IMPORTANT NOTE #####From October 2024, this project is being updated by Dr. Juan Armando Torres Munguía. In case of questions, requests, or collaborations, you can contact me via GitHub or here. Updated data can be found in data-monthly-updated-1996-2025.zip. You can also access the updated data here: https://github.com/jatorresmunguia/disease_outbreak_news.

Outbreak database for travel-associated infections

🇬🇧 영국

MS Access system drawing from MOLIS, cases associated with specific outbreaks and clusters of salmonella infections

🇬🇧 영국

Description of the included studies.

Healthcare-related infections caused by extended-spectrum beta-lactamase (ESBL)-producing Klebsiella spp. are of major concern. To control transmission, deep understanding of the transmission mechanisms is needed. This systematic review aimed to identify risk factors and sources, clonal relatedness using molecular techniques, and the most effective control strategies for ESBL-producing Klebsiella spp. A systematic search of PubMed, Embase, and Outbreak Database was performed. We identified 2771 articles from November 25th, 1960 until April 7th, 2014 of which 148 were included in the systematic review and 23 in a random-effects meta-analysis study. The random-effects meta-analyses showed that underlying disease or condition (odds ratio [OR] = 6.25; 95% confidence interval [CI] = 2.85 to 13.66) generated the highest pooled estimate. ESBL-producing Klebsiella spp. were spread through person-to-person contact and via sources in the environment; we identified both monoclonal and polyclonal presence. Multi-faceted interventions are needed to prevent transmission of ESBL-producing Klebsiella spp.

Covid19Kerala.info-Data is a consolidated multi-source open dataset of metadata from the COVID-19 outbreak in the Indian state of Kerala. It is created and maintained by volunteers of ‘Collective for Open Data Distribution-Keralam’ (CODD-K), a nonprofit consortium of individuals formed for the distribution and longevity of open-datasets. Covid19Kerala.info-Data covers a set of correlated temporal and spatial metadata of SARS-CoV-2 infections and prevention measures in Kerala. Static releases of this dataset snapshots are manually produced from a live database maintained as a set of publicly accessible Google sheets. This dataset is made available under the Open Data Commons Attribution License v1.0 (ODC-BY 1.0).

Schema and data package Datapackage with schema definition is accessible at https://codd-k.github.io/covid19kerala.info-data/datapackage.json. Provided datapackage and schema are based on Frictionless data Data Package specification.

Temporal and Spatial Coverage

This dataset covers COVID-19 outbreak and related data from the state of Kerala, India, from January 31, 2020 till the date of the publication of this snapshot. The dataset shall be maintained throughout the entirety of the COVID-19 outbreak.

The spatial coverage of the data lies within the geographical boundaries of the Kerala state which includes its 14 administrative subdivisions. The state is further divided into Local Self Governing (LSG) Bodies. Reference to this spatial information is included on appropriate data facets. Available spatial information on regions outside Kerala was mentioned, but it is limited as a reference to the possible origins of the infection clusters or movement of the individuals.

Longevity and Provenance

The dataset snapshot releases are published and maintained in a designated GitHub repository maintained by CODD-K team. Periodic snapshots from the live database will be released at regular intervals. The GitHub commit logs for the repository will be maintained as a record of provenance, and archived repository will be maintained at the end of the project lifecycle for the longevity of the dataset.

Data Stewardship

CODD-K expects all administrators, managers, and users of its datasets to manage, access, and utilize them in a manner that is consistent with the consortium’s need for security and confidentiality and relevant legal frameworks within all geographies, especially Kerala and India. As a responsible steward to maintain and make this dataset accessible— CODD-K absolves from all liabilities of the damages, if any caused by inaccuracies in the dataset.

License

This dataset is made available by the CODD-K consortium under ODC-BY 1.0 license. The Open Data Commons Attribution License (ODC-By) v1.0 ensures that users of this dataset are free to copy, distribute and use the dataset to produce works and even to modify, transform and build upon the database, as long as they attribute the public use of the database or works produced from the same, as mentioned in the citation below.

Disclaimer

Covid19Kerala.info-Data is provided under the ODC-BY 1.0 license as-is. Though every attempt is taken to ensure that the data is error-free and up to date, the CODD-K consortium do not bear any responsibilities for inaccuracies in the dataset or any losses—monetary or otherwise—that users of this dataset may incur.

ObjectivesSeveral automated algorithms for epidemiological surveillance in hospitals have been proposed. However, the usefulness of these methods to detect nosocomial outbreaks remains unclear. The goal of this review was to describe outbreak detection algorithms that have been tested within hospitals, consider how they were evaluated, and synthesize their results.MethodsWe developed a search query using keywords associated with hospital outbreak detection and searched the MEDLINE database. To ensure the highest sensitivity, no limitations were initially imposed on publication languages and dates, although we subsequently excluded studies published before 2000. Every study that described a method to detect outbreaks within hospitals was included, without any exclusion based on study design. Additional studies were identified through citations in retrieved studies.ResultsTwenty-nine studies were included. The detection algorithms were grouped into 5 categories: simple thresholds (n = 6), statistical process control (n = 12), scan statistics (n = 6), traditional statistical models (n = 6), and data mining methods (n = 4). The evaluation of the algorithms was often solely descriptive (n = 15), but more complex epidemiological criteria were also investigated (n = 10). The performance measures varied widely between studies: e.g., the sensitivity of an algorithm in a real world setting could vary between 17 and 100%.ConclusionEven if outbreak detection algorithms are useful complementary tools for traditional surveillance, the heterogeneity in results among published studies does not support quantitative synthesis of their performance. A standardized framework should be followed when evaluating outbreak detection methods to allow comparison of algorithms across studies and synthesis of results.

Please cite the following paper when using this dataset: N. Thakur, “MonkeyPox2022Tweets: The first public Twitter dataset on the 2022 MonkeyPox outbreak,” Preprints, 2022, DOI: 10.20944/preprints202206.0172.v2

Abstract The world is currently facing an outbreak of the monkeypox virus, and confirmed cases have been reported from 28 countries. Following a recent “emergency meeting”, the World Health Organization just declared monkeypox a global health emergency. As a result, people from all over the world are using social media platforms, such as Twitter, for information seeking and sharing related to the outbreak, as well as for familiarizing themselves with the guidelines and protocols that are being recommended by various policy-making bodies to reduce the spread of the virus. This is resulting in the generation of tremendous amounts of Big Data related to such paradigms of social media behavior. Mining this Big Data and compiling it in the form of a dataset can serve a wide range of use-cases and applications such as analysis of public opinions, interests, views, perspectives, attitudes, and sentiment towards this outbreak. Therefore, this work presents MonkeyPox2022Tweets, an open-access dataset of Tweets related to the 2022 monkeypox outbreak that were posted on Twitter since the first detected case of this outbreak on May 7, 2022. The dataset is compliant with the privacy policy, developer agreement, and guidelines for content redistribution of Twitter, as well as with the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management.

Data Description The dataset consists of a total of 255,363 Tweet IDs of the same number of tweets about monkeypox that were posted on Twitter from 7th May 2022 to 23rd July 2022 (the most recent date at the time of dataset upload). The Tweet IDs are presented in 6 different .txt files based on the timelines of the associated tweets. The following provides the details of these dataset files. • Filename: TweetIDs_Part1.txt (No. of Tweet IDs: 13926, Date Range of the Tweet IDs: May 7, 2022 to May 21, 2022) • Filename: TweetIDs_Part2.txt (No. of Tweet IDs: 17705, Date Range of the Tweet IDs: May 21, 2022 to May 27, 2022) • Filename: TweetIDs_Part3.txt (No. of Tweet IDs: 17585, Date Range of the Tweet IDs: May 27, 2022 to June 5, 2022) • Filename: TweetIDs_Part4.txt (No. of Tweet IDs: 19718, Date Range of the Tweet IDs: June 5, 2022 to June 11, 2022) • Filename: TweetIDs_Part5.txt (No. of Tweet IDs: 47718, Date Range of the Tweet IDs: June 12, 2022 to June 30, 2022) • Filename: TweetIDs_Part6.txt (No. of Tweet IDs: 138711, Date Range of the Tweet IDs: July 1, 2022 to July 23, 2022)

The dataset contains only Tweet IDs in compliance with the terms and conditions mentioned in the privacy policy, developer agreement, and guidelines for content redistribution of Twitter. The Tweet IDs need to be hydrated to be used.

Rates of confirmed COVID-19 in Ottawa Wards, excluding LTC and RH cases, and number of cases in LTCH and RH in Ottawa Wards. Data are provided for all cases (i.e. cumulative), cases reported within 30 days of the data pull (i.e. last 30 days), and cases reported within 14 days of the data pull (i.e. last 14 days). Based on the most up to date information available at 2pm from the COVID-19 Ottawa Database (The COD) on the day prior to publication.Rates of confirmed COVID-19 in Ottawa Wards, excluding LTC and RH cases, and number of cases in LTCH and RH in Ottawa Wards. Data are provided for all cases (i.e. cumulative), cases reported within 30 days of the data pull (i.e. last 30 days), and cases reported within 14 days of the data pull (i.e. last 14 days). Based on the most up to date information available at 2pm from the COVID-19 Ottawa Database (The COD) on the day prior to publication. You can see the map on Ottawa Public Health's website.Accuracy: Points of consideration for interpretation of the data:Data extracted by Ottawa Public Health at 2pm from the COVID-19 Ottawa Database (The COD) on May 12th, 2020. The COD is a dynamic disease reporting system that allow for continuous updates of case information. These data are a snapshot in time, reflect the most accurate information that OPH has at the time of reporting, and the numbers may differ from other sources. Cases are assigned to Ward geography based on their postal code and Statistics’ Canada’s enhanced postal code conversion file (PCCF+) released in January 2020. Most postal codes have multiple geographic coordinates linked to them. Thus, when available, postal codes were attributed to a XY coordinates based on the Single Link Identifier provided by Statistics’ Canada’s PCCF+. Otherwise, postal codes that fall within the municipal boundaries but whose SLI doesn’t, were attributed to the first XY coordinates within Ottawa listed in the PCCF+. For this reason, results for rural areas should be interpreted with caution as attribution to XY coordinates is less likely to be based on an SLI and rural postal codes typically encompass a much greater surface area than urban postal codes (e.i. greater variability in geographic attribution, less precision in geographic attribution). Population estimates are based on the 2016 Census. Rates calculated from very low case numbers are unstable and should be interpreted with caution. Low case counts have very wide 95% confidence intervals, which are the lower and upper limit within which the true rate lies 95% of the time. A narrow confidence interval leads to a more precise estimate and a wider confidence interval leads to a less precise estimate. In other words, rates calculated from very low case numbers fluctuate so much that we cannot use them to compare different areas or make predictions over time.Update Frequency: Biweekly Attributes:Ward Number – numberWard Name – textCumulative rate (per 100 000 population), excluding cases linked to outbreaks in LTCH and RH – cumulative number of residents with confirmed COVID-19 in a Ward, excluding those linked to outbreaks in LTCH and RH, divided by the total population of that WardCumulative number of cases, excluding cases linked to outbreaks in LTCH and RH - cumulative number of residents with confirmed COVID-19 in a Ward, excluding cases linked to outbreaks in LTCH and RHCumulative number of cases linked to outbreaks in LTCH and RH - Number of residents with confirmed COVID-19 linked to an outbreak in a long-term care home or retirement home by WardRate (per 100 000 population) in the last 30 days, excluding cases linked to outbreaks in LTCH and RH –number of residents with confirmed COVID-19 in a Ward reported in the 30 days prior to the data pull, excluding those linked to outbreaks in LTCH and RH, divided by the total population of that WardNumber of cases in the last 30 days, excluding cases linked to outbreaks in LTCH and RH - cumulative number of residents with confirmed COVID-19 in a Ward reported in the 30 days prior to the data pull, excluding cases linked to outbreaks in LTCH and RHNumber of cases in the last 30 days linked to outbreaks in LTCH and RH - Number of residents with confirmed COVID-19, reported in the 30 days prior to the data pull, linked to an outbreak in a long-term care home or retirement home by WardRate (per 100 000 population) in the last 14 days, excluding cases linked to outbreaks in LTCH and RH –number of residents with confirmed COVID-19 in a Ward reported in the 30 days prior to the data pull, excluding those linked to outbreaks in LTCH and RH, divided by the total population of that WardNumber of cases in the last 14 days, excluding cases linked to outbreaks in LTCH and RH - cumulative number of residents with confirmed COVID-19 in a Ward reported in the 30 days prior to the data pull, excluding cases linked to outbreaks in LTCH and RHContact: OPH Epidemiology Team

Salmonellosis is one of the most common causes of diarrhea, affecting 1/10 of the global population. Salmonellosis outbreaks (SO) pose a severe threat to the healthcare systems of developing regions. To elucidate the patterns of SO in China, we conducted a systematic review and meta-analysis encompassing 1,134 reports across 74 years, involving 89,050 patients and 270 deaths. A rising trend of SO reports has been observed since the 1970s, with most outbreaks occurring east of the Hu line, especially in coastal and populated regions. It is estimated to have an overall attack rate of 36.66% (95% CI, 33.88-39.45%), and antimicrobial resistance towards quinolone (49.51%) and beta-lactam (73.76%) remains high. Furthermore, we developed an online website, the Chinese Salmonellosis Outbreak Database (CSOD), for visual presentation and data-sharing purposes. This study indicated that healthcare-associated SO required further attention, and our study served as a foundational step in pursuing outbreak intervention and prediction.

Aim of this study was to explore how the COVID-19 outbreak influenced emergency admissions for ureteral lithiasis, complications, hospitalizations and management of the hospitalized patients in 3 urologic Departments in Italy

COVID-19: No. of Tests: New: Northeast: Rio Grande do Norte data was reported at 0.000 Unit in 29 May 2024. This stayed constant from the previous number of 0.000 Unit for 28 May 2024. COVID-19: No. of Tests: New: Northeast: Rio Grande do Norte data is updated daily, averaging 0.000 Unit from Jan 2020 (Median) to 29 May 2024, with 1610 observations. The data reached an all-time high of 5,701.000 Unit in 24 May 2021 and a record low of 0.000 Unit in 29 May 2024. COVID-19: No. of Tests: New: Northeast: Rio Grande do Norte data remains active status in CEIC and is reported by Ministry of Health. The data is categorized under High Frequency Database’s Disease Outbreaks – Table BR.HLA002: Disease Outbreaks: COVID-19: Number of Tests: Mild to Moderate Cases. This tests series refers to mild to moderate cases suspected of COVID-19

MDS: COVID-19: Confirmed Cases: To Date: Central West: Dist Federal data was reported at 963,555.000 Person in 03 May 2025. This records an increase from the previous number of 963,449.000 Person for 02 May 2025. MDS: COVID-19: Confirmed Cases: To Date: Central West: Dist Federal data is updated daily, averaging 839,020.000 Person from Feb 2020 (Median) to 03 May 2025, with 1895 observations. The data reached an all-time high of 963,555.000 Person in 03 May 2025 and a record low of 0.000 Person in 06 Mar 2020. MDS: COVID-19: Confirmed Cases: To Date: Central West: Dist Federal data remains active status in CEIC and is reported by Ministry of Health. The data is categorized under High Frequency Database’s Disease Outbreaks – Table BR.HLA001: Disease Outbreaks: COVID-19: Confirmed Cases. Current day data is released daily between 6PM and 7PM Brazil Time. Weekend data are updated following Monday morning, Hong Kong Time.

Serves as clearinghouse for monoclonal antibodies against SARS-CoV-2. Database will catalog contributed antibodies in searchable resource and provide interactive analysis tools for comparisons among them. Most potent antibodies will guide development of vaccines to stop current outbreak and protect against future pandemics.

The dataset is an output of the project BARCOVID - The impact of the Covid-19 crisis on Collective Agreements in Europe. It contains the text items of online newsletters collected between March 2020 and February 2022. The newsletters were published by EU-level institutions and social partners on the national levels in the EU and Candidate Countries. The database captures a policy discourse about policy measures in the EU and Candidate Countries during the pandemic COVID-19. The data were analyzed and published in the quarterly reports. For more information see: https://wageindicator.org/about/projects/barcovid

Electronic Supplementary Material 1 for Garske et al, 2016, Heterogeneities in the Case Fatality Ratio in the West African Ebola Outbreak 2013 - 2016, Phil. Trans. R. Soc. B. doi: 10.1098/rstb.2016.0308. Dataset of the 33,338 confirmed, probable and suspected EVD cases officially reported to WHO by 28 September 2015 and recorded in the VHF database. Data dictionary given in ESM 2

Database of the official acts of Regional Offices of Public Health (ROPH) targeting Marginalized Roma Communities (MRCs) in the first and second wave of the COVID-19 pandemic in Slovakia. The database serves as supplementary material for the forthcoming chapter “Targeting Marginalized Roma Communities in Slovakia: An analysis of official measures during the Covid-19 pandemic”.

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 the first reported coronavirus case in Washington State on Jan. 21, 2020, 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.