As of April 7, 2022, the total number of COVID-19 cases in Singapore amounted to around 1.1 million. There has been a decrease in daily cases in Singapore this week, though the number is still expected to rise largely due to the highly-contagious Omicron variant.

Overcoming the COVID-19 pandemic Singapore was one of the few countries worldwide that had managed to successfully control the spread of COVID-19. This was done through imposing a strict lockdown period during the beginning of the pandemic in 2020, introducing and enforcing hygiene and social-distancing rules, and effective contact tracing, among others. The measures in place had the intended impact, as the number of daily recorded cases have decreased to manageable levels. Furthermore, community transmission has been reduced to just several cases a week; the majority of the daily new cases of COVID-19 recorded were from overseas arrivals.

Recovering from the economic impact of COVID-19 The closure of businesses, compounded by the global restrictions on movement, had had an adverse effect on its economy. Singapore went through its worse recession on record, while the resident unemployment rate increased. However, with restrictions in the country easing, economists have raised their forecasts for economic growth in Singapore for 2021.

Singapore is currently one out of more than 200 countries and territories battling the novel coronavirus. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

On November 4, 2022, Singapore recorded 3,128 new confirmed cases of COVID-19. Although the number of daily cases is started to decline, Singapore is still expecting a rise in cases caused by the highly-contagious Omicron variant.

Singapore is currently one out of more than 200 countries and territories battling the novel coronavirus. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

As of April 7, 2022, 416 people in Singapore were hospitalized due to COVID-19. Out of these, 44 cases required oxygen supplementation, while 15 in the ICU. To date, 1,290 deaths have so far been attributed to COVID-19.

State of the coronavirus (COVID-19) pandemic in Singapore As of February 2, 2022, Singapore had registered more than 362 thousand confirmed cases of COVID-19. Despite having an 88 percent COVID-19 vaccination rate, the country has been going through a surge in COVID-19 infections now caused by the highly-contagious Omicron variant. This has led to delays in its plans to reopen the country for a 'return to normal'.

Gradual return to normalcy? Due to the current increase in COVID-19 infections, Singapore has pushed back plans to remove the restrictions imposed to control the pandemic, with the Prime Minister estimating that it would be another three to six months before the 'new normal' could begin. This was to prevent the healthcare system from being overstressed. While vaccination rates remain high, hospitalization rates have increased, with the majority of those hospitalized being unvaccinated.

Singapore is currently one out of more than 200 countries and territories battling the novel coronavirus. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

In 2022, there was a total of 2,511 reported cases of tuberculosis in Singapore. This was an increase in tuberculosis cases compared to the ones reported in the previous year, which was 2,314 cases.

Forecast: Trunks and Cases of Leather Market Size Value in Singapore 2022 - 2026 Discover more data with ReportLinker!

Singapore SG: Tuberculosis Treatment Success Rate: % of New Cases data was reported at 79.000 % in 2015. This records an increase from the previous number of 78.000 % for 2014. Singapore SG: Tuberculosis Treatment Success Rate: % of New Cases data is updated yearly, averaging 79.000 % from Dec 2000 (Median) to 2015, with 16 observations. The data reached an all-time high of 83.000 % in 2011 and a record low of 71.000 % in 2000. Singapore SG: Tuberculosis Treatment Success Rate: % of New Cases data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Singapore – Table SG.World Bank.WDI: Health Statistics. Tuberculosis treatment success rate is the percentage of all new tuberculosis cases (or new and relapse cases for some countries) registered under a national tuberculosis control programme in a given year that successfully completed treatment, with or without bacteriological evidence of success ('cured' and 'treatment completed' respectively).; ; World Health Organization, Global Tuberculosis Report.; Weighted average;

Singapore SG: Tuberculosis Case Detection Rate: All Forms data was reported at 87.000 % in 2016. This stayed constant from the previous number of 87.000 % for 2015. Singapore SG: Tuberculosis Case Detection Rate: All Forms data is updated yearly, averaging 87.000 % from Dec 2000 (Median) to 2016, with 17 observations. The data reached an all-time high of 87.000 % in 2016 and a record low of 87.000 % in 2016. Singapore SG: Tuberculosis Case Detection Rate: All Forms data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Singapore – Table SG.World Bank.WDI: Health Statistics. Tuberculosis case detection rate (all forms) is the number of new and relapse tuberculosis cases notified to WHO in a given year, divided by WHO's estimate of the number of incident tuberculosis cases for the same year, expressed as a percentage. Estimates for all years are recalculated as new information becomes available and techniques are refined, so they may differ from those published previously.; ; World Health Organization, Global Tuberculosis Report.; Weighted average;

Trunks and Cases of Leather Market Size Value Per Capita in Singapore, 2021 Discover more data with ReportLinker!

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretability. We also formatted the data into a standard data format.

Each Project Tycho dataset contains case counts for a specific condition (e.g. measles) and for a specific country (e.g. The United States). Case counts are reported per time interval. In addition to case counts, datasets include information about these counts (attributes), such as the location, age group, subpopulation, diagnostic certainty, place of acquisition, and the source from which we extracted case counts. One dataset can include many series of case count time intervals, such as "US measles cases as reported by CDC", or "US measles cases reported by WHO", or "US measles cases that originated abroad", etc.

Depending on the intended use of a dataset, we recommend a few data processing steps before analysis: - Analyze missing data: Project Tycho datasets do not include time intervals for which no case count was reported (for many datasets, time series of case counts are incomplete, due to incompleteness of source documents) and users will need to add time intervals for which no count value is available. Project Tycho datasets do include time intervals for which a case count value of zero was reported. - Separate cumulative from non-cumulative time interval series. Case count time series in Project Tycho datasets can be "cumulative" or "fixed-intervals". Cumulative case count time series consist of overlapping case count intervals starting on the same date, but ending on different dates. For example, each interval in a cumulative count time series can start on January 1st, but end on January 7th, 14th, 21st, etc. It is common practice among public health agencies to report cases for cumulative time intervals. Case count series with fixed time intervals consist of mutually exclusive time intervals that all start and end on different dates and all have identical length (day, week, month, year). Given the different nature of these two types of case count data, we indicated this with an attribute for each count value, named "PartOfCumulativeCountSeries".

In 2022, there was four reported cases of measles in Singapore. This was a steep decrease in comparison to the cases reported in the previous years. The decline in the measles cases in Singapore in that year was likely due to the social distancing measures implemented during the COVID-19 pandemic, as well as Singapore's high rate of measles vaccinations.

Statistics illustrates market overview of watch cases; n.e.s. in heading no. 9111 in Singapore from 2007 to 2024.

Statistics illustrates market overview of clock cases and similar cases for other goods of chapter 91; other than watch cases in Singapore from 2007 to 2024.

Statistics illustrates consumption, production, prices, and trade of Packing Cases, Boxes and Similar Packings in Singapore from 2007 to 2024.

Singapore Crime Cases Recorded: Miscellaneous Crimes data was reported at 5,639.000 Number in 2017. This records a decrease from the previous number of 5,663.000 Number for 2016. Singapore Crime Cases Recorded: Miscellaneous Crimes data is updated yearly, averaging 4,179.000 Number from Dec 2005 (Median) to 2017, with 13 observations. The data reached an all-time high of 5,663.000 Number in 2016 and a record low of 3,699.000 Number in 2009. Singapore Crime Cases Recorded: Miscellaneous Crimes data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Singapore – Table SG.G077: Public Safety.

Singapore Exports of wood marquetry and inlaid wood, caskets and cases of wood, statuettes of wood to Switzerland was US$18.28 Thousand during 2023, according to the United Nations COMTRADE database on international trade.

Statistics illustrates consumption, production, prices, and trade of Watch cases and parts thereof in Singapore from Jan 2019 to Feb 2025.

In 2023, crime cases in Singapore increased to around 70,342 from around 53,862 cases in 2022. The overall crime cases in Singapore had been increasing since 2017.

Robbery rate of Singapore increased by 4.89% from 1.2 cases per 100,000 population in 2017 to 1.3 cases per 100,000 population in 2018. Since the 22.80% drop in 2016, robbery rate sank by 25.33% in 2018. "Robbery” means the theft of property from a person; overcoming resistance by force or threat of force. Where possible; the category “Robbery” should include muggings (bag-snatching) and theft with violence; but should exclude pick pocketing and extortion. (UN-CTS M3.5)

Singapore Exports of watch cases and to Hong Kong was US$330.94 Thousand during 2023, according to the United Nations COMTRADE database on international trade. Singapore Exports of watch cases and to Hong Kong - data, historical chart and statistics - was last updated on May of 2025.

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretabilty. We also formatted the data into a standard data format. Each Project Tycho dataset contains case counts for a specific condition (e.g. measles) and for a specific country (e.g. The United States). Case counts are reported per time interval. In addition to case counts, datsets include information about these counts (attributes), such as the location, age group, subpopulation, diagnostic certainty, place of aquisition, and the source from which we extracted case counts. One dataset can include many series of case count time intervals, such as "US measles cases as reported by CDC", or "US measles cases reported by WHO", or "US measles cases that originated abroad", etc. Depending on the intended use of a dataset, we recommend a few data processing steps before analysis:

Analyze missing data: Project Tycho datasets do not inlcude time intervals for which no case count was reported (for many datasets, time series of case counts are incomplete, due to incompleteness of source documents) and users will need to add time intervals for which no count value is available. Project Tycho datasets do include time intervals for which a case count value of zero was reported. Separate cumulative from non-cumulative time interval series. Case count time series in Project Tycho datasets can be "cumulative" or "fixed-intervals". Cumulative case count time series consist of overlapping case count intervals starting on the same date, but ending on different dates. For example, each interval in a cumulative count time series can start on January 1st, but end on January 7th, 14th, 21st, etc. It is common practice among public health agencies to report cases for cumulative time intervals. Case count series with fixed time intervals consist of mutually exxclusive time intervals that all start and end on different dates and all have identical length (day, week, month, year). Given the different nature of these two types of case count data, we indicated this with an attribute for each count value, named "PartOfCumulativeCountSeries".