As of March 10, 2023, the death rate from COVID-19 in the state of New York was 397 per 100,000 people. New York is one of the states with the highest number of COVID-19 cases.

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 the first reported coronavirus case in Washington State on Jan. 21, 2020, 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.

Differences in COVID-19 testing and tracing across countries, as well as changes in testing within each country over time, make it difficult to estimate the true (population) infection rate based on the confirmed number of cases obtained through RNA viral testing. We applied a backcasting approach to estimate a distribution for the true (population) cumulative number of infections (infected and recovered) for 15 developed countries. Our sample comprised countries with similar levels of medical care and with populations that have similar age distributions. Monte Carlo methods were used to robustly sample parameter uncertainty. We found a strong and statistically significant negative relationship between the proportion of the population who test positive and the implied true detection rate. Despite an overall improvement in detection rates as the pandemic has progressed, our estimates showed that, as at 31 August 2020, the true number of people to have been infected across our sample of 15 countries was 6.2 (95% CI: 4.3–10.9) times greater than the reported number of cases. In individual countries, the true number of cases exceeded the reported figure by factors that range from 2.6 (95% CI: 1.8–4.5) for South Korea to 17.5 (95% CI: 12.2–30.7) for Italy.

Identifying changes in the reproduction number, rate of spread, and doubling time during the course of the COVID-19 outbreak whilst accounting for potential biases due to delays in case reporting at the local authority level in the United Kingdom.

Background: More than 1 year after the beginning of the international spread of coronavirus 2019 (COVID-19), the reasons explaining its apparently lower reported burden in Africa are still to be fully elucidated. Few studies previously investigated the potential reasons explaining this epidemiological observation using data at the level of a few African countries. However, an updated analysis considering the various epidemiological waves and variables across an array of categories, with a focus on African countries might help to better understand the COVID-19 pandemic on the continent. Thus, we investigated the potential reasons for the persistently lower transmission and mortality rates of COVID-19 in Africa.Methods: Data were collected from publicly available and well-known online sources. The cumulative numbers of COVID-19 cases and deaths per 1 million population reported by the African countries up to February 2021 were used to estimate the transmission and mortality rates of COVID-19, respectively. The covariates were collected across several data sources: clinical/diseases data, health system performance, demographic parameters, economic indicators, climatic, pollution, and radiation variables, and use of social media. The collinearities were corrected using variance inflation factor (VIF) and selected variables were fitted to a multiple regression model using the R statistical package.Results: Our model (adjusted R-squared: 0.7) found that the number of COVID-19 tests per 1 million population, GINI index, global health security (GHS) index, and mean body mass index (BMI) were significantly associated (P < 0.05) with COVID-19 cases per 1 million population. No association was found between the median life expectancy, the proportion of the rural population, and Bacillus Calmette–Guérin (BCG) coverage rate. On the other hand, diabetes prevalence, number of nurses, and GHS index were found to be significantly associated with COVID-19 deaths per 1 million population (adjusted R-squared of 0.5). Moreover, the median life expectancy and lower respiratory infections rate showed a trend towards significance. No association was found with the BCG coverage or communicable disease burden.Conclusions: Low health system capacity, together with some clinical and socio-economic factors were the predictors of the reported burden of COVID-19 in Africa. Our results emphasize the need for Africa to strengthen its overall health system capacity to efficiently detect and respond to public health crises.

Identifying changes in the reproduction number, rate of spread, and doubling time during the course of the COVID-19 outbreak whilst accounting for potential biases due to delays in case reporting both nationally and subnationally in the United States of America. These results are impacted by changes in testing effort, increases and decreases in testing effort will increase and decrease reproduction number estimates respectively.

For English, see below The reproduction number R gives the average number of people infected by one person with COVID-19. To estimate this reproduction number, we use the number of reported COVID-19 hospital admissions per day in the Netherlands. This number of hospital admissions is tracked by the NICE Foundation (National Intensive Care Evaluation). Because a COVID-19 admission is passed on with some delay in the reporting system, we correct the number of admissions for this delay [1]. The first day of illness is known for a large proportion of the reported cases. This information is used to estimate the first day of illness for hospital admissions. By displaying the number of COVID-19 admissions per date of the first day of illness, it is immediately possible to see whether the number of infections is increasing, peaking or decreasing. For the calculation of the reproduction number, it is also necessary to know the length of time between the first day of illness of a COVID-19 case and the first day of illness of his or her infector. This duration is an average of 4 days for SARS-CoV-2 variants in 2020 and 2021, and an average of 3.5 days for more recent variants, calculated on the basis of COVID-19 reports to the GGD. With this information, the value of the reproduction number is calculated as described in Wallinga & Lipsitch 2007 [2]. Until June 12, 2020, the reproduction number was calculated on the basis of COVID-19 hospital admissions, and until March 15, 2023, the reproduction number was calculated on the basis of COVID-19 reports to the GGDs. [1] van de Kassteele J, Eilers PHC, Wallinga J. Nowcasting the Number of New Symptomatic Cases During Infectious Disease Outbreaks Using Constrained P-spline Smoothing. Epidemiology. 2019;30(5):737-745. doi:10.1097/EDE.0000000000001050. [2] Wallinga J, Lipsitch M. How generation intervals shape the relationship between growth rates and reproductive numbers. Proc Biol Sci. 2007;274(1609):599-604. doi:10.1098/rspb.2006.3754. Description of the variables: Version: Version number of the dataset. When the content of the dataset is structurally changed (so not the daily update or a correction at record level), the version number will be adjusted (+1) and also the corresponding metadata in RIVMdata (https://data.rivm.nl) . Version 2 update (February 8, 2022): - In the calculation of the reproduction number, the date of the positive test result is now used instead of the GGD notification date. Version 3 update (February 17, 2022): - The calculation of the reproduction number now takes into account different generation times for different variants. For the variants up to and including Delta, the average generation time is 4 days, from Omikron it is 3.5 days. The reproduction number published here is a weighted average of the reproduction numbers per variant. Version 4 update (September 1, 2022): - From September 1, 2022, this dataset is split into two parts. The first part contains the dates from the start of the pandemic to October 3, 2021 (week 39) and contains "tm" in the file name. This data will no longer be updated. The second part contains the data from October 4, 2021 (week 40) and is updated every Tuesday and Friday. - Until August 31, the published reproduction number was calculated with the data of the day before publication. From September 1, the published reproduction number is calculated with the data of the day of publication. Version 5 update (March 31, 2023): - From March 15, 2023, the reproduction number is calculated based on COVID-19 hospital admissions according to the NICE hospital registration. From June 13, 2020 to March 14, 2023, the reproduction number was calculated on the basis of COVID-19 reports to the GGD. However, the number of reports is strongly determined by the test policy, and is less suitable as a basis for calculating the reproduction number due to the adjusted test policy as of March 10, 2023 and the closure of the GGD test lanes as of March 17, 2023. Until 12 June 2020, the reproduction number was also calculated on the basis of hospital admissions, but then as reported to the GGD. Date: Date for which the reproduction number was estimated Rt_low: Lower bound 95% confidence interval Rt_avg: Estimated reproduction number Rt_up: Upper bound 95% confidence interval population: patient population with value “hosp” for hospitalized patients or “testpos” for test positive patients For recent R estimates, the reliability is not great, because the reliability depends on the time between infection and becoming ill and the time between becoming ill and reporting. Therefore, the variable Rt_avg is absent in the last two weeks. -------------------------------------------------- --------------------------------------------- Covid-19 reproduction number The reproduction number R gives the average number of people infected by one person with COVID-19. To estimate this reproduction number, we use the number of reported COVID-19 hospital admissions per day in the Netherlands. This number of hospital admissions is tracked by the NICE Foundation (National Intensive Care Evaluation). Because a COVID-19 admission is reported with some delay in the reporting system, we correct the number of admissions for this delay [1]. The first day of illness is known for a large proportion of the reported cases. This information is used to estimate the first day of illness for hospital admissions. By displaying the number of COVID-19 admissions per date of the first day of illness, it is immediately possible to see whether the number of infections is increasing, peaking or decreasing. To calculate the reproduction number, it is also necessary to know the length of time between the first day of illness of a COVID-19 case and the first day of illness of his or her infector. This duration is an average of 4 days for SARS-CoV-2 variants in 2020 and 2021, and an average of 3.5 days for more recent variants, calculated on the basis of COVID-19 reports to the PHS. With this information, the value of the reproduction number is calculated as described in Wallinga & Lipsitch 2007 [2]. Until June 12, 2020, the reproduction number was calculated on the basis of COVID-19 hospital admissions, and until March 15, 2023, the reproduction number was calculated on the basis of COVID-19 reports to the GGDs. [1] van de Kassteele J, Eilers PHC, Wallinga J. Nowcasting the Number of New Symptomatic Cases During Infectious Disease Outbreaks Using Constrained P-spline Smoothing. Epidemiology. 2019;30(5):737-745. doi:10.1097/EDE.0000000000001050. [2] Wallinga J, Lipsitch M. How generation intervals shape the relationship between growth rates and reproductive numbers. Proc Biol Sci. 2007;274(1609):599-604. doi:10.1098/rspb.2006.3754. Description of the variables: Version: Version number of the dataset. When the content of the dataset is structurally changed (so not the daily update or a correction at record level), the version number will be adjusted (+1) and also the corresponding metadata in RIVMdata (https://data.rivm.nl). Version 2 update (February 8, 2022): - In the calculation of the reproduction number, the date of the positive test result is now used instead of the PHS notification date. Version 3 update (February 17, 2022): - The calculation of the reproduction number now takes into account different generation times for different variants. For the variants up to and including Delta, the average generation time is 4 days, from Omikron it is 3.5 days. The reproduction number published here is a weighted average of the reproduction numbers per variant. Version 4 update (September 1, 2022): - As of September 1, 2022, this dataset is split into two parts. The first part contains the dates from the start of the pandemic till October 3, 2021 (week 39) and contains "tm" in the file name. This data will no longer be updated. The second part contains the data from October 4, 2021 (week 40) and is updated every Tuesday and Friday. - Until August 31, the published reproduction number was calculated with the data of the day before publication. From September 1, the published reproduction number is calculated with the data of the day of publication. Version 5 update (March 31, 2023): - As of March 15, 2023, the reproduction number is calculated based on COVID-19 hospital admissions according to the NICE hospital registry. From June 13, 2020 to March 14, 2023, the reproduction number was calculated on the basis of COVID-19 reports to the PHS. However, the number of reports is strongly determined by the test policy, and is less suitable as a basis for calculating the reproduction number due to the adjusted test policy as of March 10, 2023 and the closure of the PHS test lanes as of March 17, 2023. Until 12 June 2020, the reproduction number was also calculated on the basis of hospital admissions, but then as reported to the PHS. Date: Date for which the reproduction number was estimated Rt_low: Lower limit 95% confidence interval Rt_avg: Estimated reproduction number Rt_up: Upper bound 95% confidence interval population: patient population with value “hosp” for hospitalized patients or “testpos” for test positive patients For recent R estimates, the reliability is not great, because the reliability depends on the time between infection and becoming ill and the time between becoming ill and reporting. Therefore, the variable Rt_avg is absent in the last two weeks.

Identifying changes in the reproduction number, rate of spread, and doubling time during the course of the COVID-19 outbreak whilst accounting for potential biases due to delays in case reporting both nationally and subnationally in Italy. These results are impacted by changes in testing effort, increases and decreases in testing effort will increase and decrease reproduction number estimates respectively.

Used R-codes and full set of figures

Comparison of per capita rates for COVID-19 infection, hospitalization and death for residents across three phases of the epidemic; State of Indiana.

Identifying changes in the reproduction number, rate of spread, and doubling time during the course of the COVID-19 outbreak whilst accounting for potential biases due to delays in case reporting both nationally and subnationally. These results are impacted by changes in testing effort, increases and decreases in testing effort will increase and decrease reproduction number estimates respectively.

Daily situation for Tennessee counties as reported by the Tennessee Department of Health. The data are posted on the department's coronavirus disease web page: https://www.tn.gov/health/cedep/ncov.html. Date on testing results and deaths was posted beginning March 31, 2020.CountyNS (County GNIS code)NAMELSAD (Legal/statistical area) -County of residence of COVID-19 casesCounty identifier (GEOID) - County FIPS codeCombined statistical area code (CBSAFP) - Metropolitan/Micropolitan Area codeCore-based area name (CBSA_TITLE) - Metropolitan/Micropolitan Area nameCore-based statistical area type (MSA_TYPE) - Core-based statistical area typeCore-based area county type (MSA_COUNTY_TYPE) - Type of county in core-based statistical areasHealth Department Region (HEALTH_DEPT_REG)Health Department Type (HEALTH_DEPT_TYPE)TN ECD Urban Rural Classification (ECD_URBAN_RURAL_CLASS)Positive Tests (TEST_POS) - Total number of people ever to test positive for COVID-19Negative Tests (TEST_NEG) - Total number of people with a negative COVID-19 test resultTotal Tests (TEST_TOT) - Total number of COVID-19 tests with reported resultNew Tests (TEST_NEW) - Number of new tests results posted since the previous dayTotal Cases (CASES_TOT) - Total number of people ever to have a confirmed or probably case of COVID-19 by countyNew Cases (CASES_NEW) - The number of new cases reported to have a confirmed case of COVID-19 since the report on the previous dayTotal Hospitalizations (HOSPITALIZED_TOT) - Number of patients that were ever hospitalized during their illness, it does not indicate the number of patients currently hospitalizeNew Hospitalizations (HOSPITALIZED_NEW) - Number of patients that were ever hospitalized in the previous 24-hour period. Does not indicate the number of patients currently hospitalizedTotal Recovered (RECOV_TOT) - Total Number of inactive/recovered COVID cases. Includes people 14 days beyond illness onset date, specimen collection date, investigation report date, or investigation start date.New Recovered (RECOV_NEW) - Change in the number of new inactive/recovered cases since the previous day.Total Deaths (DEATHS_TOT) - Number of COVID-19 related deaths that were ever reported by countyNew Deaths (DEATHS_NEW) - Number of COVID-19 related deaths that were reported since the previous dayActive Cases (ACTIVE_TOT) - Calculated as the total number of confirmed COVID-19 cases, less the number of recovered and deaths reportedNew Active Cases (ACTIVE_NEW) - Change in the number of active COVID-19 cases since the previous dayPopulation Estimate 2019 (POPESTIMATE2019) - 2019 vintage estimated population for counties by the U.S. Census BureauNOWcast Current (NOWCast_CURRENT) - UTK COVID-19 NOWCast estimate of the number of new daily casesEffective Rate Transmission (EffectiveR) - Effective reproduction or R is an estimate of the average number of new infections caused by a single infected individualEffect Rate Transmission Label (EffectiveR_LABEL)

Note: The date last updated is 2022-02-28, the dataset is no longer provided.

The R value, also known as the reproduction number, describes whether cases are currently increasing, decreasing or staying the same. It tells us the average number of people that someone with COVID-19 will infect.

For example, if the R value is:

• at 1, then an infected person will infect one other person, on average
• below 1, it means the rate of transmission was decreasing during that period
• above 1, the transmission rate was increasing

COVID-19 R values are updated weekly.

Data from https://www.alberta.ca/covid-19-alberta-data.aspx; updated 2022-03-18 16:08 with data as of end of day 2022-03-17.

Identifying changes in the reproduction number, rate of spread, and doubling time during the course of the COVID-19 outbreak whilst accounting for potential biases due to delays in case reporting both nationally and subnationally in Colombia. These results are impacted by changes in testing effort, increases and decreases in testing effort will increase and decrease reproduction number estimates respectively.

Identifying changes in the reproduction number, rate of spread, and doubling time during the course of the COVID-19 outbreak whilst accounting for potential biases due to delays in case reporting both nationally and subnationally. These results are impacted by changes in testing effort, increases and decreases in testing effort will increase and decrease reproduction number estimates respectively.

Repository of data, code, and analysis for manuscript titled "Scaling COVID-19 rates with population size in the United States".

Identifying changes in the reproduction number, rate of spread, and doubling time during the course of the COVID-19 outbreak whilst accounting for potential biases due to delays in case reporting both nationally and subnationally in the United Kingdom.

Identifying changes in the reproduction number, rate of spread, and doubling time during the course of the COVID-19 outbreak whilst accounting for potential biases due to delays in case reporting both nationally and subnationally in India. These results are impacted by changes in testing effort, increases and decreases in testing effort will increase and decrease reproduction number estimates respectively.

Identifying changes in the reproduction number, rate of spread, and doubling time during the course of the COVID-19 outbreak whilst accounting for potential biases due to delays in case reporting both nationally and subnationally in Belgium. These results are impacted by changes in testing effort, increases and decreases in testing effort will increase and decrease reproduction number estimates respectively.

Chinese_City_Temp is the Coronavirus Data from 'coronavirus' package in R with two new variables cumulative and percentage increase added in. Chinese_Temperature_Date is data for each province pulled out from mid Jan to mid Feb. Final_Data_Set_5_Day_Lag is the combination of the two with a 5 day lag.

(Stata) Analysis Do - does temperature regressions Humidity Do - does humidity regressions