Difference between revisions of "COVID-19 FOSS initiatives"
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| − | ===[https:// | + | ===[https://uscovid-19map.org/ U. S. Spread of COVID-19 Map and Analytics]=== |
==== U. S. A. - SharedGeo.org==== | ==== U. S. A. - SharedGeo.org==== | ||
In support of nationwide public service efforts to help individuals and organizations understand the rapidly expanding nature of the COVID-19 virus crisis in the U.S., [https://www.sharedgeo.org SharedGeo.org] created a series of maps and related analytics which includes a by-county time-lapse view of the spread. <br /> | In support of nationwide public service efforts to help individuals and organizations understand the rapidly expanding nature of the COVID-19 virus crisis in the U.S., [https://www.sharedgeo.org SharedGeo.org] created a series of maps and related analytics which includes a by-county time-lapse view of the spread. <br /> | ||
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*https://www.sharedgeo.org/COVID-19/ | *https://www.sharedgeo.org/COVID-19/ | ||
*https://uscovid-19map.org/ | *https://uscovid-19map.org/ | ||
| + | |||
| + | ===[https://stompcovidph.com STOMP COVID-19 | PH]=== | ||
| + | ====[https://bnhr.xyz BNHR]==== | ||
| + | The COVID-19 Status Tracking, Overview, and Mapping Project (#STOMPCOVIDPH) is a platform utilizing free and open technologies to provide open, granular, and up-to-date COVID-19 data, statistics, maps, visualizations, and analyses of the Philippine situation. It shows case information and statistics, maps and visualizations, and allows for data to be downloaded so it can be used by others for their own purposes. | ||
| + | |||
| + | To learn more, visit [https://stompcovidph.com the #STOMPCOVID website]. | ||
| + | * [https://stompcovidph.com Home] | ||
| + | * [https://www.stompcovidph.com/stats/local.html Local Statistics] | ||
| + | * [https://www.stompcovidph.com/maps Maps] | ||
| + | * [https://www.stompcovidph.com/contribute Contribute Data] | ||
| + | |||
| + | ====FOSS employed ==== | ||
| + | The platform uses CARTO for the backend through their [https://carto.com/grants/#form grant program]. Mapbox, Charts.JS, and other JavaScript libraries are used for the frontend. | ||
| + | |||
| + | |||
| + | ===[https://mapscloud.udg.edu/covid19 COVID-19 incidence in Spain]=== | ||
| + | ==== COVID-19 incidence by autonomic regions in Spain ==== | ||
| + | The [https://www.udg.edu/en/sigte Geographical Information Systems and Remote Sensing Service (SIGTE)] of the University of Girona has developed a dynamic map that shows the official aggregated data on the evolution of patients, recovered, and deceased by COVID-19 in the autonomic regions of Spain. The time span of this map comprises the start of the pandemic up until May 20 2020, when the [https://www.mscbs.gob.es/en/profesionales/saludPublica/ccayes/alertasActual/nCov-China/documentos/COVID19_Estrategia_vigilancia_y_control_e_indicadores.pdf COVID-19 Early Detection, Surveillance, and Control Strategy] came into force. Due to the entry into force of this strategy, only the new daily cases are provided at the provincial level, leaving aside the recovered and the deceased.<br /> | ||
| + | |||
| + | According to the counting criteria of the [https://www.mscbs.gob.es/en/profesionales/saludPublica/ccayes/alertasActual/nCov-China/situacionActual.htm Ministry of Health, Consumer Affairs, and Social Welfare] before the entry into force of the Strategy mentioned above, only patients with a positive result in the PCR test are counted as sick people. In contrast, recovered and deceased patients include patients diagnosed by PCR + and rapid tests, so that the number of discharges may at some point exceed the number of patients. A script with Python was designed to update the data shown on the web map daily with the [https://cnecovid.isciii.es/covid19/#documentaci%C3%B3n-y-datos officials] provided by the Carlos III Health Institute. | ||
| + | |||
| + | * https://mapscloud.udg.edu/covid19 | ||
| + | |||
| + | ====FOSS employed ==== | ||
| + | The application is build using PostgreSQL+PostGIS, OpenLayers, D3.js, and Python. The background map is provided by Mapbox. <br /> | ||
| + | |||
| + | |||
| + | ===[https://mapscloud.udg.edu/covid19 COVID-19 incidence in Catalonia]=== | ||
| + | ==== COVID-19 incidence by municipality in Catalonia ==== | ||
| + | The [https://www.udg.edu/en/sigte Geographical Information Systems and Remote Sensing Service (SIGTE)] of the University of Girona has developed a tool to access the situation of the SARS-CoV-2 in any municipality of Catalonia. Its main aim is to provide the public with an aggregated value showing the number of confirmed cases of SARS-CoV-2 (using a PCR test) over the last 7 days for every 100.000 inhabitants for every municipality of Catalonia. | ||
| + | |||
| + | This value is then used to draw a map showing, in red, the municipalities with more than 50 new cases in the last week for every 100.000 inhabitants. It also gives access to a detailed view of the 7-day situation of the municipality selected and an archive view showing the same value for every day since the beginning of the tests. | ||
| + | |||
| + | The site uses the official data provided by the [https://analisi.transparenciacatalunya.cat/Salut/Registre-de-casos-de-COVID-19-realitzats-a-Catalun/jj6z-iyrp Department of Health] of the Generalitat de Catalunya as well as the population data provided by the [http://www.idescat.cat/poblacio/ Statistical Institute of Catalonia (Idescat)]. Both data are extracted dynamically with JavaScript using their respective APIs. | ||
| + | |||
| + | * https://mapscloud.udg.edu/covidmunicipis/ | ||
| + | |||
| + | ====FOSS employed ==== | ||
| + | The site needs no backend and is built using JavaScript, MaterializeCSS and Leaflet. The background map is provided by Mapbox. <br /> | ||
Latest revision as of 23:45, 20 September 2020
Introduction
Our generation is facing one of the most difficult challenges of its time with consequences that will unravel for a long time, in all aspects of our existence. With much of our world in lockdown, our attention turns towards the spread and evolution of the SARS-CoV-2 virus around the globe. Given the geospatial dimension of the threat, some of the OSGeo community members might be using their knowledge and experience to design and implement various applications to track, visualise or predict the COVID-19 pandemic evolution.
This wiki page is dedicated to collect COVID-19 related initiatives developed using FOSS solutions. We invite anyone involved in such an initiative or has knowledge of one, to acknowledge their efforts by including it in this list.
COVID-19 FOSS initiatives
Romania, geo-spatial.org (Romanian OSGeo Local Chapter)
As soon as Romania registered its first official COVID-19 patient on February 26, the geo-spatial.org volunteers have collaboratively begun to work on an application based on the Romanian official data to disseminate to the public the evolution of the pandemic. The application presents visualizations related to the dimensions of the outbreak in Romania - confirmed cases, recovered and deaths - together with a series of statistics, as well as other relevant aspects covering quarantine and applied restrictions, hospital infrastructure, marginalised communities, border traffic. Due to the un-structured and often modified manner in which the Romanian authorities are sharing COVID-19 information, a significant part of the team's efforts are invested into collecting more detailed information from the local and national media, match it to the official reporting, structure and deliver it in a more relevant manner. The collected, cleaned data can be accessed as JSON format using a dedicate API (the API is momentarily static, nevertheless, we will analyse the possibility of filter set-ups for future releases):
- https://covid19.geo-spatial.org/api/dashboard/getCasesByCounty
- https://covid19.geo-spatial.org/api/dashboard/getDeadCasesByCounty
- https://covid19.geo-spatial.org/api/dashboard/getHealthCasesByCounty
- https://covid19.geo-spatial.org/api/dashboard/getDailyCaseReport
- https://covid19.geo-spatial.org/api/statistics/getCaseRelation
- https://covid19.geo-spatial.org/api/dashboard/getPercentageByGender
- https://covid19.geo-spatial.org/api/dashboard/getCasesByAgeGroup
- https://covid19.geo-spatial.org/api/dashboard/getDailyCases
FOSS employed
The application is build using Node.js, PostgreSQL+PostGIS, R on the backend side, OpenLayers, Angular, charts.js, Plotly and D3.js for the frontend side and it is available under un MIT license on Github.
The infrastructure is supported by SAGE group from University of West Timișoara and CARTO, through their grant program.
U. S. Spread of COVID-19 Map and Analytics
In support of nationwide public service efforts to help individuals and organizations understand the rapidly expanding nature of the COVID-19 virus crisis in the U.S., SharedGeo.org created a series of maps and related analytics which includes a by-county time-lapse view of the spread.
To view and learn more, go to SharedGeo's U.S. Spread of COVID-19 Maps and Analytics website.
STOMP COVID-19 | PH
BNHR
The COVID-19 Status Tracking, Overview, and Mapping Project (#STOMPCOVIDPH) is a platform utilizing free and open technologies to provide open, granular, and up-to-date COVID-19 data, statistics, maps, visualizations, and analyses of the Philippine situation. It shows case information and statistics, maps and visualizations, and allows for data to be downloaded so it can be used by others for their own purposes.
To learn more, visit the #STOMPCOVID website.
FOSS employed
The platform uses CARTO for the backend through their grant program. Mapbox, Charts.JS, and other JavaScript libraries are used for the frontend.
COVID-19 incidence in Spain
COVID-19 incidence by autonomic regions in Spain
The Geographical Information Systems and Remote Sensing Service (SIGTE) of the University of Girona has developed a dynamic map that shows the official aggregated data on the evolution of patients, recovered, and deceased by COVID-19 in the autonomic regions of Spain. The time span of this map comprises the start of the pandemic up until May 20 2020, when the COVID-19 Early Detection, Surveillance, and Control Strategy came into force. Due to the entry into force of this strategy, only the new daily cases are provided at the provincial level, leaving aside the recovered and the deceased.
According to the counting criteria of the Ministry of Health, Consumer Affairs, and Social Welfare before the entry into force of the Strategy mentioned above, only patients with a positive result in the PCR test are counted as sick people. In contrast, recovered and deceased patients include patients diagnosed by PCR + and rapid tests, so that the number of discharges may at some point exceed the number of patients. A script with Python was designed to update the data shown on the web map daily with the officials provided by the Carlos III Health Institute.
FOSS employed
The application is build using PostgreSQL+PostGIS, OpenLayers, D3.js, and Python. The background map is provided by Mapbox.
COVID-19 incidence in Catalonia
COVID-19 incidence by municipality in Catalonia
The Geographical Information Systems and Remote Sensing Service (SIGTE) of the University of Girona has developed a tool to access the situation of the SARS-CoV-2 in any municipality of Catalonia. Its main aim is to provide the public with an aggregated value showing the number of confirmed cases of SARS-CoV-2 (using a PCR test) over the last 7 days for every 100.000 inhabitants for every municipality of Catalonia.
This value is then used to draw a map showing, in red, the municipalities with more than 50 new cases in the last week for every 100.000 inhabitants. It also gives access to a detailed view of the 7-day situation of the municipality selected and an archive view showing the same value for every day since the beginning of the tests.
The site uses the official data provided by the Department of Health of the Generalitat de Catalunya as well as the population data provided by the Statistical Institute of Catalonia (Idescat). Both data are extracted dynamically with JavaScript using their respective APIs.
FOSS employed
The site needs no backend and is built using JavaScript, MaterializeCSS and Leaflet. The background map is provided by Mapbox.