Updates

• Notice of data discontinuation: Since the start of the pandemic, AP has reported case and death counts from data provided by Johns Hopkins University. Johns Hopkins University has announced that they will stop their daily data collection efforts after March 10. As Johns Hopkins stops providing data, the AP will also stop collecting daily numbers for COVID cases and deaths. The HHS and CDC now collect and visualize key metrics for the pandemic. AP advises using those resources when reporting on the pandemic going forward.

  • CDC Weekly case and death counts (national and state level)
  • CDC County level cases and deaths
  • HHS New hospital admissions
  • CDC NowCast COVID variant proportions (national and regional level)

• April 9, 2020

  • The population estimate data for New York County, NY has been updated to include all five New York City counties (Kings County, Queens County, Bronx County, Richmond County and New York County). This has been done to match the Johns Hopkins COVID-19 data, which aggregates counts for the five New York City counties to New York County.

• April 20, 2020

  • Johns Hopkins death totals in the US now include confirmed and probable deaths in accordance with CDC guidelines as of April 14. One significant result of this change was an increase of more than 3,700 deaths in the New York City count. This change will likely result in increases for death counts elsewhere as well. The AP does not alter the Johns Hopkins source data, so probable deaths are included in this dataset as well.

• April 29, 2020

  • The AP is now providing timeseries data for counts of COVID-19 cases and deaths. The raw counts are provided here unaltered, along with a population column with Census ACS-5 estimates and calculated daily case and death rates per 100,000 people. Please read the updated caveats section for more information.

• September 1st, 2020

  • Johns Hopkins is now providing counts for the five New York City counties individually.

• February 12, 2021

  • The Ohio Department of Health recently announced that as many as 4,000 COVID-19 deaths may have been underreported through the state’s reporting system, and that the "daily reported death counts will be high for a two to three-day period."
  • Because deaths data will be anomalous for consecutive days, we have chosen to freeze Ohio's rolling average for daily deaths at the last valid measure until Johns Hopkins is able to back-distribute the data. The raw daily death counts, as reported by Johns Hopkins and including the backlogged death data, will still be present in the new_deaths column.

• February 16, 2021

  - Johns Hopkins has reconciled Ohio's historical deaths data with the state.

  Overview

The AP is using data collected by the Johns Hopkins University Center for Systems Science and Engineering as our source for outbreak caseloads and death counts for the United States and globally.

The Hopkins data is available at the county level in the United States. The AP has paired this data with population figures and county rural/urban designations, and has calculated caseload and death rates per 100,000 people. Be aware that caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.

This data is from the Hopkins dashboard that is updated regularly throughout the day. Like all organizations dealing with data, Hopkins is constantly refining and cleaning up their feed, so there may be brief moments where data does not appear correctly. At this link, you’ll find the Hopkins daily data reports, and a clean version of their feed.

The AP is updating this dataset hourly at 45 minutes past the hour.

To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

Queries

Use AP's queries to filter the data or to join to other datasets we've made available to help cover the coronavirus pandemic

• Filter cases by state here

• Rank states by their status as current hotspots. Calculates the 7-day rolling average of new cases per capita in each state: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=481e82a4-1b2f-41c2-9ea1-d91aa4b3b1ac

• Find recent hotspots within your state by running a query to calculate the 7-day rolling average of new cases by capita in each county: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=b566f1db-3231-40fe-8099-311909b7b687&showTemplatePreview=true

• Join county-level case data to an earlier dataset released by AP on local hospital capacity here. To find out more about the hospital capacity dataset, see the full details.

• Pull the 100 counties with the highest per-capita confirmed cases here

• Rank all the counties by the highest per-capita rate of new cases in the past 7 days here. Be aware that because this ranks per-capita caseloads, very small counties may rise to the very top, so take into account raw caseload figures as well.

Interactive

The AP has designed an interactive map to track COVID-19 cases reported by Johns Hopkins.

@(https://datawrapper.dwcdn.net/nRyaf/15/)

Interactive Embed Code

<iframe title="USA counties (2018) choropleth map Mapping COVID-19 cases by county" aria-describedby="" id="datawrapper-chart-nRyaf" src="https://datawrapper.dwcdn.net/nRyaf/10/" scrolling="no" frameborder="0" style="width: 0; min-width: 100% !important;" height="400"></iframe><script type="text/javascript">(function() {'use strict';window.addEventListener('message', function(event) {if (typeof event.data['datawrapper-height'] !== 'undefined') {for (var chartId in event.data['datawrapper-height']) {var iframe = document.getElementById('datawrapper-chart-' + chartId) || document.querySelector("iframe[src*='" + chartId + "']");if (!iframe) {continue;}iframe.style.height = event.data['datawrapper-height'][chartId] + 'px';}}});})();</script>

Caveats

• This data represents the number of cases and deaths reported by each state and has been collected by Johns Hopkins from a number of sources cited on their website.
• In some cases, deaths or cases of people who've crossed state lines -- either to receive treatment or because they became sick and couldn't return home while traveling -- are reported in a state they aren't currently in, because of state reporting rules.
• In some states, there are a number of cases not assigned to a specific county -- for those cases, the county name is "unassigned to a single county"
• This data should be credited to Johns Hopkins University's COVID-19 tracking project. The AP is simply making it available here for ease of use for reporters and members.
• Caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.
• Population estimates at the county level are drawn from 2014-18 5-year estimates from the American Community Survey.
• The Urban/Rural classification scheme is from the Center for Disease Control and Preventions's National Center for Health Statistics. It puts each county into one of six categories -- from Large Central Metro to Non-Core -- according to population and other characteristics. More details about the classifications can be found here.

Johns Hopkins timeseries data - Johns Hopkins pulls data regularly to update their dashboard. Once a day, around 8pm EDT, Johns Hopkins adds the counts for all areas they cover to the timeseries file. These counts are snapshots of the latest cumulative counts provided by the source on that day. This can lead to inconsistencies if a source updates their historical data for accuracy, either increasing or decreasing the latest cumulative count. - Johns Hopkins periodically edits their historical timeseries data for accuracy. They provide a file documenting all errors in their timeseries files that they have identified and fixed here

Attribution

This data should be credited to Johns Hopkins University COVID-19 tracking project

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.

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.

Authoritative COVID-19 information and not violating patient rights—Can we do both?With the coronavirus disease 2019 (COVID-19) pandemic now affecting people everywhere on the globe, the sheer scale means we are facing problems we have never before faced in modern times. Health authorities are more than ever in need of authoritative information like where current and upcoming hotspots are in order to decide on how best to prepare and respond. To get this information, collection of actual, accurate, authentic, and location-based patient information is a must. However, legislation to protect citizens’ rights puts restrictions on how and what data collection, analysis, and dissemination of personal information can be done. So, we have a situation where health authorities need good, reliable patient data but face difficulties in obtaining, processing, and distributing it. The challenge we have is how to collect, analyze, and disseminate localized patient information while at the same time ensuring that we protect the individual’s rights._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

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

Since its emergence in Wuhan, China, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has spread very rapidly around the world, resulting in a global pandemic. Though the vaccination process has started, the number of COVID-affected patients is still quite large. Hence, an analysis of hotspot mutations of the different evolving virus strains needs to be carried out. In this regard, multiple sequence alignment of 71,038 SARS-CoV-2 genomes of 98 countries over the period from January 2020 to June 2021 is performed using MAFFT followed by phylogenetic analysis in order to visualize the virus evolution. These steps resulted in the identification of hotspot mutations as deletions and substitutions in the coding regions based on entropy greater than or equal to 0.3, leading to a total of 45 unique hotspot mutations. Moreover, 10,286 Indian sequences are considered from 71,038 global SARS-CoV-2 sequences as a demonstrative example that gives 52 unique hotspot mutations. Furthermore, the evolution of the hotspot mutations along with the mutations in variants of concern is visualized, and their characteristics are discussed as well. Also, for all the non-synonymous substitutions (missense mutations), the functional consequences of amino acid changes in the respective protein structures are calculated using PolyPhen-2 and I-Mutant 2.0. In addition to this, SSIPe is used to report the binding affinity between the receptor-binding domain of Spike protein and human ACE2 protein by considering L452R, T478K, E484Q, and N501Y hotspot mutations in that region.

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.

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 Cases in England

Tracking Infection Risk at a Local Level

By Dan Winchester [source]

About this dataset

This dataset contains the total number of confirmed COVID-19 cases in each English Upper Tier Local Authority over the past eight days. Aggregated from Public Health England data, this dataset provides unprecedented insight into how quickly the virus has been able to spread in local communities throughout England. Despite testing limitations, understanding these localized patterns of infection can help inform important public health decisions by local authorities and healthcare workers alike.

It is essential to bear in mind that this data is likely an underestimation of true infection rates due to limited testing -- it is critical not to underestimate the risk the virus poses on a local scale! Use this dataset at your own discretion with caution and care; consider supplementing it with other health and socio-economic metrics for a holistic picture of regional trends over time.

This dataset features information surrounding GSS codes and names as well as total numbers of recorded COVID-19 cases per English Upper Tier Local Authority on January 5th 2023 (TotalCases_2023-01-05)

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

Research Ideas

• Comparing the total cases in each local authority to population density of the region, to identify areas with higher incidence of virus
• Tracking changes in total cases over a period of time to monitor trend shifts and detect possible outbreak hotspots
• Establishing correlations between the spread of COVID-19 and other non-coronavirus related health issues, such as mental health or cardiovascular risk factors

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: utla_by_day.csv | Column name | Description | |:--------------------------|:------------------------------------------------------------------------------------------------------| | GSS_CD | Government Statistical Service code for the local authority. (String) | | GSS_NM | Name of the local authority. (String) | | TotalCases_2023-01-05 | Total number of confirmed COVID-19 cases in the local authority on the 5th of January 2023. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Dan Winchester.

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

The COVID-19 pandemic resulting from the spread of SARS-CoV-2 spurred devastating health and economic crises around the world. Neutralizing antibodies and licensed vaccines were developed to combat COVID-19, but progress was slow. In addition, variants of the receptor-binding domain (RBD) of the spike protein confer resistance of SARS-CoV-2 to neutralizing antibodies, nullifying the possibility of human immunity. Therefore, investigations into the RBD mutations that disrupt neutralization through convalescent antibodies are urgently required. In this study, we comprehensively and systematically investigated the binding stability of RBD variants targeting convalescent antibodies and revealed that the RBD residues F456, F490, L452, L455, and K417 are immune-escaping hotspots, and E484, F486, and N501 are destabilizing residues. Our study also explored the possible modes of actions of emerging SARS-CoV-2 variants. All results are consistent with experimental observations of attenuated antibody neutralization and clinically emerging SARS-CoV-2 variants. We identified possible immune-escaping hotspots that could further promote resistance to convalescent antibodies. The results provide valuable information for developing and designing novel monoclonal antibody drugs to combat emerging SARS-CoV-2 variants.

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

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.

Attributes of prospective space-time clusters (hotspots) for COVID-19 from 1/23-5/20/2020 at the county level.

In addition to vaccine and impactful treatments, mitigation strategies represent an effective way to combat the COVID-19 virus and an invaluable resource in this task is numerical modeling that can reveal key factors in COVID-19 pandemic development. On the other hand, it has become evident that regional infection curves of COVID-19 exhibit complex patterns which often differ from curves predicted by forecasting models. The wide variations in attack rate observed among different social strata suggest that this may be due to social heterogeneity not accounted for by regional models. We investigated this hypothesis by developing and using a new Stochastic Heterogeneous Epidemic Model that focuses on subpopulations that are vulnerable in the sense of having an increased likelihood of spreading infection among themselves. We found that the isolation or embedding of vulnerable sub-clusters in a major population hub generated complex stochastic infection patterns which included multiple peaks and growth periods, an extended plateau, a prolonged tail, or a delayed second wave of infection. Embedded vulnerable groups became hotspots that drove infection despite efforts of the main population to socially distance, while isolated groups suffered delayed but intense infection. Amplification of infection by these hotspots facilitated transmission from one urban area to another, causing the epidemic to hopscotch in a stochastic manner to places it would not otherwise reach; whereas vaccination only in hotspot populations stopped geographic spread of infection. Our results suggest that social heterogeneity is a key factor in the formation of complex infection propagation patterns. Thus, the mitigation and vaccination of vulnerable groups is essential to control the COVID-19 pandemic worldwide. The design of our new model allows it to be applied in future studies of real-world scenarios on any scale, limited only by computing memory and the ability to determine the underlying topology and parameters.

The unique count is the unique number of groups flagged, and the total count is the total number of groups flagged (may be flagged more than once) across a 15-week semester. Using time window k, LS,k and LH,k are the likelihoods of a hotspot group for sections and housing, respectively. The relative risk of the two groups RRk (from Eq 8) is also shown with a 95% confidence interval (CI).

Hypothesized risk indicators informing the transmission scenarios, their rationale for inclusion, description and sources. Original rasters were warped to 0.25 decimal degrees and World Geodetic System (WGS 84). Complete data description available in Table S2. from Muylaert et al.

The COVID-19 epidemic in December 2019 had a significant negative impact on people’s health and economies all across the world. The most effective preventive measure against COVID-19 is vaccination. Therefore, the development and production of COVID-19 vaccines is booming worldwide. This study aimed to analyze the current state of that research and its development tendency by bibliometrics. We conducted a thorough search of the Web of Science Core Collection. VOSviewer1.6.18 was used to perform the bibliometric analysis of these papers. A total of 6,325 papers were finally included. The USA maintained a top position worldwide. Shimabukuro Tom T and Harvard University were the most prolific author and institution. The Vaccines was the most published journal. The research hotspots of COVID-19 vaccines can be classified into vaccine hesitancy, vaccine safety and effectiveness, vaccine immunogenicity, and adverse reactions to vaccines. Studies on various vaccination types have also concentrated on efficacy against continuously developing virus strains, immunogenicity, side effects, and safety.

Number of problematic courses found, false discovery rate and sensitivity for competing FDR methods when the spike factor equals 2. “FDR (33%)” and “Sensitivity (33%)” describe the performance of FDR methods among courses with at least 33% of students spiked. Numbers that are italicized indicate a significant difference (0.001 level) from the SimBa method using a t-test.

Sum of variable importances for all levels of a given categorical type.