The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

COVID-19 Cases in England

Tracking Infection Risk at a Local Level

By Dan Winchester [source]

About this dataset

This dataset contains the total number of confirmed COVID-19 cases in each English Upper Tier Local Authority over the past eight days. Aggregated from Public Health England data, this dataset provides unprecedented insight into how quickly the virus has been able to spread in local communities throughout England. Despite testing limitations, understanding these localized patterns of infection can help inform important public health decisions by local authorities and healthcare workers alike.

It is essential to bear in mind that this data is likely an underestimation of true infection rates due to limited testing -- it is critical not to underestimate the risk the virus poses on a local scale! Use this dataset at your own discretion with caution and care; consider supplementing it with other health and socio-economic metrics for a holistic picture of regional trends over time.

This dataset features information surrounding GSS codes and names as well as total numbers of recorded COVID-19 cases per English Upper Tier Local Authority on January 5th 2023 (TotalCases_2023-01-05)

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Research Ideas

• Comparing the total cases in each local authority to population density of the region, to identify areas with higher incidence of virus
• Tracking changes in total cases over a period of time to monitor trend shifts and detect possible outbreak hotspots
• Establishing correlations between the spread of COVID-19 and other non-coronavirus related health issues, such as mental health or cardiovascular risk factors

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: utla_by_day.csv | Column name | Description | |:--------------------------|:------------------------------------------------------------------------------------------------------| | GSS_CD | Government Statistical Service code for the local authority. (String) | | GSS_NM | Name of the local authority. (String) | | TotalCases_2023-01-05 | Total number of confirmed COVID-19 cases in the local authority on the 5th of January 2023. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Dan Winchester.

Provisional age-standardised mortality rates for deaths due to COVID-19 by sex, local authority and deprivation indices, and numbers of deaths by middle-layer super output area.

Provisional counts of the number of deaths registered in England and Wales, including deaths involving coronavirus (COVID-19), by local authority, health board and place of death in the latest weeks for which data are available. The occurrence tabs in the 2021 edition of this dataset were updated for the last time on 25 October 2022.

Vaccination has been critical to the decline in infectious disease prevalence in recent centuries. Nonetheless, vaccine refusal has increased in recent years, with complacency associated with reductions in disease prevalence highlighted as an important contributor. We exploit a natural experiment in Glasgow at the beginning of the 20th century to investigate whether prior local experience of an infectious disease matters for vaccination decisions. Our study is based on smallpox surveillance data and administrative records of parental refusal to vaccinate their infants. We analyse variation between administrative units of Glasgow in cases and deaths from smallpox during two epidemics over the period 1900–1904, and vaccine refusal following its legalisation in Scotland in 1907 after a long period of compulsory vaccination. We find that lower local disease incidence and mortality during the epidemics were associated with higher rates of subsequent vaccine refusal. This finding indicates that complacency influenced vaccination decisions in periods of higher infectious disease risk, responding to local prior experience of the relevant disease, and has not emerged solely in the context of the generally low levels of infectious disease risk of recent decades. These results suggest that vaccine delivery strategies may benefit from information on local variation in incidence. Methods Overview of the record This record provides the main analysis dataset for the following paper: Angelopoulos K, Stewart G, Mancy R. 2022 Local infectious disease experience influences vaccine refusal rates: a natural experiment. Proc. R. Soc. B 20221986. https://doi.org/10.1098/rspb.2022.1986 It also serves as a portal for the remaining code and datasets used in the paper. Specifically, the upload consists of:

The main analysis dataset, hosted directly in DataDryad as an Excel spreadsheet. This is the final dataset used to conduct the statistical analysis reported in the paper. 'Related Works: Software': a link to the R code used for the data transformations and analysis, and hosted on Zenodo. Note that this also includes a copy of the main dataset and the shapefiles in (3) below. 'Related Works: Dataset': a link to the shapefiles used to generate maps and other spatial manipulations required for the manuscript, and hosted on Zenodo

Each Related Work contains its own README files and/or appropriate documentation. The following is a description of the methods for constructing the main dataset (1).

Main dataset construction and processing COV_Main_Dataset.xlsx contains data relating to smallpox and conscientious object to smallpox vaccination (COV), alongside socioeconomic variables, at municipal ward and registration district level for Glasgow 1900-1913. It contains the following worksheets:

Data dictionary: provides descriptions of variables and explains abbreviations used. Ward_All: provides variables at municipal ward level, for all 25 wards that existed in Glasgow in 1900. W_Drop_Blyt_Exch: provides variables at municipal ward level, excluding two atypical central wards, Blythswood and Exchange. RD_All: provides variables at registration district level, for all 20 registration districts, or parts of these registration districts that cover the area of the 25 municipal wards that existed in 1900. RD_Drop_Blyt: provides variables at registration district level, excluding Blythswood where the maternity hospital was situated and where data are atypical.

To construct this dataset, data were first manually transcribed into Excel, primarily from Medical Officer of Health reports and census reports. They were then processed in R as described in the manuscript (code and further documentation are available in 'Related Works: Software', a repository that also includes the raw dataset as transcribed). The file provided here is the final dataset used for the statistical analysis reported in the paper.

BackgroundBorder control mitigates local infections but bears a heavy economic cost, especially for tourism-reliant countries. While studies have supported the efficacy of border control in suppressing cross-border transmission, the trade-off between costs from imported and secondary cases and from lost economic activities has not been studied. This case study of Singapore during the COVID-19 pandemic aims to understand the impacts of varying quarantine length and testing strategies on the economy and health system. Additionally, we explored the impact of permitting unvaccinated travelers to address emerging equity concerns. We assumed that community transmission is stable and vaccination rates are high enough that inbound travelers are not dissuaded from traveling.MethodsThe number of travelers was predicted considering that longer quarantine reduces willingness to travel. A micro-simulation model predicted the number of COVID-19 cases among travelers, the resultant secondary cases, and the probability of being symptomatic in each group. The incremental net monetary benefit (INB) of Singapore was quantified under each border-opening policy compared to pre-opening status, based on tourism receipts, cost/profit from testing and quarantine, and cost and health loss due to COVID-19 cases.ResultsCompared to polymerase chain reaction (PCR), rapid antigen test (ART) detects fewer imported cases but results in fewer secondary cases. Longer quarantine results in fewer cases but lower INB due to reduced tourism receipts. Assuming the proportion of unvaccinated travelers is small (8% locally and 24% globally), allowing unvaccinated travelers will accrue higher INB without exceeding the intensive care unit (ICU) capacity. The highest monthly INB from all travelers is $2,236.24 m, with 46.69 ICU cases per month, achieved with ARTs at pre-departure and on arrival without quarantine. The optimal policy in terms of highest INB is robust under changes to various model assumptions. Among all cost-benefit components, the top driver for INB is tourism receipts.ConclusionsWith high vaccination rates locally and globally alongside stable community transmission, opening borders to travelers regardless of vaccination status will increase economic growth in the destination country. The caseloads remain manageable without exceeding ICU capacity, and costs of cases are offset by the economic value generated from travelers.

Most COVID-19 cases in Ukraine were recorded in the capital Kyiv, measured at over 441.4 thousand as of February 22, 2022. The Odesa Oblast had the second-highest number of infections at around 324.4 thousand. In total, around 4.78 million cases of COVID-19 were confirmed in the country as of that date. Ukraine’s adaptive quarantine After the nationwide lockdown from March 12 to May 21, 2020, Ukraine was placed under the so-called 'adaptive quarantine' by the national government, meaning that most restrictive measures have been lifted, except for the areas with high infection rates. There, confinement measures were to be taken by local authorities. Red, orange, yellow, and green risk levels have been assigned on the regional level according to the data provided by the Ministry of Health. The 'adaptive quarantine' was later extended until October 31, 2020.

Economic implications for the Ukrainian economy

The IMF estimated Ukraine’s GDP to reach 151.5 billion U.S. dollars in 2020. Furthermore, the country’s national debt was forecast to increase to over 110 billion U.S. dollars by 2025. The financial fund of approximately seven billion U.S. dollars was created by the Cabinet of Ministers and the Bank of Ukraine in an attempt to stabilize the economy and strengthen the national currency Ukrainian hryvnia. However, despite the efforts, Ukraine could require assistance from the international community to overcome the crisis caused by the pandemic. In July 2020, Ukraine signed a memorandum with the European Commission to receive macro-financial assistance (MFA) funds in the form of long-term loans worth up to 1.2 billion euros from the European Union.

This data is a statistical data organized by month on the status of confirmed COVID-19 cases collected in Mokpo-si, Jeollanam-do. The provided items include the number of new confirmed cases and the cumulative number of confirmed cases each month, and are organized so that you can check the infection trend and the extent of spread by period within the region. This data can be used as basic data necessary for health administration such as establishing quarantine policies, infectious disease response strategies, and health resource distribution, and the purpose is to systematically inform citizens and researchers of the COVID-19 situation within the region. It is also highly utilized in the fields of infectious disease management and public health research. We aim to contribute to policy decisions and information provision by opening it as public data.

This report provides statistical data on the number of confirmed cases and deaths each month from February 2020, when the first case of COVID-19 occurred in Cheongdo-gun, Gyeongsangbuk-do, to the end of December 2021. This data is organized so that the COVID-19 epidemic pattern in Cheongdo-gun can be identified in time series, and the number of confirmed cases and deaths by month is organized by item. It can also be used by residents and external organizations to understand Cheongdo-gun's quarantine response performance and the local COVID-19 situation, and can also be used as reference material in the future in areas such as infectious disease prevention education, disaster information disclosure, and designing a public medical response system.

This data contains the number of COVID-19-related deaths aggregated by month for each administrative district in Eunpyeong-gu, and is composed of items such as year, administrative district code, month, and number of deaths. It is useful for evaluating quarantine policies, analyzing vulnerable health areas, and establishing medical response strategies because it allows quantitative comparison of the scale of damage by region. In addition, by deriving the mortality rate per capita, it is possible to identify the distribution of high-risk groups and establish customized response policies. It can also be used as basic data for verifying the spatial effectiveness of quarantine-related policies or studying regional health inequalities after COVID-19. For more information, please contact Seoul Open Data or your public health center.

Infectious disease transmission is an inherently spatial process in which a host’s home location and their social mixing patterns are important, with the mixing of infectious individuals often different to that of susceptible individuals. Although incidence data for humans have traditionally been aggregated into low-resolution data sets, modern representative surveillance systems such as electronic hospital records generate high volume case data with precise home locations. Here, we use a gridded spatial transmission model of arbitrary resolution to investigate the theoretical relationship between population density, differential population movement and local variability in incidence. We show analytically that a uniform local attack rate is typically only possible for individual pixels in the grid if susceptible and infectious individuals move in the same way. Using a population in Guangdong, China, for which a robust quantitative description of movement is available (a travel kernel), and a natural history consistent with pandemic influenza; we show that local cumulative incidence is positively correlated with population density when susceptible individuals are more connected in space than infectious individuals. Conversely, under the less intuitively likely scenario, when infectious individuals are more connected, local cumulative incidence is negatively correlated with population density. The strength and direction of correlation changes sign for other kernel parameter values. We show that simulation models in which it is assumed implicitly that only infectious individuals move are assuming a slightly unusual specific correlation between population density and attack rate. However, we also show that this potential structural bias can be corrected by using the appropriate non-isotropic kernel that maps infectious-only code onto the isotropic dual-mobility kernel. These results describe a precise relationship between the spatio-social mixing of infectious and susceptible individuals and local variability in attack rates. More generally, these results suggest a genuine risk that mechanistic models of high-resolution attack rate data may reach spurious conclusions if the precise implications of spatial force-of-infection assumptions are not first fully characterized, prior to models being fit to data.

A lookup file between 2019 Local Authority Districts to 2020 Covid Infection Survey Geography in the United Kingdom, as at 1 October 2020. (File size - 48KB) Field Names - LAD19CD, LAD19NM, CIS20CD, FIDField Types - Text, Text, Text, NumericField Lengths - 9, 35, 9FID = The FID, or Feature ID is created by the publication process when the names and codes / lookup products are published to the Open Geography portal.

A lookup file between 2018 Local Authority Districts to 2020 Covid Infection Survey Geography in the United Kingdom, as at 1 October 2020. (File size - 48KB) Field Names - LAD18CD, LAD18NM, LAD18NMW, CIS20CD, FIDField Types - Text, Text, Text, Text, NumericField Lengths - 9, 28, 28, 9FID = The FID, or Feature ID is created by the publication process when the names and codes / lookup products are published to the Open Geography portal. REST URL of Feature Access Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/LAD18_CIS20_EN_LU_v1_5fbc82fc73e6407facd67a1c5e4cc043/FeatureServer

A figure showing how increased loss rates due to pathogen infection can increase the total local density of a wild host population.

COVID-19 was first detected in Brazil on March 1, 2020, making it the first Latin American country to report a case of the novel coronavirus. Since then, the number of infections has risen drastically, reaching approximately 38 million cases by May 11, 2025. Meanwhile, the first local death due to the disease was reported in March 19, 2020. Four years later, the number of fatal cases had surpassed 700,000. The highest COVID-19 death toll in Latin America With a population of more than 211 million inhabitants as of 2023, Brazil is the most populated country in Latin America. This nation is also among the most affected by COVID-19 in number of deaths, not only within the Latin American region, but also worldwide, just behind the United States. These figures have raised a debate on how the Brazilian government has dealt with the pandemic. In fact, according to a study carried out in May 2021, more than half of Brazilians surveyed disapproved of the way in which former president Jair Bolsonaro had been dealing with the health crisis. In comparison, a third of respondents had a similar opinion about the Ministry of Health. Brazil’s COVID-19 vaccination campaign rollout Brazil’s vaccination campaign started at the beginning of 2021, when a nurse from São Paulo became the first person in the country to get vaccinated against the disease. A few years later, roughly 88 percent of the Brazilian population had received at least one vaccine dose, while around 81 percent had already completed the basic immunization scheme. With more than 485.2 million vaccines administered as of March 2023, Brazil was the fourth country with the most administered doses of the COVID-19 vaccine globally, after China, India, and the United States.Find the most up-to-date information about the coronavirus pandemic in the world under Statista’s COVID-19 facts and figures site.

Context

On December 31, 2019, Chinese officials informed the first case of COVID-19 in Wuhan (China). Around the end of January, 2020, many countries (the U.S., the UK, South Korea, etc.), including Vietnam reported their first COVID-19 cases.

Since then, each country has their own specific strategy to contain the outbreak. Most of the countries have now shifted from the containment (early tracking, isolating the infection sources) to serious mitigation (tactics to reduce transmission) paradigms. Although loosing some F0 cases, Vietnam still has remained safely in the containment stage.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3439828%2Fbe8a17529fc1b48e3c44be94afe75529%2FVietnam_trend.png?generation=1588195825303050&alt=media" alt="">

Vietnam currently has only 270 COVID-19 confirmed cases in total with NO FATALITIES. And now, Vietnam is on its 13 straight days with no new local transmitted cases and 5 straight days without any imported cases (Updated on April 29, 2020). This leave us so many question to ask.

1. What has happened in Vietnam? Was the number of COVID-19 cases reported by Vietnamese officials undercounted? Did testing work well in Vietnam?

2. Did the Vietnam government suppressed information about their local COVID-19 pandemic? And if not, with such the 'real' low number of cases and no death, how did Vietnam contain the virus?

3. What did we know about the Vietnam COVID-19 patients? Is there characteristics of the patients that helps slow down the infection rate in Vietnam?

One remarkable thing about Vietnam health care system is the fact that privacy laws are not as stringent as in the US, Canada or the EU. Therefore, COVID-19 patient data in Vietnam is publicly available. For some cases, detail gets seriously down to their names, their personal contacts, daily activities and even their habits.

To help answer some of the above questions, I decided to collect the Vietnam data and study it independently using all the information available on the internet. I hope this dataset will provide some insights into the COVID-19 pandemic at the specific country level.

Collection methodology

• Data was acquired by web scrapping with manually curated from the Vietnam Ministry of Health's website (https://ncov.moh.gov.vn/) and other mainstream media in Vietnam (which were cited specifically in each data row).
• The world data was obtained from the data repository for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). https://github.com/CSSEGISandData/COVID-19

Disclaimer:

• This is my personal work with no link to any organization. Although this analysis is data-driven, my comments reflect my personal perspectives.
• My results are based on the data collected from the Vietnam Health Ministry website and the mainstream media in Vietnam. The data might be biased and reflects what is publicly available on the internet. However, it can served as a good reference for someone who are curious about the COVID-19 pandemic in Vietnam. Some information conflicts (about the data) would be explained in detail in the exploration notebook that goes together with this dataset.

A full report and visualization for this dataset can be found in my Medium site.

Dataset was updated until May 10, 2020.

Relationship of nursing home deaths with local infection rates in staff and nursing home neighborhoods.

Statistics table of cases by region, age group, and gender since 2015 (Disease name: Zika virus infection, Type of date: Onset date, Type of case: Confirmed case, Source of infection: Local, Imported)

Note: Effective 3/31/25, this dataset is no longer being updated.

This dataset includes information on all positive tests of individuals for COVID-19 infection performed in New York State beginning March 1, 2020, when the first case of COVID-19 was identified in the state. The primary goal of publishing this dataset is to provide users timely information about local disease spread and COVID-19 case rates by age group. The data will be updated weekly, reflecting tests reported by 12:00 AM three days prior to the date of the update.

Total positives includes both PCR and antigen positive test results.

Note: This is an updated version of the statewide cases by age dataset that includes all reported cases, both first infections and reinfections. An archived version of the prior dataset, which includes only first infections, is available: https://health.data.ny.gov/d/h8ay-wryy