Pakistan recorded 1580631 Coronavirus Cases since the epidemic began, according to the World Health Organization (WHO). In addition, Pakistan reported 30656 Coronavirus Deaths. This dataset includes a chart with historical data for Pakistan Coronavirus Cases.

In past 24 hours, Pakistan, Asia had N/A new cases, N/A deaths and N/A recoveries.

Pakistan COVID-19 Dataset

This is the data repository for the 2019 Novel Coronavirus cases in Pakistan.

Daily reports (daily_reports)

This folder contains daily case reports. All timestamps are in UTC (GMT+0). Provincial Data is only available from 11th April 2020, previous reports have data of Pakistan as whole.

File naming convention

YYYY-MM-DD.csv in UTC.

Field description

• Province_State: Province, state or dependency name.
• Country_Region: Country, region or sovereignty name.
• Last Update: YYYY-MM-DD HH:mm:ss (24 hour format, in UTC).
• Lat and Long_: Latitude and Longitude locations on the map. All points shown on the map are based on geographic centroids, and are not representative of a specific address, building or any location at a spatial scale finer than a province/state.
• Confirmed: Counts include confirmed and probable (where reported).
• Deaths: Counts include confirmed and probable (where reported).
• Recovered: Recovered cases are estimates based on local media reports, and state and local reporting when available, and therefore may be substantially lower than the true number.
• Active: Active cases = total cases - total recovered - total deaths.
• Incident_Rate: Incidence Rate = cases per 100,000 persons.
• Case_Fatality_Ratio (%): Case-Fatality Ratio (%) = Number recorded deaths / Number cases.
• All cases, deaths, and recoveries reported are based on the date of initial report.

Combined report (combined_report.csv)

This file contains all the daily cases reports combined into one.

Data sources

• Government of Pakistan
• JHU CSSE COVID-19 Dataset

Content

The coronavirus, or COVID-19, is inciting panic for a number of reasons. It's a new virus, meaning no one has immunity, and there is no vaccine. Its novelty means that scientists aren't sure yet how it behaves they have little history to go on.

This dataset contains updated information on the number of COVID-19 cases in Pakistan. Please note that this is a time-series data so that it will be updated on a daily bases.

This dataset contain d data from 19/12/2019. Upvote if you like it...

Acknowledgements

Wikipedia 2020 Coronavirus pandemic data/ Pakistan medical cases Ministry of National Health and Services Regulations and Coordinations COVID-19 Health Advisory Platform

Inspiration

1. Data will be updated on a daily bases
2. More information will be uploaded

Analysis of ‘Pakistan Corona Virus Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/zusmani/pakistan-corona-virus-citywise-data on 30 September 2021.

--- Dataset description provided by original source is as follows ---

Context

Pakistan witnessed its first Corona virus patient on February 26th 2020. It's a bumpy ride since then. The cases are increasing gradually and we haven't seen the worst yet. While, there are few government resources for cumulative updates, there is no place where you can find the city level patients data. It's also not possible to find the running chronological tally of patients as they test positive. We have decided to create our own dataset for all the researchers out there with such details so we can model the infection spread and forecast the situation in coming days. We hope, by doing so, we will be able to inform policy makers on various intervention models, and healthcare professionals to be ready for the influx of new patients. We certainly hope, that this little contribution will go a long way for saving lives in Pakistan

Content

The dataset contains seven columns for date, number of cases, number of deaths, number of people recovered, travel history of those cases, and location of the cases (province and city).

The first version has the data from first case of February 26 2020 to April 19, 2020. We intend to publish weekly updates

Acknowledgements

Users are allowed to use, copy, distribute and cite the dataset as follows: “Zeeshan-ul-hassan Usmani, Sana Rasheed, Pakistan Corona Virus Data, Kaggle Dataset Repository, April 19, 2020.”

Inspiration

Some ideas worth exploring:

Can we find the spread factor for the Corona virus in Pakistan?

How long it takes for a positive case to infect another in Pakistan?

How we can use this data to simulate lock down scenarios and find its impact on country's economy? Here is a good
read to get started - http://zeeshanusmani.com/urdu/corona-economic-impact/

How does Pakistan Corona virus spread compare against its neighbors and other developed counties?

What would be the impact of this infection spread on country's economy and people living under poverty? Here are two briefs to get you started

http://zeeshanusmani.com/urdu/corona/ http://zeeshanusmani.com/urdu/corona-what-to-learn/

How do we visualize this dataset to inform policy makers? Here is one example https://zeeshanusmani.com/corona/

Can we predict the number of cases in next 10 days and a month?

--- Original source retains full ownership of the source dataset ---

As of May 2, 2023, the outbreak of the coronavirus disease (COVID-19) had been confirmed in almost every country in the world. The virus had infected over 687 million people worldwide, and the number of deaths had reached almost 6.87 million. The most severely affected countries include the U.S., India, and Brazil.

COVID-19: background information COVID-19 is a novel coronavirus that had not previously been identified in humans. The first case was detected in the Hubei province of China at the end of December 2019. The virus is highly transmissible and coughing and sneezing are the most common forms of transmission, which is similar to the outbreak of the SARS coronavirus that began in 2002 and was thought to have spread via cough and sneeze droplets expelled into the air by infected persons.

Naming the coronavirus disease Coronaviruses are a group of viruses that can be transmitted between animals and people, causing illnesses that may range from the common cold to more severe respiratory syndromes. In February 2020, the International Committee on Taxonomy of Viruses and the World Health Organization announced official names for both the virus and the disease it causes: SARS-CoV-2 and COVID-19, respectively. The name of the disease is derived from the words corona, virus, and disease, while the number 19 represents the year that it emerged.

This dataset demonstrates the fear of Coronavirus (COVID-19) among the people of Khyber Pakhtunkhwa (Pakistan), their preventive behaviour, mental health condition, and level of anxiety during the pandemic. To gauge these constructs, a questionnaire was developed with the help of various scales – Fear of COVID-19 Scale (FCV-19S), Positive Mental Health Scale (PMHS), and General Anxiety Disorder Scale (GAD). At the time of data collection, the COVID-19 cases were emerging rapidly in the country due to which the KPK province was also facing lock-down and other mobility restrictions to limit the spread of viral infection. Keeping in view the prevalent emergency conditions, the research tool was developed in Google form and disseminated online for the collection of data. The informed consent of the respondents was obtained electronically, and they participated voluntarily in this survey research. Social media apps like Facebook, WhatsApp, LinkedIn, and personal contacts were also used for speedy collection of data. All the questions in the questionnaire were mandatory and the respondents could not send their responses by skipping any of them, so there is no missing value in the dataset. A total of 501 responses were received out of which 208 were females. For the convenience of the participants, every question in the questionnaire was translated into the Urdu language. All the responses were automatically saved online into a .xlsx spreadsheet and later that data was converted to digitized form by developing a coding frame. There are two main sections in this dataset, first is about the socio-demographic information (gender, age, marital status, employment status, area of residence and education) of the participants and the second demonstrates the fear, mental health, preventive behaviour, and anxiety while in the second section, the responses were rated on Likert scale. This dataset could be beneficial to the researchers and policymakers as they can get further insight to develop better skills and practices from a rapidly evolving situation.

IntroductionThe mental health of South Asian women has been observed to be in regression lately, with sexual harassment as one of the major factors accounting for mental health deterioration, especially for women who leave their homes frequently for work and study. The COVID-19 pandemic not only augmented the mental health distress of the general female population but the rise in sexual violence against women is being consistently reported around the globe. Based on this background, we adopted a two-pronged strategy to assess whether working women and students aged 18–55 experienced a rise in sexual harassment in the 18 months after lifting the COVID-19 lockdowns. Secondly, using the well-validated psychometric test, DASS-21, we evaluated the psychiatric outcome of this change on the mental health of those women.Study designThe study was designed as a quantitative, cross-sectional survey-based research.MethodologyA total of 303 women participated in this study. Personal interviews through a specifically designed questionnaire and psychometric test DASS-21 were administered to assess the mental health state of working women and female students, aged between 18 and 55 years old. The mean age of the participants was 37 ± 2.8. The study population was further categorized into two main groups of limited and frequent interactions based on varying levels of the frequency of leaving home and interacting with male strangers in their daily routine. Data were analyzed and the correlation between limited/frequent interaction and DASS-21 total scores and sub-scores of depression, anxiety and stress, and other sociodemographic variables were investigated using the Chi-square test, whereas psychosocial predictors of mental distress were evaluated using multiple linear regression analysis after matching limited and frequent interaction groups using a 1:1 propensity score-matched pair method for sociodemographic covariates.ResultsOverall, approximately 50% of our study population experienced changes in the behavior of male strangers that could be categorized as harassment in their daily life interactions, whereas 33.66% of participants experienced relatively more sexual harassment post-pandemic than before it. This observation was significantly correlated with the frequency of male interaction (χ2 = 5.71, p 60 on the DASS21-total score, whereas 29.04% scored >21 on the depression scale. Alarmingly, >40% of the women in the frequent interaction group scored in the extremely severe range of anxiety and depression. Moreover, in the regression analysis, out of all the factors analyzed, the extent of everyday interaction with male strangers, an increase in fear of sexual crimes, and a self-perceived increase in mental distress during the 18 months post-pandemic were found to be highly statistically significant predictors of mental distress not only for total DASS 21 but also for the sub-scales of depression, anxiety, and stress.ConclusionIn Pakistan, women experienced a rise in sexual harassment cases post–COVID–19. An increase in sexual harassment was found to be a predictor of negative mental health in the form of depression, anxiety, and stress.

Dataset

This dataset was created by Abdul Bari

Contents

Distribution of observed and simulated daily new cases, daily deaths, and daily recover cases of COVID-19 in Pakistan.

COVID-19 Confirmed Cases by Division and District, The Punjab

As of April 13, 2024, India had the highest number of confirmed deaths due to the outbreak of the novel coronavirus in the Asia-Pacific region, with over 533 thousand deaths. Comparatively, Indonesia, which had the second highest number of coronavirus deaths in the Asia-Pacific region, recorded approximately 162 thousand COVID-19 related deaths as of April 13, 2024. Contrastingly, Bhutan had reported 21 deaths due to COVID-19 as of April 13, 2024.

Context

With COVID-19 spread world-wide, it is essential to have a clear understanding of its spread.

Content

The data has date-wise cases for each province/region of Pakistan.

Acknowledgements

The data is compiled from Pakistan govt website http://covid.gov.pk/

Inspiration

I would like to see how much the spread will grow in coming days and would it become as bad as other nations in the world?

Records of reported Counts of COVID-19 case counts in Pakistan from 2019-2021. Download is a zipped CSV file with readme.

Context

Pakistan witnessed its first Corona virus patient on February 26th 2020. It's a bumpy ride since then. The cases are increasing gradually and we haven't seen the worst yet. While, there are few government resources for cumulative updates, there is no place where you can find the city level patients data. It's also not possible to find the running chronological tally of patients as they test positive. We have decided to create our own dataset for all the researchers out there with such details so we can model the infection spread and forecast the situation in coming days. We hope, by doing so, we will be able to inform policy makers on various intervention models, and healthcare professionals to be ready for the influx of new patients. We certainly hope, that this little contribution will go a long way for saving lives in Pakistan

Content

The dataset contains seven columns for date, number of cases, number of deaths, number of people recovered, travel history of those cases, and location of the cases (province and city).

The first version has the data from first case of February 26 2020 to April 19, 2020. We intend to publish weekly updates

Acknowledgements

Users are allowed to use, copy, distribute and cite the dataset as follows: “Zeeshan-ul-hassan Usmani, Sana Rasheed, Pakistan Corona Virus Data, Kaggle Dataset Repository, April 19, 2020.”

Inspiration

Some ideas worth exploring:

Can we find the spread factor for the Corona virus in Pakistan?

How long it takes for a positive case to infect another in Pakistan?

How we can use this data to simulate lock down scenarios and find its impact on country's economy? Here is a good
read to get started - http://zeeshanusmani.com/urdu/corona-economic-impact/

How does Pakistan Corona virus spread compare against its neighbors and other developed counties?

What would be the impact of this infection spread on country's economy and people living under poverty? Here are two briefs to get you started

http://zeeshanusmani.com/urdu/corona/ http://zeeshanusmani.com/urdu/corona-what-to-learn/

How do we visualize this dataset to inform policy makers? Here is one example https://zeeshanusmani.com/corona/

Can we predict the number of cases in next 10 days and a month?

On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased its collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit the following sources: World Health Organization (WHO)For more information, visit the Johns Hopkins Coronavirus Resource Center.-- Esri COVID-19 Trend Report for 3-9-2023 --0 Countries have Emergent trend with more than 10 days of cases: (name : # of active cases) 41 Countries have Spreading trend with over 21 days in new cases curve tail: (name : # of active cases)Monaco : 13, Andorra : 25, Marshall Islands : 52, Kyrgyzstan : 79, Cuba : 82, Saint Lucia : 127, Cote d'Ivoire : 148, Albania : 155, Bosnia and Herzegovina : 172, Iceland : 196, Mali : 198, Suriname : 246, Botswana : 247, Barbados : 274, Dominican Republic : 304, Malta : 306, Venezuela : 334, Micronesia : 346, Uzbekistan : 356, Afghanistan : 371, Jamaica : 390, Latvia : 402, Mozambique : 406, Kosovo : 412, Azerbaijan : 427, Tunisia : 528, Armenia : 594, Kuwait : 716, Thailand : 746, Norway : 768, Croatia : 847, Honduras : 1002, Zimbabwe : 1067, Saudi Arabia : 1098, Bulgaria : 1148, Zambia : 1166, Panama : 1300, Uruguay : 1483, Kazakhstan : 1671, Paraguay : 2080, Ecuador : 53320 Countries may have Spreading trend with under 21 days in new cases curve tail: (name : # of active cases)61 Countries have Epidemic trend with over 21 days in new cases curve tail: (name : # of active cases)Liechtenstein : 48, San Marino : 111, Mauritius : 742, Estonia : 761, Trinidad and Tobago : 1296, Montenegro : 1486, Luxembourg : 1540, Qatar : 1541, Philippines : 1915, Ireland : 1946, Brunei : 2010, United Arab Emirates : 2013, Denmark : 2111, Sweden : 2149, Finland : 2154, Hungary : 2169, Lebanon : 2208, Bolivia : 2838, Colombia : 3250, Switzerland : 3321, Peru : 3328, Slovakia : 3556, Malaysia : 3608, Indonesia : 3793, Portugal : 4049, Cyprus : 4279, Argentina : 5050, Iran : 5135, Lithuania : 5323, Guatemala : 5516, Slovenia : 5689, South Africa : 6604, Georgia : 7938, Moldova : 8082, Israel : 8746, Bahrain : 8932, Netherlands : 9710, Romania : 12375, Costa Rica : 12625, Singapore : 13816, Serbia : 14093, Czechia : 14897, Spain : 17399, Ukraine : 19568, Canada : 24913, New Zealand : 25136, Belgium : 30599, Poland : 38894, Chile : 41055, Australia : 50192, Mexico : 65453, United Kingdom : 65697, France : 68318, Italy : 70391, Austria : 90483, Brazil : 134279, Korea - South : 209145, Russia : 214935, Germany : 257248, Japan : 361884, US : 6440500 Countries may have Epidemic trend with under 21 days in new cases curve tail: (name : # of active cases) 54 Countries have Controlled trend: (name : # of active cases)Palau : 3, Saint Kitts and Nevis : 4, Guinea-Bissau : 7, Cabo Verde : 8, Mongolia : 8, Benin : 9, Maldives : 10, Comoros : 10, Gambia : 12, Bhutan : 14, Cambodia : 14, Syria : 14, Seychelles : 15, Senegal : 16, Libya : 16, Laos : 17, Sri Lanka : 19, Congo (Brazzaville) : 19, Tonga : 21, Liberia : 24, Chad : 25, Fiji : 26, Nepal : 27, Togo : 30, Nicaragua : 32, Madagascar : 37, Sudan : 38, Papua New Guinea : 38, Belize : 59, Egypt : 60, Algeria : 64, Burma : 65, Ghana : 72, Haiti : 74, Eswatini : 75, Guyana : 79, Rwanda : 83, Uganda : 88, Kenya : 92, Burundi : 94, Angola : 98, Congo (Kinshasa) : 125, Morocco : 125, Bangladesh : 127, Tanzania : 128, Nigeria : 135, Malawi : 148, Ethiopia : 248, Vietnam : 269, Namibia : 422, Cameroon : 462, Pakistan : 660, India : 4290 41 Countries have End Stage trend: (name : # of active cases)Sao Tome and Principe : 1, Saint Vincent and the Grenadines : 2, Somalia : 2, Timor-Leste : 2, Kiribati : 8, Mauritania : 12, Oman : 14, Equatorial Guinea : 20, Guinea : 28, Burkina Faso : 32, North Macedonia : 351, Nauru : 479, Samoa : 554, China : 2897, Taiwan* : 249634 -- SPIKING OF NEW CASE COUNTS --20 countries are currently experiencing spikes in new confirmed cases:Armenia, Barbados, Belgium, Brunei, Chile, Costa Rica, Georgia, India, Indonesia, Ireland, Israel, Kuwait, Luxembourg, Malaysia, Mauritius, Portugal, Sweden, Ukraine, United Kingdom, Uzbekistan 20 countries experienced a spike in new confirmed cases 3 to 5 days ago: Argentina, Bulgaria, Croatia, Czechia, Denmark, Estonia, France, Korea - South, Lithuania, Mozambique, New Zealand, Panama, Poland, Qatar, Romania, Slovakia, Slovenia, Switzerland, Trinidad and Tobago, United Arab Emirates 47 countries experienced a spike in new confirmed cases 5 to 14 days ago: Australia, Austria, Bahrain, Bolivia, Brazil, Canada, Colombia, Congo (Kinshasa), Cyprus, Dominican Republic, Ecuador, Finland, Germany, Guatemala, Honduras, Hungary, Iran, Italy, Jamaica, Japan, Kazakhstan, Lebanon, Malta, Mexico, Micronesia, Moldova, Montenegro, Netherlands, Nigeria, Pakistan, Paraguay, Peru, Philippines, Russia, Saint Lucia, Saudi Arabia, Serbia, Singapore, South Africa, Spain, Suriname, Thailand, Tunisia, US, Uruguay, Zambia, Zimbabwe 194 countries experienced a spike in new confirmed cases over 14 days ago: Afghanistan, Albania, Algeria, Andorra, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burma, Burundi, Cabo Verde, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Comoros, Congo (Brazzaville), Congo (Kinshasa), Costa Rica, Cote d'Ivoire, Croatia, Cuba, Cyprus, Czechia, Denmark, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Estonia, Eswatini, Ethiopia, Fiji, Finland, France, Gabon, Gambia, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kiribati, Korea - South, Kosovo, Kuwait, Kyrgyzstan, Laos, Latvia, Lebanon, Lesotho, Liberia, Libya, Liechtenstein, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Marshall Islands, Mauritania, Mauritius, Mexico, Micronesia, Moldova, Monaco, Mongolia, Montenegro, Morocco, Mozambique, Namibia, Nauru, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, North Macedonia, Norway, Oman, Pakistan, Palau, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Rwanda, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, Samoa, San Marino, Sao Tome and Principe, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovakia, Slovenia, Solomon Islands, Somalia, South Africa, South Sudan, Spain, Sri Lanka, Sudan, Suriname, Sweden, Switzerland, Syria, Taiwan*, Tajikistan, Tanzania, Thailand, Timor-Leste, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkey, Tuvalu, US, Uganda, Ukraine, United Arab Emirates, United Kingdom, Uruguay, Uzbekistan, Vanuatu, Venezuela, Vietnam, West Bank and Gaza, Yemen, Zambia, Zimbabwe Strongest spike in past two days was in US at 64,861 new cases.Strongest spike in past five days was in US at 64,861 new cases.Strongest spike in outbreak was 424 days ago in US at 1,354,505 new cases. Global Total Confirmed COVID-19 Case Rate of 8620.91 per 100,000Global Active Confirmed COVID-19 Case Rate of 37.24 per 100,000Global COVID-19 Mortality Rate of 87.69 per 100,000 21 countries with over 200 per 100,000 active cases.5 countries with over 500 per 100,000 active cases.3 countries with over 1,000 per 100,000 active cases.1 country with over 2,000 per 100,000 active cases.Nauru is worst at 4,354.54 per 100,000.

The data consist of COVID-19 cases and its relevant parameters for a few countries including: Pakistan, Bangladesh, India and Afghanistan on daily basis from December 31, 2019 to August 19, 2020 acquired from https://ourworldindata.org/coronavirus-source-data.

The forecast results based on the average of 100 simulations.

Background: The coronavirus disease 2019 (COVID-19) pandemic has become a major public health crisis worldwide, and the Eastern Mediterranean is one of the most affected areas.Materials and Methods: We use a data-driven approach to assess the characteristics, situation, prevalence, and current intervention actions of the COVID-19 pandemic. We establish a spatial model of the spread of the COVID-19 pandemic to project the trend and time distribution of the total confirmed cases and growth rate of daily confirmed cases based on the current intervention actions.Results: The results show that the number of daily confirmed cases, number of active cases, or growth rate of daily confirmed cases of COVID-19 are exhibiting a significant downward trend in Qatar, Egypt, Pakistan, and Saudi Arabia under the current interventions, although the total number of confirmed cases and deaths is still increasing. However, it is predicted that the number of total confirmed cases and active cases in Iran and Iraq may continue to increase.Conclusion: The COVID-19 pandemic in Qatar, Egypt, Pakistan, and Saudi Arabia will be largely contained if interventions are maintained or tightened. The future is not optimistic, and the intervention response must be further strengthened in Iran and Iraq. The aim of this study is to contribute to the prevention and control of the COVID-19 pandemic.

Forecasting accuracy of different time series models for cumulative recovered cases.