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.

Dataset from Ministry of Health. For more information, visit https://data.gov.sg/datasets/d_37c77bafba57a15da0da74326d6cc077/view

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.

Context

Since the beginning of the Covid-19 outbreak, Singapore's MOH has been providing daily press releases to update citizens on the confirmed cases, their background and so on.

Content

Each row represents a confirmed case, with attributes like gender, age, case-related information which I extracted from the MOH's daily press releases.

Acknowledgements

MOH's Press Releases. https://www.moh.gov.sg/news-highlights

Inspiration

To see the trends in Covid-19 spread in Singapore.

Context

From World Health Organization - On 31 December 2019, WHO was alerted to several cases of pneumonia in Wuhan City, Hubei Province of China. The virus did not match any other known virus. This raised concern because when a virus is new, we do not know how it affects people.

So daily level information on the affected people can give some interesting insights when it is made available to the broader data science community.

Johns Hopkins University has made an excellent dashboard using the affected cases data. Data is extracted from the google sheets associated and made available here.

Edited: Now data is available as csv files in the Johns Hopkins Github repository. Please refer to the github repository for the Terms of Use details. Uploading it here for using it in Kaggle kernels and getting insights from the broader DS community.

Content

2019 Novel Coronavirus (2019-nCoV) is a virus (more specifically, a coronavirus) identified as the cause of an outbreak of respiratory illness first detected in Wuhan, China. Early on, many of the patients in the outbreak in Wuhan, China reportedly had some link to a large seafood and animal market, suggesting animal-to-person spread. However, a growing number of patients reportedly have not had exposure to animal markets, indicating person-to-person spread is occurring. At this time, it’s unclear how easily or sustainably this virus is spreading between people - CDC

This dataset has daily level information on the number of affected cases, deaths and recovery from 2019 novel coronavirus. Please note that this is a time series data and so the number of cases on any given day is the cumulative number.

The data is available from 22 Jan, 2020.

Column Description

Main file in this dataset is covid_19_data.csv and the detailed descriptions are below.

covid_19_data.csv

• Sno - Serial number
• ObservationDate - Date of the observation in MM/DD/YYYY
• Province/State - Province or state of the observation (Could be empty when missing)
• Country/Region - Country of observation
• Last Update - Time in UTC at which the row is updated for the given province or country. (Not standardised and so please clean before using it)
• Confirmed - Cumulative number of confirmed cases till that date
• Deaths - Cumulative number of of deaths till that date
• Recovered - Cumulative number of recovered cases till that date

Apart from that these two files have individual level information

COVID_open_line_list_data.csv This file is originally obtained from this link

COVID19_line_list_data.csv This files is originally obtained from this link

Country level datasets If you are interested in knowing country level data, please refer to the following Kaggle datasets: South Korea - https://www.kaggle.com/kimjihoo/coronavirusdataset Italy -
https://www.kaggle.com/sudalairajkumar/covid19-in-italy

Acknowledgements

• Johns Hopkins University for making the data available for educational and academic research purposes
• MoBS lab - https://www.mobs-lab.org/2019ncov.html World Health Organization (WHO): https://www.who.int/
• DXY.cn. Pneumonia. 2020. http://3g.dxy.cn/newh5/view/pneumonia.
• BNO News: https://bnonews.com/index.php/2020/02/the-latest-coronavirus-cases/
• National Health Commission of the People’s Republic of China (NHC): http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml
• China CDC (CCDC): http://weekly.chinacdc.cn/news/TrackingtheEpidemic.htm
• Hong Kong Department of Health: https://www.chp.gov.hk/en/features/102465.html
• Macau Government: https://www.ssm.gov.mo/portal/ Taiwan CDC: https://sites.google.com/cdc.gov.tw/2019ncov/taiwan?authuser=0
• US CDC: https://www.cdc.gov/coronavirus/2019-ncov/index.html
• Government of Canada: https://www.canada.ca/en/public-health/services/diseases/coronavirus.html
• Australia Government Department of Health: https://www.health.gov.au/news/coronavirus-update-at-a-glance
• European Centre for Disease Prevention and Control (ECDC): https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-cases
• Ministry of Health Singapore (MOH): https://www.moh.gov.sg/covid-19
• Italy Ministry of Health: http://www.salute.gov.it/nuovocoronavirus

Inspiration

Some useful insi...

On June 25, 2020, 305 patients with COVID-19 were discharged from Singapore hospitals and self-isolation facilities. As of that date, around 42.7 thousand people had been infected with the novel coronavirus in Singapore, and around 36.6 thousand people have recovered. The country has since introduced a stimulus package worth 48 billion Singapore dollars to help the Singapore economy, which had been badly hit by the pandemic.

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.

A data set on COVID-19 pandemic in China, which covers daily statistics of confirmed cases (new and cumulative), recoveries (new and cumulative) and deaths (new and cumulative) at city/county level. All data are extracted from Chinese government reports.

I continue to work on improving this Dataset and will upload as soon as I have an improved version of it. I don't own this dataset, I have merely tried to enrich the data that is gathered from multiple sources by John Hopkins CSSE.

Context

COVID-19 is perhaps the biggest historical event of our lifetime with the kind of destruction and disruption it has already caused to the people around the world. I wanted to build a dashboard summarizing the events from beginning to date and that's the reason I worked on combining all the daily reports into one file.

Content

This file consists of incidents reported from across the world Jan 22 onwards. Incidents are categorized into Confirmed, Deaths and Recovered. Country/Region and/or Province/State information is available. Geo-coordinates are available but these are missing for countries like China

Acknowledgements

This data belongs to John Hopkins CSSE which they gathered from multiple sources. Below is from JHU Github account, please read before using the dataset.

This is the data repository for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). Also, Supported by ESRI Living Atlas Team and the Johns Hopkins University Applied Physics Lab (JHU APL).

Visual Dashboard (desktop): https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6

Visual Dashboard (mobile): http://www.arcgis.com/apps/opsdashboard/index.html#/85320e2ea5424dfaaa75ae62e5c06e61

Lancet Article: An interactive web-based dashboard to track COVID-19 in real time

Provided by Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE): https://systems.jhu.edu/

Data Sources:

World Health Organization (WHO): https://www.who.int/ DXY.cn. Pneumonia. 2020. http://3g.dxy.cn/newh5/view/pneumonia. BNO News: https://bnonews.com/index.php/2020/02/the-latest-coronavirus-cases/ National Health Commission of the People’s Republic of China (NHC): http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml China CDC (CCDC): http://weekly.chinacdc.cn/news/TrackingtheEpidemic.htm Hong Kong Department of Health: https://www.chp.gov.hk/en/features/102465.html Macau Government: https://www.ssm.gov.mo/portal/ Taiwan CDC: https://sites.google.com/cdc.gov.tw/2019ncov/taiwan?authuser=0 US CDC: https://www.cdc.gov/coronavirus/2019-ncov/index.html Government of Canada: https://www.canada.ca/en/public-health/services/diseases/coronavirus.html Australia Government Department of Health: https://www.health.gov.au/news/coronavirus-update-at-a-glance European Centre for Disease Prevention and Control (ECDC): https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-cases Ministry of Health Singapore (MOH): https://www.moh.gov.sg/covid-19 Italy Ministry of Health: http://www.salute.gov.it/nuovocoronavirus 1Point3Arces: https://coronavirus.1point3acres.com/en WorldoMeters: https://www.worldometers.info/coronavirus/

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

Contact Us:

Email: jhusystems@gmail.com

Terms of Use:

This GitHub repo and its contents herein, including all data, mapping, and analysis, copyright 2020 Johns Hopkins University, all rights reserved, is provided to the public strictly for educational and academic research purposes. The Website relies upon publicly available data from multiple sources, that do not always agree. The Johns Hopkins University hereby disclaims any and all representations and warranties with respect to the Website, including accuracy, fitness for use, and merchantability. Reliance on the Website for medical guidance or use of the Website in commerce is strictly prohibited.

Inspiration

COVID-19 is perhaps the biggest historical event of our lifetime with the kind of destruction and disruption it has already caused to the people around the world. I wanted to build a dashboard summarizing the events from beginning to date and that's the reason I worked on combining all the daily reports into one file.

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 July 13, 2022, 58 percent of Singaporean respondents stated that they had been avoiding public places during the COVID-19 outbreak, up from 44 percent on Feb 21, 2020. Singapore is experiencing a decrease in the number of confirmed daily cases, although the country is still expecting a rise in cases caused by the highly-contagious Omicron variant.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

As of April 2020, entering the first week of circuit breaker measures to restrain the spread of COVID-19 in Singapore, 46 percent of the respondents stated they left their homes once during the previous day. In response to the growing number of new cases, Singapore announced on April 3 a set of preventive "circuit breaker" measures, to be applied from April 7 to June 1.

In 2024, there were 13,655 reported cases of dengue fever and dengue hemorrhagic fever. This was an increase from the number of cases reported in the previous year. Largest outbreak in Singapore Dengue is one of Singapore’s most pressing endemic infectious disease. Since Singapore was declared malaria-free by the World Health Organization in 1982, the island-state has been focusing on fighting dengue and other infectious diseases such as HIV and tuberculosis. However, unlike malaria, Singapore has not been able to eradicate this mosquito-borne disease. The National Environment Agency (NEA) stated that Singapore saw one of the biggest dengue outbreaks in its history in 2020, reaching more than 35,000 cases. More dengue cases during Circuit Breaker period According to NEA, the increase in dengue infections were due to several factors: a change in the dominant dengue serotype, meaning fewer people would have immunity against it; the warmer months which allows mosquitoes to breed easily; and the impact of the ”circuit breaker” measures to control the COVID-19 pandemic in Singapore.During the two months of the “circuit breaker” period ending June 1, 2020, there were five times more cases of Aedes mosquito larvae detected in housing areas compared to the two months preceding it. As of May 1, 2020, 54 percent of Singaporean respondents stated that they had been avoiding going to work during the COVID-19 outbreak, up from 11 percent on Feb 21, 2020. With more people staying at home, and as the Aedes mosquitoes are active during the daytime, this period could have led to a higher number of dengue cases. To reduce the case numbers, since March 2020, the Inter-Agency Dengue Task Force (IADTF), including Town Councils, started to remove potential mosquito breeding grounds in public spaces and residences.