Dataset Open Access

# Suitability Map of COVID-19 Virus Spread

Gianpaolo Coro

### Citation Style Language JSON Export

{
"publisher": "Zenodo",
"DOI": "10.5281/zenodo.3725831",
"title": "Suitability Map of COVID-19 Virus Spread",
"issued": {
"date-parts": [
[
2020,
3,
20
]
]
},
"abstract": "<p>This image&nbsp;reports a Maximum Entropy model that&nbsp;estimates <em>suitable </em>locations for COVID-19 spread, i.e. places that could favour the spread of the virus just in terms of environmental parameters.</p>\n\n<p>The model was trained just on locations in <em>Italy </em>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:</p>\n\n<ul>\n\t<li>Average Annual Surface Air Temperature in 2018 (NASA)</li>\n\t<li>Average Annual Precipitation in 2018 (NASA)</li>\n\t<li>CO2 emission (natural+artificial) averaged between January 1979 and&nbsp;December 2013 (Copernicus Atmosphere Monitoring Service)</li>\n\t<li>Elevation (NOAA ETOPO2)</li>\n\t<li>Population per 0.5&deg; cell (NASA Gridded Population of the World)</li>\n</ul>\n\n<p>A higher resolution map, the model file (in ASC format) and all parameters used are also attached.</p>\n\n<p>The model indicates highest correlation with&nbsp;<em>infection rate</em> for CO2 around 0.03 gCm^&minus;2day^&minus;1, for Temperature around 11.8 &deg;C, and for Precipitation around 0.3 kg m^-2&nbsp; s^-1, whereas Elevation and Population density are&nbsp;poorly correlated with <em>infection rate</em>.</p>\n\n<p><strong>One interesting result is that the model indicates, among others, the Hubei region in China as a high-probability location</strong>, <strong>and Iran (around Teheran) as a suited location for virus&#39; spread, but the model was not trained on these regions, i.e. it did not know about the actual spread in these regions.</strong></p>\n\n<p><strong>Evaluation: </strong></p>\n\n<p>A <em>risk score</em> was calculated for&nbsp;each country/region reported by the JHU&nbsp;monitoring system (<a href=\"https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6\">https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6</a>). This score is calculated as&nbsp;the summed normalised probability&nbsp;in the populated locations divided by their total surface. This score represents how much the zone would potentially foster&nbsp;the virus&#39; spread.</p>\n\n<p>We assessed the reliability of this score, by selecting the country/regions that reported the <em>highest rates of infection</em>. These zones were selected&nbsp;as those with a rate higher than the upper confidence of a log-normal distribution of the rates.</p>\n\n<p>The agreement between the two maps (<a href=\"https://zenodo.org/api/files/23b09ea2-e5eb-415d-9f6c-fd3b5abfe6c9/covid_high_rate_vs_high_risk_24_03_2020.png\">covid_high_rate_vs_high_risk.png</a>, where violet dots indicate <em>high infection rates </em>and countries&#39; colours indicate estimated <em>high risk score</em>) is the following:</p>\n\n<p><strong>Accuracy </strong>(overall percentage of correctly predicted high-rate zones):&nbsp;<strong>77.25%</strong><br>\n<strong>Kappa </strong>(agreement between the two maps): <strong>0.46</strong> (Good, according to Fleiss&#39; intepretation of the score)&nbsp;</p>\n\n<p><strong>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&#39;s spread should be further investigated.</strong></p>\n\n<p>&nbsp;</p>",
"author": [
{
"family": "Gianpaolo Coro"
}
],
"note": "This experiment was done using the DataMiner cloud computing system of the D4Science e-Infrastructure and the BiodiversityLab Virtual Reseach Environment https://services.d4science.org/group/biodiversitylab/",
"version": "5",
"type": "dataset",
"id": "3725831"
}
1,088
330
views