CPSC's epidemiological data include reports of incidents involving death, injury, or potential injury that are associated with consumer products. The online Clearinghouse posts summary information from death certificates (DTHS), medical examiner reports (MECAP reports), reports published on Saferproducts.gov, Newsclips, and other submissions from consumers, healthcare professionals, state, federal, and local agencies (IPII), and public safety entities.

The National Epidemiological Surveillance Network (RENAVE) coordinates surveillance integrating notification and epidemiological research of cases of communicable diseases, outbreaks or microorganisms

Data and code repository for the Open COVID-19 Data Working Group: a global and multi-organizational initative that aims to enable rapid sharing of trusted and open public health data to advance the response to infectious diseases.

This database includes confirmed cases of COVID-19 in line list format. It spans the timeframe between 1 December 2019 to 5 February 2020.

My PhD thesis with title "Investigating Changes in COVID-19 Epidemiological Parameters from Different Perspectives" focus on using line list data (anonymized), patient hospitalization data (anonymized) and viral load data (anonymized) to improve the estimatin of different key epidemiological parameters during the COVID-19 pandemic in Hong Kong.This dataset contains supporting data for reproducibility, it has 6 subfolders correspond to 6 chapters of the thesis (chapters 2, 4, 5, 6, 7, 8) where contain figures and data analyses, each sub folder contains data and R code for reproducing the figures and other analytical results, with README file accompanied with each sub folder.In chapter 2, I provided an overview of the COVID-19 pandemic in Hong Kong and worldwide, and thus used datasets contain case incidence data and a R code to generate incidence figure. I also conducted a systematic review of the latent period estimation, and I provided the endnote library with spreadsheet of the endnote output that contain my paper screening process, which are included in subfolder dataset chapter 2.In chapter 4, I did a detailed statistical analyses of the changing serial interval of COVID-19 in Hong Kong, and thus sub folder dataset chapter 4 contained anonymized transmission pair line list data for estimating the serial interval, I provided R codes and essential subset of the data output for reproducibility of my results. The related published work is on American Journal of Epidemiology, in README chapter4.txt I have put the DOI of this paper.In chapter 5, I developed an inferential framework to infer the generation interval on temporal time scale, sub folder dataset chapter 5 contained public available line list data from mainland China, and R codes and essential subset of the data output for reproducibility of my results. The related published work is on Nature Communications, and the data and code are also available on github, I have out the DOI and github link in README chapter5.txt.In chapter 6, I investigated the superspreading potential and setting-specific generation interval in Hong Kong, subfolder dataset chapter 6 contained simplified and anonymized transmission cluster size information, and related R code to reproduce the result, and also the R code for modelling buildig and estimation summary of the generation interval estimates.In chapter 7, I estimated the latent period of COVID-19 based on different settings in Hong Kong, sub folder dataset chapter 7 contained processed and anonymized viral load record and transmission pair information of COVID-19 cases in Hong Kong, and related R code to reproduce the result, together with two spreadsheets for estimation summary. The entire R programming process contain a lot of R scripts, which I put two sub folders (R and Stan) under sub folder dataset chapter 7, and also put the original Github link for R programming of the method in README chapter 7.txtIn chapter 8, I analyzed the length of stay in hospital of COVID-19 patients in Hong Kong and the potential association with vaccination status. In sub folder dataset chapter 8 I put a simplified and anonymized dataset of patient's hospitalization record regarding their vaccination status and length of stay in hospital for the analysis. I also put R code and essential subset of the data output to reproduce the result.

Data products about the epidemiological, social and economic dimensions of the outbreak. Includes datasets, dashboards, statistics, analyses, trends, charts and maps. Also includes a list of locations where people may have been exposed to the virus.

Data from the National Epidemiology Unit.

Epidemiology is the branch of medicine which deals with the incidence, distribution, and possible control of diseases and other factors relating to health.

This dataset contains two files: (i) An annotated corpus ("epi_info_corpus‧xlsx") containing 486 manually annotated sentences extracted from 32 animal disease-related news articles. These news articles were obtained from the database of an event-based biosurveillance system dedicated to animal health surveillance, PADI-web (https://padi-web.cirad.fr/en/). The first sheet (‘article_metadata’) provides metadata about the news articles : (1) id_article, the unique id of a news article, (2) title, the title of the news article, (3) source, the name of the news article website, (3) publication_date, the publication date of the news article (mm-dd-yyyy) and (4) URL, the web URL of the news article. The second sheet (‘annot_sentences’) contains the annotated sentences: each row corresponds to a sentence from a news article. Each sentence has two distinct labels, Event type and Information type. The set of columns is : (1) id_article, the id of the news article to which the sentence belongs, (2) id_sentence, the unique id of the sentence, indicating its position in the news content (integer ranging from 1 to n, n being the total number of sentences in the news article), (3) sentence_text, the sentence textual content, (4) event_type, the Event type label and (5) information_type, the Information type label. Event type labels indicate the relation between the sentence and the epidemiological context, i‧e. current event (CE), risk event (RE), old event (OE), general (G) and irrelevant (IR). Information type labels indicate the type of epidemiological information, i‧e descriptive epidemiology (DE), distribution (DI), preventive and control measures (PCM), economic and political consequences (EPC), transmission pathway (TP), concern and risk factors (CRF), general epidemiology (GE) and irrelevant (IR). (ii) The annotation guidelines ("epi_info_guidelines‧doc") providing a detailed description of each category.

Global AI in Epidemiology Market size is expected to be worth around USD 6,041.0 million by 2033 from USD 549.1 million in 2023

Percentages are relative to the number of primary PCD research studies.

Research integrity and research fairness have gained considerable momentum in the past decade and have direct implications for global health epidemiology. Research integrity and research fairness principles should be equally nurtured to produce high quality impactful research – but bridging the two can lead to practical and ethical dilemmas. In order to provide practical guidance to researchers and epidemiologist, we set out to develop good epidemiological practice guidelines specifically for global health epidemiology, targeted at stakeholders involved in the commissioning, conduct, appraisal and publication of global health research.

We developed preliminary guidelines based on targeted online searches on existing best practices for epidemiological studies and sought to align these with key elements of global health research and research fairness. We validated these guidelines through a Delphi consultation study, to reach a consensus among a wide representation of stakeholders.

A total of 45 experts provided input on the first round of GEP e-Delphi consultation, and 40 in the second. Respondents covered a range of organisations (including for example academia, ministries, NGOs, research funders, technical agencies) involved in epidemiological studies from countries around the world. A selection of eight experts were invited for a face-to-face meeting. The final guidelines consists of a set of six standards and 42 accompanying criteria including study preparation, study protocol and ethical review, data collection, data management, analysis, reporting and dissemination.

This database only includes anonymised responses of participants who agreed to their data being shared in this depository , i.e.19 out of the 45 (Round 1) and 40 (Round 2) participants.

Background: When a new pathogen emerges, consistent case reporting is critical for public health surveillance. Tracking cases geographically and over time is key for understanding the spread of an infectious disease and effectively designing interventions to contain and mitigate an epidemic. In this paper we describe the reporting systems on COVID-19 in Southeast Asia during the first wave in 2020, and highlight the impact of specific reporting methods.Methods: We reviewed key epidemiological variables from various sources including a regionally comprehensive dataset, national trackers, dashboards, and case bulletins for 11 countries during the first wave of the epidemic in Southeast Asia. We recorded timelines of shifts in epidemiological reporting systems and described the differences in how epidemiological data are reported across countries and timepoints.Results: Our findings suggest that countries in Southeast Asia generally reported precise and detailed epidemiological data during the first wave of the pandemic. Changes in reporting rarely occurred for demographic data, while reporting shifts for geographic and temporal data were frequent. Most countries provided COVID-19 individual-level data daily using HTML and PDF, necessitating scraping and extraction before data could be used in analyses.Conclusion: Our study highlights the importance of more nuanced analyses of COVID-19 epidemiological data within and across countries because of the frequent shifts in reporting. As governments continue to respond to impacts on health and the economy, data sharing also needs to be prioritised given its foundational role in policymaking, and in the implementation and evaluation of interventions.

EDDi Print Manual - An Epidemic of Haemolytic Uraemic Syndrome in Hamburg Summary: Building on a problem-based self-study approach, EDDi participants will be introduced to the principles and practices of infectious disease epidemiology as well as concepts related to the investigation of disease outbreaks, with particular focus on: Describing infectious disease events and evaluating local outbreak scenarios Learning about basic methods of epidemiological outbreak investigation, study designs, and tools of epidemiology (e.g., epidemiological curves, outbreak maps, technical terms) Interpreting and recognizing associations between outbreak events and relevant influencing factors by applying basic techniques of infectious disease epidemiology (especially descriptive methods) Making informed decisions in outbreak investigation based on epidemiological evidence Extra: Getting to know the structure of the German disease surveillance and reporting system as well as background information on the Hanseatic city of Hamburg Preparation of the Table Top Exercise Resources 1 EDDi Case Study: Description of the case scenario including tasks 2 EDDi Investigation Notebook: Reference materials relevant to the case study 3 EDDi Solution Guide: Guide for facilitators including solutions Approximate time (classroom teaching set-up) Approx. 2.5-3 hours (estimate) = approx. four course units á 45 minutes; the course units may be spread over two sessions á 1.5 hours Welcome Tutorial and Epidemiological Cheat Sheet As the Table Top Exercise requires a basic understanding of infectious disease epidemiology concepts and terminology, refreshing some background knowledge through the EDDi Welcome Tutorial is recommended, either individually or in small groups. There is a total of six educational videos to prepare for the different tasks of the exercise. The tutorial can be given to students as preparatory homework and could be further supplemented with additional questions. In addition, students may be advised to familiarize themselves with the Epidemiology Cheat Sheet and bring a copy. Materials needed Although extensive, we advise printing the EDDi Case Study and EDDi Investigation Notebook (one set per group). The printouts may be reused provided that all tasks are solved by the students on an extra sheet. A calculator is needed for some of the tasks. Teaching Facilitators To facilitate the exercise, we recommend having one facilitator (e.g., lecturer, tutor, experienced student) per working group who can assist with any problems. For each facilitator, we recommend providing one EDDi Solution Guide. If this is not possible, one solution guide can be given to each group to assist with any questions or problems. Workflow #1 Make sure you have completed all preparations and all printouts are available. #2 Before starting the exercise, explain the overall outline of the exercise (learning objectives, resources needed, time to complete the exercise) and assign the course to working groups (3-5 individuals and, if possible, one facilitator per group). Each group shall be provided with one EDDi Case Study and one EDDi Investigation Notebook printout. The Case Study provides the main storyline and tasks and points to important reference materials in the Investigation Notebook. The groups will then work on the exercise independently. #3 During the exercise, assistance can be sought through the facilitators or the Solution Guide. Make sure to keep an eye on the time! #4 The exercise should be closed with a final discussion and exchange of findings and experiences (approx. 15-20 minutes). To learn more about the real outbreak event, further reading materials can be found in the Investigation Notebook and shared with the class. Extra: EDDi Artwork Table Top Exercise Artwork by Annabell Koenen-Rindfrey, licensed under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/. The artwork was developed using Affinity Designer 1.9.9 (iPad version). Epidemiological trends by Juliane Boenecke, licensed under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/. Graphs were developed using software R Studio (Version 1.0.136), packages rgdal, sp, ggplot2, ggsave, readr and base. If you would like to revise, rewrite, or build a new narrative upon this case study, all materials are available under an open license. This resource is published under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/). The license's terms and conditions for further use and distribution apply. For further information on EDDi visit https://linktr.ee/e.d.d.i.

Genomic and epidemiological data were matched with public information, and the data quality was verified by manual curation. We collected 159,672 genomic records and 2,571,532 epidemiological records. A total of 20,431 genome–case pairs were generated.Finally, four data files are generated in .tsv format.

Contains downloadable datasets on COVID-19 cases in Tennessee and datasets pertaining to COVID-19 vaccinations at state and county level. The datasets contains tests, deaths, hospitalization and, demographics information

Crash type by driver age group based on the number of current drivers (n = 327) over the last 5 years.

Costs per case of hospitalized and outpatient CAP (all cause) by countries and age group.

Number of data sources by disorder. Note that some studies report data for more than one disorder.*In total 96,349 data sources were identified for the review series (ie. high and low prevalence disorders and disorders with onset in childhood).

aNumber of towns shown in parentheses.