BackgroundCoronavirus disease 2019 (COVID-19) emerged in 2019 and has since caused a global pandemic. Since its emergence, COVID-19 has hugely impacted healthcare, including pediatrics. This study aimed to explore the current status and hotspots of pediatric COVID-19 research using bibliometric analysis.MethodsThe Institute for Scientific Information Web of Science core collection database was searched for articles on pediatric COVID-19 to identify original articles that met the criteria. The retrieval period ranged from the creation of the database to September 20, 2021. A total of 3,561 original articles written in English were selected to obtain data, such as author names, titles, source publications, number of citations, author affiliations, and countries where the studies were conducted. Microsoft Excel (Microsoft, Redmond, WA) was used to create charts related to countries, authors, and institutions. VOSviewer (Center for Science and Technology Studies, Leiden, The Netherlands) was used to create visual network diagrams of keyword, author, and country co-occurrence.ResultsWe screened 3,561 publications with a total citation frequency of 30,528. The United States had the most published articles (1188 articles) and contributed the most with author co-occurrences. The author with the most published articles was Villani from the University of Padua, Italy. He also contributed the most co-authored articles. The most productive institution was Huazhong University of Science and Technology in China. The institution with the most frequently cited published articles was Shanghai Jiao Tong University in China. The United States cooperated most with other countries. Research hotspots were divided into two clusters: social research and clinical research. Besides COVID-19 and children, the most frequent keywords were pandemic (251 times), mental health (187 times), health (172 times), impact (148 times), and multisystem inflammatory syndrome in children (MIS-C) (144 times).ConclusionPediatric COVID-19 has attracted considerable attention worldwide, leading to a considerable number of articles published over the past 2 years. The United States, China, and Italy have leading roles in pediatric COVID-19 research. The new research hotspot is gradually shifting from COVID-19 and its related clinical studies to studies of its psychological and social impacts on children.

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐟𝐮𝐥 𝐃𝐲𝐧𝐚𝐦𝐢𝐜 𝐃𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝 𝐨𝐟 𝐂𝐨𝐯𝐢𝐝-𝟏𝟗 𝐏𝐚𝐧𝐝𝐞𝐦𝐢𝐜 🚑

Hello Kaggle Community!👋 Check out my new Data Analysis Project on Covid-19 Pandemic. I strive to Discover Insights and Crunch Numbers into Narratives, ensuring Clean Data for Optimal use. This dual approach caters to both Technical and Non-Technical Audiences, making the Data readily Understandable. Then, I delve into Insights revealed by the comprehensive Dashboard, Extracting Valuable Conclusions from the Analysis 📊📈

𝐃𝐚𝐭𝐚-𝐃𝐫𝐢𝐯𝐞𝐧 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐟𝐫𝐨𝐦 𝐭𝐡𝐢𝐬 𝐃𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝:

1. 𝐆𝐥𝐨𝐛𝐚𝐥 𝐒𝐭𝐚𝐭𝐮𝐬: 𝐌𝐨𝐧𝐢𝐭𝐨𝐫 𝐭𝐡𝐞 𝐭𝐨𝐭𝐚𝐥 𝐧𝐮𝐦𝐛𝐞𝐫 𝐨𝐟 𝐂𝐎𝐕𝐈𝐃-𝟏𝟗 𝐜𝐚𝐬𝐞𝐬 𝐰𝐨𝐫𝐥𝐝𝐰𝐢𝐝𝐞. For specific actions and precautions, prioritize local public health guidelines and advisories.

2. 𝐇𝐨𝐭𝐬𝐩𝐨𝐭𝐬: 𝐄𝐱𝐩𝐥𝐨𝐫𝐞 𝐭𝐡𝐞 𝐭𝐨𝐩 𝟓 𝐜𝐨𝐮𝐧𝐭𝐫𝐢𝐞𝐬 𝐰𝐢𝐭𝐡 𝐭𝐡𝐞 𝐦𝐨𝐬𝐭 𝐜𝐨𝐧𝐟𝐢𝐫𝐦𝐞𝐝 𝐜𝐚𝐬𝐞𝐬. Research the latest travel advisories and restrictions imposed by these countries before making decisions.

3. 𝐂𝐨𝐧𝐭𝐢𝐧𝐞𝐧𝐭𝐚𝐥 𝐁𝐫𝐞𝐚𝐤𝐝𝐨𝐰𝐧: 𝐓𝐫𝐚𝐜𝐤 𝐭𝐡𝐞 𝐜𝐨𝐧𝐭𝐢𝐧𝐞𝐧𝐭𝐬 𝐰𝐢𝐭𝐡 𝐭𝐡𝐞 𝐡𝐢𝐠𝐡𝐞𝐬𝐭 𝐜𝐚𝐬𝐞𝐥𝐨𝐚𝐝𝐬. Use this broader view to inform travel or event decisions.

4. 𝐌𝐨𝐫𝐭𝐚𝐥𝐢𝐭𝐲 𝐑𝐚𝐭𝐞𝐬: 𝐌𝐨𝐧𝐢𝐭𝐨𝐫 𝐜𝐨𝐮𝐧𝐭𝐫𝐢𝐞𝐬 𝐰𝐢𝐭𝐡 𝐭𝐡𝐞 𝐡𝐢𝐠𝐡𝐞𝐬𝐭 𝐝𝐞𝐚𝐭𝐡 𝐩𝐞𝐫𝐜𝐞𝐧𝐭𝐚𝐠𝐞𝐬. Exercise heightened caution and hygiene measures in these areas.

5. 𝐑𝐞𝐜𝐨𝐯𝐞𝐫𝐲 𝐏𝐫𝐨𝐠𝐫𝐞𝐬𝐬: 𝐓𝐫𝐚𝐜𝐤 𝐭𝐡𝐞 𝐧𝐮𝐦𝐛𝐞𝐫 𝐨𝐟 𝐫𝐞𝐜𝐨𝐯𝐞𝐫𝐞𝐝 𝐂𝐎𝐕𝐈𝐃-𝟏𝟗 𝐜𝐚𝐬𝐞𝐬 𝐠𝐥𝐨𝐛𝐚𝐥𝐥𝐲. Stay informed about advancements in treatment and vaccinations for optimism.

6. 𝐓𝐨𝐭𝐚𝐥 𝐃𝐞𝐚𝐭𝐡𝐬: 𝐍𝐮𝐦𝐛𝐞𝐫 𝐨𝐟 𝐩𝐞𝐨𝐩𝐥𝐞 𝐰𝐡𝐨 𝐜𝐨𝐮𝐥𝐝𝐧'𝐭 𝐒𝐮𝐫𝐯𝐢𝐯𝐞 𝐭𝐡𝐞 𝐏𝐚𝐧𝐝𝐞𝐦𝐢𝐜. Focus on recovery efforts and preventative measures for protection.

Navigate to Kaggle to preview dynamicity of this dashboard (Link in the comments).

𝐓𝐨𝐨𝐥 𝐔𝐬𝐞𝐝: Microsoft Excel

DataAnalytics #DataAnalysis #DataAnalyst #DataStorytelling #BusinessIntelligence #DataVisualization #DataDrivenInsights

As of March 10, 2023, the state with the highest rate of COVID-19 cases was Rhode Island followed by Alaska. Around 103.9 million cases have been reported across the United States, with the states of California, Texas, and Florida reporting the highest numbers of infections.

From an epidemic to a pandemic The World Health Organization declared the COVID-19 outbreak as a pandemic on March 11, 2020. The term pandemic refers to multiple outbreaks of an infectious illness threatening multiple parts of the world at the same time; when the transmission is this widespread, it can no longer be traced back to the country where it originated. The number of COVID-19 cases worldwide is roughly 683 million, and it has affected almost every country in the world.

The symptoms and those who are most at risk Most people who contract the virus will suffer only mild symptoms, such as a cough, a cold, or a high temperature. However, in more severe cases, the infection can cause breathing difficulties and even pneumonia. Those at higher risk include older persons and people with pre-existing medical conditions, including diabetes, heart disease, and lung disease. Those aged 85 years and older have accounted for around 27 percent of all COVID deaths in the United States, although this age group makes up just two percent of the total population

Description: The COVID-19 dataset used for this EDA project encompasses comprehensive data on COVID-19 cases, deaths, and recoveries worldwide. It includes information gathered from authoritative sources such as the World Health Organization (WHO), the Centers for Disease Control and Prevention (CDC), and national health agencies. The dataset covers global, regional, and national levels, providing a holistic view of the pandemic's impact.

Purpose: This dataset is instrumental in understanding the multifaceted impact of the COVID-19 pandemic through data exploration. It aligns perfectly with the objectives of the EDA project, aiming to unveil insights, patterns, and trends related to COVID-19. Here are the key objectives: 1. Data Collection and Cleaning: • Gather reliable COVID-19 datasets from authoritative sources (such as WHO, CDC, or national health agencies). • Clean and preprocess the data to ensure accuracy and consistency. 2. Descriptive Statistics: • Summarize key statistics: total cases, recoveries, deaths, and testing rates. • Visualize temporal trends using line charts, bar plots, and heat maps. 3. Geospatial Analysis: • Map COVID-19 cases across countries, regions, or cities. • Identify hotspots and variations in infection rates. 4. Demographic Insights: • Explore how age, gender, and pre-existing conditions impact vulnerability. • Investigate disparities in infection rates among different populations. 5. Healthcare System Impact: • Analyze hospitalization rates, ICU occupancy, and healthcare resource allocation. • Assess the strain on medical facilities. 6. Economic and Social Effects: • Investigate the relationship between lockdown measures, economic indicators, and infection rates. • Explore behavioral changes (e.g., mobility patterns, remote work) during the pandemic. 7. Predictive Modeling (Optional): • If data permits, build simple predictive models (e.g., time series forecasting) to estimate future cases.

Data Sources: The primary sources of the COVID-19 dataset include the Johns Hopkins CSSE COVID-19 Data Repository, Google Health’s COVID-19 Open Data, and the U.S. Economic Development Administration (EDA). These sources provide reliable and up-to-date information on COVID-19 cases, deaths, testing rates, and other relevant variables. Additionally, GitHub repositories and platforms like Medium host supplementary datasets and analyses, enriching the available data resources.

Data Format: The dataset is available in various formats, such as CSV and JSON, facilitating easy access and analysis. Before conducting the EDA, the data underwent preprocessing steps to ensure accuracy and consistency. Data cleaning procedures were performed to address missing values, inconsistencies, and outliers, enhancing the quality and reliability of the dataset.

License: The COVID-19 dataset may be subject to specific usage licenses or restrictions imposed by the original data sources. Proper attribution is essential to acknowledge the contributions of the WHO, CDC, national health agencies, and other entities providing the data. Users should adhere to any licensing terms and usage guidelines associated with the dataset.

Attribution: We acknowledge the invaluable contributions of the World Health Organization (WHO), the Centers for Disease Control and Prevention (CDC), national health agencies, and other authoritative sources in compiling and disseminating the COVID-19 data used for this EDA project. Their efforts in collecting, curating, and sharing data have been instrumental in advancing our understanding of the pandemic and guiding public health responses globally.

COVID-19 in Muslim vs. Non-Muslim Countries

A Comparative Study of 50 Muslim-Majority and 50 Richest Non-Muslim Countries

About Dataset

This dataset contains information on COVID-19 cases and deaths in 50 Muslim-majority countries compared to the 50 richest non-Muslim countries. The aim of the dataset is to investigate the differences in COVID-19 incidence between these two groups and to explore potential reasons for these disparities. The Muslim-majority countries in the sample had more than 50.0% Muslims, while the non-Muslim countries were selected based on their GDP, excluding any Muslim-majority countries listed. The data was collected on September 18, 2020, and includes information on the percentage of Muslim population per country, GDP, population count, and total number of COVID-19 cases and deaths. The dataset was transferred via an Excel spreadsheet on September 23, 2020 and analyzed using three different Average Treatment Methods (ATE) to validate the results. The dataset was published as a preprint and is associated with a manuscript titled "Fifty Muslim-majority countries have fewer COVID-19 cases and deaths than the 50 richest non-Muslim countries". The manuscript can be accessed via the following Link The sources of the data are also provided in the manuscript. The percentage of Muslim population per country was obtained from World Population Review and can be accessed at Link The GDP per country, population count, and total number of COVID-19 cases and deaths were obtained from Worldometers and can be accessed at Link

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Research Ideas / Data Use

• Comparative analysis: Researchers can use this dataset to compare the COVID-19 cases and deaths between Muslim-majority and non-Muslim countries. This can help to identify any disparities or differences in the response to the pandemic.
• Trend analysis: Over time, this dataset can be used to track the changes in the COVID-19 cases and deaths in Muslim-majority and non-Muslim countries. This can help to identify trends and patterns that may inform future research.
• Geographical analysis: This dataset can be used to explore the geographical distribution of COVID-19 cases and deaths in Muslim-majority and non-Muslim countries. This can help to identify hotspots and areas that may require special attention.
• Demographic analysis: Researchers can use the data to explore the impact of demographic factors on the spread and severity of the pandemic in Muslim-majority and non-Muslim countries. This can help to identify any patterns or correlations that may inform future research and policy decisions.
• Economic analysis: The data can be used to explore the economic impact of the pandemic on Muslim-majority and non-Muslim countries. By comparing the GDP and other economic indicators in these countries, researchers can identify any patterns or trends that may inform economic policy decisions.

Acknowledgements

if this dataset was used in your work or studies, please credit the original source Please Credit ↑ ⠀

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. More Information

Purpose: The coronavirus disease 2019 (COVID-19) outbreak, which began in December 2019, has not been completely controlled; therefore, COVID-19 has received much attention from countries around the world. Many related clinical studies, such as clinical trials, have been published, but to the knowledge of the authors, there has been no bibliometric analysis of these publications focusing on clinical research studies on COVID-19.Methods: Global publications on COVID-19 from January 2020 to December 2020 were extracted from the Web of Science (WOS) collection database. The VOSviewer software and CiteSpace were employed to perform a bibliometric study. In addition, we obtained information on relevant clinical trials from the website http://clinicaltrials.gov.Results: China published most of the articles in this field and had the highest number of citations and H-index. The Journal of Medical Virology published most of the articles related to COVID-19. In terms of institutions, Huazhong University of Science and Technology had the most publications, and Wang, JW received the highest number of citations.Conclusion: The diagnosis, prevention, and prognosis of COVID-19 are still the focus of attention at present. The overall analysis of the disease were identified as the emerging topics from the perspectives of epidemiology and statistics. However, finding an effective treatment remains the focus of clinical trials.

BackgroundPrecise public health and clinical interventions for the COVID-19 pandemic has spurred a global rush on SARS-CoV-2 variant tracking, but current approaches to variant tracking are challenged by the flood of viral genome sequences leading to a loss of timeliness, accuracy, and reliability. Here, we devised a new co-mutation network framework, aiming to tackle these difficulties in variant surveillance.MethodsTo avoid simultaneous input and modeling of the whole large-scale data, we dynamically investigate the nucleotide covarying pattern of weekly sequences. The community detection algorithm is applied to a co-occurring genomic alteration network constructed from mutation corpora of weekly collected data. Co-mutation communities are identified, extracted, and characterized as variant markers. They contribute to the creation and weekly updates of a community-based variant dictionary tree representing SARS-CoV-2 evolution, where highly similar ones between weeks have been merged to represent the same variants. Emerging communities imply the presence of novel viral variants or new branches of existing variants. This process was benchmarked with worldwide GISAID data and validated using national level data from six COVID-19 hotspot countries.ResultsA total of 235 co-mutation communities were identified after a 120 weeks' investigation of worldwide sequence data, from March 2020 to mid-June 2022. The dictionary tree progressively developed from these communities perfectly recorded the time course of SARS-CoV-2 branching, coinciding with GISAID clades. The time-varying prevalence of these communities in the viral population showed a good match with the emergence and circulation of the variants they represented. All these benchmark results not only exhibited the methodology features but also demonstrated high efficiency in detection of the pandemic variants. When it was applied to regional variant surveillance, our method displayed significantly earlier identification of feature communities of major WHO-named SARS-CoV-2 variants in contrast with Pangolin's monitoring.ConclusionAn efficient genomic surveillance framework built from weekly co-mutation networks and a dynamic community-based variant dictionary tree enables early detection and continuous investigation of SARS-CoV-2 variants overcoming genomic data flood, aiding in the response to the COVID-19 pandemic.

Background: Disaster epidemiology has not attracted enough attention in the past few decades and still faces significant challenges. This study aimed to systematically analyze the evolving trends and research hotspots in disaster epidemiology and provide insights into disaster epidemiology.Methods: We searched the Scopus and Web of Science Core Collection (WoSCC) databases between 1985 and 2020 to identify relevant literature on disaster epidemiology. The retrieval strategies were TITLE-ABS-KEY (disaster epidemiology) and TS = (disaster AND epidemiology). Bibliometrix, VOSviewer 1.6.6 and SigmaPlot 12.5 were used to analyze the key bibliometric indicators, including trends and annual publications, the contributions of countries, institutions, journals and authors, and research hotspots.Results: A total of 1,975 publications were included. There was an increasing trend in publications over the past 35 years. The USA was the most productive country. The most frequent institutions and journals were Fukushima Medical University and Prehospital and Disaster Medicine. Galea S made significant contributions to this field. “Epidemiology” was the highest-frequency keyword. COVID-19 was highly cited after 2019. Three research hotspots were identified: (i) the short- and long-term adverse health effects of disasters on the population; (ii) COVID-19 pandemic and emergency preparedness; and (iii) disaster management.Conclusions: In recent decades, the USA was a global leader in disaster epidemiology. Disaster management, the short- and long-term health effects of disasters, and the COVID-19 pandemic reflected the research focuses. Our results suggest that these directions will remain research hotspots in the future. International collaboration is also expected to widen and deepen in the field of disaster epidemiology.

Abstract

Abstract

Background: Hand washing with soap is crucial for infection transmission prevention. However, despite its effectiveness in reducing infections, globally the proportion of individuals who comply is low at only 19%, varying between developed (48-72%) and developing countries (5-25%). In Africa, basic hand washing facility coverage is at 15%, and in Kenya, the same is estimated at 18%. During the COVID-19 pandemic, awareness and hand washing practices increased globally including Kenya. However, hand-washing adoption often declines soon after crises/pandemics. Informal settlements, such as Viwandani, are harder hit by handwashing challenges because of limitations in access to water and handwashing facilities. Moreover, these communities are more vulnerable to other non-hygiene-related infectious diseases. Data on hand washing practices is sparse more so among populations living in informal settlements. Also, there is need to identify interventions for sustained hand washing with soap in these communities. Objectives: To explore handwashing practice among the slum population, in a post-pandemic era. Specifically, the study will 1) assess adherence to and techniques of handwashing used in main hand washing hotspots in slum residents of Viwandani, Nairobi, 2) assess perceptions, facilitators, and barriers to sustaining adherence to hand washing with soap after COVID-19 by slum residents in Viwandani, Nairobi, 3) explore the motivation and mechanism through which hand washing with soap can be sustained among some slum residents in Viwandani, Nairobi and 4) assess availability and readiness of handwashing facilities at identified hand washing hotspots in Viwandani, Nairobi. Methods: This will be a qualitative study using direct observation, key informant interviews (KIIs), focus group discussions (FGDs), and in-depth interviews (IDIs) to collect information on adherence to and techniques of handwashing, perceptions, facilitators, and barriers to sustaining handwashing with soap, as well as the motivations and mechanisms through which handwashing with soap can be sustained including availability and readiness of handwashing facilities. The work will conclude with a consultative workshop to propose a pilot concept for sustained hand washing with soap in Viwandani. First, the research team, with the assistance of the community advisory committee (CAC) members, familiar with the local set up will identify hot spots for handwashing. The CAC is a dedicated group that helps identify local health needs and develops ways to address those needs using community approach. The CAC is composed of members elected by respective constituent groups that they represent. The members represent government, local leaders/village leaders, the youth, women, older persons, school administrators, healthcare providers, faith-based organizations/community-based organizations/local non-governmental organizations, community health volunteers, media/education and entertainment organizations, religious groups and people living with disabilities. Then, we will conduct covert observations at the identified hotspots across Viwandani, focusing on both handwashing facilities and their users. Each hotspot will have two observation sessions in which several individuals may be observed, one session in the morning (9:00 AM to 1:00 PM) and another in the afternoon (1:00 PM to 5:00 PM). From each observation session, we will purposively select one individual for IDI, meaning that we will conduct 2 in-depth interviews from each observation site. In addition, we will engage CAC members in FGDs to further explore the community motivation and the mechanisms for sustained hand washing with soap. We will also gather additional insights from KIIs drawn from individuals representing facilities in the hotspot list. These will be institutional leaders or owners of these hotspots or focal persons who are well informed about hand washing with soap. Lastly, we will convene a consultative workshop bringing together representatives from the County health officials, local administration,interview participants, CAC, and representatives of the facilities within the hotspots to collaboratively propose a pilot concept for sustained hand washing with soap in Viwandani. We will conduct thematic analysis of the data.

Significance: In resource-constrained slum environments, where costly interventions like sanitation upgrades may not be feasible and the risk for transmission of infectious diseases is high, it is crucial to understand how existing resources are utilized for handwashing with soap. This project will generate insights into current practices, identifying factors that influence the use of available resources, explore motivation mechanisms and assess availability and readiness of facilities for hand washing with soap in Viwandani. The findings will inform the design or improvement of sustainable handwashing interventions, contributing to more effective disease prevention strategies.

Duration: 12 months (March 2024 to February 2025)

Budget: USD 10,000

Lay summary

Washing hands with soap is important for preventing the spread of pathogens. But not many people around the world do it regularly - only about 19%. This varies depending on where you live, with richer countries having higher rates (around 48-72%) and poorer countries having lower rates (about 5-25%). During the COVID-19 pandemic, governments including Kenyan, ran campaigns to get people to wash their hands more, and they set up lots of handwashing stations. More people started washing their hands because they feared getting sick. As a result, besides prevention of COVID-19 transmission, additional benefits were realized including reduction of diarrheal and other respiratory infections. But in the past, when there have been outbreaks of diseases, people start washing their hands more, but then they stop again soon after. A survey in Nairobi found that after six months, most of the handwashing stations were still working, and lots of people were using them properly. But a year later, fewer people were using them, and some of the stations were abandoned.

Through this study, we would like to understand how people in the slums of Viwandani in Nairobi are washing their hands after the COVID-19 pandemic. We will work with the community to come up with ways to encourage people to keep washing their hands regularly. Specifically, we will engage CAC members to identify hotspots for handwashing with soap in their community, then observe people in the identified hotspots to see how they wash their hands in places where they're supposed to. Out of those that we observe, we will pick out some and talk to them to find out what they think about washing their hands with soap and what makes it hard for them to keep doing it, as well as what motivates some people to keep washing their hands and how we can help others do the same. Additionally, we will hold discussions with the CAC team that did the hotspot mapping to gather more information on the community perspective of hand washing with soap. We will also talk to key informants to gather further insights. Finally, we will hold a workshop to bring together representatives from the County health officials, local administration, interview participants, the CAC, and representatives from the facilities in the hotspot list. They will collaboratively propose a pilot concept for Viwandani community that can encourage regular hand washing with soap. We will analyze the data to find common themes and insights. This study appreciates that in poor areas like slums, it's not easy to do big things like upgrade sanitation systems. So, it's important to focus on simple things like washing hands with soap, which can help stop diseases from spreading. But even though washing hands is cheap and effective, not many people keep doing it regularly. This study will help us understand why and propose ways to fix it, as suggested by the community itself.

The study will last for 12 months, from March 2024 to February 2025.

The budget for the study is $10,000.

Geographic coverage

County coverage, Urabn informal settlement, Nairobi county (Viwandani informal settlement)

Analysis unit

The study observed handwashing practices, conditions of handwashing facilities, their availability and readiness in Viwandani after COVID-19. The study also assessed individual, institutional and administrative perceptions, facilitators and barriers to sustaining adherence to handwashing with soap as well as motivations and mechanisms through whuch handwashing with soap can be sustained among residents in Viwandani after COVID-19.

Universe

The study focuses residents residint within Viwandani, leaders of institutions identified during the hotspot mapping, health professionals and local administrative leaders.

Sampling procedure

A purposive sampling strategy was employed to recruit participants for hotspot mapping, in-depth interviews (IDIs), focus group discussions (FGDs), and key informant interviews (KIIs). This method was appropriate as it allowed deliberate selection of individuals and groups with relevant knowledge and experiences critical to the study objectives.

Sampling deviation

We intended conduct 600 covert observations, 50 in-depth interviews (IDIs), 10-15 key informant interviews (KIIs), and 2 focus group discussions (FGDs). We managed to complete 596 covert bservations, 42 IDIs, 11 KIIs and both FGDs. This deviation from th indeded sample size was due to low traffic in some of the handwashing stations and refusal to participate in the study.To mitigate this, we did replacement for the refusals.

Mode of data collection

Face-to-face

ObjectiveDuring the period of COVID-19, government regulation (GR) played an important role in healthcare. This study examines the current research situation of GR in healthcare, discusses the research hotspots, the most productive authors and countries, and the most common journals, and analyzes the changes in GR in healthcare before and after the outbreak of COVID-19.MethodsThis study followed PRISMA guidelines to collect literature on GR in healthcare. And the VOSviewer software was used to perform a quantitative analysis of these documents to obtain a visual map, including year, country, institution, journal, author, and research topic.ResultsA total of 1,830 papers that involved 976 academic journals, 3,178 institutions, and 133 countries were identified from 1985 to 2023. The United States was the country with the highest production (n = 613), followed by the United Kingdom (n = 289). The institution with the largest number of publications was the University of London in the UK (n = 103); In the author collaboration network, the biggest cluster is Bomhoff M, Bouwman R, Friele R, et al. The top five journals in terms of the number of articles were BMC Health Services Research (n = 70), Plos One (n = 35), Health Policy (n = 33), Social Science & Medicine (n = 29), Health Policy and Planning (n = 29), and Frontiers in Public Health (n = 27). The existing literature mainly focused on “health policy,” “public health,” “China,” “mental health,” “India,” “qualitative research,” “legislation,” and “governance,” et al. Since 2020, research on “COVID-19” has also become a priority in the domain of healthcare.ConclusionThis study reveals the overall performance of the literature on GR published in healthcare. Healthcare needs GR, especially in response to the COVID-19 epidemic, which has played an irreplaceable role. The outbreak of COVID-19 not only tested the health systems of various countries, but also changed GR in healthcare. With the end of COVID-19, whether these changes will end remains to be further studied.

Urban slums are hotspots of infectious diseases like COVID-19 as was seen in the waves of 2020 and 2021. One of the primary reasons why slums are disproportionately affected is their location in inaccessible and uninhabitable zones, crowded and poorly ventilated living spaces, unsanitary conditions and common facilities (water taps, common toilets, etc.). Staying at home during pandemics is hardly an option for slum dwellers as it often means giving up work and even basic necessities. This paper aims to understand the habitat vulnerabilities of slums in the two Indian megacities of Pune and Surat which were the worst hit during both waves. The study is done at the level of wards, which is the smallest administrative boundary, taking the habitat vulnerability (congestion and access to basic services). To identify the explanatory variables which increase the vulnerability of slums to infectious diseases, literature study is done on the triggering factors which affect habitat vulnerability derived from common characteristics and definitions of slum. The aim of the research is to categorize the slums into 3 levels of risk zones and map them subsequently. This study will help in formulating a model to prioritize the allocation of sparse resources in developing countries to tackle the habitat vulnerabilities of the slum dwellers especially during health emergencies of contagious diseases like COVID-19.

Results of multiple linear regression analysis performed between COVID-19 incidence and Google Health Trends search queries from four selected terms. (XLSX)

Context

The first case of the COVID-19 pandemic in Kerala (which was also the first in all of India) was confirmed in Thrissur on 30 January 2020.The number of active cases initially peaked at 266 on 6 April before declining. For the first time in over 45 days, there were no new cases on 1 May.However, following the return of Keralites from other countries and states, more cases were reported in mid-May, with the biggest single-day spike (195 cases) on 27 June. As of 30 June, there have been 4442 confirmed cases with 2304 (51.87%) recoveries and 24 deaths in the state.Kerala has one of the lowest mortality rate of 0.53% among all states in India.Kerala's success in containing COVID-19 has been widely praised both nationally and internationally.

Content

Patients age details in AgeInterval.csv file District wise Patient details in DistictData.csv file. List of Active Hotspots across kerala Hotspots.csv. Detais of infection type in InfectionType.csv file. List of Peoples in Observations across Kerala Observations.csv. Complete patient details in PatientData.csv file. Increase in Day by day Sum_by_Day.csv. List of Peoples in Quarantine in quarrentine.csv

Acknowledgements

Thanks to covid19kerala.info for making the data available to general public.

IntroductionAs the first bibliometric analysis of COVID-19 and immune responses, this study will provide a comprehensive overview of the latest research advances. We attempt to summarize the scientific productivity and cooperation across countries and institutions using the bibliometric methodology. Meanwhile, using clustering analysis of keywords, we revealed the evolution of research hotspots and predicted future research focuses, thereby providing valuable information for the follow-up studies.MethodsWe selected publications on COVID-19 and immune response using our pre-designed search strategy. Web of Science was applied to screen the eligible publications for subsequent bibliometric analyses. GraphPad Prism 8.0, VOSviewer, and CiteSpace were applied to analyze the research trends and compared the contributions of countries, authors, institutions, and journals to the global publications in this field.ResultsWe identified 2,200 publications on COVID-19 and immune response published between December 1, 2019, and April 25, 2022, with a total of 3,154 citations. The United States (611), China (353), and Germany (209) ranked the top three in terms of the number of publications, accounting for 53.3% of the total articles. Among the top 15 institutions publishing articles in this area, four were from France, four were from the United States, and three were from China. The journal Frontiers in Immunology published the most articles (178) related to COVID-19 and immune response. Alessandro Sette (31 publications) from the United States were the most productive and influential scholar in this field, whose publications with the most citation frequency (3,633). Furthermore, the development and evaluation of vaccines might become a hotspot in relevant scope.ConclusionsThe United States makes the most indispensable contribution in this field in terms of publication numbers, total citations, and H-index. Although publications from China also take the lead regarding quality and quantity, their international cooperation and preclinical research need to be further strengthened. Regarding the citation frequency and the total number of published articles, the latest research progress might be tracked in the top-ranking journals in this field. By analyzing the chronological order of the appearance of retrieved keywords, we speculated that vaccine-related research might be the novel focus in this field.

BackgroundThe COVID-19 pandemic has significantly impacted public health, putting people with Alzheimer's disease at significant risk. This study used bibliometric analysis method to conduct in-depth research on the relationship between COVID-19 and Alzheimer's disease, as well as to predict its development trends.MethodsThe Web of Science Core Collection was searched for relevant literature on Alzheimer's and Coronavirus-19 during 2019–2023. We used a search query string in our advanced search. Using Microsoft Excel 2021 and VOSviewer software, a statistical analysis of primary high-yield authors, research institutions, countries, and journals was performed. Knowledge networks, collaboration maps, hotspots, and regional trends were analyzed using VOSviewer and CiteSpace.ResultsDuring 2020–2023, 866 academic studies were published in international journals. United States, Italy, and the United Kingdom rank top three in the survey; in terms of productivity, the top three schools were Harvard Medical School, the University of Padua, and the University of Oxford; Bonanni, Laura, from Gabriele d'Annunzio University (Italy), Tedeschi, Gioacchino from the University of Campania Luigi Vanvitelli (Italy), Vanacore, Nicola from Natl Ctr Dis Prevent and Health Promot (Italy), Reddy, P. Hemachandra from Texas Tech University (USA), and El Haj, Mohamad from University of Nantes (France) were the authors who published the most articles; The Journal of Alzheimer's Disease is the journals with the most published articles; “COVID-19,” “Alzheimer's disease,” “neurodegenerative diseases,” “cognitive impairment,” “neuroinflammation,” “quality of life,” and “neurological complications” have been the focus of attention in the last 3 years.ConclusionThe disease caused by the COVID-19 virus infection related to Alzheimer's disease has attracted significant attention worldwide. The major hot topics in 2020 were: “Alzheimer' disease,” COVID-19,” risk factors,” care,” and “Parkinson's disease.” During the 2 years 2021 and 2022, researchers were also interested in “neurodegenerative diseases,” “cognitive impairment,” and “quality of life,” which require further investigation.

England. (XLSX)

Data covering the period from 01/03/2020 to 20/05/2021. (XLSX)

England and Wales. (XLSX)

Data covering the period from 01/03/2020 to 20/05/2021. (XLSX)