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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.
\n\nThe 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:
\n\nA higher resolution map, the model file (in ASC format) and all parameters used are also attached.
\n\nThe 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.
\n\nOne 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.
\n\nEvaluation:
\n\nA 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.
\n\nWe 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.
\n\nThe 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:
\n\nAccuracy (overall percentage of correctly predicted high-rate zones): 77.25%
\nKappa (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.
\n\n", "publication_date": "2020-03-20", "publisher": "Zenodo", "references": [ { "reference": "Coro, G., Panichi, G., Scarponi, P., & 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." } ], "resource_type": { "id": "dataset", "title": { "de": "Datensatz", "en": "Dataset" } }, "rights": [ { "description": { "en": "The Creative Commons Attribution license allows re-distribution and re-use of a licensed work on the condition that the creator is appropriately credited." }, "icon": "cc-by-icon", "id": "cc-by-4.0", "props": { "scheme": "spdx", "url": "https://creativecommons.org/licenses/by/4.0/legalcode" }, "title": { "en": "Creative Commons Attribution 4.0 International" } } ], "subjects": [ { "subject": "COVID-19" }, { "subject": "Coronavirus" }, { "subject": "Maximum Entropy" }, { "subject": "Temperature" }, { "subject": "Precipitation" }, { "subject": "Carbon Dioxide" }, { "subject": "CO2" }, { "subject": "Corona virus" } ], "title": "Suitability Map of COVID-19 Virus Spread", "version": "5" }, "parent": { "access": { "owned_by": { "user": 55267 } }, "communities": { "default": "10f33f78-3f29-41b6-bb10-f757a8f03cb8", "entries": [ { "access": { "member_policy": "open", "members_visibility": "public", "record_policy": "open", "review_policy": "open", "visibility": "public" }, "children": { "allow": false }, "created": "2020-03-16T11:40:44.487619+00:00", "custom_fields": {}, "deletion_status": { "is_deleted": false, "status": "P" }, "id": "10f33f78-3f29-41b6-bb10-f757a8f03cb8", "links": {}, "metadata": { "curation_policy": "
The Coronavirus Disease Research Community - COVID-19 is curated by a selected team of experts nominated by OpenAIRE* (see list below). Each time a Zenodo user wants to add a record into the community, an email is sent to the curators that will decide whether to include the record or not.
\r\n\r\nOnly records that may be relevant to the Corona Virus Disease (COVID-19) or the SARS-CoV-2 should be included in this community. The Community curators are not able to edit records, therefore they may ask the corresponding authors to modify the record metadata when necessary, to provide the readers/users with more detailed information according to the FAIR principle of Open Science.
\r\n\r\nIf after its acceptance, a record is subsequently found not to be compliant, we reserve the right to remove it from the community.
\r\n\r\nThe curation team is reachable through the following email address for further clarification or information: covid19@openaire.eu.
\r\n\r\nCurator List:
\r\n\r\n* OpenAIRE: open access and open science training and support since 2009. OpenAIRE is the largest aggregator of European Commission funded research outputs and beyond, also delivering on-demand services for research communities.
\r\n", "page": "This community collects research outputs that may be relevant to the Coronavirus Disease (COVID-19) or the SARS-CoV-2. Scientists are encouraged to upload their outcome in this collection to facilitate sharing and discovery of information. Although Open Access articles and datasets are recommended, also closed and restricted access material are accepted. All types of research outputs can be included in this Community (Publication, Poster, Presentation, Dataset, Image, Video/Audio, Software, Lesson, Other).
\r\n\r\nThe recent Corona Virus Disease (COVID-19) outbreak is requiring unseen efforts of collaboration of the scientific community that need to act fast and to share results in an unpredictable manner. In order to facilitate the Scientist efforts, this community was created to collect all research results that could be relevant for the scientific community working on the Corona Virus Disease (COVID-19) and SARS-CoV-2.
\r\n\r\nAlthough Open Access articles and datasets are recommended, also closed and restricted access material are accepted. All types of research outputs can be included in this Community (Publication, Poster, Presentation, Dataset, Image, Video/Audio, Software, Lesson, Other).
\r\n\r\nWhen depositing a resource that is linked to other resources (not limited to the records deposited in Zenodo but also in other repositories), please make sure that your record is linked to all the other related elements already available, in order to adhere to the FAIR principles of Open Science to maximise the reusability of research results.
\r\n\r\n", "title": "Coronavirus Disease Research Community - COVID-19" }, "revision_id": 0, "slug": "covid-19", "updated": "2021-02-23T14:39:53.029415+00:00" } ], "ids": [ "10f33f78-3f29-41b6-bb10-f757a8f03cb8" ] }, "id": "3719140", "pids": { "doi": { "client": "datacite", "identifier": "10.5281/zenodo.3719140", "provider": "datacite" } } }, "pids": { "doi": { "client": "datacite", "identifier": "10.5281/zenodo.3725831", "provider": "datacite" }, "oai": { "identifier": "oai:zenodo.org:3725831", "provider": "oai" } }, "revision_id": 11, "stats": { "all_versions": { "data_volume": 21079940145.0, "downloads": 2664, "unique_downloads": 1621, "unique_views": 4805, "views": 5203 }, "this_version": { "data_volume": 7541727707.0, "downloads": 809, "unique_downloads": 458, "unique_views": 868, "views": 972 } }, "status": "published", "updated": "2020-07-14T22:34:27.862022+00:00", "versions": { "index": 5, "is_latest": false } }