This dataset shows the cases of Coronavirus (COVID-19) in Vietnam. The dataset information will be updated according to the announcements from the ministry of health in Vietnam. The data is updated frenquently along with the data of Ministry of Vietnam. Note: The first case of COVID-19 in Vietnam was first announced on January 22, 2020, including a 66-year-old Chinese man (#1) traveling from Wuhan to Hanoi to visit his son living in Vietnam, and his 28-year-old son (# 2), who is believed to have contracted the disease from his father when they met in Nha Trang. This dataset is updated as the case progresses, thus requiring the public to understand and verify the data that ODV has published.

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.

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.

New Covid cases per million people in Vietnam, March, 2023 The most recent value is 4 new Covid cases per million people as of March 2023, no change compared to the previous value of 4 new Covid cases per million people. Historically, the average for Vietnam from February 2020 to March 2023 is 3090 new Covid cases per million people. The minimum of 0 new Covid cases per million people was recorded in February 2020, while the maximum of 62648 new Covid cases per million people was reached in March 2022. | TheGlobalEconomy.com

COVID-19 total cases by province in Vietnam 27.04.2021-03.08.2021 COVID-19 new cases by province in Vietnam 27.04.2021-03.08.2021

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.

On April 27, 2021, the fourth wave of the coronavirus disease 2019 (COVID-19) pandemic originating from the Delta variant of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) began in Vietnam. The adoption of travel restrictions, coupled with rapid vaccination and mask-wearing, is a global strategy to prevent the spread of COVID-19. Although trade-off between health and economic development are unavoidable in this situation, little evidence that is specific to Vietnam in terms of movement restrictions, vaccine coverage, and real-time COVID-19 cases is available. Our research question is whether travel restrictions and vaccine coverage are related to changes in the incidence of COVID-19 in each province in Vietnam. We used Google's Global Mobility Data Source, which reports different mobility types, along with reports of vaccine coverage and COVID-19 cases retrieved from publicly and freely available datasets, for this research. Starting from the 50th case per province and incorporating a 14-day period to account for exposure and illness, we examined the association between changes in mobility (from day 27 to 04–03/11/2021) and the ratio of the number of new confirmed cases on a given day to the total number of cases in the past 14 days of indexing (the potentially contagious group in the population) per million population by making use of LOESS regression and logit regression. In two-thirds of the surveyed provinces, a reduction of up to 40% in commuting movement (to the workplace, transit stations, grocery stores, and entertainment venues) was related to a reduction in the number of cases, especially in the early stages of the pandemic. Once both movement and disease prevalence had been mitigated, further restrictions offered little additional benefit. These results indicate the importance of early and decisive actions during the pandemic.

COVID-19 infected case was first detected on January 22, 2020 in Vietnam. Determined to leave no one behind, the central government has been working closely to make timely policy decisions to deal with the disease. A series of decisions, official letters, telegrams and related policies are collected and recorded by ODV in this data set. The dataset is updated all three days frequently.

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

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.

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.

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.

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

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.

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.

Descriptive statistics of the daily return of 24 sectors in Vietnam.

The yearly market risks of 24 sectors in Vietnam, 2012–2021, using VaR (panel A) and CVaR (panel B).

After the COVID-19 outbreak, the Ministry of Planning and Investment discussed two possible scenarios and its impact on the country's GDP. The initial GDP growth projection before the virus outbreak was at 6.8 percent. In the case that the coronavirus can be contained in the second quarter of 2020, the GDP was projected to increase by only 6.09 percent. In the same scenario, the agriculture will grow at a rate of 2.35 percent, the industry sector will grow at a rate of 7.1 percent and the services sector will grow at a rate of 6.47 percent.