TITLE: Suitability Map of COVID-19 Virus Spread
AUTHORS: Gianpaolo Coro

[DESCRIPTION]
<div class = "text-blocks"><div style = "text-align :; float : ;"><img style = "" src = "https://s3.amazonaws.com/protocols-files/public/d24555ea6537eeef4db42c4aa461f4a348d6f4fd099a232a2c61450ea74c051a/b27z7vcw.png" /></div><div class = "text-block"><span>This image reports a Maximum Entropy model that estimates </span><span style = "font-style:italic;">suitable </span><span>locations for COVID-19 spread, i.e. places that could favour the spread of the virus just in terms of environmental parameters.</span></div><div class = "text-block"><span>The model was trained just on locations in </span><span style = "font-style:italic;">Italy </span><span>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:</span></div><div class = "text-block"><ul style = "list-style-type:disc;"><li style = "counter-reset:ol0;list-style-type:disc;">Average Annual Surface Air Temperature in 2018 (NASA)</li><li style = "counter-reset:ol0;list-style-type:disc;">Average Annual Precipitation in 2018 (NASA)</li><li style = "counter-reset:ol0;list-style-type:disc;">CO2 emission (natural+artificial) averaged between January 1979 and December 2013 (Copernicus Atmosphere Monitoring Service)</li><li style = "counter-reset:ol0;list-style-type:disc;">Elevation (NOAA ETOPO2)</li><li style = "counter-reset:ol0;list-style-type:disc;">Population per 0.5° cell (NASA Gridded Population of the World)</li></ul></div><div class = "text-block">The model file (in ASC format) and all parameters used are attached.</div><div class = "text-block">A higher resolution map and also the model file (in ASC format) and all parameters are available at the external link (Zenodo).</div><div class = "text-block"><span>The model indicates highest correlation with </span><span style = "font-style:italic;">infection rate</span><span> 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 </span><span style = "font-style:italic;">infection rate</span><span>.</span></div><div class = "text-block"><span style = "font-weight:bold;">One interesting result is that the model indicates, among others, the Hubei region in China as a high-probability location</span><span>, </span><span style = "font-weight:bold;">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.</span></div><div class = "text-block"><span style = "font-weight:bold;">Evaluation:</span></div><div class = "text-block"><span>A </span><span style = "font-style:italic;">risk score </span><span>was calculated for each country/region reported by the JHO monitoring system (</span><a href="https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html" style = "text-decoration:underline;color:blue;cursor:pointer;"><span style = ":;">https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html</span></a><span>). 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.</span></div><div class = "text-block"><span>We assessed the reliability of this score, by selecting the country/regions that reported the</span><span style = "font-style:italic;">highest rates of infection</span><span>. These zones were selected as those with a rate higher than the upper confidence of a log-normal distribution of the rates.</span></div><div class = "text-block"><span>The agreement between the two maps (</span><a href="https://zenodo.org/api/files/23b09ea2-e5eb-415d-9f6c-fd3b5abfe6c9/covid_high_rate_vs_high_risk_24_03_2020.png" style = "text-decoration:underline;color:blue;cursor:pointer;"><span style = ":;">covid_high_rate_vs_high_risk.png</span></a><span>, where violet dots indicate</span><span style = "font-style:italic;">high infection rates</span><span>and countries' colours indicate estimated</span><span style = "font-style:italic;">high risk score</span><span>) is the following:</span></div><div class = "text-block"><span style = "font-weight:bold;">Accuracy</span><span>(overall percentage of correctly predicted high-rate zones): </span><span style = "font-weight:bold;">77.25%</span><span style = "font-weight:bold;"></span></div><div class = "text-block"><span style = "font-weight:bold;">Kappa</span><span>(agreement between the two maps):</span><span style = "font-weight:bold;">0.46</span><span>(Good, according to Fleiss' intepretation of the score) </span></div><div class = "text-block"><span style = "font-weight:bold;">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.</span></div><div class = "text-block"><span style = "font-weight:bold;">Files</span></div><div class = "text-block">Name (Size)                                                                              </div><div class = "text-block"><a href="#" style = "text-decoration:underline;color:blue;cursor:pointer;"><span style = ":;font-weight:bold;"> </span></a><span style = ":;font-weight:bold;"> </span><span style = "font-weight:bold;font-weight:bold;">(</span><span style = "font-weight:bold;">5.7 MB</span><span>)                                                             </span></div><div class = "text-block">md5:dea4e66a1c66d0dfc3b0872adfaa020f</div><div class = "text-block"><a href="#" style = "text-decoration:underline;color:blue;cursor:pointer;"><span style = ":;"> </span></a><span style = ":;font-weight:bold;">(</span><span style = "font-weight:bold;">47.3 MB</span><span>)                                                  </span></div><div class = "text-block">md5:069727a6c5656d276c475606c9b96d47</div><div class = "text-block"><a href="#" style = "text-decoration:underline;color:blue;cursor:pointer;"><span style = ":;"> </span></a><span style = ":;font-weight:bold;">(</span><span style = "font-weight:bold;">1.9 MB</span><span>)                                                                                                       </span></div><div class = "text-block">md5:ca91c4d56654b77bf572eef1a42af7a5</div><div class = "text-block"><a href="#" style = "text-decoration:underline;color:blue;cursor:pointer;"><span style = ":;"> </span></a><span style = ":;font-weight:bold;">(</span><span style = "font-weight:bold;">5.1 MB</span><span>)                                                                                </span></div><div class = "text-block">md5:0ed217e20ab32aad4ab96e5403670ee4</div><div class = "text-block"><a href="#" style = "text-decoration:underline;color:blue;cursor:pointer;"><span style = ":;"> </span></a><span style = ":;font-weight:bold;">(</span><span style = "font-weight:bold;">2.8 MB</span><span>) </span></div><div class = "text-block">md5:79639fd3540c68450d86fde288edb264</div><div class = "text-block"><a href="#" style = "text-decoration:underline;color:blue;cursor:pointer;"><span style = ":;"> </span></a><span style = ":;font-weight:bold;">(</span><span style = "font-weight:bold;">4.6 MB</span><span style = "font-weight:bold;">)</span></div><div class = "text-block">md5:57aa6c172b3fc036c08d0560f01436ba</div><div class = "text-block"><a href="#" style = "text-decoration:underline;color:blue;cursor:pointer;"><span style = ":;"> </span></a><span style = ":;font-weight:bold;">(</span><span style = "font-weight:bold;">5.5 MB</span><span>) </span></div><div class = "text-block">md5:3ab587ea0e0fbe3fcbd9ea6b7844271a</div><div class = "text-block"><a href="#" style = "text-decoration:underline;color:blue;cursor:pointer;"><span style = ":;"> </span></a><span style = ":;font-weight:bold;">(</span><span style = "font-weight:bold;">4.7 MB</span><span>) </span></div><div class = "text-block">md5:7ea930f59e5ff627a18383f02737f78d</div><div style = "text-align :; float : ;"><img style = "" src = "https://s3.amazonaws.com/protocols-files/public/d24555ea6537eeef4db42c4aa461f4a348d6f4fd099a232a2c61450ea74c051a/b3rbbcacp.png" /></div><div class = "text-block"><span style = "font-weight:bold;">References</span></div></div>

[STEPS]