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Suitability Map of COVID-19 Virus Spread

Gianpaolo Coro


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        <foaf:name>Gianpaolo Coro</foaf:name>
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            <foaf:name>ISTI-CNR</foaf:name>
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    <dct:title>Suitability Map of COVID-19 Virus Spread</dct:title>
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    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2020</dct:issued>
    <dcat:keyword>COVID-19</dcat:keyword>
    <dcat:keyword>Coronavirus</dcat:keyword>
    <dcat:keyword>Maximum Entropy</dcat:keyword>
    <dcat:keyword>Temperature</dcat:keyword>
    <dcat:keyword>Precipitation</dcat:keyword>
    <dcat:keyword>Carbon Dioxide</dcat:keyword>
    <dcat:keyword>CO2</dcat:keyword>
    <dcat:keyword>Corona virus</dcat:keyword>
    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2020-03-20</dct:issued>
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    <dct:description>&lt;p&gt;This image&amp;nbsp;reports a Maximum Entropy model that&amp;nbsp;estimates &lt;em&gt;suitable &lt;/em&gt;locations for COVID-19 spread, i.e. places that could favour the spread of the virus just in terms of environmental parameters.&lt;/p&gt; &lt;p&gt;The model was trained just on locations in &lt;em&gt;Italy &lt;/em&gt;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:&lt;/p&gt; &lt;ul&gt; &lt;li&gt;Average Annual Surface Air Temperature in 2018 (NASA)&lt;/li&gt; &lt;li&gt;Average Annual Precipitation in 2018 (NASA)&lt;/li&gt; &lt;li&gt;CO2 emission (natural+artificial) averaged between January 1979 and&amp;nbsp;December 2013 (Copernicus Atmosphere Monitoring Service)&lt;/li&gt; &lt;li&gt;Elevation (NOAA ETOPO2)&lt;/li&gt; &lt;li&gt;Population per 0.5&amp;deg; cell (NASA Gridded Population of the World)&lt;/li&gt; &lt;/ul&gt; &lt;p&gt;A higher resolution map, the model file (in ASC format) and all parameters used are also attached.&lt;/p&gt; &lt;p&gt;The model indicates highest correlation with&amp;nbsp;&lt;em&gt;infection rate&lt;/em&gt; for CO2 around 0.03 gCm^&amp;minus;2day^&amp;minus;1, for Temperature around 11.8 &amp;deg;C, and for Precipitation around 0.3 kg m^-2&amp;nbsp; s^-1, whereas Elevation and Population density are&amp;nbsp;poorly correlated with &lt;em&gt;infection rate&lt;/em&gt;.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;One interesting result is that the model indicates, among others, the Hubei region in China as a high-probability location&lt;/strong&gt;, &lt;strong&gt;and Iran (around Teheran) as a suited location for virus&amp;#39; spread, but the model was not trained on these regions, i.e. it did not know about the actual spread in these regions.&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Evaluation: &lt;/strong&gt;&lt;/p&gt; &lt;p&gt;A &lt;em&gt;risk score&lt;/em&gt; was calculated for&amp;nbsp;each country/region reported by the JHU&amp;nbsp;monitoring system (&lt;a href="https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6"&gt;https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6&lt;/a&gt;). This score is calculated as&amp;nbsp;the summed normalised probability&amp;nbsp;in the populated locations divided by their total surface. This score represents how much the zone would potentially foster&amp;nbsp;the virus&amp;#39; spread.&lt;/p&gt; &lt;p&gt;We assessed the reliability of this score, by selecting the country/regions that reported the &lt;em&gt;highest rates of infection&lt;/em&gt;. These zones were selected&amp;nbsp;as those with a rate higher than the upper confidence of a log-normal distribution of the rates.&lt;/p&gt; &lt;p&gt;The agreement between the two maps (&lt;a href="https://zenodo.org/api/files/23b09ea2-e5eb-415d-9f6c-fd3b5abfe6c9/covid_high_rate_vs_high_risk_24_03_2020.png"&gt;covid_high_rate_vs_high_risk.png&lt;/a&gt;, where violet dots indicate &lt;em&gt;high infection rates &lt;/em&gt;and countries&amp;#39; colours indicate estimated &lt;em&gt;high risk score&lt;/em&gt;) is the following:&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Accuracy &lt;/strong&gt;(overall percentage of correctly predicted high-rate zones):&amp;nbsp;&lt;strong&gt;77.25%&lt;/strong&gt;&lt;br&gt; &lt;strong&gt;Kappa &lt;/strong&gt;(agreement between the two maps): &lt;strong&gt;0.46&lt;/strong&gt; (Good, according to Fleiss&amp;#39; intepretation of the score)&amp;nbsp;&lt;/p&gt; &lt;p&gt;&lt;strong&gt;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&amp;#39;s spread should be further investigated.&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;</dct:description>
    <dct:description xml:lang="">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/</dct:description>
    <dct:description xml:lang="">{"references": ["Coro, G., Panichi, G., Scarponi, P., &amp; Pagano, P. (2017). Cloud computing in a distributed e\u2010infrastructure using the web processing service standard. Concurrency and Computation: Practice and Experience, 29(18), e4219."]}</dct:description>
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