COVID-19: Epidemiology & infectious modelling datasets

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 : nicresearchgroup@gmail.com) 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

CoVID-19 prevalnce data of six geographical region and twelve distinct countries.

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.

This Data article has the purpose to show basic information about scientific data of the principal factors determining the diffusion of COVID-19 in Italy (Coccia, 2020) based on Environmental pollution, Atmospheric environment, Demographic aspects and Respiratory disorders of people. In particular, these data presents critical items for specific statistical analyses. This dataset has the purpose to provide scientific data of the principal factors determining the diffusion of COVID-19 in Italy based on Environmental pollution (Air Pollution), Atmospheric Factors (Wind Speed), Demographic aspects (Density of Population per km 2) and respiratory disorders of people (e.g., Lung cancer). In particular, these data presents critical items that can be useful for specific statistical analyses.

Real-time tracking of the spatial diffusion of airborne diseases, and especially COVID-19 is in the focal point of both recent academic studies and policymaking. Airborne pathogens are handed over by interpersonal encounters. Therefore, agent-based modelling provides a useful approach to grasp the complex and interrelated nature of spatiotemporal movement and the geographical spread of infectious diseases. Although technology development rendered it to be feasible to track the spatial spread of infected individuals, the spatial scale of data retrieval can cause challenging bottlenecks for academic analysis. Samples on community-scale, for instance, by crowdsourced data as well as the global level of international aircraft movements are addressed. However, regional-scale spread of airborne diseases conveyed by human mobility rarely comes into focus. By directing our efforts to the level of countrywide diffusion, we aim to disclose the spatial component of airborne pathogens’ infection carried over by interpersonal encounters. The mobile cell dataset we applied here is especially suitable to estimate the number of interpersonal encounters, that is enabled by co-locating the same space with an infected person within a definite timeframe. Consequently, we considered mobile phone data driven co-location as ‘locational chance’ of airborne pathogen spreading. The volume of spread, as we argue, is dependent on the interpersonal connections. According to the current results, the geographical spread of COVID-19 is dominantly carried over by latently infected individuals, who transmit the disease without showing any symptoms. We modelled the interpersonal encounters of a set of randomly chosen latent infected as an indicator of the further geographical spread of the disease. We applied two various sets of models running: one, that is based on real archive data, and the other, that simulates current mobility patterns ordered by relocation restrictions.

Daily Coronavirus Disease (Covid-19) prevalence data from 25th of January 2020 until 29th of April 2020 were collected from the records of the Ministry of Health Malaysia and Excel 2019 was used to build a time-series database. All fully-anonymized, laboratory-confirmed cases were abstracted on Covid-19 in which 5,945 cases represented Covid-19 infection in 16 states in Malaysia as recorded by the Ministry of Health Malaysia. The non-parametric Mann-Kendall Test (MK) statistical test has usually been used to assess the significance of a trend at a site. Meanwhile, the prediction model was developed based on Singular Spectrum Analysis (SSA) which called it as Recurrent Forecasting (RF-SSA) to predict the new daily confirmed Covid-19 cases for a short-term period.

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.

For analyzing data in the VOS viewer, we used ISI, PubMed, and Scopus as a database. Data were obtained from the Web of Science (WOS), PubMed, and Scopus on March 02, 2020, and updated on March 10. to analyzed in VOS viewer software. The keywords were divided into three clusters: Generally, the lesser the distance between two key-terms, the greater the number of co-occurrences of the terms. The larger circle represents the more frequent keyword