1, for Temperature around 11.8 °C, and for Precipitation around 0.3 kg m^-2  s^-1, whereas Elevation and Population density are poorly correlated with infection rate.One interesting result is that the model indicates, among others, the Hubei region in China as a high-probability location, and Iran (around Teheran) as a suited location for virus' spread, but the model was not trained on these regions, i.e. it did not know about the actual spread in these regions.Evaluation:A risk score was calculated for each country/region reported by the JHO monitoring system (https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html). This score is calculated as the summed normalised probability in the populated locations divided by their total surface. This score represents how much the zone would potentially foster the virus' spread.We assessed the reliability of this score, by selecting the country/regions that reported thehighest rates of infection. These zones were selected as those with a rate higher than the upper confidence of a log-normal distribution of the rates.The agreement between the two maps (covid_high_rate_vs_high_risk.png, where violet dots indicatehigh infection ratesand countries' colours indicate estimatedhigh risk score) is the following:Accuracy(overall percentage of correctly predicted high-rate zones): 77.25%Kappa(agreement between the two maps):0.46(Good, according to Fleiss' intepretation of the score) This assessment demonstrates that our map can be used to estimate the risk of a certain country to have a high rate of infection, and indicates that the influence of environmental parameters on virus's spread should be further investigated.FilesName (Size) (5.7 MB) md5:dea4e66a1c66d0dfc3b0872adfaa020f (47.3 MB) md5:069727a6c5656d276c475606c9b96d47 (1.9 MB) md5:ca91c4d56654b77bf572eef1a42af7a5 (5.1 MB) md5:0ed217e20ab32aad4ab96e5403670ee4 (2.8 MB) md5:79639fd3540c68450d86fde288edb264 (4.6 MB)md5:57aa6c172b3fc036c08d0560f01436ba (5.5 MB) md5:3ab587ea0e0fbe3fcbd9ea6b7844271a (4.7 MB) md5:7ea930f59e5ff627a18383f02737f78dReferences

Affiliations: CNR

External URL: https://zenodo.org/record/3719184

Version: 4

Publication Date: 2020

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