Taiwan Province of China

Data cited at: Our World in data-https://ourworldindata.org/coronavirus-source-data 

These Community Mobility Reports aim to provide insights into what has changed in response to policies aimed at combating COVID-19. The reports chart movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential.

Note: For Measures-"Lower Credible (50%), Upper Credible(50%),Probability of Control", the data is available as of 25th June 2020 and there will be no further update for these measures as discontinued at source level. Latest estimates of the number of confirmed cases by date of infection, the effective reproduction number, and the doubling time (when negative this corresponds to the halving time) in each region. The mean and 90% credible interval is shown. Data cited at https://epiforecasts.io/covid/posts/global/

Note: ECDC will cease to update this dataset on 1 December 2023. Note:Data cited at European Centre for Disease Prevention and Control, an agency of the European Union: https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwideThe population data in the databases are from the World Bank.

Note: Daily data is getting released with a time gap of T-2. For example, 16-March-2020 data is released on 18-March-2020   This dataset gives Google Web Search Trend for search item - "coronavirus" for past 90 days. Interest over time: Numbers represent search interest relative to the highest point on the chart for the given region and time. A value of 100 is the peak popularity for the term. A value of 50 means that the term is half as popular. A score of 0 means there was not enough data for this term.

In December 2022, IHME paused its COVID-19 modeling. IHME has developed projections for total and daily deaths, daily infections and testing, hospital resource use, and social distancing due to COVID-19 for a number of countries. Forecasts at the subnational level are included for select countries. The projections for total deaths, daily deaths, and daily infections and testing each include a reference scenario: Current projection, which assumes social distancing mandates are re-imposed for 6 weeks whenever daily deaths reach 8 per million (0.8 per 100k). They also include two additional scenarios: Mandates easing, which reflects continued easing of social distancing mandates, and mandates are not re-imposed; and Universal Masks, which reflects 95% mask usage in public in every location. Hospital resource use forecasts are based on the Current projection scenario. Social distancing forecasts are based on the Mandates easing scenario. These projections are produced with a model that incorporates data on observed COVID-19 deaths, hospitalizations, and cases, information about social distancing and other protective measures, mobility, and other factors. They include uncertainty intervals and are being updated daily with new data. These forecasts were developed in order to provide hospitals, policy makers, and the public with crucial information about how expected need aligns with existing resources, so that cities and countries can best prepare. Dataset contains Observed and Projected data

Data cited at: Prof.Prof. Lauren Gardner; Center for Systems Science and Engineering at John Hopkins University, blog Post -  https://systems.jhu.edu/research/public-health/ncov/   On December 31, 2019, the World Health Organization (WHO) was informed of an outbreak of “pneumonia of unknown cause” detected in Wuhan City, Hubei Province, China – the seventh-largest city in China with 11 million residents. As of February 04, 2020, there are over 24,502 cases confirmed globally, including cases in at least 30 regions in China and 30 countries.  Interests: In-Market Segments Knoema All Users   Knoema modified the original dataset to include calculations per million.   https://knoema.com/WBPEP2018Oct https://knoema.com/USICUBDS2020 https://knoema.com/NBSCN_P_A_A0301 https://knoema.com/IMFIFSS2017Nov https://knoema.com/AUDSS2019 https://knoema.com/UNAIDSS2017 https://knoema.com/UNCTADPOPOCT2019Nov https://knoema.com/WHOWSS2018 https://knoema.com/KPMGDHC2019

We define our day as midnight-to-midnight, Pacific time. Cities represent usage in greater metropolitan areas and are stably defined during this period. In many countries/regions and cities, relative volume has increased since January 13th, consistent with normal, seasonal usage of Apple Maps. Day of week effects are important to normalize as you use this data. Data that is sent from users’ devices to the Maps service is associated with random, rotating identifiers so Apple doesn’t have a profile of your movements and searches. Apple Maps has no demographic information about our users, so we can’t make any statements about the representativeness of our usage against the overall population. This information will be available for a limited time during the COVID‑19 pandemic.

University of Oxford baseline measure of variation in governments’ responses is the COVID-19 Government Response Stringency Index. This composite measure is a simple additive score of seven indicators measured on an ordinal scale, rescaled to vary from 0 to 100. Please note that this measure is for comparative purposes only, and should not necessarily be interpreted as rating of the appropriateness or effectiveness of a country's response.   Data cited at: Hale, Thomas and Samuel Webster (2020). Oxford COVID-19 Government Response Tracker. Data use policy: Creative Commons Attribution CC BY standard.

This data has been collected, cleaned and analyzed by  James Tozer  and  Martín González .

World Health Organization 2023 data.who.int, WHO Coronavirus (COVID-19) dashboard > Data [Dashboard]. https://data.who.int/dashboards/covid19/data

Data cited at: World Uncertainty Index (WUI), developed by Hites Ahir (International Monetary Fund), Nicholas Bloom (Stanford University) and Davide Furceri (International Monetary Fund).