Context

• Does a central/fedral government have the power/authority to completely isolate a province/state?
• How quickly can a government react to the situation?
• Is the government able to effectively allocate and move the resources to where they are needed the most in a timely manner?
• What is the risk appetite of the leader?
• What is the risk appetite of the goverment?

The spread of COVID-19 is more than just a technical and scientific question. How and how well can a country contain the spread of the virus also depends on its leader and regime characteristics.

Content

This dataset contains 5 leader characteristics to profile the leader of a country. - whether the leader is elected - tenure - age - gender - military experience

This dataset also contains 1 regime characteristic that outlines the political structure. Notably, - presidential democracy: Democracy in which the executive is distinct from the legislative branch and considerable decision-making authority is granted to the executive. - parliamentary Democracy: Democracy in which legislatures are more powerful and executives are less autonomous. - single-party systems: Power is held by the head of a party. Executive power is effectively checked by the party or ruling committee.

Additionally, this dataset contains other politial factors that may affect how the leader and government react to the situation, including anticipated election in the near term and whether there were previous civil conflicts.

Acknowledgements

The source of the dataset is updated by Bell, Curtis at OEFRESESARCH.ORG (citation below) and is altered for the COVID-19 forcasting challenge.

Bell, Curtis. 2016. The Rulers, Elections, and Irregular Governance Dataset (REIGN). Broomfield, CO: OEF Research. Available at oefresearch.org

Detailed variable descriptions can be found at https://oefresearch.org/sites/default/files/REIGN_descriptions.pdf

Changelog:

Update September 20, 2021: Data and overview updated to reflect data used in the September 15 story Over Half of States Have Rolled Back Public Health Powers in Pandemic. It includes 303 state or local public health leaders who resigned, retired or were fired between April 1, 2020 and Sept. 12, 2021. Previous versions of this dataset reflected data used in the Dec. 2020 and April 2021 stories.

Overview

Across the U.S., state and local public health officials have found themselves at the center of a political storm as they combat the worst pandemic in a century. Amid a fractured federal response, the usually invisible army of workers charged with preventing the spread of infectious disease has become a public punching bag.

In the midst of the coronavirus pandemic, at least 303 state or local public health leaders in 41 states have resigned, retired or been fired since April 1, 2020, according to an ongoing investigation by The Associated Press and KHN.

According to experts, that is the largest exodus of public health leaders in American history.

Many left due to political blowback or pandemic pressure, as they became the target of groups that have coalesced around a common goal — fighting and even threatening officials over mask orders and well-established public health activities like quarantines and contact tracing. Some left to take higher profile positions, or due to health concerns. Others were fired for poor performance. Dozens retired. An untold number of lower level staffers have also left.

The result is a further erosion of the nation’s already fragile public health infrastructure, which KHN and the AP documented beginning in 2020 in the Underfunded and Under Threat project.

Findings

The AP and KHN found that:

• One in five Americans live in a community that has lost its local public health department leader during the pandemic
• Top public health officials in 28 states have left state-level departments ## Using this data To filter for data specific to your state, use this query

To get total numbers of exits by state, broken down by state and local departments, use this query

Methodology

KHN and AP counted how many state and local public health leaders have left their jobs between April 1, 2020 and Sept. 12, 2021.

The government tasks public health workers with improving the health of the general population, through their work to encourage healthy living and prevent infectious disease. To that end, public health officials do everything from inspecting water and food safety to testing the nation’s babies for metabolic diseases and contact tracing cases of syphilis.

Many parts of the country have a health officer and a health director/administrator by statute. The analysis counted both of those positions if they existed. For state-level departments, the count tracks people in the top and second-highest-ranking job.

The analysis includes exits of top department officials regardless of reason, because no matter the reason, each left a vacancy at the top of a health agency during the pandemic. Reasons for departures include political pressure, health concerns and poor performance. Others left to take higher profile positions or to retire. Some departments had multiple top officials exit over the course of the pandemic; each is included in the analysis.

Reporters compiled the exit list by reaching out to public health associations and experts in every state and interviewing hundreds of public health employees. They also received information from the National Association of City and County Health Officials, and combed news reports and records.

Public health departments can be found at multiple levels of government. Each state has a department that handles these tasks, but most states also have local departments that either operate under local or state control. The population served by each local health department is calculated using the U.S. Census Bureau 2019 Population Estimates based on each department’s jurisdiction.

KHN and the AP have worked since the spring on a series of stories documenting the funding, staffing and problems around public health. A previous data distribution detailed a decade's worth of cuts to state and local spending and staffing on public health. That data can be found here.

Attribution

Findings and the data should be cited as: "According to a KHN and Associated Press report."

Is Data Missing?

If you know of a public health official in your state or area who has left that position between April 1, 2020 and Sept. 12, 2021 and isn't currently in our dataset, please contact authors Anna Maria Barry-Jester annab@kff.org, Hannah Recht hrecht@kff.org, Michelle Smith mrsmith@ap.org and Lauren Weber laurenw@kff.org.

From 15/08/2020, I am no longer updating these files. Instead, I am directly reading data files from the Covid-19 Repository at John Hopkins University.

Context

I have created these datasets specifically for my analysis notebooks:

https://www.kaggle.com/aiaiaidavid/how-spain-became-leader-in-covid-19-infections

And others I am working on.

Content

These datasets contain covid-19 confirmed, recovered and detah cases time series for the following 10 world countries:

Europe: Spain, Italy, France, Germany and UK
Rest of the world: Australia, Brazil, Canada, Iran and USA

Note the files for 27072020 had two countries (Iran and Australia) removed.

Full data is obtained from the COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University:

https://github.com/CSSEGISandData/COVID-19

Acknowledgements

Thank you to the community of AI Saturdays Spain, which introduced me into Jupyter Notebooks and Kaggle, which has open up a new world of opportunities for me.

While Twitter has grown popular among political leaders as a means of computer-mediated mass media communication alternative, the COVID-19 pandemic required new strategies for socio-political communication to handle such a crisis. Using the case of India, which was one of the worst-hit countries and is also the world’s largest democracy, this research explicates how political leaders responded to the COVID-19 crisis on Twitter during the first wave as it was the first time such a crisis occurred. Theoretical frameworks of discursive leadership and situational crisis communication theory have been used to analyze interactions based on the usage patterns, the content of communication, the extent of usage in relation to the severity of the crisis, and the possible role of leaders’ position along with the status of their political party. The sample consisted of tweets posted by six prominent political leaders in India across the four consecutive lockdown periods from 25th March to 31st May 2020. A total of 4,158 tweets were scrapped and after filtering for retweets, the final dataset consisted of 2,809 original tweets. Exploratory data analysis, sentiment analysis, and content analysis were conducted. It was found that the tweets had an overall positive sentiment, an important crisis management strategy. Four main themes emerged: crisis management information, strengthening followers’ resilience and trust, reputation management, and leaders’ proactiveness. By focusing on such discursive aspects of crisis management, the study comprehensively highlights how political interactions on twitter integrated with politics and governance to handle COVID-19 in India. The study has implications for the fields of digital media interaction, political communication, public relations, and crisis leadership.

How well have populist leaders responded to the COVID-19 pandemic? There is a growing literature dedicated to populism and health outcomes. However, the ongoing pandemic provides us with a unique opportunity to study whether populist leaders fared better or worse than their non-populist counterparts by using a much larger sample size. While there has been a fruitful debate over whether populism is responsible for worse health outcomes, much of the focus has centered around the overall effect of having populist parties in power, without testing for different explanatory mechanisms. We argue that populist leaders fuel mass political polarization, which increases the overall level of hostility among the population and reduces their willingness to comply with anti-COVID measures and, more generally, contribute to public good. We test this theory using the expert-coded V-Party Dataset which contains variables for the ideological characteristics for parties around the world, as well as weekly excess mortality from the World Mortality Dataset. In addition to the OLS regression analysis, we employ a causal mediation framework to account for the order of succession of populism and political polarization. Our empirical results corroborate our main hypothesis that populism fuels political polarization, which is, in turn, associated with higher excess mortality during the ongoing pandemic. Our results are robust to alternative model specifications.

How did Brazil become a global epicenter of the outbreak? After seeming to ease, is the virus making a comeback?

A world leader in infections and deaths.

Latin America became an epicenter of the coronavirus pandemic in May, driven by Brazil’s ballooning caseload. Ten months after its first known case, Brazil has had more than 7.9 million cases and over 200,000 deaths.

In early June, Brazil began averaging about 1,000 deaths per day from Covid-19, joining the United States — and later India — as the countries with the world’s largest death tolls.

This dataset contains information about COVID-19 in Brazil extracted on the date 16/06/2021. It is the most updated dataset available about Covid in Brazil

Features:

🔍 date: date that the data was collected. format YYYY-MM-DD.
🔍 state: Abbreviation for States. Example: SP
🔍 city: Name of the city (if the value is NaN, they are referring to the State, not the city)
🔍 place_type: Can be City or State
🔍 order_for_place: Number that identifies the registering order for this location. The line that refers to the first log is going to be shown as 1, and the following information will start the count as an index.
🔍 is_last: Show if the line was the last update from that place, can be True or False
🔍 city_ibge_code: IBGE Code from the location
🔍confirmed: Number of confirmed cases.
🔍deaths: Number of deaths.
🔍estimated_population: Estimated population for this city/state in 2020. Data from IBGE
🔍estimated_population_2019: Estimated population for this city/state in 2019. Data from IBGE.
🔍confirmed_per_100k_inhabitants: Number of confirmed cases per 100.000 habitants (based on estimated_population).
🔍death_rate: Death rate (deaths / confirmed cases).

Acknowledgements

This dataset was downloaded from the URL bello. Thanks, Brasil.IO! Their main goal is to make all Brazilian data available to the public DATASET URL: https://brasil.io/dataset/covid19/files/ Cities map file https://geoftp.ibge.gov.br/organizacao_do_territorio/malhas_territoriais/malhas_municipais/municipio_2020/Brasil/BR/

Similar Datasets

COVID-19 - https://www.kaggle.com/rafaelherrero/covid19-brazil-full-cases-17062021 COVID-19 - https://www.kaggle.com/imdevskp/corona-virus-report MERS - https://www.kaggle.com/imdevskp/mers-outbreak-dataset-20122019 Ebola Western Africa 2014 Outbreak - https://www.kaggle.com/imdevskp/ebola-outbreak-20142016-complete-dataset H1N1 | Swine Flu 2009 Pandemic Dataset - https://www.kaggle.com/imdevskp/h1n1-swine-flu-2009-pandemic-dataset SARS 2003 Pandemic - https://www.kaggle.com/imdevskp/sars-outbreak-2003-complete-dataset HIV AIDS - https://www.kaggle.com/imdevskp/hiv-aids-dataset

The COVID-19 pandemic triggered a globally spread—but differently timed—implementation of school closures and other disruptive containment measures as governments worldwide intervened to curb transmission of disease. This study argues that the timing of such disruptive interventions reflects how governments balance the principles of precaution and proportionality in their pandemic decision-making. A theory is proposed of how their trade-off is impacted by two interacting institutional factors: electoral democratic institutions, which incentivize political leaders to increasingly favor precaution, and high state administrative capacity, which instead makes a proportional strategy involving later containment measures more administratively and politically feasible. Global patterns consistent with this theory are documented among 170 countries in early 2020, using Cox models of school closures and other non-pharmaceutical interventions. Corroborating the theorized mechanisms, additional results indicate that electoral competition prompts democratic leaders’ faster response, and that this mechanism is weaker where professional state agencies have more influence over policy-making.

Demo

https://james.iamcebu.com/images/demo-face-mask.gif" alt="Facemask Usage Detection Demo">

Inspiration

As COVID-19 continues to spread across the world, leaders and individuals are finding ways to halt the spread of the virus. The World Health Organization (WHO) recommended in March 2020 to wear a face-covering to prevent people from inhaling out tiny droplets that may carry the virus. Properly mask wearing means the virus transmission can be lowered.

Code and Reasearch Paper

If you use this dataset, can you please cite this study. The GitHub code is also provided. Research Paper: https://www.jardcs.org/abstract.php?id=5699 Python Code: https://github.com/jamesnogra/ImproperMaskDetector

About the Data

The data consists of four folders, fully_covered, not_covered, not_face, and partially_covered. The folder fully_covered contains faces of people that are wearing face mask properly/correctly according to WHO standards. Folder partially_covered contains face images that the face mask only covers the mouth but not the nose openings. The folder not_covered are face images of people not wearing face mask at all. The not_face folder, which can be optional in training your data, are images that are detected in OpenCV face detection library that are obviously not faces of people.

Contact

If you want the full paper, just email me at jamesnogra@gmail.com or you can visit the information website for this study at https://james.iamcebu.com/#face-mask-detection.

There's a joke among HR specialists that the most requested report from company stakeholders is an annual list of employee birthdays. The problem is that even in 2023, the joke isn’t too far from the truth. HR has come a long way of late. During Covid, not to mention the Great Resignation that followed, leaders everywhere came to depend on people insights.

But here’s the reality: much of the HR data is still stuck in HR, and HR professionals generally lag behind with respect to analytics and data visualization competency. The purpose of this use case is to dig deeper than reading rows and columns, and get insights from that amount of data to understand those people, the company’s strategy and make a move forward in the data world.

The HR-related dataset is provided by Dr. Carla Patalano and Dr. Rich, which is used in one of their graduate MSHRM courses called HR Metrics and Analytics, at New England College of Business. Therefore, we have no general problem to study but a multitude of HR KPI to uncover.

Inspiration

Here are some questions I answered: Is the absence of our employee due to a low satisfaction ? Is the performance of our employees influenced by the manager? What about the state of diversity in our company ? Are our employees paid enough, compared with the market in 2023, in the USA ?

The Thailand Economic Monitor (TEM) reports on key developments in Thailand’s economy over the past six months, situates these changes in the context of global trends and Thailand’s longer-term economic trajectory, and updates Thailand’s economic and social welfare outlook. Each edition of the TEM also provides an in-depth examination of selected economic and policy issues and an analysis of Thailand’s medium-term development challenges. The TEM is intended for a wide audience, including policymakers, business leaders, financial-market participants, and the community of analysts and professionals engaged in Thailand’s evolving economy. The TEM is produced by the staff of the World Bank’s Bangkok office, consisting of Kiatipong Ariyapruchya, Arvind Nair (task team leaders), Phonthanat Uruhamanon, Ralph van Doorn, Mahama Samir Bandaogo, Harry Edmund Moroz, Francesca Lamanna, Judy Yang, Ratchada Anantavrasilpa, Ana Maria Aviles, Nikola Kojucharov, Smita Kuriakose, Wouter Schalken, Radu Tatucu and Sutayut Osornprasop. Birgit Hansl, Ndiame Diop, and Souleymane Coulibaly provided overall guidance. The team is grateful to, Andrew Blackman, Achim Schmillen and Ergys Islamaj for their constructive peer review comments. Clarissa Crisostomo David, Kanitha Kongrukgreatiyos and Buntarika Sangarun are responsible for external communications related to the TEM, as well as the production and design of this edition.

Boris Johnson - United Kingdom: Boris Johnson has been Prime Minister since July 24, 2019. He is the leader of the Conservative Party. Johnson's tenure has been marked by significant events such as Brexit, the COVID-19 pandemic, and efforts to revitalize the UK economy.

Imran Khan - Pakistan: Imran Khan became Prime Minister on August 18, 2018. He leads the Pakistan Tehreek-e-Insaf (PTI) party. Khan, a former cricketer turned politician, has focused on anti-corruption measures, economic reforms, and diplomacy with neighboring countries.

Sheikh Hasina - Bangladesh: Sheikh Hasina has served as Prime Minister since January 6, 2009. She leads the Awami League party. Hasina's leadership has emphasized economic development, infrastructure projects, and social welfare programs in Bangladesh.

Joe Biden - United States: Joe Biden is the President of the United States, not a Prime Minister. He assumed office on January 20, 2021, leading the Democratic Party. Biden's presidency has prioritized issues such as the COVID-19 pandemic response, climate change, racial equity, and infrastructure reform.

Justin Trudeau - Canada: Justin Trudeau has been Prime Minister since November 4, 2015. He leads the Liberal Party. Trudeau is known for his progressive policies on climate change, immigration, and social issues, as well as his efforts to strengthen Canada's international relations.

Narendra Modi - India: Narendra Modi has been Prime Minister since May 26, 2014. He leads the Bharatiya Janata Party (BJP). Modi's tenure has been marked by economic reforms, infrastructure development, digital initiatives, and a proactive foreign policy approach.

Notes:

1.**Boris Johnson - United Kingdom**

2.**Imran Khan - Pakistan**

3.**Sheikh Hasina - Bangladesh**

4.**Joe Biden - United States** (Note: Joe Biden is not a Prime Minister; he is the President of the United States)

5.**Justin Trudeau - Canada**

6.**Narendra Modi - India**

COVID-19 has had significant effects on the field of veterinary medicine. Adaptation to pandemic-related and post-pandemic challenges requires engagement from all levels of the professional pipeline, including veterinary college students. Insights gained from this group may inform curriculum design, help the veterinary profession innovate, maximize opportunities for positive change, and avoid negative outcomes. The current study aimed to understand the potential impacts of the COVID-19 pandemic on veterinary medicine, as foreseen by second-year veterinary students in an online discussion during a public health course in the spring of 2020. Twenty-one percent of the 113 students agreed to participate in this qualitative research study. We used an inductive coding process and distilled the student responses into descriptive themes to capture diverse perspectives and understand possible post-pandemic pathways for the veterinary profession. Four themes emerged from the student discussion posts, describing how veterinarians might be affected by the COVID-19 pandemic: (1) economic and social impacts, (2) adapting to challenges, (3) collaborations to improve public health, and (4) disparities and diversity. These themes are a starting point for discussion and innovation as veterinarians plan for the post-pandemic world; further investigation will provide additional guidance for veterinary leaders.