A. SUMMARY This archived dataset includes data for population characteristics that are no longer being reported publicly. The date on which each population characteristic type was archived can be found in the field “data_loaded_at”.

B. HOW THE DATASET IS CREATED Data on the population characteristics of COVID-19 cases are from:  * Case interviews  * Laboratories  * Medical providers    These multiple streams of data are merged, deduplicated, and undergo data verification processes.  

Race/ethnicity * We include all race/ethnicity categories that are collected for COVID-19 cases. * The population estimates for the "Other" or “Multi-racial” groups should be considered with caution. The Census definition is likely not exactly aligned with how the City collects this data. For that reason, we do not recommend calculating population rates for these groups.

Gender * The City collects information on gender identity using these guidelines.

Skilled Nursing Facility (SNF) occupancy * A Skilled Nursing Facility (SNF) is a type of long-term care facility that provides care to individuals, generally in their 60s and older, who need functional assistance in their daily lives.  * This dataset includes data for COVID-19 cases reported in Skilled Nursing Facilities (SNFs) through 12/31/2022, archived on 1/5/2023. These data were identified where “Characteristic_Type” = ‘Skilled Nursing Facility Occupancy’.

Sexual orientation * The City began asking adults 18 years old or older for their sexual orientation identification during case interviews as of April 28, 2020. Sexual orientation data prior to this date is unavailable. * The City doesn’t collect or report information about sexual orientation for persons under 12 years of age. * Case investigation interviews transitioned to the California Department of Public Health, Virtual Assistant information gathering beginning December 2021. The Virtual Assistant is only sent to adults who are 18+ years old. https://www.sfdph.org/dph/files/PoliciesProcedures/COM9_SexualOrientationGuidelines.pdf">Learn more about our data collection guidelines pertaining to sexual orientation.

Comorbidities * Underlying conditions are reported when a person has one or more underlying health conditions at the time of diagnosis or death.

Homelessness Persons are identified as homeless based on several data sources: * self-reported living situation * the location at the time of testing * Department of Public Health homelessness and health databases * Residents in Single-Room Occupancy hotels are not included in these figures. These methods serve as an estimate of persons experiencing homelessness. They may not meet other homelessness definitions.

Single Room Occupancy (SRO) tenancy * SRO buildings are defined by the San Francisco Housing Code as having six or more "residential guest rooms" which may be attached to shared bathrooms, kitchens, and living spaces. * The details of a person's living arrangements are verified during case interviews.

Transmission Type * Information on transmission of COVID-19 is based on case interviews with individuals who have a confirmed positive test. Individuals are asked if they have been in close contact with a known COVID-19 case. If they answer yes, transmission category is recorded as contact with a known case. If they report no contact with a known case, transmission category is recorded as community transmission. If the case is not interviewed or was not asked the question, they are counted as unknown.

C. UPDATE PROCESS This dataset has been archived and will no longer update as of 9/11/2023.

D. HOW TO USE THIS DATASET Population estimates are only available for age groups and race/ethnicity categories. San Francisco population estimates for race/ethnicity and age groups can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS).

This dataset includes many different types of characteristics. Filter the “Characteristic Type” column to explore a topic area. Then, the “Characteristic Group” column shows each group or category within that topic area and the number of cases on each date.

New cases are the count of cases within that characteristic group where the positive tests were collected on that specific specimen collection date. Cumulative cases are the running total of all San Francisco cases in that characteristic group up to the specimen collection date listed.

This data may not be immediately available for recently reported cases. Data updates as more information becomes available.

To explore data on the total number of cases, use the ARCHIVED: COVID-19 Cases Over Time dataset.

E. CHANGE LOG

• 9/11/2023 - data on COVID-19 cases by population characteristics over time are no longer being updated. The date on which each population characteristic type was archived can be found in the field “data_loaded_at”.
• 6/6/2023 - data on cases by transmission type have been removed. See section ARCHIVED DATA for more detail.
• 5/16/2023 - data on cases by sexual orientation, comorbidities, homelessness, and single room occupancy have been removed. See section ARCHIVED DATA for more detail.
• 4/6/2023 - the State implemented system updates to improve the integrity of historical data.
• 2/21/2023 - system updates to improve reliability and accuracy of cases data were implemented.
• 1/31/2023 - updated “population_estimate” column to reflect the 2020 Census Bureau American Community Survey (ACS) San Francisco Population estimates.
• 1/5/2023 - data on SNF cases removed. See section ARCHIVED DATA for more detail.
• 3/23/2022 - ‘Native American’ changed to ‘American Indian or Alaska Native’ to align with the census.
• 1/22/2022 - system updates to improve timeliness and accuracy of cases and deaths data were implemented.
• 7/15/2022 - reinfections added to cases dataset. See section SUMMARY for more information on how reinfections are identified.

Objective Daily COVID-19 data reported by the World Health Organization (WHO) may provide the basis for political ad hoc decisions including travel restrictions. Data reported by countries, however, is heterogeneous and metrics to evaluate its quality are scarce. In this work, we analyzed COVID-19 case counts provided by WHO and developed tools to evaluate country-specific reporting behaviors. Methods In this retrospective cross-sectional study, COVID-19 data reported daily to WHO from 3rd January 2020 until 14th June 2021 were analyzed. We proposed the concepts of binary reporting rate and relative reporting behavior and performed descriptive analyses for all countries with these metrics. We developed a score to evaluate the consistency of incidence and binary reporting rates. Further, we performed spectral clustering of the binary reporting rate and relative reporting behavior to identify salient patterns in these metrics. Results Our final analysis included 222 countries and regions. Reporting scores varied between -0.17, indicating discrepancies between incidence and binary reporting rate, and 1.0 suggesting high consistency of these two metrics. Median reporting score for all countries was 0.71 (IQR 0.55 to 0.87). Descriptive analyses of the binary reporting rate and relative reporting behavior showed constant reporting with a slight “weekend effect” for most countries, while spectral clustering demonstrated that some countries had even more complex reporting patterns. Conclusion The majority of countries reported COVID-19 cases when they did have cases to report. The identification of a slight “weekend effect” suggests that COVID-19 case counts reported in the middle of the week may represent the best data basis for political ad hoc decisions. A few countries, however, showed unusual or highly irregular reporting that might require more careful interpretation. Our score system and cluster analyses might be applied by epidemiologists advising policymakers to consider country-specific reporting behaviors in political ad hoc decisions. Methods Data collection COVID-19 data was downloaded from WHO. Using a public repository, we have added the countries' full names to the WHO data set using the two-letter abbreviations for each country to merge both data sets. The provided COVID-19 data covers January 2020 until June 2021. We uploaded the final data set used for the analyses of this paper. Data processing We processed data using a Jupyter Notebook with a Python kernel and publically available external libraries. This upload contains the required Jupyter Notebook (reporting_behavior.ipynb) with all analyses and some additional work, a README, and the conda environment yml (env.yml).

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus causing coronavirus disease 2019 (COVID-19) was announced as an outbreak by the World Health Organization (WHO) in January 2020 and as a pandemic in March 2020. The majority of infected individuals have experienced no or only mild symptoms, ranging from fully asymptomatic cases to mild pneumonic disease. However, a minority of infected individuals develop severe respiratory symptoms. The objective of this study was to identify susceptible HLA alleles and clinical markers that can be used in risk prediction model for the early identification of severe COVID-19 among hospitalized COVID-19 patients. A total of 137 patients with mild COVID-19 (mCOVID-19) and 53 patients with severe COVID-19 (sCOVID-19) were recruited from the Center Hospital of the National Center for Global Health and Medicine (NCGM), Tokyo, Japan for the period of February–August 2020. High-resolution sequencing-based typing for eight HLA genes was performed using next-generation sequencing. In the HLA association studies, HLA-A*11:01:01:01 [Pc = 0.013, OR = 2.26 (1.27–3.91)] and HLA-C*12:02:02:01-HLA-B*52:01:01:02 [Pc = 0.020, OR = 2.25 (1.24–3.92)] were found to be significantly associated with the severity of COVID-19. After multivariate analysis controlling for other confounding factors and comorbidities, HLA-A*11:01:01:01 [P = 3.34E-03, OR = 3.41 (1.50–7.73)], age at diagnosis [P = 1.29E-02, OR = 1.04 (1.01–1.07)] and sex at birth [P = 8.88E-03, OR = 2.92 (1.31–6.54)] remained significant. The area under the curve of the risk prediction model utilizing HLA-A*11:01:01:01, age at diagnosis, and sex at birth was 0.772, with sensitivity of 0.715 and specificity of 0.717. To the best of our knowledge, this is the first article that describes associations of HLA alleles with COVID-19 at the 4-field (highest) resolution level. Early identification of potential sCOVID-19 could help clinicians prioritize medical utility and significantly decrease mortality from COVID-19.

A. SUMMARY This archived dataset includes data for population characteristics that are no longer being reported publicly. The date on which each population characteristic type was archived can be found in the field “data_loaded_at”.

To access the dataset that continues to refresh daily, navigate to this page: COVID-19 Deaths by Population Characteristics Over Time.   The dataset contains data on the following population characteristics that are no longer being reported publicly:

• Skilled Nursing Facility Occupancy
• Sexual orientation
• Comorbidities
• Homelessness
• Single room occupancy (SRO) tenancy
• Transmission Type

B. HOW THE DATASET IS CREATED COVID-19 deaths are suspected to be associated with COVID-19. This means COVID-19 is listed as a cause of death or significant condition on the death certificate.    Data on the population characteristics of COVID-19 deaths are from:  * Case interviews  * Laboratories  * Medical providers    These multiple streams of data are merged, deduplicated, and undergo data verification processes.      Skilled Nursing Facility (SNF) occupancy * A Skilled Nursing Facility (SNF) is a type of long-term care facility that provides care to individuals, generally in their 60s and older, who need functional assistance in their daily lives.  * This dataset includes data for COVID-19 deaths reported in Skilled Nursing Facilities (SNFs) through 12/31/2022, archived on 1/5/2023. These data were identified where “Characteristic_Type” = ‘Skilled Nursing Facility Occupancy’.

Sexual orientation    * The City began asking adults 18 years old or older for their sexual orientation identification during case interviews as of April 28, 2020. Sexual orientation data prior to this date is unavailable. * The City doesn’t collect or report information about sexual orientation for persons under 12 years of age. * Case investigation interviews transitioned to Virtual Assistant information gathering starting December 2021. The California Department of Public Health, Virtual Assistant is only sent to adults who are 18+ years old. Learn more about our data collection guidelines pertaining to sexual orientation.

Comorbidities * Underlying conditions are reported when a person has one or more underlying health conditions at the time of diagnosis or death.

Homelessness Persons are identified as homeless based on several data sources: * self-reported living situation * the location at the time of testing * Department of Public Health homelessness and health databases * Residents in Single-Room Occupancy hotels are not included in these figures. These methods serve as an estimate of persons experiencing homelessness. They may not meet other homelessness definitions.

Single Room Occupancy (SRO) tenancy * SRO buildings are defined by the San Francisco Housing Code as having six or more "residential guest rooms" which may be attached to shared bathrooms, kitchens, and living spaces. * The details of a person's living arrangements are verified during case interviews.

Transmission type * Information on transmission of COVID-19 is based on case interviews with individuals who have a confirmed positive test. Individuals are asked if they have been in close contact with a known COVID-19 case. If they answer yes, transmission category is recorded as contact with a known case. If they report no contact with a known case, transmission category is recorded as community transmission. If the case is not interviewed or was not asked the question, they are counted as unknown.

C. UPDATE PROCESS This dataset will only update when any population characteristics are archived. Data for existing characteristic types will not change but new characteristic types may be added.   D. HOW TO USE THIS DATASET This dataset may include different types of characteristics. Filter the “Characteristic Type” column to explore a topic area. Then, the “Characteristic Group” column shows each group or category within that topic area and the number of deaths on each date.

New deaths are the count of deaths within that characteristic group on that specific date. Cumulative deaths are the running total of all San Francisco COVID-19 deaths in that characteristic group up to the date listed.

E. CHANGE LOG

• 6/6/2023 - data on deaths by transmission type are no longer being updated. This data is currently through 6/1/2023 (as of 6/6/2023) and will not include any new data after this date.
• 5/16/2023 - data on deaths by sexual orientation, comorbidities, homelessness, and single room occupancy are no longer being updated. This data is currently through 5/11/2023 (as of 5/16/2023) and will not include any new data after this date.
• 1/5/2023 - data on SNF deaths are no longer being updated. SNF data is currently through 12/31/2022 (as of 1/5/2023) and will not include any new data after this date.

The California Department of Public Health (CDPH) aggregates confirmed cases of COVID-19 by sewershed restricted locations. Confirmed cases are defined as individuals with a positive molecular test, which tests for viral genetic material, such as a polymerase chain reaction test.

Since wastewater data available starts from January 1st, 2021, rather than the beginning of the COVID-19 pandemic in 2020, the cumulative counts of the confirmed cases variable are shown as “NA”.

Please note that values less than 5 for confirmed cases are masked (shown as “Masked”) if the sewershed population size is 50,000 or fewer, in accordance with de-identification guidelines. Values less than 3 for cases are masked (shown as “Masked”) if the sewershed population size is between 50,001 and 250,000. For no confirmed cases reported, values are set as zero.

This dataset is published by the Colorado Department of Public Health and Environment and contains the number of COVID-19 positive cases by county, county rate of infection per 100,000 persons, death data by county, statewide COVID-19 prevalence data and associated statewide COVID-19 related statistics. Data is assembled and published Monday-Friday beginning July 26, 2021. Further information concerning case data can be found at https://covid19.colorado.gov/data/.

This is the Zenodo archive for the manuscript "Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada" (Mucaki EJ, Shirley BC and Rogan PK. F1000Research 2021, 10:1312, DOI: 10.12688/f1000research.75891.1). This study aimed to produce community-level geo-spatial mapping of patterns and clusters of symptoms, and of confirmed COVID-19 cases, in near real-time in order to support decision-making. This was accomplished by area-to-area geostatistical analysis, space-time integration, and spatial interpolation of COVID-19 positive individuals. This archive will contain data and image files from this study, which were too numerous to be included in the manuscript for this study. It also provides all program files pertaining to the Geostatistical Epidemiology Toolbox (Geostatistical analysis software package to be used in ArcGIS), as well as all other scripts described in this manuscript and other software developed (cluster, outlier, streak identification and pairing)..

We also provide a guide which provides a general description of the contents of the four sections in this archive (Documentation_for_Sections_of_Zenodo_Archive.docx). If you have any intent to utilize the data provided in Section 3, we greatly advise you to review this document as it describes the output of all geostatistical analyses performed in this study in detail.

Data Files:

Section 1. "Section_1.Tables_S1_S7.Figures_S1_S11.zip"

This section contains all additional tables and figures described in the manuscript "Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada". Additional tables S1 to S7 are presented in an Excel document. These 7 tables provide summary statistics of various geostatistical tests described in the study (“Section 1 – Tables S1-S4”) and lists all identified single and paired high-case cluster streaks (“Section 1 – Tables S5-S7”). This section also contains 11 additional figures referred to in the manuscript (“Section 1 – Figures S1-S11”) both individually and within a Word document which describes them.

Section 2. "Section_2.Localized_Hotspot_Lists.zip"

All localized hotspots (identified through kriging analysis) were catalogued for each municipality evaluated (Hamilton, Kitchener/Waterloo, London, Ottawa, Toronto, Windsor/Essex). These files indicate the FSA in which the hotspot was identified, the date in which it was identified (utilizing 3-day case data at the postal code level), the amount of cases which occurred within the FSA within these 3 dates, the range of cases interpolated by kriging analysis (between 5-10, 10-15, 15-20, 20-25, 25-30, 30-35, 35-40, 40-50, >50), and whether or not the FSA was deemed a hotspot by Gi* relative to the rest of Ontario on any of the three dates evaluated. Please see Section 4 for map images of these localized hotspots.

Section 3. "Section_3.All-Data_Files.Kriging_GiStar_Local_and_GlobalMorans.2020_2021"

Section 3 – All output files from the geostatistical tests performed in this study are provided in this section. This includes the output from Ontario-wide FSA-level Gi* and Cluster and Outlier analyses, and PC-level Cluster and Outlier, Spatial Autocorrelation, and kriging analysis of 6 municipal regions. It also includes kriging analysis of 7 other municipal regions adjacent to Toronto (Ajax, Brampton, Markham, Mississauga, Pickering, Richmond Hill and Vaughan). This section also provides data files from our analyses of stratified case data (by age, gender, and at-risk condition). All coordinates presented in these data files are given in “PCS_Lambert_Conformal_Conic” format. Case values between 1-5 were masked (appear as “NA”).

Section 4. "Section_4.All_Map_Images_of_Geostat_Analyses.zip"

Sets of image files which map the results of our geostatistical analyses onto a map of Ontario or within the municipalities evaluated (Hamilton, Kitchener/Waterloo, London, Ottawa, Toronto, Windsor/Essex) are provided. This includes: Kriging analysis (PC-level), Local Moran's I cluster and outlier analysis (FSA and PC-level), normal and space-time Gi* analysis, and all images for all analyses performed on stratified data (by age, gender and at-risk condition). Kriging contour maps are also included for 7 other municipal regions adjacent to Toronto (Ajax, Brampton, Markham, Mississauga, Pickering, Richmond Hill and Vaughan).

Software:

This Zenodo archive also provides all program files pertaining to the Geostatistical Epidemiology Toolbox (Geostatistical analysis software package to be used in ArcGIS), as well as all other scripts described in this manuscript. This geostatistical toolbox was developed by CytoGnomix Inc., London ON, Canada and is distributed freely under the terms of the GNU General Public License v3.0. It can be easily modified to accommodate other Canadian provinces and, with some additional effort, other countries.

This distribution of the Geostatistical Epidemiology Toolbox does not include postal code (PC) boundary files (which are required for some of the tools included in the toolbox). The PC boundary shapefiles used to test the toolbox were obtained from DMTI (https://www.dmtispatial.com/canmap/) through the Scholar's Geoportal at the University of Western Ontario (http://geo2.scholarsportal.info/). The distribution of these files (through sharing, sale, donation, transfer, or exchange) is strictly prohibited. However, any equivalent PC boundary shape file should suffice, provided it contains polygon boundaries representing postal code regions (see guide for more details).

Software File 1. "Software.GeostatisticalEpidemiologyToolbox.zip"

The Geostatistical Epidemiology Toolbox is a set of custom Python-based geoprocessing tools which function as any built-in tool in the ArcGIS system. This toolbox implements data preprocessing, geostatistical analysis and post-processing software developed to evaluate the distribution and progression of COVID-19 cases in Canada. The purpose of developing this toolbox is to allow external users without programming knowledge to utilize the software scripts which generated our analyses and was intended to be used to evaluate Canadian datasets. While the toolbox was developed for evaluating the distribution of COVID-19, it could be utilized for other purposes.

The toolbox was developed to evaluate statistically significant distributions of COVID-19 case data at Canadian Forward Sortation Area (FSA) and Postal Code-level in the province of Ontario utilizing geostatistical tools available through the ArcGIS system. These tools include: 1) Standard Gi* analysis (finds areas where cases are significantly spatially clustered), 2) spacetime based Gi* analysis (finds areas where cases are both spatially and temporally clustered), 3) cluster and outlier analysis (determines if high case regions are an regional outlier or part of a case cluster), 4) spatial autocorrelation (determines the cases in a region are clustered overall) and, 5) Empirical Bayesian Kriging analysis (creates contour maps which define the interpolation of COVID-19 cases in measured and unmeasured areas). Post-processing tools are included that import these all of the preceding results into the ArcGIS system and automatically generate PNG images.

This archive also includes a guide ("UserManual_GeostatisticalEpidemiologyToolbox_CytoGnomix.pdf") which describes in detail how to set up the toolbox, how to format input case data, and how to use each tool (describing both the relevant input parameters and the structure of the resultant output files).

Software File 2: “Software.Additional_Programs_for_Cluster_Outlier_Streak_Idendification_and_Pairing.zip"

In the manuscript associated with this archive, Perl scripts were utilized to evaluate postal code-level Cluster and Outlier analysis to identify significantly, highly clustered postal codes over consecutive periods (i.e., high-case cluster “streaks”). The identified streaks are then paired to those in close proximity, based on the neighbors of each postal code from PC centroid data ("paired streaks"). Multinomial logistic regression models were then derived in the R programming language to measure the correlation between the number of cases reported in each paired streak, the interval of time separating each streak, and the physical distance between the two postal codes. Here, we provide the 3 Perl scripts and the R markdown file which perform these tasks:

“Ontario_City_Closest_Postal_Code_Identification.pl”

Using an input file with postal code coordinates (by centroid), this program identifies the nearest neighbors to all postal codes for a given municipal region (the name of this region is entered on the command line). Postal code centroids were calculated in ArcGIS using the “Calculate Geometry” function against DMTI postal code boundary files (not provided). Input from other sources could be used, however, as long as the input includes a list of coordinates with a unique label associated with a particular municipality.

The output of this program (for the same municipal region being evaluated) is required for the following two Perl scripts:

“Local_Morans_Analysis.Recurrent_Clustered_PC_Identifier.pl”

This program uses the output of postal code-level Cluster and Outlier analysis for a municipality (these files are available in a second Zenodo archive: doi.org/10.5281/zenodo.5585812) and the output from “Ontario_City_Closest_Postal_Code_Identification.pl” (for the same municipal region) as input to identify high-case clustered postal codes that occur consecutively over a course of several dates (referred to as high-case cluster “streaks”). The script allows for a single day in which the PC was either not clustered or did not meet the minimum case count threshold of ≥ 6 cases within the 3-day sliding window (i.e. if

Reggio Emilia, September 2020-March 2021.

Note: This web page provides data on health facilities only. To file a complaint against a facility, please see: https://www.cdph.ca.gov/Programs/CHCQ/LCP/Pages/FileAComplaint.aspx Skilled Nursing Facility (SNF) testing and case data for the COVID-19 response. For details on the SNF COVID-19 data, please visit this site: https://www.cdph.ca.gov/Programs/CID/DCDC/Pages/COVID-19/SNFsCOVID_19.aspx Please note that values of less than eleven (11) are masked (shown as blank) in accordance with de-identification guidelines. This means the cumulative sum in this dataset will not match the totals from the dashboard due to data artifact from small cell size suppression.

The California Department of Public Health (CDPH) aggregates confirmed cases of COVID-19 by sewershed restricted locations. Confirmed cases are defined as individuals with a positive molecular test, which tests for viral genetic material, such as a polymerase chain reaction test. Since wastewater data available starts from January 1st, 2021, rather than the beginning of the COVID-19 pandemic in 2020, the cumulative counts of the confirmed cases variable are shown as “NA”. Please note that values less than 5 for confirmed cases are masked (shown as “Masked”) if the sewershed population size is 50,000 or fewer, in accordance with de-identification guidelines. Values less than 3 for cases are masked (shown as “Masked”) if the sewershed population size is between 50,001 and 250,000. For no confirmed cases reported, values are set as zero.

The continuity and smooth running of the telecommunications system plays an important role during this pandemic. The smooth running of the telecommunications system cannot be separated from the maintenance function of the equipment used to support the telecommunications system. PT. HIJ is one of the partners of the company PT. Telekomunikasi Indonesia, which is engaged in the maintenance of telecommunications equipment. One of the projects handled by PT. HIJ is a telecommunications equipment maintenance project in the East Java area network. As is known, in a telecommunications equipment maintenance project there are several risks that have an impact on the completion of the project. The results of the identified risk assessment will be developed further into risk mitigation, i.e. actions that can reduce the identified risk. As a result of the research, five risk aspects were identified: maintenance, finance and accounting, human resources (HR), procurement, and operational. According to the risk assessment result, there are 19 risks that can be classified based on risk level, with 7 in the moderate level of risk, 5 in the high level of risk, and 7 in the extreme level of risk. As a result of the research, it is suggested that risk mitigation be implemented for 5 risks with a high level of risk and 7 risks with an extreme level of risk.

The present repository includes :

1. Survey answers - 200 people, anonymized. Quizz about animals and vegetals that are and are not present in the game ANIMAL CROSSING NEW HORIZONS. Personal questions (age,, gender, location). Self assesment of "Naturalistic Fiber". At the end, question about the video game Animal CROSSING, and then free space
2. Translated R code, based on the present dataset. Runs figures and tests used in our paper "Video games as a tool for ecological learning : the case of "Animal Crossing : New Horizons" during Covid-19 quarantine"
3. The survey itself is available (in french unfortunately) at the following link : https://forms.gle/GgwULMcg8KnBqDt26,
   if the link is broken, please don't hesitate to contact me
4. Nintendo Game Content guide at the following link : https://www.nintendo.co.jp/networkservice_guideline/en/index.html
5. Appendix (S1 : table of raw datas ; S2 : Normal and QQ plots)
6. Approval of Ethical Research Committee of Université de Sherbrooke (Canada, QC)
   

" [...] furthermore, after reviewing the application for review, no ethical issues were identified by the committee.The committee notes that:The data were collected from a population of individuals who do not a priori present the characteristics of a vulnerable population; The risks associated with participation in the research are minimal; The data collected are anonymous; Individuals have been informed that the data may be used for scientific purposes; However, we remind you that in the future, any research project, as defined in the policy, must be approved by the research ethics committee before proceeding with the collection of data. Therefore, please accept this letter in lieu of an ethics certificate from the Research Ethics Board - Education and Social Sciences of the Université de Sherbrooke. This letter may be used when submitting for publication or presentation of the results of this study or for any results of this study or for any other request related to the ethical approval of this project. "

Mme Ariane Tessier
Coordonnatrice à l'éthique de la recherche - Université de Sherbrooke, CA QC

If you have any problem with the present ressources, or if you want to work and publish works containing these datas, please contact me at :
simoncoroller.biologie@gmail.com CC : Simon.Coroller@usherbrooke.ca

I would gladly discuss with you !

Best wishes.

COROLLER & FLINOIS

IntroductionCOVID-19-associated acute respiratory distress syndrome (CARDS) poses significant challenges in resource-limited settings. This case series explores the role of bronchoscopy and molecular techniques in identifying superimposed infections in CARDS patients during the second wave of the pandemic in Ecuador.MethodsNine critically ill CARDS patients underwent bronchoscopy and molecular testing to detect co-infections and superinfections. Clinical presentations, diagnostic findings, and outcomes were analyzed.ResultsBronchoscopy and molecular techniques identified a range of secondary infections, including multidrug-resistant pathogens such as Acinetobacter baumannii and Klebsiella pneumoniae. The case series highlights the complexities of managing severe COVID-19 cases in resource-constrained environments.DiscussionEarly identification of microorganisms using PCR methods allows for rapid and accurate diagnosis, facilitating targeted management of critically ill CARDS patients. The study underscores the importance of advanced diagnostic tools and adaptable strategies in pandemic situations, particularly in low-resource settings.

The data set 'COVID-19 deaths in Germany' provides the deaths related to COVID-19 in Germany. In addition, in addition to the number of deaths transmitted, the case-deceased share is calculated.Death information is one of the content requiring reporting and transmission. Different approaches were taken in the identification of deaths and the assessment of the relevant information in the health authorities. As a result, there could be an underestimation of the number of deaths on the one hand, and an overestimation of the proportion of the deceased of an infectious disease on the other. Detailed information on data collection and interpretation can be found in the data set documentation.

Italy was the first and hardest-hit Western nation by the coronavirus pandemic (COVID-19). The identification of the first case, on 20th February, created widespread panic as residents in Italy began stockpiling food. Social media posts pictured near empty supermarket aisles. Soon after, this rush to hoard food spread across many other European nations. But those rushing to the supermarkets were the fortunate ones who could afford to do so. On 30th March, Pope Francis noted: ‘We’re beginning to see people who are hungry because they can’t work’,1 and pleaded for help. COVID-19 and the lockdown have placed the global economy under tremendous strain but are also increasing the threat of longer term food insecurity. Notwithstanding problems of cross-national data comparability, it is clear that food insecurity is already widespread in many high-income countries.2 In the aftermath of the global financial crisis in 2008, an estimated 13.5 million European households were tipped into food insecurity,3 while the current recession is already much deeper and is expected to last longer.4,5,6

The PHIRI Federated Research Infrastructure (FRI) is supported by a containerized reproducible solution for data analysis to be deployed on-premises by each participant partner. This solution is based on the identification of the relevant data sources for each cases study (including the demonstration pilot), the development of the common data models and the analytical pipelines, and enables the FAIR reporting of the rapid cycle outputs. The aggregated dataset is produced by an analysis script integrated within the PHIRI App for PHIRI Use Case A local analyses performed on data from Austria. Input data conforms to the respective Common Data Model. The aggregated output dataset is disseminated within WP6 of the PHIRI project to allow for aggregated and comparative analyses across participating countries. If you wish to contribute to the PHIRI - Use Case A analyses, please contact the WP6 Coordinator through the PHIRI website.

In case of a global crisis, such as the COVID-19 pandemic, inclusive identities are essential for coordinated action and for pro-social behavior on behalf of vulnerable groups. We tested how identification with all humanity vs. the national ingroup play a role in supporting vulnerable groups by prosocial action on one hand, and on the other hand, how these factors mobilize people to be willing to put pressure on authorities for the interest of their communities. We hypothesized that identification with all humanity (compared to national identity) leads to empathy for vulnerable groups and prosocial action intention on behalf of them to a higher degree, and unlike national identity, it also predicts political action intention. Data was collected with an online survey at four timepoints in Hungary. Our path analyses showed that both human and national identity predicted empathy and prosocial action intentions toward groups in need. Human identification was a positive, and national identification a negative predictor of political action intention. While both identification with all humanity and national identity united people in caring for others in a crisis, the two forms of identification divided them in questioning governmental measures. Identification with all humanity made people not only sensitive to vulnerable groups, but critical to the government and made them more willing to challenge political decisions. Identification with all humanity became a predictor of political action intention, showing that solidarity could manifest both in prosocial and political action tendency in the context of COVID-19.

COVID-19 testing rates and percent positivity, excluding long-term care home (LTCH) residents, by Ottawa Neighbourhood Study neighbourhoods. Data are a compilation of data extracted weekly by the Institute for Clinical Evaluative Sciences (ICES) from iPHIS Plus and Public Health Ontario's COVID-19 laboratory data (Chung H. et al., 2021).Date created:Data effective May 2021. Uploaded to Open Data on May 30, 2021.Update frequency: MonthlyAccuracy - Points of consideration for interpretation of the data:Testing data were compiled by the Institute for Clinical Evaluative Sciences (ICES) and are based on information extracted from iPHIS Plus and Public Health Ontario's COVID-19 laboratory data (Chung H, Fung K, Ishiguro L, Paterson M, et al. Characteristics of COVID-19 diagnostic test recipients, Applied Health Research Questions (AHRQ) # 2021 0950 080 000. Toronto: Institute for Clinical Evaluative Sciences; 2020). Data are updated monthly and are provided by IC/ES as weekly counts. Monthly aggregates are based on the start date of weekly counts.Individuals who have testing episodes on multiple days in a weekly testing period are only counted once per week. Those who have both negative and positive test results within a weekly testing period are considered positive. Any and all testing episodes after an individual's first confirmed positive COVID-19 test (since 15 January 2020) are excluded from subsequent weekly counts (both numerator and denominator). Testing date represents the date of specimen collection. Due to the time required for transportation and processing of specimens, it takes 6 days for approximately 95% of results to be finalized and reported for a given testing date. Tested individuals include those whose result is confirmed positive, negative, indeterminate, or pending. For individuals confirmed positive using their record in the Ontario Ministry of Health integrated Public Health Information System (iPHIS), their public health unit (PHU) assignment was based on their diagnosing PHU. For all others, PHU assignment was based on the postal code in RPDB as of the testing date. The current Registered Persons Database (RPDB), which has basic demographic information on anyone who has ever received an Ontario health card number, is updated up to 30 April 2021.Only COVID-19 testing by standard polymerase chain reaction are reported. Tests done by other methods, such as rapid point-of-care, are excluded.Rates and percent positivity calculated from very low case counts, or for small populations, are unstable and should be interpreted with caution. For this reason, testing rates and percent positivity are not presented for neighbourhoods with populations of less than 2000 persons or when counts (i.e. total number tested or total number tested positive) are between 1-6 for a given neighbourhood.Confirmed cases are those with a confirmed COVID-19 laboratory result as per the Ministry of Health Public health management of cases and contacts of COVID-19 in Ontario. March 25, 2020 version 6.0.The province has had to limit testing to priority groups in the early stages of the pandemic. Since only a small fraction of all the persons who were infected with the COVID-19 virus were tested, the number of reported confirmed community cases underestimates the actual number of infections. Information on overall infection rates in Canada will not be available until large studies on COVID-19 antibody presence in blood serum are conducted. Based on available information, the actual number of infections may lie from 5 to 30 times or more than the reported number of cases (1). Surveillance testing for COVID-19 began in long term care facilities on April 25, 2020.Reference: 1. Richterich P. Severe underestimation of COVID-19 case numbers: Effect of epidemic growth rate and test restrictions. medRxiv. April 2020: 2020.04.13. doi.org/10.1101/2020.04.13.20064220Attributes - Data Fields:ONS ID – Ottawa Neighbourhood Study (ONS) neighbourhood identification number.ONS Name – ONS neighbourhood name.Month – The year and month of data, based on the start date of weekly counts.Testing rate (per 1000 population), excluding LTCH residents – number of Ottawa residents tested for COVID-19 during the month of interest, excluding LTCH residents, divided by the total population of that neighbourhood and multiplied by 1000.% Positivity (Excluding LTCH) – number of Ottawa residents tested for COVID-19 during the month of interest that received a positive test result divided by the total number of Ottawa residents tested during that month. Both the numerator and the denominator exclude LTCH residents.

The study included all the deceased participant’s demographics, case history, and SARS-CoV-2 testing results that were entered into an Excel sheet and analyzed using STATA and EpiInfo. Deceased participants were given a unique study identification code. The variables were grouped and presented as frequencies and percentages, with (OR) values comparing the Delta wave period and the Omicron wave period as descriptive statistics. The Odds ratio (OR) of testing positive for SARS-CoV-2 during the two waves associated with sudden and unexpected death as the outcome was calculated using EpiInfo (version 7.2.0.1) Software using Fisher’s exact test. The odds ratios of each variable and the p-value for statistical significance were determined.

Since 2 April 2021, these files are no longer updated. Their communication will resume, but we are not in a position to give you a precise date to date. ### Vaccination against COVID-19 From the start of the vaccination campaign, the health authorities were provided with information to enable daily monitoring of the progress and deployment of the campaign on the territory. These, collected from institutions for the elderly and vaccination centres, were transmitted by the Regional Health Agencies. At the same time, Health Insurance has developed the Vaccine Covid Information System (VAC-SI), which is now fully operational after an analysis of the completeness and completeness of the data. The Vaccine Covid information system is powered by healthcare professionals carrying out vaccinations. Based on the use of these data, Santé Publique France publishes in open data the vaccine coverage indicators. #### What data? Data from the Vaccine Covid information system allows a near-real-time count (J-1) of the number of people who have been injected with Covid vaccine, taking into account the number of doses received, the vaccine, age, sex and geographical level (national, regional and departmental). The indicators available in open access on this dataset relate to the daily number of people with vaccinated comorbidities by date of injection (as well as this cumulative number), by age group They are declined on a scale: national, regional and departmental. Persons with co-morbidities are identified a priori by Cnam on the basis of the recommendations of the High Health Authority (HAS) for priority persons for COVID-19 Vaccination as identified as confirmed risk of serious form or death. Identification is carried out in particular for persons benefiting from long-term effects (ALD), or by targeting CIM codes. In particular, people with: diabetes, chronic kidney failure, COPD and respiratory failure, high blood pressure, heart failure, solid organ transplantation or allograft of haematopoietic stem cells, obesity, cancer and malignant haematologic diseases undergoing treatment with chemotherapy, some rare diseases (see list on the Ministry of Health website), trisomy 21. Only those for whom the department could be located are shown on the maps. Due to the time frame for entry into Covid Vaccine after vaccination in certain structures, a delay is needed to consolidate the data. **From 07/04/2021, the age of vaccinated persons will be calculated from the date of birth (and not the year of birth only). This implies, within Ehpad/USLS, a slight increase in immunisation coverage among professionals, which is accompanied by a slight decrease in immunisation coverage among residents. Overall, for all Vacsi indicators, this leads to some variations in age distributions. #### Precaution of data use Although the new injections are now seized over the water in Vaccin Covid, some injections since the end of December have not yet been entered in Vaccine Covid. In some regions, the Vaccine Covid data are now more complete than the increase made by the ARS, but in others this is not the case yet — especially in Île de France, Provence Alpes Côte d’Azur and Auvergne Rhône Alpes. The data that will now be published every day by Santé publique France at the date of injection will make it possible to report on this catch-up. In the departmental files, if a line for a number of vaccinated at a given date is missing, it is that there was no vaccination that day in the department. The age classes used are as follows: * 0: All ages * 24: 18-24 * 29: 25-29 * 39: 30-39 * 49: 40-49 * 59: 50-59 * 64: 60-64 The region (column “reg”) follows the codification of the INSEE Official Geographical Code, it is codified as follows: * 01: Guadeloupe * 02: Martinique * 03: Guyana * 04: The Meeting * 11: Ile-de-France * 24: Centre-Val de Loire * 27: Burgundy-Franche-Comté * 28: Normandy * 32: Haute-de-France * 44: Great East * 52: Country of the Loire * 53: Brittany * 75: New-Aquitaine * 76: Occitania * 84: Auvergne-Rhône-Alpes * 93: Provence-Alpes-Côte d’Azur * 94: Corsica