We selected Epidemiology & infectious modelling datasets that are indexed by the Mendeley Data Search engine on the 2019-present COVID-19 / Coronavirus pandemic. The aim was to make it easier to find potentially relevant datasets for this specific topic.
Contributors:Rohit Salgotra, Supreet Singh, Urvinder Singh, Sriparna Saha, Amir H Gandomi
This dataset consists of COVID-19 time series data of India since March 24th, 2020.
The data set is for all the States and Union Territories of India and is divided into five parts, including
i) Confirmed cases;
ii) Death Count;
iii) Recovered Cases;
iv) Temperature of that place; and
v) Percentage humidity in the region.
The data set also provides basic details of confirmed cases and death count for all the countries of the world updated daily since 30 January 2020.
The end user can contact the corresponding author (Rohit Salgotra : firstname.lastname@example.org) for more details.
The Authors can Refer to and CITE our latest Papers on COVID:
1. Salgotra, Rohit, Mostafa Gandomi, and Amir H. Gandomi. "Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming." Chaos, Solitons & Fractals (2020): 109945.
2. Salgotra, Rohit, Mostafa Gandomi, and Amir H. Gandomi. "Evolutionary modelling of the COVID-19 pandemic in fifteen most affected countries." Chaos, Solitons & Fractals 140 (2020): 110118.
3. Mousavi, Mohsen, et al. "COVID-19 Time Series Forecast Using Transmission Rate and Meteorological Parameters as Features." IEEE Computational Intelligence Magazine 15.4 (2020): 34-50.
[Dataset is updated Once a Week]
This a data about the corona virus COVID-19. It contains the actual reported data. Also, it includes the predicted COVID-19 data in the future based on a model developed to predict in the future. The model used will be published in one of the journals later and will be found on my profile with title "Optimistic Prediction Model For the COVID-19 Coronavirus Pandemic based on the Reported Data Analysis".
The daily folder contains the daily data. The predicted folder contains the predicted data for each country. The total cases folder contains the total cases for each country. he section folder contains a latex code for plotting the figures for each country. Also the source file from European Centre for Disease Prevention and Control is included. More updated files available in the website of European Centre for Disease Prevention and Control.
17-04-2020: Esta proposta de análise faz uso de projeções com um modelo modelo compartimental SEIR baseado em iniciativas anteriores (ver aba referências). A partir dos dados de entrada na aba Modelo, são conduzidas as projeções. [This spreadsheet do projections with a SEIR compartment based on previous initiatives (see references tab). Results are based on the input data on the Model tab]
25-05-2020: Nova versão do modelo com correções e atualizações. Nesta versão:
- Incluída a condução de simulações de Monte Carlo
- Estimativas de transmissão de acordo nível de isolamento social disponibilizado por InLoco (https://mapabrasileirodacovid.inloco.com.br)
17-08-2020: Nova versão do modelo com atualizações. Nesta versão:
- O Fator de calibração (tau) ajustado de acordo com a série histórica do nível de isolamento social disponibilizado por InLoco (https://mapabrasileirodacovid.inloco.com.br) e do número acumulado de óbitos por Covid-19 no Distrito Federal até a data de 30/07/2020
This DB based on all available reports by the Communicational center of Government of the Russian Federation.
Official Russian COVID-19 data published daily by the Government of Russia (on the Russian language) in the form of raw data is a daily updated report in a pdf form. Each piece has daily updates. We are providing a working link on every cell of data in the dataset. This DB is an attempt to manually collect critical variables from the report into a machine-readable format. These datasets are ready to be used for analysis and modeling.
Variables: location; date; new cases [diagnosed]; cases [cumulative]; recovered [new]; recovered [cumulative]; deaths [new]; deaths [cumulative]; tests [new tests administered]; tests [cumulative]; test_positive [cumulative]; hospitalization [cumulative]; icu [cumulative or population]; on_invasive_ventilators [cumulative or population]; test_negative [cumulative]; hospital beds; web links.
All Data divided by date (time) and regions (Oblast) of the Russian Federation.
The high sensitivity of COVID-19 and the need for high accuracy calculations necessitate collecting the required data sets from reliable sources. Thus, all information was collected and categorized from reputable sources such as WHO (World Health Organization) and worldometers site (www.worldometers.info). The worldometers site contains information such as daily mortality statistics, recovery, and newly confirmed cases.
Research data including observation data is obtained from a collection of Iranian samples’ reports in three parts (i.e. death, confirmed and recovered). This countrywide daily information is confirmed by the WHO. It should be noted that the relevant data was collected between February 19 and May 16, 2020.
This is a database with the information on the conferimed cases and deaths, per million people in Colombia, from 20 March 2020 to 10 May 2020.
Sources of information to create the dataset:
1. INS (Instituto Nacional de Salud) National Health Institute
2. DANE (Departamento Administrativo Nacional de Estadistica) National Department of Statistics
This dataset contains the confirmed cases of COVID-19 as recorded daily by the Worl Health Organization Country by Country. The dataset was used to build machine Learning models for the real-time forecast of COVID-19 spread in Nigeria.