Retrieved from Official Github account of Malaysia's Ministry of Health on 04-Aug-2023.

Imported database : - cases_state.csv: Daily recorded COVID-19 cases at state level. - deaths_state.csv: Daily deaths due to COVID-19 at state level. - hospital.csv: Flow of patients to/out of hospitals, with capacity and utilisation. - icu.csv: Capacity and utilisation of intensive care unit (ICU) beds. - vax_state.csv: Vaccinations (daily and cumulative, by dose type and brand) at state level. - vax_malaysia.csv: Vaccinations (daily and cumulative, by dose type and brand) at country level. - population.csv Malaysia's population by state (last updated from DOSM 2020 census, as published in 2022).

*Database mainly focused on state-level analysis only.*

Full available datasets from MOH (including country-level data) please refer to link above.

In past 24 hours, Malaysia, Asia had N/A new cases, N/A deaths and N/A recoveries.

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.

On March 11, 2023, Malaysia had approximately five million confirmed cases of COVID-19. Over the past week, Malaysia has seen a decrease in the number of new cases each day, but still expects an increase due to the highly-contagious Omicron variant.

Malaysia is currently one out of more than 200 countries and territories battling with the novel coronavirus. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Malaysia recorded 5079436 Coronavirus Cases since the epidemic began, according to the World Health Organization (WHO). In addition, Malaysia reported 37028 Coronavirus Deaths. This dataset includes a chart with historical data for Malaysia Coronavirus Cases.

As of November 4, 2023, Malaysian states of Putrajaya and Kuala Lumpur had respectively around 36.1 and 30.6 coronavirus (COVID-19) confirmed cases per 100,000 people, the highest in the country. Malaysia is experiencing a decrease in cases, although the country still expecting a rise due to the highly contagious variant of Omicron.

Malaysia is currently one out of more than 200 countries and territories battling with the novel coronavirus. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Dataset

This dataset was created by Khairul Hafiz

Released under Data files © Original Authors

Contents

Dataset

This dataset was created by Jesen Do

Contents

Dataset of knowledge, attitudes and practices towards COVID-19 among Malaysian

This Project Tycho dataset includes a CSV file with COVID-19 data reported in MALAYSIA: 2019-12-30 - 2021-07-31. It contains counts of cases and deaths. Data for this Project Tycho dataset comes from: "COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University", "European Centre for Disease Prevention and Control Website", "World Health Organization COVID-19 Dashboard". The data have been pre-processed into the standard Project Tycho data format v1.1.

This dataset contains COVID-19 specific information from Malaysia, which includes the number of cases across Malaysian states and vaccination information.

Taken directly from the Malaysian Ministry of Health's COVID-19 repository, which also includes data descriptions.

The number of COVID-19 vaccination doses administered in Malaysia rose to 72625370 as of Oct 27 2023. This dataset includes a chart with historical data for Malaysia Coronavirus Vaccination Total.

Context

The dataset consists of COVID-19 cases in Malaysia from 27 March 2020 to 15 April 2021. This dataset is collected for the purpose of creating better visualizations for the COVID-19 cases in Malaysia. All of the data is web scraped from https://kpkesihatan.com/ by using BeautifulSoup library.

The data is also available in GitHub, along with the scripts made to scrape the data. There is also a Web Application made to show the visualizations.

Originally I planned to update the data daily but I find that it seems too tedious for me to do this alone without some sort of automated scripts or schedulers. I have been wondering how to do this efficiently with automation or schedulers, if someone knows how to do this efficiently, please reach out to me by emailing or message in LinkedIn, the links can be found in my GitHub, thank you very much.

Content

There are three CSV files and one GeoJSON file: - all_2020-03-27_2021-04-15.csv: all daily cases excluding state data - state_all.csv: all daily cases for each state - state_cumu.csv: all daily cumulative cases for each state - malaysia_state_province_boundary.geojson: Malaysia's GeoJSON map file

The columns consist of: 1. Date 2. Recovered 3. Cumulative Recovered 4. Imported Case (many NaN values till the end of 2020) 5. Local Case (many NaN values) 6. Active Case (many NaN values but can be inferred) 7. New Case 8. Cumulative Case 9. ICU - Number of patients admitted into Intensive Care Unit 10. Ventilator - Number of patients who need ventilator in ICU 11. Death 12. Cumulative Death 13. URL - link to the original webpage

Acknowledgements

Thanks to Info GIS MAP.com that provides Malaysia's GeoJSON file to create Choropleth maps.

Inspiration

Hopefully, there will be people utilizing the scripts or the data to create better visualizations.

Dataset for the manuscript "Key predictors of COVID-19 vaccine hesitancy in Malaysia - An integrated framework"

BackgroundSporadic outbreaks of COVID-19 remain a threat to public healthcare, especially if vaccination levels do not improve. As Malaysia begins its transition into the endemic phase, it is essential to identify the key determinants of COVID-19 vaccination intention amongst the pockets of the population who are still hesitant. Therefore, focusing on a sample of individuals who did not register for the COVID-19 vaccination, the current study integrated two widely used frameworks in the public health domain—the health belief model (HBM) and the theory of reasoned action (TRA)—to examine the inter-relationships of the predictors of vaccination intention amongst these individuals.MethodologyPrimary data from 117 respondents who did not register for the COVID-19 vaccination were collected using self-administered questionnaires to capture predictors of vaccination intention amongst individuals in a Malaysian context. The partial least squares structural equation modeling (PLS-SEM) technique was used to analyze the data.ResultsSubjective norms and attitude play key mediating roles between the HBM factors and vaccination intention amongst the unregistered respondents. In particular, subjective norms mediate the relationship between cues to action and vaccination intention, highlighting the significance of important others to influence unregistered individuals who are already exposed to information from mass media and interpersonal discussions regarding vaccines. Trust, perceived susceptibility, and perceived benefits indirectly influence vaccination intention through attitude, indicating that one’s attitude is vital in promoting behavioral change.ConclusionThis study showed that the behavioral factors could help understand the reasons for vaccine refusal or acceptance, and shape and improve health interventions, particularly among the vaccine-hesitant group in a developing country. Therefore, policymakers and key stakeholders can develop effective strategies or interventions to encourage vaccination amongst the unvaccinated for future health pandemics by targeting subjective norms and attitude.

COVID-19 UMT - Google Forms Digital Printed File is the online form generated from the Google platform, the Google Form to conducted the survey entitled "Study on the effects of COVID-19 on Malaysian fisheries industry by UMT". The online survey by Institute of Tropical Aquaculture and Fisheries, Universiti Malaysia Terengganu looks at the current challenges and actions during COVID-19 pandemic on Malaysian fisheries and aquaculture sectors for policy formulating preparation by the relevant agencies.

Malaysia Central Govt COVID-19 Fund: Quarterly data was reported at 0.000 MYR mn in Dec 2024. This stayed constant from the previous number of 0.000 MYR mn for Sep 2024. Malaysia Central Govt COVID-19 Fund: Quarterly data is updated quarterly, averaging 0.000 MYR mn from Mar 1996 (Median) to Dec 2024, with 116 observations. The data reached an all-time high of 22,147.000 MYR mn in Jun 2020 and a record low of 0.000 MYR mn in Dec 2024. Malaysia Central Govt COVID-19 Fund: Quarterly data remains active status in CEIC and is reported by Bank Negara Malaysia. The data is categorized under Global Database’s Malaysia – Table MY.F001: Central Government Finance.

Countries around the world are gearing for the transition of the coronavirus disease 2019 (COVID-19) from pandemic to endemic phase but the emergence of new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants could lead to a prolonged pandemic. SARS-CoV-2 has continued to evolve as it optimizes its adaptation to the human host and the successive waves of COVID-19 have been linked to the explosion of particular variant of concern. As the genetic diversity and epidemiological landscape of SARS-CoV-2 differ from country to country, this study aims to provide insights into the variants that are circulating in Malaysia. Whole genome sequencing was performed for 204 SARS-CoV-2 from COVID-19 cases and an additional 18,667 SARS-CoV-2 genome sequences were retrieved from the GISAID EpiCoV database for clade, lineage and genetic variation analyses. Complete genome sequences with high coverage were then used for phylogeny investigation and the resulting phylogenetic tree was constructed from 8,716 sequences. We found that the different waves of COVID-19 in Malaysia were dominated by different clades with the L and O clade for first and second wave, respectively, whereas the progressive replacement by G, GH, and GK of the GRA clade were observed in the subsequence waves. Continuous monitoring of the genetic diversity of SARS-CoV-2 is important to identify the emergence and dominance of new variant in different locality so that the appropriate countermeasures can be taken to effectively contain the spread of SARS-CoV-2.

We aim to explore the impact of the COVID-19 pandemic on psychological stress and well-being of primary healthcare workers (HCWs) in Malaysia.

The objective of this study is to understand the epidemiology of COVID-19 in Sabah from March 2020 through October 2021 and to determine the factors associated with COVID-19 severity. The data used in this study were provided by the Surveillance Unit, Sabah State Health Department, Ministry of Health Malaysia. Individuals aged ≤5 years old and ≥ 65 years old (AOR=1.87, 95% CI: 1.77–1.99), non-citizens of Malaysia (AOR=1.46, 95% CI: 1.30–1.64), male gender (AOR=1.06, 95% CI: 1.01–1.12), native Sabahan (AOR=1.30, 95% CI: 1.19–1.42), presence of symptoms of COVID-19 infection (AOR=23.33, 95% CI: 20.75-26.23), presence of comorbidity (AOR=1.80, 95% CI: 1.67-1.94), high exposure risk of COVID-19 infection (AOR=0.44, 95% CI: 0.28-0.71), and incomplete COVID-19 vaccination (AOR=8.53, 95% CI: 7.35-9.89) were statistically significantly associated with developing severe COVID-19 infection.