Details of completed (processed) COVID-19 antigen tests carried out in NHS hospitals in Northern Ireland.

Data forming the Covid-19 Second Generation Surveillance Systems data set relate to demographic and diagnostic information from Pillar 1 swab testing in PHE labs and NHS hospitals for those with a clinical need, and health and care workers and Pillar 2 Swab testing in the community at drive through test centres, walk in centres, home kits returned by posts, care homes, prisons etc).

Timescales for dissemination can be found under 'Our Service Levels' at the following link: https://digital.nhs.uk/services/data-access-request-service-dars/data-access-request-service-dars-process

A. SUMMARY This dataset includes COVID-19 tests by resident neighborhood and specimen collection date (the day the test was collected). Specifically, this dataset includes tests of San Francisco residents who listed a San Francisco home address at the time of testing. These resident addresses were then geo-located and mapped to neighborhoods. The resident address associated with each test is hand-entered and susceptible to errors, therefore neighborhood data should be interpreted as an approximation, not a precise nor comprehensive total.

In recent months, about 5% of tests are missing addresses and therefore cannot be included in any neighborhood totals. In earlier months, more tests were missing address data. Because of this high percentage of tests missing resident address data, this neighborhood testing data for March, April, and May should be interpreted with caution (see below)

Percentage of tests missing address information, by month in 2020 Mar - 33.6% Apr - 25.9% May - 11.1% Jun - 7.2% Jul - 5.8% Aug - 5.4% Sep - 5.1% Oct (Oct 1-12) - 5.1%

To protect the privacy of residents, the City does not disclose the number of tests in neighborhoods with resident populations of fewer than 1,000 people. These neighborhoods are omitted from the data (they include Golden Gate Park, John McLaren Park, and Lands End).

Tests for residents that listed a Skilled Nursing Facility as their home address are not included in this neighborhood-level testing data. Skilled Nursing Facilities have required and repeated testing of residents, which would change neighborhood trends and not reflect the broader neighborhood's testing data.

This data was de-duplicated by individual and date, so if a person gets tested multiple times on different dates, all tests will be included in this dataset (on the day each test was collected).

The total number of positive test results is not equal to the total number of COVID-19 cases in San Francisco. During this investigation, some test results are found to be for persons living outside of San Francisco and some people in San Francisco may be tested multiple times (which is common). To see the number of new confirmed cases by neighborhood, reference this map: https://sf.gov/data/covid-19-case-maps#new-cases-maps

B. HOW THE DATASET IS CREATED COVID-19 laboratory test data is based on electronic laboratory test reports. Deduplication, quality assurance measures and other data verification processes maximize accuracy of laboratory test information. All testing data is then geo-coded by resident address. Then data is aggregated by analysis neighborhood and specimen collection date.

Data are prepared by close of business Monday through Saturday for public display.

C. UPDATE PROCESS Updates automatically at 05:00 Pacific Time each day. Redundant runs are scheduled at 07:00 and 09:00 in case of pipeline failure.

D. HOW TO USE THIS DATASET San Francisco population estimates for geographic regions 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).

Due to the high degree of variation in the time needed to complete tests by different labs there is a delay in this reporting. On March 24 the Health Officer ordered all labs in the City to report complete COVID-19 testing information to the local and state health departments.

In order to track trends over time, a data user can analyze this data by "specimen_collection_date".

Calculating Percent Positivity: The positivity rate is the percentage of tests that return a positive result for COVID-19 (positive tests divided by the sum of positive and negative tests). Indeterminate results, which could not conclusively determine whether COVID-19 virus was present, are not included in the calculation of percent positive. Percent positivity indicates how widespread COVID-19 is in San Francisco and it helps public health officials determine if we are testing enough given the number of people who are testing positive. When there are fewer than 20 positives tests for a given neighborhood and time period, the positivity rate is not calculated for the public tracker because rates of small test counts are less reliable.

Calculating Testing Rates: To calculate the testing rate per 10,000 residents, divide the total number of tests collected (positive, negative, and indeterminate results) for neighborhood by the total number of residents who live in that neighborhood (included in the dataset), then multiply by 10,000. When there are fewer than 20 total tests for a given neighborhood and time period, the testing rate is not calculated for the public tracker because rates of small test counts are less reliable.

Read more about how this data is updated and validated daily: https://sf.gov/information/covid-19-data-questions

E. CHANGE LOG

• 1/12/2024 - This dataset will stop updating as of 1/12/2024
• 6/21/2023 - A small number of additional COVID-19 testing records were released as part of our ongoing cleaning efforts.
• 1/31/2023 - updated “acs_population” column to reflect the 2020 Census Bureau American Community Survey (ACS) San Francisco Population estimates.
• 1/31/2023 - implemented system updates to streamline and improve our geo-coded data, resulting in small shifts in our testing data by geography.
• 1/31/2023 - renamed column “last_updated_at” to “data_as_of”.
• 1/31/2023 - removed the “multipolygon” column. To access the multipolygon geometry column for each geography unit, refer to COVID-19 Cases and Deaths Summarized by Geography.
• 4/16/2021 - dataset updated to refresh with a five-day data lag.

COVID-19 Dataset for Correlation Between Early Government Interventions in the Northeastern United States and Peak COVID-19 Disease Burden by Joel Mintz. File Type: Excel Contents: Tab 1 ("Raw")=Raw Data as Downloaded directly from COVID Tracking Project, sorted by date Tab 2-14 ("State Name') = Data Sorted by State Tab 2-14 Headers: Column 1: Population per state, as recorded by latest American Community Survey, maximum (peak) COVID-19 outcome, with date on which outcome occurred. Column 2: Date on which numbers were recorded* Column 3: State Name* Column 4: Number of reported positive COVID-19 tests* Column 5: Number of reported negative COVID-19 tests* Column 6: Pending COVID-19 tests* Column 7: Currently Hospitalized* Column 8: Cumulatively Hospitalized* Column 9: Currently in ICU* Column 10: Cumulatively in ICU* Column 11: Currently on Ventilator Support* Column 12: Cumulatively on Ventilator Support* Column 13: Total Recovered* Column 14: Cumulative Mortality* *Provided in Original Raw Data Column 15: Total Tests Administered (Column 4+Column 5) Column 16: Placeholder Column 17: % of total population tested Column 18: New Cases Per day Column 19: Change in new cases per day Column 20: Positive cases per day per capita in number per/ hundreds of thousands: (Column 18/total population*100000) Column 21: Change in Positive cases per day per capita in number per/ hundreds of thousands: (Column 19/total population*100000) Column 22: Hospitalizations per day per capita in number per/ hundreds of thousands Column 23: Change in Hospitalizations per day per capita in number per/ hundreds of thousands Column 24: Deaths per day per capita in number per/ hundreds of thousands Column 25: Change in Deaths per day per capita in number per/ hundreds of thousands Column 26-31: Columns 20-25 with an applied 5 day moving average filter Column 32: Adjusted hospitalization: (Subtract number of hospitalizations from the initial number of hospitalzations where reporting bean) Column 33: Adjusted hospitalizations per day per capita Column 34: Adjusted hospitalizations per day per capita, with applied 5 day moving average filter

This is a dataset created for the Medicaid Scorecard website (https://www.medicaid.gov/state-overviews/scorecard/index.html), and is not intended for use outside that application.

This is a dataset created for the Medicaid Scorecard website (https://www.medicaid.gov/state-overviews/scorecard/index.html), and is not intended for use outside that application.

Second Generation Surveillance System (SGSS) is the national laboratory reporting system used in England to capture routine laboratory data on infectious diseases and antimicrobial resistance. The SARS-CoV-2 testing started in UK laboratories on 24/02/2020, with the SGSS data reflecting testing (swab samples, PCR test method) offered to those in hospital and NHS key workers (i.e. Pillar 1). The CPRD-SGSS linked data currently contain positive tests results only.

This publication was archived on 12 October 2023. Please see the Viral Respiratory Diseases (Including Influenza and COVID-19) in Scotland publication for the latest data. This dataset provides information on number of new daily confirmed cases, negative cases, deaths, testing by NHS Labs (Pillar 1) and UK Government (Pillar 2), new hospital admissions, new ICU admissions, hospital and ICU bed occupancy from novel coronavirus (COVID-19) in Scotland, including cumulative totals and population rates at Scotland, NHS Board and Council Area levels (where possible). Seven day positive cases and population rates are also presented by Neighbourhood Area (Intermediate Zone 2011). Information on how PHS publish small are COVID figures is available on the PHS website. Information on demographic characteristics (age, sex, deprivation) of confirmed novel coronavirus (COVID-19) cases, as well as trend data regarding the wider impact of the virus on the healthcare system is provided in this publication. Data includes information on primary care out of hours consultations, respiratory calls made to NHS24, contact with COVID-19 Hubs and Assessment Centres, incidents received by Scottish Ambulance Services (SAS), as well as COVID-19 related hospital admissions and admissions to ICU (Intensive Care Unit). Further data on the wider impact of the COVID-19 response, focusing on hospital admissions, unscheduled care and volume of calls to NHS24, is available on the COVID-19 Wider Impact Dashboard. Novel coronavirus (COVID-19) is a new strain of coronavirus first identified in Wuhan, China. Clinical presentation may range from mild-to-moderate illness to pneumonia or severe acute respiratory infection. COVID-19 was declared a pandemic by the World Health Organisation on 12 March 2020. We now have spread of COVID-19 within communities in the UK. Public Health Scotland no longer reports the number of COVID-19 deaths within 28 days of a first positive test from 2nd June 2022. Please refer to NRS death certificate data as the single source for COVID-19 deaths data in Scotland. In the process of updating the hospital admissions reporting to include reinfections, we have had to review existing methodology. In order to provide the best possible linkage of COVID-19 cases to hospital admissions, each admission record is required to have a discharge date, to allow us to better match the most appropriate COVID positive episode details to an admission. This means that in cases where the discharge date is missing (either due to the patient still being treated, delays in discharge information being submitted or data quality issues), it has to be estimated. Estimating a discharge date for historic records means that the average stay for those with missing dates is reduced, and fewer stays overlap with records of positive tests. The result of these changes has meant that approximately 1,200 historic COVID admissions have been removed due to improvements in methodology to handle missing discharge dates, while approximately 820 have been added to the cumulative total with the inclusion of reinfections. COVID-19 hospital admissions are now identified as the following: A patient's first positive PCR or LFD test of the episode of infection (including reinfections at 90 days or more) for COVID-19 up to 14 days prior to admission to hospital, on the day of their admission or during their stay in hospital. If a patient's first positive PCR or LFD test of the episode of infection is after their date of discharge from hospital, they are not included in the analysis. Information on COVID-19, including stay at home advice for people who are self-isolating and their households, can be found on NHS Inform. Data visualisation of Scottish COVID-19 cases is available on the Public Health Scotland - Covid 19 Scotland dashboard. Further information on coronavirus in Scotland is available on the Scottish Government - Coronavirus in Scotland page, where further breakdown of past coronavirus data has also been published.

CPRD GOLD linked Second Generation Surveillance System (SGSS) data contains SARS-CoV-2 testing (swab samples, PCR test method) offered to those in hospital and NHS key workers (i.e. Pillar 1) and includes positive tests results only.

QResearch GP data is linked to Second Generation Surveillance System (SGSS) data contains SARS-CoV-2 testing (swab samples, PCR test method) offered to those in hospital and NHS key workers (i.e. Pillar 1) and includes positive tests results only

Data from Phase 1 testing of a single ALFA Oscillating Water Column (OWC) device at the O.H. Hinsdale Wave Research Laboratory (HWRL) at Oregon State University in Fall of 2016. Contains two zip files of raw data, one of project data, and a diagram of the device with dimensions. A "readme" file in the project data archive under "Docs" explains the project data.

CPRD Aurum linked Second Generation Surveillance System (SGSS) data contains SARS-CoV-2 testing (swab samples, PCR test method) offered to those in hospital and NHS key workers (i.e. Pillar 1) and includes positive tests results only.

This submission of data includes all the 1/50th scale testing data completed on the Wave Energy Prize for the Principle Power team, and includes: - 1/50th test data (raw & processed) - 1/50th test data video and pictures - 1/50th Test plans and testing documents - SSTF_Submission (summarized results)

A. SUMMARY This dataset contains COVID-19 positive confirmed cases aggregated by several different geographic areas and by day. COVID-19 cases are mapped to the residence of the individual and shown on the date the positive test was collected. In addition, 2016-2020 American Community Survey (ACS) population estimates are included to calculate the cumulative rate per 10,000 residents.

Dataset covers cases going back to 3/2/2020 when testing began. This data may not be immediately available for recently reported cases and data will change to reflect as information becomes available. Data updated daily.

Geographic areas summarized are: 1. Analysis Neighborhoods 2. Census Tracts 3. Census Zip Code Tabulation Areas

B. HOW THE DATASET IS CREATED Addresses from the COVID-19 case data are geocoded by the San Francisco Department of Public Health (SFDPH). Those addresses are spatially joined to the geographic areas. Counts are generated based on the number of address points that match each geographic area for a given date.

The 2016-2020 American Community Survey (ACS) population estimates provided by the Census are used to create a cumulative rate which is equal to ([cumulative count up to that date] / [acs_population]) * 10000) representing the number of total cases per 10,000 residents (as of the specified date).

COVID-19 case data undergo quality assurance and other data verification processes and are continually updated to maximize completeness and accuracy of information. This means data may change for previous days as information is updated.

C. UPDATE PROCESS Geographic analysis is scripted by SFDPH staff and synced to this dataset daily at 05:00 Pacific Time.

D. HOW TO USE THIS DATASET San Francisco population estimates for geographic regions 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 can be used to track the spread of COVID-19 throughout the city, in a variety of geographic areas. Note that the new cases column in the data represents the number of new cases confirmed in a certain area on the specified day, while the cumulative cases column is the cumulative total of cases in a certain area as of the specified date.

Privacy rules in effect To protect privacy, certain rules are in effect: 1. Any area with a cumulative case count less than 10 are dropped for all days the cumulative count was less than 10. These will be null values. 2. Once an area has a cumulative case count of 10 or greater, that area will have a new row of case data every day following. 3. Cases are dropped altogether for areas where acs_population < 1000 4. Deaths data are not included in this dataset for privacy reasons. The low COVID-19 death rate in San Francisco, along with other publicly available information on deaths, means that deaths data by geography and day is too granular and potentially risky. Read more in our privacy guidelines

Rate suppression in effect where counts lower than 20 Rates are not calculated unless the cumulative case count is greater than or equal to 20. Rates are generally unstable at small numbers, so we avoid calculating them directly. We advise you to apply the same approach as this is best practice in epidemiology.

A note on Census ZIP Code Tabulation Areas (ZCTAs) ZIP Code Tabulation Areas are special boundaries created by the U.S. Census based on ZIP Codes developed by the USPS. They are not, however, the same thing. ZCTAs are areal representations of routes. Read how the Census develops ZCTAs on their website.

Rows included for Citywide case counts Rows are included for the Citywide case counts and incidence rate every day. These Citywide rows can be used for comparisons. Citywide will capture all cases regardless of address quality. While some cases cannot be mapped to sub-areas like Census Tracts, ongoing data quality efforts result in improved mapping on a rolling bases.

Related dataset See the dataset of the most recent cumulative counts for all geographic areas here: https://data.sfgov.org/COVID-19/COVID-19-Cases-and-Deaths-Summarized-by-Geography/tpyr-dvnc

E. CHANGE LOG

• 9/11/2023 - data on COVID-19 cases by geography over time are no longer being updated. This data is currently through 9/6/2023 and will not include any new data after this date.
• 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 “acs_population” column to reflect the 2020 Census Bureau American Community Survey (ACS) San Francisco Population estimates.
• 1/31/2023 - implemented system updates to streamline and improve our geo-coded data, resulting in small shifts in our case data by geography.
• 1/31/2023 - renamed column “last_updated_at” to “data_as_of”.
• 1/31/2023 - removed the “multipolygon” column. To access the multipolygon geometry column for each geography unit, refer to COVID-19 Cases and Deaths Summarized by Geography.
• 1/22/2022 - system updates to improve timeliness and accuracy of cases and deaths data were implemented.
• 4/16/2021 - dataset updated to refresh with a five-day data lag.

Data from the 1/20th scale testing data completed on the Wave Energy Prize for the M3 Wave team, including the 1/20th scale test plan, raw test data, video, photos, and data analysis results. The top level objective of the 1/20th scale device testing is to obtain the necessary measurements required for determining Average Climate Capture Width per Characteristic Capital Expenditure (ACE) and the Hydrodynamic Performance Quality (HPQ), key metrics for determining the Wave Energy Prize (WEP) winners.

This submission of data includes all the 1/50th scale testing data completed on the Wave Energy Prize for the M3 Wave team, and includes: - 1/50th test data (raw & processed) - 1/50th test data video and pictures - 1/50th Test plans and testing documents - SSTF_Submission (summarized results)

The mechanics of olivine deformation play a key role in long-term planetary processes, including the response of the lithosphere to tectonic loading or the response of the solid Earth to tidal forces, and in short-term processes, such as post-seismic creep within the upper mantle. Previous studies have emphasized the importance of grain-size effects in the deformation of olivine. Most of our understanding of the role of grain boundaries in the deformation of olivine is inferred from comparison of experiments on single crystals to experiments on polycrystalline samples, as there are no direct studies of the mechanical properties of individual grain boundaries in olivine. In this study, we use high-precision mechanical testing of synthetic forsterite bicrystals with well characterized interfaces to directly observe and quantify the mechanical properties of olivine grain boundaries. We conduct in-situ micropillar compression tests at high-temperature (700°C) on bicrystals containing low-angle (4• tilt about [100] on (014)) and high-angle (60• tilt about [100] on (011)) boundaries. During the in-situ tests, we observe differences in deformation style between the pillars containing the grain boundary and the pillars in the crystal interior. In the pillars containing the grain boundary, the interface is oriented at ∼ 45° to the loading direction to promote shear. In-situ observations and analysis of the mechanical data indicate that pillars containing the grain boundary consistently support elastic loading to higher stresses than the pillars without a grain boundary. Moreover, the pillars without the grain boundary sustain larger plastic strain. Post-deformation microstructural characterization confirms that under the conditions of these deformation experiments, sliding did not occur along the grain boundary. These observations support the hypothesis that grain boundaries are stronger relative to the crystal interior at these conditions. This data set is associated with the pre-print manuscript with the DOI: 10.22541/essoar.167979601.17867144/v1

For English, see below This file contains the following numbers: - Number per VOC, VOI and VUM detected per week - Total number of measurements, the denominator, per weekly sample This is split into the WHO (https://www.who .int/en/activities/tracking-SARS-CoV-2-variants/) and/or ECDC (https://www.ecdc.europa.eu/en/covid-19/variants-concern) Variant or Concern ( VOC), Variant of Interest (VOI) and Variant Under Monitoring (VUM). The week to which a sample belongs is based on the date of sampling. The numbers are based on the random sample from the germ surveillance, which means that samples belonging to outbreaks are not included in the data. The file is structured as follows: - One record per VOC, VOI and VUM designated SARS-CoV-2 variant per week. This file is updated weekly on Fridays. The way this information is generated is different from the rapid tests and PCR tests. More advanced machines are used that have a longer lead time than, for example, the machines used for PCR testing. Due to all the logistics processes, it is therefore not feasible to form a representative picture of the last two weeks: these are therefore not reported. Additionally, the germ surveillance project has been operational since October 2020 with an increasing number of weekly samples until mid-early January 2021, therefore older data is not available. For all reported data, the instructions, definitions and footnotes as stated on https://www.rivm.nl/coronavirus-covid-19/virus/varianten are leading. N.B.: Due to internationally changing tribal name definitions based on advancing scientific insight, the records in the data presented here can be adjusted. Changelog: Version 2 update (October 29, 2021): - A WHO_category column has been added with the current variant category (VOC/VOI/VUM) as assigned by WHO. - In addition to the VOC and VOI categories, the VUM category is now also included in the file. Version 3 update (December 10, 2021): - A column May_include_samples_listed_before has been added with a value TRUE it is possible that the reported Variant_cases aggregate samples that are already included in a previous variant in the table. When this is not possible, the value is FALSE. Version 4 update (July 8, 2022): - The May_include_samples_listed_before column has been replaced by an Is_subvariant_of column. If this variant is a subvariant of another variant mentioned, this column contains a value that corresponds to the Variant_code of the other variant. The numbers (Variant_cases) of this subvariant are a subset of those of the other variant. Description of the variables: Version: Version number of the dataset. When the content of the dataset is structurally changed (so not the weekly update or a correction at record level), the version number will be adjusted (+1) and also the corresponding metadata in RIVM data (data.rivm.nl). Date_of_report: Date and time when the data file was last updated by RIVM. Notation: YYYY-MM-DD hh:mm:ss. Date_of_statistics_week_start: The date of the Monday - first day of that week - for which the numbers per week are presented. The last day of the week is Sunday. Notation: YYYY-MM-DD. Variant_code: Scientific name of SARS-CoV-2 variant based on Pangolin nomenclature. Can contain letters, numbers and periods. Variant_name: Current WHO label of SARS-CoV-2 variant. Consists of letters only. ECDC_category: Indicates whether it is a Variant of Concern (VOC), Variant of Interest (VOI), Variant under Monitoring (VUM), or De-escalated Variant (DEV) according to ECDC's current definitions. For more information see also: https://www.ecdc.europa.eu/en/covid-19/variants-concern. WHO_category: Indicates whether it is a Variant of Concern (VOC), Variant of Interest (VOI) or Variant under Monitoring (VUM) according to the current WHO definitions. For more info see also: https://www.who.int/en/activities/tracking-SARS-CoV-2-variants/ Is_subvariant_of: If this variant is a subvariant of another variant mentioned, this column contains a value that corresponds to the Variant_code of the other variant. The numbers (Variant_cases) of this subvariant are a subset of those of the other variant. Sample_size: Shows the total sample size in that week. Consists of whole numbers only. Variant_cases: Shows for how many cases from the sample in the week in question the specific VOC, VOI or VUM was found. Consists of whole numbers only. -------------------------------------------------- --------------------------------------------- Covid-19 reporting of SARS-CoV-2 variants in the Netherlands through the random sample of RT -PCR positive samples in the national surveillance of virus variants. This file contains the following numbers: - Number per VOC, VOI and VUM detected per week - Total number of measurements, the denominator, per weekly sample This is split into the WHO (https://www.who.int/en/activities/tracking-SARS-CoV-2-variants/) and/or ECDC (https://www.ecdc.europa.eu/en/covid-19/variants-concern) designated Variant of Concern (VOC), Variant of Interest (VOI) and Variant Under Monitoring (VUM). The week to which a sample belongs is based on the date of sampling. The numbers are based on the random sample from the virus variant surveillance, which means that samples belonging to outbreaks are not included in the data. The file is structured as follows: - One record per VOC, VOI and VUM noted SARS-CoV-2 variant per week. This file is updated weekly on Fridays. The way this information is generated is different from the rapid tests and PCR tests. More advanced machines are used that have a longer run time than, for example, the machines used for PCR testing. Due to all the logistics processes, it is therefore not feasible to form a representative picture of the most recent two weeks: these are not reported for that reason. Additionally, the virus variant surveillance project has been operational since October 2020 with an increasing number of weekly samples until mid-early January 2021, therefore older data is not available. For all reported data, the instructions, definitions and footnotes as stated on https://www.rivm.nl/coronavirus-covid-19/virus/varianten are leading. Please note, due to internationally changing variant name definitions based on advancing scientific insight, the records in the data presented here can be adjusted. Changelog: Version 2 update (October 29, 2021): - A WHO_category column has been added with the current variant category (VOC/VOI/VUM) as assigned by the WHO. - In addition to the VOC and VOI categories, the VUM category is now also included in the file. Version 3 update (December 10, 2021): - A column May_include_samples_listed_before has been added with a value TRUE whenever it is possible for the reported Variant_cases to aggregate samples that have already been included in a previous variant in the table. When this is not possible, the value is FALSE. Version 4 update (July 8, 2022): - The May_include_samples_listed_before column has been replaced by an Is_subvariant_of column. If this variant is a subvariant of another variant mentioned, this column contains a value that corresponds to the Variant_code of the other variant. The numbers (Variant_cases) of this subvariant are a subset of those of the other variant. Description of the variables: Version: Version number of the dataset. When the content of the dataset is structurally changed (so not the weekly update or a correction at record level), the version number will be adjusted (+1) and also the corresponding metadata in RIVM data (data.rivm.nl). Date_of_report: Date and time when the database was last updated by the RIVM. Notation: YYYY-MM-DD hh:mm:ss. Date_of_statistics_week_start: The date of the Monday - first day of that week - for which the numbers per week are presented. The last day of the week is Sunday. Notation: YYYY-MM-DD. Variant_code: Scientific name of SARS-CoV-2 variant based on Pangolin nomenclature. Can contain letters, numbers and periods. Variant_name: Current WHO label of SARS-CoV-2 variant. Consists of letters only. ECDC_category: Indicates whether it is a Variant of Concern (VOC), Variant of Interest (VOI), Variant under Monitoring (VUM), or De-escalated Variant (DEV) according to ECDC's current definitions. For more information see also: https://www.ecdc.europa.eu/en/covid-19/variants-concern. WHO_category: Indicates whether it is a Variant of Concern (VOC), Variant of Interest (VOI) or Variant under Monitoring (VUM) according to the current WHO definitions. For more information see also: https://www.who.int/en/activities/tracking-SARS-CoV-2-variants/ Is_subvariant_of: If this variant is a subvariant of another variant that has been mentioned, this column contains a value that corresponds to the Variant_code of the other variant. The numbers (Variant_cases) of this subvariant are a subset of those of the other variant. Sample_size: Shows the total sample size in that week. Consists of whole numbers only. Variant_cases: Shows for how many cases from the sample from that week the specific VOC, VOI or VUM was found. Consists of whole numbers only.

A free, publicly available Malaysia Covid-19 dataset.

Data Descriptions

28 variables. Include:

New case New case (7 day rolling average) Recovered Active case Local cases Imported case ICU Death Cumulative deaths

People tested Cumulative people tested Positivity rate Positivity rate (7 day rolling average)

Data Sources

Column 1 to 22 are Twitter data, which the Tweets are retrieved from Health DG @DGHisham timeline with Twitter API. A typical covid situation update Tweet is written in a relatively fixed format. Data wrangling are done in Python/Pandas, numerical values extracted with Regular Expression (RegEx). Missing data are added manually from Desk of DG (kpkesihatan).

Column 23 ['remark'] is my own written remark regarding the Tweet status/content.

Column 24 ['Cumulative people tested'] data is transcribed from an image on MOH COVID-19 website. Specifically, the first image under TABURAN KES section in each Situasi Terkini daily webpage of http://covid-19.moh.gov.my/terkini. If missing, the image from CPRC KKM Telegram or KKM Facebook Live video is used. Data in this column, dated from 1 March 2020 to 11 Feb 2021, are from Our World in Data, their data collection method as stated here.

Why does this dataset exist?

MOH does not publish any covid data in csv/excel format as of today, they provide the data as is, along with infographics that are hardly informative. In an undisclosed email, MOH doesn't seem to understand my request for them to release the covid public health data for anyone to download and do their analysis if they do wish.

To be updated periodically

A simple visualization dashboard is now published on Tableau Public. It's is updated daily. Do check it out! More charts to be added in the near future

Inspiration

Create better visualizations to help fellow Malaysians understand the Covid-19 situation. Empower the data science community.