As of November 15, 2023, Ha Noi had 1,646,923 confirmed cases of COVID-19, followed by 629,018 cases in Ho Chi Minh City. There were 11,619,990 cumulative confirmed cases of coronavirus in Vietnam. The country is currently responding to a new COVID-19 variant with aggressive contact tracing and mass testing.

COVID-19 development in Ha Noi The first four infections in the country’s capital were one 26-year-old female, one 27-year-old male, one 64-year-old female and one 61-year-old male. The 26-year-old female was patient 17, who returned from Europe on flight VN0054 from Vietnam Airlines. There were 130 people in close contact with patient 17 and another 226 people identified as having been in close contact with the aforementioned 130 people. Those who were in close contact to patient 17 were brought into quarantine, and the residential area was put under lockdown.

Measures during COVID-19 in Ho Chi Minh City

The People’s Council of Ho Chi Minh City has approved a financial package of 2.75 trillion Vietnamese dong to fight the COVID-19 epidemic. The financial package will be used to provide meal subsidies to people under quarantine, as well as daily allowances for medical workers, military staff and other forces that are engaged in the work of epidemic control. Part of the financial package will be reserved for a possible increase in patients and people that would need to be quarantined. Furthermore, teachers and staff members who would lose income during this time but are not entitled to unemployment benefits will receive one million Vietnamese dong in support each month.

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

As of November 15, 2023, there have been 11,619,990 total infections of coronavirus in Vietnam. At the moment, 10,639,962 patients have recovered. Vietnam has recorded 43,206 deaths related to the COVID-19 pandemic so far, most of which occurred during the current outbreaks in Ha Noi.

COVID-19 development in Vietnam

On January 30, 2020, the first two patients with COVID-19 in Vietnam were diagnosed. They were a male from Wuhan and his son, who was living in Long An and whom the father was visiting. Both father and son were tested positive and treated in a hospital. Although the number of infections was contained after that, travel activities have again led to a steady increase in COVID-19 cases. Patient 17, who returned from Europe, as well as patient 34, who returned from Washington via Qatar, were in contact with several citizens in Vietnam before the infection was determined, which started a chain of infections.

Measures against COVID-19 in Vietnam

Beginning April 1, 2020, Vietnam went into 15 days of nationwide social distancing and self-isolation after the latest directive signed by the Prime Minister. Until now, there have been three major outbreaks happening across the country, leading to several lockdowns in some regions. Vietnam was one of the first countries to close its border and suspended international commercial flights in March 2020. Almost all visitors coming to Vietnam currently need pre-approvals from the Vietnamese embassy and have to go through centralized quarantine for 14 days. The country also started its vaccination campaign in March 2021, with the front-line health care workers being the first group to be vaccinated. Additionally, Vietnam has also been developing its own vaccines, which are expected to be in use at the end of 2021.

Vietnam recorded 43201 Coronavirus Deaths since the epidemic began, according to the World Health Organization (WHO). In addition, Vietnam reported 11590617 Coronavirus Cases. This dataset includes a chart with historical data for Vietnam Coronavirus Deaths.

Context

On December 31, 2019, Chinese officials informed the first case of COVID-19 in Wuhan (China). Around the end of January, 2020, many countries (the U.S., the UK, South Korea, etc.), including Vietnam reported their first COVID-19 cases.

Since then, each country has their own specific strategy to contain the outbreak. Most of the countries have now shifted from the containment (early tracking, isolating the infection sources) to serious mitigation (tactics to reduce transmission) paradigms. Although loosing some F0 cases, Vietnam still has remained safely in the containment stage.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3439828%2Fbe8a17529fc1b48e3c44be94afe75529%2FVietnam_trend.png?generation=1588195825303050&alt=media" alt="">

Vietnam currently has only 270 COVID-19 confirmed cases in total with NO FATALITIES. And now, Vietnam is on its 13 straight days with no new local transmitted cases and 5 straight days without any imported cases (Updated on April 29, 2020). This leave us so many question to ask.

1. What has happened in Vietnam? Was the number of COVID-19 cases reported by Vietnamese officials undercounted? Did testing work well in Vietnam?

2. Did the Vietnam government suppressed information about their local COVID-19 pandemic? And if not, with such the 'real' low number of cases and no death, how did Vietnam contain the virus?

3. What did we know about the Vietnam COVID-19 patients? Is there characteristics of the patients that helps slow down the infection rate in Vietnam?

One remarkable thing about Vietnam health care system is the fact that privacy laws are not as stringent as in the US, Canada or the EU. Therefore, COVID-19 patient data in Vietnam is publicly available. For some cases, detail gets seriously down to their names, their personal contacts, daily activities and even their habits.

To help answer some of the above questions, I decided to collect the Vietnam data and study it independently using all the information available on the internet. I hope this dataset will provide some insights into the COVID-19 pandemic at the specific country level.

Collection methodology

• Data was acquired by web scrapping with manually curated from the Vietnam Ministry of Health's website (https://ncov.moh.gov.vn/) and other mainstream media in Vietnam (which were cited specifically in each data row).
• The world data was obtained from the data repository for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). https://github.com/CSSEGISandData/COVID-19

Disclaimer:

• This is my personal work with no link to any organization. Although this analysis is data-driven, my comments reflect my personal perspectives.
• My results are based on the data collected from the Vietnam Health Ministry website and the mainstream media in Vietnam. The data might be biased and reflects what is publicly available on the internet. However, it can served as a good reference for someone who are curious about the COVID-19 pandemic in Vietnam. Some information conflicts (about the data) would be explained in detail in the exploration notebook that goes together with this dataset.

A full report and visualization for this dataset can be found in my Medium site.

Dataset was updated until May 10, 2020.

The outbreak of the novel coronavirus in Wuhan, China, saw infection cases spread throughout the Asia-Pacific region. By April 13, 2024, India had faced over 45 million coronavirus cases. South Korea followed behind India as having had the second highest number of coronavirus cases in the Asia-Pacific region, with about 34.6 million cases. At the same time, Japan had almost 34 million cases. At the beginning of the outbreak, people in South Korea had been optimistic and predicted that the number of cases would start to stabilize. What is SARS CoV 2?Novel coronavirus, officially known as SARS CoV 2, is a disease which causes respiratory problems which can lead to difficulty breathing and pneumonia. The illness is similar to that of SARS which spread throughout China in 2003. After the outbreak of the coronavirus, various businesses and shops closed to prevent further spread of the disease. Impacts from flight cancellations and travel plans were felt across the Asia-Pacific region. Many people expressed feelings of anxiety as to how the virus would progress. Impact throughout Asia-PacificThe Coronavirus and its variants have affected the Asia-Pacific region in various ways. Out of all Asia-Pacific countries, India was highly affected by the pandemic and experienced more than 50 thousand deaths. However, the country also saw the highest number of recoveries within the APAC region, followed by South Korea and Japan.

The data set provides numbers of COVID-19 infections in Vietnam and some neighboring countries in Asia. In the data set in Vietnam, there is a classification of the total number of new cases, new cases and new infections in the community. The number of infections in the community is the number of unknown infections. The case data is published on a daily basis and is aggregated to the time of current statistics.

Context

COVID-19 total cases by province in Vietnam 27.04.2021-29.07.2021 COVID-19 new cases by province in Vietnam 27.04.2021-29.07.2021

Acknowledgements

Sang, Nguyen Minh (2021), “COVID-19 cases by province in Vietnam 27.04.2021-29.07.2021”, Mendeley Data, V1, doi: 10.17632/nmxsnx4z64.1

COVID-19 cases by province in Vietnam 27.05.2021-25.07.2021

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3553471%2F3e65ec900106b13b3042fc7d424bd569%2FVn%20Map.png?generation=1584296146474224&alt=media" alt="">

Context

Vietnam SARS-CoV-2 | COVID-19 Compiled Data from Several Reliable Sources such as VnExpress.net, Ministry of Health.

Content

Brief summary of cases 17 - 53. 14/3/2020: Confirmed: 53, Recovered: 16, Death: 0

Column Headers: - Date: Date of reporting case - Case: Case ID - Gender - Age - Origin: Last known location before reported - (Potential) Infection Source: Additional travel information - Current Location: Last known treatment location - Confirmed: Tested Positive - Recovered: Recovered | Tested Negative | No Longer Quarantined - Death - Source Information: References

Acknowledgements

NA

Inspiration

I'm interested in SARS-CoV-2 | COVID-19 spread.

Abstract The global but uneven course of the Covid-19 pandemic highlights the importance of international cooperation and negotiation on such matters as financial assistance, medical equipment provision, vaccine development and distribution, and other pandemic response measures. This article will present a theoretical overview of “health diplomacy” and analyze the case of Vietnam within this framework, showing how the country’s political response to the pandemic demonstrates an increasingly proactive engagement in health diplomacy. The article argues that health diplomacy will become more relevant for international relations in the time to come and that the case of Vietnam might yield valuable lessons.

The effects of the Covid-19 pandemic, policy responses and macroeconomic fundamentals on the market risks across 24 Vietnamese sectors.

Abstract

The main objective of this project is to collect household data for the ongoing assessment and monitoring of the socio-economic impacts of COVID-19 on households and family businesses in Vietnam. The estimated field work and sample size of households in each round is as follows:

Round 1 June fieldwork- approximately 6300 households (at least 1300 minority households) Round 2 August fieldwork - approximately 4000 households (at least 1000 minority households) Round 3 September fieldwork- approximately 4000 households (at least 1000 minority households) Round 4 December- approximately 4000 households (at least 1000 minority households) Round 5 - pending discussion

Geographic coverage

National, regional

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2020 Vietnam COVID-19 High Frequency Phone Survey of Households (VHFPS) uses a nationally representative household survey from 2018 as the sampling frame. The 2018 baseline survey includes 46980 households from 3132 communes (about 25% of total communes in Vietnam). In each commune, one EA is randomly selected and then 15 households are randomly selected in each EA for interview. Out of the 15 households, 3 households have information collected on both income and expenditure (large module) as well as many other aspects. The remaining 12 other households have information collected on income, but do not have information collected on expenditure (small module). Therefore, estimation of large module includes 9396 households and are representative at regional and national levels, while the whole sample is representative at the provincial level.

We use the large module of to select the households for official interview of the VHFPS survey and the small module households as reserve for replacement. The sample size of large module has 9396 households, of which, there are 7951 households having phone number (cell phone or line phone).

After data processing, the final sample size is 6,213 households.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire for Round 1 consisted of the following sections Section 2. Behavior Section 3. Health Section 4. Education & Child caring Section 5A. Employment (main respondent) Section 5B. Employment (other household member) Section 6. Coping Section 7. Safety Nets Section 8. FIES

Cleaning operations

Data cleaning began during the data collection process. Inputs for the cleaning process include available interviewers’ note following each question item, interviewers’ note at the end of the tablet form as well as supervisors’ note during monitoring. The data cleaning process was conducted in following steps: • Append households interviewed in ethnic minority languages with the main dataset interviewed in Vietnamese. • Remove unnecessary variables which were automatically calculated by SurveyCTO • Remove household duplicates in the dataset where the same form is submitted more than once. • Remove observations of households which were not supposed to be interviewed following the identified replacement procedure. • Format variables as their object type (string, integer, decimal, etc.) • Read through interviewers’ note and make adjustment accordingly. During interviews, whenever interviewers find it difficult to choose a correct code, they are recommended to choose the most appropriate one and write down respondents’ answer in detail so that the survey management team will justify and make a decision which code is best suitable for such answer. • Correct data based on supervisors’ note where enumerators entered wrong code. • Recode answer option “Other, please specify”. This option is usually followed by a blank line allowing enumerators to type or write texts to specify the answer. The data cleaning team checked thoroughly this type of answers to decide whether each answer needed recoding into one of the available categories or just keep the answer originally recorded. In some cases, that answer could be assigned a completely new code if it appeared many times in the survey dataset.
• Examine data accuracy of outlier values, defined as values that lie outside both 5th and 95th percentiles, by listening to interview recordings. • Final check on matching main dataset with different sections, where information is asked on individual level, are kept in separate data files and in long form. • Label variables using the full question text. • Label variable values where necessary.

Response rate

The target for Round 1 is to complete interviews for 6300 households, of which 1888 households are located in urban area and 4475 households in rural area. In addition, at least 1300 ethnic minority households are to be interviewed. A random selection of 6300 households was made out of 7951 households for official interview and the rest as for replacement. However, the refusal rate of the survey was about 27 percent, and households from the small module in the same EA were contacted for replacement and these households are also randomly selected.

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.

Disease Severity and Vaccination Propensity: A COVID-19 Case Study From Vietnam

Geographic coverage

National, regional

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2020 Vietnam COVID-19 High Frequency Phone Survey of Households (VHFPS) uses a nationally representative household survey from 2018 as the sampling frame. The 2018 baseline survey includes 46,980 households from 3132 communes (about 25% of total communes in Vietnam). In each commune, one EA is randomly selected and then 15 households are randomly selected in each EA for interview. We use the large module of to select the households for official interview of the VHFPS survey and the small module households as reserve for replacement. After data processing, the final sample size for Round 2 is 3,935 households.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire for Round 2 consisted of the following sections

Section 2. Behavior Section 3. Health Section 5. Employment (main respondent) Section 6. Coping Section 7. Safety Nets Section 8. FIES

Cleaning operations

Data cleaning began during the data collection process. Inputs for the cleaning process include available interviewers’ note following each question item, interviewers’ note at the end of the tablet form as well as supervisors’ note during monitoring. The data cleaning process was conducted in following steps: • Append households interviewed in ethnic minority languages with the main dataset interviewed in Vietnamese. • Remove unnecessary variables which were automatically calculated by SurveyCTO • Remove household duplicates in the dataset where the same form is submitted more than once. • Remove observations of households which were not supposed to be interviewed following the identified replacement procedure. • Format variables as their object type (string, integer, decimal, etc.) • Read through interviewers’ note and make adjustment accordingly. During interviews, whenever interviewers find it difficult to choose a correct code, they are recommended to choose the most appropriate one and write down respondents’ answer in detail so that the survey management team will justify and make a decision which code is best suitable for such answer. • Correct data based on supervisors’ note where enumerators entered wrong code. • Recode answer option “Other, please specify”. This option is usually followed by a blank line allowing enumerators to type or write texts to specify the answer. The data cleaning team checked thoroughly this type of answers to decide whether each answer needed recoding into one of the available categories or just keep the answer originally recorded. In some cases, that answer could be assigned a completely new code if it appeared many times in the survey dataset.
• Examine data accuracy of outlier values, defined as values that lie outside both 5th and 95th percentiles, by listening to interview recordings. • Final check on matching main dataset with different sections, where information is asked on individual level, are kept in separate data files and in long form. • Label variables using the full question text. • Label variable values where necessary.

Ranking the yearly market risk of 24 sectors from 2012 to 2021 using VaR (Panel A) and CVaR (Panel B).

The purpose of this project is to write a large and in sync dataset focused patient characteristics for identify the Risk groups and characteristics human-level that impact on infection, Complication and Death as a result of the disease

for more detail about the data:

https://docs.google.com/spreadsheets/d/1awEY-04UK8wibkbZ1qfV6a-Q9YKScfP7qiAtWDsp9Jw/edit?usp=sharing

last date for update 06.12.2020

4535323 rows

Version 5:

A version that includes cleaning the data and engineering new features for more detail : https://docs.google.com/spreadsheets/d/1awEY-04UK8wibkbZ1qfV6a-Q9YKScfP7qiAtWDsp9Jw/edit?usp=sharing

Version 6:

Machine-ready version of machine learning model Consists only of INT and FLOAT for more detail : https://docs.google.com/spreadsheets/d/1awEY-04UK8wibkbZ1qfV6a-Q9YKScfP7qiAtWDsp9Jw/edit?usp=sharing

problem with dataset

• There may be duplicate cases (which come from different data systems) Focusing on countries: France, Korea, Indonesia, Tunisia, Japan, canada, new_zealand, singapore, guatemala, philippines, india, vietnam, hong kong , Toronto, Mexico.

• I did not check the credibility of the sources

• Concerns of the credibility of the Mexican government's data

• Concerns about the credibility of the data of the Chinese government

Acknowledgements and Sources

india_wiki https://www.kaggle.com/karthikcs1/covid19-coronavirus-patient-list-karnataka-india

philippines https://www.kaggle.com/sundiver/covid19-philippines-edges

france https://www.kaggle.com/lperez/coronavirus-france-dataset

korea https://www.kaggle.com/kimjihoo/coronavirusdataset

indonesia https://www.kaggle.com/ardisragen/indonesia-coronavirus-cases

tunisia https://www.kaggle.com/ghassen1302/coronavirus-tunisia

japan https://www.kaggle.com/tsubasatwi/close-contact-status-of-corona-in-japan

world https://github.com/beoutbreakprepared/nCoV2019/tree/master/latest_data

canada https://www.kaggle.com/ryanxjhan/coronaviruscovid19-canada

new_zealand https://www.kaggle.com/madhavkru/covid19-nz

singapore https://www.kaggle.com/rhodiumbeng/singapores-covid19-cases

guatemala https://www.kaggle.com/ncovgt2020/covid19-guatemala

colombia https://www.kaggle.com/sebaxtian/covid19co

mexico https://www.kaggle.com/lalish99/covid19-mx

india_data https://www.kaggle.com/samacker77k/covid19india

vietnam https://www.kaggle.com/nh

kerla https://www.kaggle.com/baburajr/covid19inkerala

hong_kong https://www.kaggle.com/teddyteddywu/covid-19-hong-kong-cases

toronto https://www.kaggle.com/divyansh22/toronto-covid19-cases

Determining the severity illness according to WHO: https://www.who.int/publications/i/item/clinical-management-of-covid-19

• Each update contains the information found in the previous version

*Thanks to all sources

*If you have any helpful information or suggestions for improvement, write

Building notebook

• netbook PART A - cleaning and conact the data: https://www.kaggle.com/shirmani/characteristics-of-corona-patient-ds-v4

• netbook PART B- features Engineering: https://www.kaggle.com/shirmani/build-characteristics-corona-patients-part-b/edit

• part C data QA https://www.kaggle.com/shirmani/qa-characteristics-corona-patients-part-c

• netbook PART D - format the data to int and float cols (model preparation): https://www.kaggle.com/shirmani/build-characteristics-corona-patients-part-d

Prepared by Lan Thuong Nguyen, a PhD. Candidate from the International Doctoral Program in Asia-Pacific Studies (IDAS) at National Chengchi University (NCCU), at the Center for Asia-Pacific Resilience and Innovation (CAPRi).

Lan Thuong Nguyen is a co-author of this project alongside an American researcher, Dr. Yen Pottinger, who has clearly defined responsibilities. Her role is sourcing and analyzing documents related to public health policies during the COVID-19 pandemic, vaccination promotion programs, communication strategies against COVID-19, and research articles and reports on vaccine acceptance rates among the Vietnamese population. Additionally, she examines public sentiment regarding the government's COVID-19 strategies and other relevant information. As a result, she searched, curated, and compiled the datasets and stored them in the depositar. She is also responsible for overseeing the storage, management, and, if necessary, customization of these data. The management process does not require additional resources or incur storage or data preparation costs. The datasets will be shared via the repository, with access requests managed by Lan Thuong Nguyen. No personal data is included in the datasets.

The project titled "Misinformation, Disinformation, and Vaccine Hesitancy in Vietnam" forms part of a broader series of studies analyzing vaccine hesitancy across various countries in the Asia-Pacific region. This research examines both the historical context and the impact of the COVID-19 pandemic, with a particular focus on the influence of misinformation and disinformation on governmental and civil society efforts to promote vaccination. It belongs to the Center for Asia-Pacific Resilience and Innovation (CAPRi). The project has been completed and posted on the Center for Asia-Pacific Resilience and Innovation (CAPRi) website.

In this case, the project aims to analyze the factors contributing to vaccine hesitancy in Vietnam, with a particular focus on the influence of misinformation and disinformation. It will examine the historical context, the role of digital and social media, and the effectiveness of governmental and public health responses in addressing these challenges during the COVID-19 pandemic. The project contains metadata on the Vietnamese vaccination program and focuses on the country's public health policy, communication strategies, and vaccination experiences.

The dataset below is part of this project. It introduces the COVID-19 prevention policies, provides an overview of the current status, and compiles academic research on vaccine acceptance, the prevalence of misinformation, and how governments are addressing these issues.

Files must be downloaded to use the entire dataset (depositar only provides limited data previews). This dataset comprises one ZIP file, one XLSX spreadsheet, and one PDF file. The ZIP files contain academic research and documents on experiences propagating COVID-19 vaccination in Vietnamese and English. They are collected for reference in this project, and each article/ research paper/ report is attached with links in this ZIP file. The XLSX spreadsheet is a collection of public health policies applicable to the country made by the author to understand how the Vietnamese government prevented, combated, and governed the anti-COVID-19 campaign. It is used for reference purposes. The PDF file is a literature review written by the author with detailed citations and references. It is conducted based on the requirements of the project manager to have an overview of Vietnam's public health policy.

In its present state, the dataset is presented primarily in Vietnamese and English.

Government intervention selection during the COVID-19 outbreak is viewed as a Multi-Criteria Decision Making (MCDM) problem in a hazy and uncertain environment in which governments and medical communities adjust their priorities in response to emerging issues and the efficacy of interventions implemented in various countries. The purpose of this study is to propose a novel hybrid Spherical Fuzzy Analytic Hierarchy Process (SF-AHP) and Fuzzy Weighted Aggregated Sum Product Assessment (WASPAS-F) model to assist stakeholders such as governors and policymakers in prioritizing government interventions to address the COVID-19 outbreak. The SF-AHP is used to compare criteria pairwise and calculate the relative weight of each criterion. The WASPAS-F technique is then used to rank 15 possible intervention strategies. In Vietnam, an empirical case study was done. Additionally, the weights of strategies at the local and global levels, as well as their ranking order, are computed. The supplementary data concludes 3 files: 1. SF-AHP to determine the relative weights of 5 criteria 2. SF- AHP in complete and partial approaches 3.WASPAS-F Results