In 2021, Suriname was the country with the highest incidence of HIV infections per 1,000 uninfected people among 24 nations in Latin America and the Caribbean, with 0.71 new cases per one thousand not infected individuals. Guyana and Jamaica ranked second and third, with a rate of 0.62 new HIV infections and 0.5 new HIV cases per 1,000 not infected population, respectively.

Given the lack of potential vaccines and effective medications, non-pharmaceutical interventions are the major option to curtail the spread of COVID-19. An accurate estimate of the potential impact of different non-pharmaceutical measures on containing, and identify risk factors influencing the spread of COVID-19 is crucial for planning the most effective interventions to curb the spread of COVID-19 and to reduce the deaths. Additive model-based bivariate causal discovery for scalar factors and multivariate Granger causality tests for time series factors are applied to the surveillance data of lab-confirmed Covid-19 cases in the US, University of Maryland Data (UMD) data, and Google mobility data from March 5, 2020 to August 25, 2020 in order to evaluate the contributions of social-biological factors, economics, the Google mobility indexes, and the rate of the virus test to the number of the new cases and number of deaths from COVID-19. We found that active cases/1,000 people, workplaces, tests done/1,000 people, imported COVID-19 cases, unemployment rate and unemployment claims/1,000 people, mobility trends for places of residence (residential), retail and test capacity were the popular significant risk factor for the new cases of COVID-19, and that active cases/1,000 people, workplaces, residential, unemployment rate, imported COVID cases, unemployment claims/1,000 people, transit stations, mobility trends (transit), tests done/1,000 people, grocery, testing capacity, retail, percentage of change in consumption, percentage of working from home were the popular significant risk factor for the deaths of COVID-19. We observed that no metrics showed significant evidence in mitigating the COVID-19 epidemic in FL and only a few metrics showed evidence in reducing the number of new cases of COVID-19 in AZ, NY and TX. Our results showed that the majority of non-pharmaceutical interventions had a large effect on slowing the transmission and reducing deaths, and that health interventions were still needed to contain COVID-19.

Based on a comparison of coronavirus deaths in 210 countries relative to their population, Peru had the most losses to COVID-19 up until July 13, 2022. As of the same date, the virus had infected over 557.8 million people worldwide, and the number of deaths had totaled more than 6.3 million. Note, however, that COVID-19 test rates can vary per country. Additionally, big differences show up between countries when combining the number of deaths against confirmed COVID-19 cases. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

The difficulties of death figures

This table aims to provide a complete picture on the topic, but it very much relies on data that has become more difficult to compare. As the coronavirus pandemic developed across the world, countries already used different methods to count fatalities, and they sometimes changed them during the course of the pandemic. On April 16, for example, the Chinese city of Wuhan added a 50 percent increase in their death figures to account for community deaths. These deaths occurred outside of hospitals and went unaccounted for so far. The state of New York did something similar two days before, revising their figures with 3,700 new deaths as they started to include “assumed” coronavirus victims. The United Kingdom started counting deaths in care homes and private households on April 29, adjusting their number with about 5,000 new deaths (which were corrected lowered again by the same amount on August 18). This makes an already difficult comparison even more difficult. Belgium, for example, counts suspected coronavirus deaths in their figures, whereas other countries have not done that (yet). This means two things. First, it could have a big impact on both current as well as future figures. On April 16 already, UK health experts stated that if their numbers were corrected for community deaths like in Wuhan, the UK number would change from 205 to “above 300”. This is exactly what happened two weeks later. Second, it is difficult to pinpoint exactly which countries already have “revised” numbers (like Belgium, Wuhan or New York) and which ones do not. One work-around could be to look at (freely accessible) timelines that track the reported daily increase of deaths in certain countries. Several of these are available on our platform, such as for Belgium, Italy and Sweden. A sudden large increase might be an indicator that the domestic sources changed their methodology.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

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.

This dataset provides values for CORONAVIRUS CASES reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

New cases detected daily in the last 14 and 7 days. Population data of more than 1,000 inhabitants

2019 Novel Coronavirus COVID-19 (2019-nCoV) Visual Dashboard and Map:
https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6

• Confirmed Cases by Country/Region/Sovereignty
• Confirmed Cases by Province/State/Dependency
• Deaths
• Recovered

Downloadable data:
https://github.com/CSSEGISandData/COVID-19

Additional Information about the Visual Dashboard:
https://systems.jhu.edu/research/public-health/ncov

This dataset has been retired as of February 17, 2023. This dataset will be kept for historical purposes, but will no longer be updated. Similar data are available on the state’s open data portal: https://data.chhs.ca.gov/dataset/covid-19-time-series-metrics-by-county-and-state.

A. DATASET DESCRIPTION This dataset contains COVID-19 positive confirmed cases aggregated by several different geographic areas and by day. COVID-19 cases are mapped to the residence of the individual and shown on the date the positive test was collected. In addition, 2019 American Community Survey (ACS) 5-year population estimates are included to calculate the cumulative rate per 10,000 residents.

Dataset covers cases going back to March 18th, 2020 when the first person in Marin County tested positive for COVID-19. This data may not be immediately available for recently reported cases and data will change to reflect as information becomes available. Data updated daily.

COVID-19 case data undergo quality assurance and other data verification processes and are continually updated to maximize completeness and accuracy of information. This means data may change for previous days as information is updated.

Geographic areas summarized are: 1. City, Town, or Community Area 2. Census Tracts 3. Census ZIP Code Tabulation Areas (ZCTAs)

B. HOW THE DATASET IS CREATED Addresses from the COVID-19 case data are geocoded by Marin County HHS. Those addresses are spatially joined to the geographic areas. Counts are generated based on the number of address points that match each geographic area for a given date.

The 2019 ACS estimates for population provided by the Census are used to create a cumulative rate which is equal to ([cumulative count up to that date] / [acs_population]) * 10000) representing the number of total cases per 10,000 residents (as of the specified date).

C. UPDATE PROCESS Geographic analysis is scripted by Marin HHS staff and synced to this dataset each day.

D. HOW TO USE THIS DATASET This dataset can be used to track the spread of COVID-19 throughout Marin County in a variety of geographic areas. Note that the new cases column in the data represents the number of new cases confirmed in a certain area on the specified day, while the cumulative cases column is the cumulative total of cases in a certain area as of the specified date.

Privacy rules in effect To protect privacy, certain rules are in effect: 1. Any area with a cumulative case count less than 10 are dropped for all days the cumulative count was less than 10. These will be null values. For example if a zip code did not have 10 cumulative cases until June 1, 2020 that location will not be included in the dataset until June 1. 2. Once an area has a cumulative case count of 10 or greater, that area will have a new row of case data every day following. 3. 3. Cases are dropped altogether for areas where acs_population < 1000. Some adjacent geographic areas may be combined until the ACS population exceeds 1,000 to still provide information for these regions.

Note: 14-day case rate or 30-day case rate where the counts are lower than 20 may be unstable. We advise caution in interpreting rates at these small numbers.

A note on Census ZIP Code Tabulation Areas (ZCTAs) ZIP Code Tabulation Areas are special boundaries created by the U.S. Census based on ZIP Codes developed by the USPS. They are not, however, the same thing. ZCTAs are areal representations of routes.

A. SUMMARY Medical provider confirmed COVID-19 cases and confirmed COVID-19 related deaths in San Francisco, CA aggregated by Census ZIP Code Tabulation Areas and normalized by 2018 American Community Survey (ACS) 5-year estimates for population data to calculate rate per 10,000 residents.

Cases and deaths are both mapped to the residence of the individual, not to where they were infected or died. For example, if one was infected in San Francisco at work but lives in the East Bay, those are not counted as SF Cases or if one dies in Zuckerberg San Francisco General but is from another county, that is also not counted in this dataset.

Dataset is cumulative and covers cases going back to March 2nd, 2020 when testing began. It is updated daily.

B. HOW THE DATASET IS CREATED Addresses from medical data are geocoded by the San Francisco Department of Public Health (SFDPH). Those addresses are spatially joined to the geographic areas. Counts are generated based on the number of address points that match each geographic area. The 2018 ACS estimates for population provided by the Census are used to create a rate which is equal to ([count] / [acs_population]) * 10000) representing the number of cases per 10,000 residents.

C. UPDATE PROCESS Geographic analysis is scripted by SFDPH staff and synced to this dataset each day.

D. HOW TO USE THIS DATASET Privacy rules in effect To protect privacy, certain rules are in effect: 1. Case counts greater than 0 and less than 10 are dropped - these will be null (blank) values 2. Cases dropped altogether for areas where acs_population < 1000

Rate suppression in effect where counts lower than 20 Rates are not calculated unless the case count is greater than or equal to 20. Rates are generally unstable at small numbers, so we avoid calculating them directly. We advise you to apply the same approach as this is best practice in epidemiology.

A note on Census ZIP Code Tabulation Areas (ZCTAs) ZIP Code Tabulation Areas are special boundaries created by the U.S. Census based on ZIP Codes developed by the USPS. They are not, however, the same thing. ZCTAs are polygonal representations of USPS ZIP Code service area routes. Read how the Census develops ZCTAs on their website.

This dataset is a filtered view of another dataset You can find a full dataset of cases and deaths summarized by this and other geographic areas.

E. CHANGE LOG

• 9/11/2023 - data on COVID-19 cases and deaths summarized by ZIP code tabulation area are no longer being updated. This data is currently through 9/6/2023 and will not include any new data after this date.

Laos LA: Incidence of Malaria: per 1,000 Population at Risk data was reported at 20.900 Number in 2015. This records an increase from the previous number of 17.800 Number for 2010. Laos LA: Incidence of Malaria: per 1,000 Population at Risk data is updated yearly, averaging 19.350 Number from Dec 2000 (Median) to 2015, with 4 observations. The data reached an all-time high of 77.500 Number in 2000 and a record low of 14.000 Number in 2005. Laos LA: Incidence of Malaria: per 1,000 Population at Risk data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Laos – Table LA.World Bank: Health Statistics. Incidence of malaria is the number of new cases of malaria in a year per 1,000 population at risk.; ; World Health Organization, Global Health Observatory Data Repository/World Health Statistics (http://apps.who.int/ghodata/).; Weighted Average;

Saudi Arabia SA: Incidence of Malaria: per 1,000 Population at Risk data was reported at 0.100 Number in 2015. This records an increase from the previous number of 0.028 Number for 2010. Saudi Arabia SA: Incidence of Malaria: per 1,000 Population at Risk data is updated yearly, averaging 0.150 Number from Dec 2000 (Median) to 2015, with 4 observations. The data reached an all-time high of 6.000 Number in 2000 and a record low of 0.028 Number in 2010. Saudi Arabia SA: Incidence of Malaria: per 1,000 Population at Risk data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Saudi Arabia – Table SA.World Bank: Health Statistics. Incidence of malaria is the number of new cases of malaria in a year per 1,000 population at risk.; ; World Health Organization, Global Health Observatory Data Repository/World Health Statistics (http://apps.who.int/ghodata/).; Weighted average;

Syria SY: Incidence of Malaria: per 1,000 Population at Risk data was reported at 0.000 Number in 2022. This stayed constant from the previous number of 0.000 Number for 2021. Syria SY: Incidence of Malaria: per 1,000 Population at Risk data is updated yearly, averaging 0.000 Number from Dec 2000 (Median) to 2022, with 23 observations. The data reached an all-time high of 0.004 Number in 2001 and a record low of 0.000 Number in 2022. Syria SY: Incidence of Malaria: per 1,000 Population at Risk data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Syrian Arab Republic – Table SY.World Bank.WDI: Social: Health Statistics. Incidence of malaria is the number of new cases of malaria in a year per 1,000 population at risk.;World Health Organization, World malaria report and Global Health Observatory Data Repository/World Health Statistics (http://apps.who.int/ghodata/).;Weighted average;This is the Sustainable Development Goal indicator 3.3.3[https://unstats.un.org/sdgs/metadata/].

In 2021, there were 0.09 new cases of HIV per 1,000 population at risk in Vietnam. The incidence rate of HIV per 1,000 population aged between 15 and 49 in Vietnam has been declining steadily year on year in the observed period.

Egypt EG: Incidence of Malaria: per 1,000 Population at Risk data was reported at 0.000 Number in 2022. This stayed constant from the previous number of 0.000 Number for 2021. Egypt EG: Incidence of Malaria: per 1,000 Population at Risk data is updated yearly, averaging 0.000 Number from Dec 2000 (Median) to 2022, with 23 observations. The data reached an all-time high of 0.000 Number in 2022 and a record low of 0.000 Number in 2022. Egypt EG: Incidence of Malaria: per 1,000 Population at Risk data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Egypt – Table EG.World Bank.WDI: Social: Health Statistics. Incidence of malaria is the number of new cases of malaria in a year per 1,000 population at risk.;World Health Organization, World malaria report and Global Health Observatory Data Repository/World Health Statistics (http://apps.who.int/ghodata/).;Weighted average;This is the Sustainable Development Goal indicator 3.3.3[https://unstats.un.org/sdgs/metadata/].

COVID-19 rate of death, or the known deaths divided by confirmed cases, was over ten percent in Yemen, the only country that has 1,000 or more cases. This according to a calculation that combines coronavirus stats on both deaths and registered cases for 221 different countries. Note that death rates are not the same as the chance of dying from an infection or the number of deaths based on an at-risk population. By April 26, 2022, the virus had infected over 510.2 million people worldwide, and led to a loss of 6.2 million. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. Note that Statista aims to also provide domestic source material for a more complete picture, and not to just look at one particular source. Examples are these statistics on the confirmed coronavirus cases in Russia or the COVID-19 cases in Italy, both of which are from domestic sources. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

A word on the flaws of numbers like this

People are right to ask whether these numbers are at all representative or not for several reasons. First, countries worldwide decide differently on who gets tested for the virus, meaning that comparing case numbers or death rates could to some extent be misleading. Germany, for example, started testing relatively early once the country’s first case was confirmed in Bavaria in January 2020, whereas Italy tests for the coronavirus postmortem. Second, not all people go to see (or can see, due to testing capacity) a doctor when they have mild symptoms. Countries like Norway and the Netherlands, for example, recommend people with non-severe symptoms to just stay at home. This means not all cases are known all the time, which could significantly alter the death rate as it is presented here. Third and finally, numbers like this change very frequently depending on how the pandemic spreads or the national healthcare capacity. It is therefore recommended to look at other (freely accessible) content that dives more into specifics, such as the coronavirus testing capacity in India or the number of hospital beds in the UK. Only with additional pieces of information can you get the full picture, something that this statistic in its current state simply cannot provide.

Kazakhstan KZ: Incidence of Malaria: per 1,000 Population at Risk data was reported at 0.000 Number in 2019. This stayed constant from the previous number of 0.000 Number for 2018. Kazakhstan KZ: Incidence of Malaria: per 1,000 Population at Risk data is updated yearly, averaging 0.000 Number from Dec 2010 (Median) to 2019, with 10 observations. The data reached an all-time high of 0.000 Number in 2019 and a record low of 0.000 Number in 2019. Kazakhstan KZ: Incidence of Malaria: per 1,000 Population at Risk data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kazakhstan – Table KZ.World Bank.WDI: Social: Health Statistics. Incidence of malaria is the number of new cases of malaria in a year per 1,000 population at risk.;World Health Organization, World malaria report and Global Health Observatory Data Repository/World Health Statistics (http://apps.who.int/ghodata/).;Weighted average;This is the Sustainable Development Goal indicator 3.3.3[https://unstats.un.org/sdgs/metadata/].

Mexico MX: Incidence of Malaria: per 1,000 Population at Risk data was reported at 0.270 Number in 2017. This records an increase from the previous number of 0.200 Number for 2016. Mexico MX: Incidence of Malaria: per 1,000 Population at Risk data is updated yearly, averaging 0.270 Number from Dec 2000 (Median) to 2017, with 9 observations. The data reached an all-time high of 3.430 Number in 2000 and a record low of 0.190 Number in 2015. Mexico MX: Incidence of Malaria: per 1,000 Population at Risk data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mexico – Table MX.World Bank.WDI: Health Statistics. Incidence of malaria is the number of new cases of malaria in a year per 1,000 population at risk.; ; World Health Organization, Global Health Observatory Data Repository/World Health Statistics (http://apps.who.int/ghodata/).; Weighted average;

Central African Republic CF: Incidence of Malaria: per 1,000 Population at Risk data was reported at 310.590 Number in 2022. This records a decrease from the previous number of 310.880 Number for 2021. Central African Republic CF: Incidence of Malaria: per 1,000 Population at Risk data is updated yearly, averaging 394.235 Number from Dec 2000 (Median) to 2022, with 23 observations. The data reached an all-time high of 444.529 Number in 2006 and a record low of 310.590 Number in 2022. Central African Republic CF: Incidence of Malaria: per 1,000 Population at Risk data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Central African Republic – Table CF.World Bank.WDI: Social: Health Statistics. Incidence of malaria is the number of new cases of malaria in a year per 1,000 population at risk.;World Health Organization, World malaria report and Global Health Observatory Data Repository/World Health Statistics (http://apps.who.int/ghodata/).;Weighted average;This is the Sustainable Development Goal indicator 3.3.3[https://unstats.un.org/sdgs/metadata/].

Georgia GE: Incidence of Malaria: per 1,000 Population at Risk data was reported at 0.000 Number in 2015. This stayed constant from the previous number of 0.000 Number for 2010. Georgia GE: Incidence of Malaria: per 1,000 Population at Risk data is updated yearly, averaging 3.800 Number from Dec 2000 (Median) to 2015, with 4 observations. The data reached an all-time high of 11.300 Number in 2000 and a record low of 0.000 Number in 2015. Georgia GE: Incidence of Malaria: per 1,000 Population at Risk data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Georgia – Table GE.World Bank: Health Statistics. Incidence of malaria is the number of new cases of malaria in a year per 1,000 population at risk.; ; World Health Organization, Global Health Observatory Data Repository/World Health Statistics (http://apps.who.int/ghodata/).; Weighted Average;

Mauritania MR: Incidence of Malaria: per 1,000 Population at Risk data was reported at 74.200 Number in 2015. This records a decrease from the previous number of 79.700 Number for 2010. Mauritania MR: Incidence of Malaria: per 1,000 Population at Risk data is updated yearly, averaging 94.950 Number from Dec 2000 (Median) to 2015, with 4 observations. The data reached an all-time high of 116.300 Number in 2005 and a record low of 74.200 Number in 2015. Mauritania MR: Incidence of Malaria: per 1,000 Population at Risk data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mauritania – Table MR.World Bank: Health Statistics. Incidence of malaria is the number of new cases of malaria in a year per 1,000 population at risk.; ; World Health Organization, Global Health Observatory Data Repository/World Health Statistics (http://apps.who.int/ghodata/).; Weighted Average;