There is a newer version of this record available.

Dataset Open Access

Suitability Map of COVID-19 Virus Spread

Gianpaolo Coro

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)

A higher resolution map, the model file (in ASC format) and all parameters used are also attached.

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 JHU monitoring system (https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6). 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 the highest 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 indicate high infection rates and countries' colours indicate estimated high 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.

 

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/
Files (77.9 MB)
Name Size
1_covid_suitability_preview.png
md5:dea4e66a1c66d0dfc3b0872adfaa020f
5.7 MB Download
2_covid_suitability_v2_Hi_Resolutionv2.png
md5:069727a6c5656d276c475606c9b96d47
47.3 MB Download
Altitude.asc
md5:ca91c4d56654b77bf572eef1a42af7a5
1.9 MB Download
CO2.asc
md5:0ed217e20ab32aad4ab96e5403670ee4
5.1 MB Download
covid_high_rate_vs_high_risk_24_03_2020.png
md5:ac354fec4c4fb60437404ea1a199cdb0
289.3 kB Download
MaxEnt_Temperature_Precipitation_Elevation_CO2.asc
md5:79639fd3540c68450d86fde288edb264
2.8 MB Download
Population.asc
md5:57aa6c172b3fc036c08d0560f01436ba
4.6 MB Download
Precipitation.asc
md5:3ab587ea0e0fbe3fcbd9ea6b7844271a
5.5 MB Download
Temperature.asc
md5:7ea930f59e5ff627a18383f02737f78d
4.7 MB Download
  • Coro, G., Panichi, G., Scarponi, P., & Pagano, P. (2017). Cloud computing in a distributed e‐infrastructure using the web processing service standard. Concurrency and Computation: Practice and Experience, 29(18), e4219.

2,012
701
views
downloads
All versions This version
Views 2,012685
Downloads 701221
Data volume 8.9 GB3.0 GB
Unique views 1,646596
Unique downloads 424143

Share

Cite as