As of November 4, 2023, Malaysia recorded over 5.1 million confirmed cases of COVID-19 and around 37.1 thousand deaths from the virus. Currently, Malaysia has successfully vaccinated over 80 percent of its population and 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.

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.

On March 11, 2023, Malaysia recorded 223 new confirmed cases of COVID-19, reflecting an increase from more than 160 cases on March 5, 2023. Malaysia is 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.

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.

Summary results for Malaysia (with redistributed cases).

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

As of February 2020, 45 percent of Malaysian respondents believed that number of COVID-19 cases would go up locally. The Central Bank of Malaysia, Bank Negara Malaysia, stated that the coronavirus outbreak will affect Malaysia’s economic growth in Q1 2020. Travel and tourism and associated sectors are predicted to be among the most affected industries in Malaysia as Malaysia's biggest tourist numbers are coming from China.

MAP estimates and associated credible intervals for Malaysia.

Comparison of reported COVID-19 cases with estimated COVID-19 cases based on reported mortality and excess counts between March 2020 and October 2021 in Malaysia

As of July 13, 2022, 67 percent of Malaysian respondents stated that they had been avoiding public places during the COVID-19 outbreak, up from 40 percent on Feb 24, 2020. Malaysia is currently experiencing a decrease in the number of confirmed daily cases of COVID-19 infections, although the country still expecting a rise due to the highly contagious variant of Omicron.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

In 2022, there were 148 government hospitals and 207 private licensed hospitals in Malaysia. During the COVID-19 pandemic, the Malaysian hospitals were prepared by the government to accommodate infected patients by increasing bed numbers. Although the country has now entered the post-COVID time, the pandemic had an impact on the healthcare system.

COVID-19 hospitals

During the COVID-19 pandemic in 2020 and 2021, many of public and private hospitals provide screenings for coronavirus. However, these are paid services. Only the high-risk groups such as elderly population who live in a nursing home and healthcare workers were provided free COVID-19 tests by the government. About 59 hospitals that are owned by the Ministry of Health handled patients under investigation (PU) and suspected positive COVID-19 cases. In July 2020, these hospitals prepared over 400 beds in the in the intensive care unit (ICU) and an additional thousand-odd ventilators for COVID-19 patients exclusively. With the availability of vaccination against the disease, the number of patients significantly decreased. As of March 2022, around 80 percent of Malaysian population have been vaccinated.

Digitalization of patient records

In 2019, the Ministry of Health announced a plan to use electronic medical record (EMR) systems across all hospitals and clinics nationwide. The digitalization of patient records would then provide ease the healthcare processes. Just like in most countries, the pandemic has also accelerated the digital evolution demand in Malaysia. To achieve this goal, the government has also improved connectivity and bandwidth infrastructure across the country. In 2019, Malaysia had a digital readiness index of 14.31 out of 25, putting it in the Accelerate Stage. In comparison, neighboring Singapore has made progress in its e-government strategy with a head start in 2011.

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.

----------------------UPDATED------UPDATED---------UPDATED----------------------- ----------------------------- (3616 COVID-19 Chest X-ray) -------------------------------

COVID-19 RADIOGRAPHY DATABASE (Winner of the COVID-19 Dataset Award by Kaggle Community)

A team of researchers from Qatar University, Doha, Qatar, and the University of Dhaka, Bangladesh along with their collaborators from Pakistan and Malaysia in collaboration with medical doctors have created a database of chest X-ray images for COVID-19 positive cases along with Normal and Viral Pneumonia images. This COVID-19, normal, and other lung infection dataset is released in stages. In the first release, we have released 219 COVID-19, 1341 normal, and 1345 viral pneumonia chest X-ray (CXR) images. In the first update, we have increased the COVID-19 class to 1200 CXR images. In the 2nd update, we have increased the database to 3616 COVID-19 positive cases along with 10,192 Normal, 6012 Lung Opacity (Non-COVID lung infection), and 1345 Viral Pneumonia images. We will continue to update this database as soon as we have new x-ray images for COVID-19 pneumonia patients.

Please find the link for downloading the whole dataset: Data

Please cite the following two articles if you are using this dataset:

-M.E.H. Chowdhury, T. Rahman, A. Khandakar, R. Mazhar, M.A. Kadir, Z.B. Mahbub, K.R. Islam, M.S. Khan, A. Iqbal, N. Al-Emadi, M.B.I. Reaz, M. T. Islam, “Can AI help in screening Viral and COVID-19 pneumonia?” IEEE Access, Vol. 8, 2020, pp. 132665 - 132676. Paper link -Rahman, T., Khandakar, A., Qiblawey, Y., Tahir, A., Kiranyaz, S., Kashem, S.B.A., Islam, M.T., Maadeed, S.A., Zughaier, S.M., Khan, M.S. and Chowdhury, M.E., 2020. Exploring the Effect of Image Enhancement Techniques on COVID-19 Detection using Chest X-ray Images. Paper Link

To view images please check image folders and references of each image are provided in the metadata.xlsx.

*****Research Team members and their affiliation***** Muhammad E. H. Chowdhury, PhD (mchowdhury@qu.edu.qa) Department of Electrical Engineering, Qatar University, Doha-2713, Qatar Tawsifur Rahman (tawsifurrahman.1426@gmail.com) Department of Biomedical Physics & Technology, University of Dhaka, Dhaka-1000, Bangladesh Amith Khandakar (amitk@qu.edu.qa) Department of Electrical Engineering, Qatar University, Doha-2713, Qatar Rashid Mazhar, MD Thoracic Surgery, Hamad General Hospital, Doha-3050, Qatar Muhammad Abdul Kadir, PhD Department of Biomedical Physics & Technology, University of Dhaka, Dhaka-1000, Bangladesh Zaid Bin Mahbub, PHD Department of Mathematics and Physics, North South University, Dhaka-1229, Bangladesh Khandakar R. Islam, MD Department of Orthodontics, Bangabandhu Sheikh Mujib Medical University, Dhaka-1000, Bangladesh Muhammad Salman Khan, PhD Department of Electrical Engineering (JC), University of Engineering and Technology, Peshawar-25120, Pakistan Prof. Atif Iqbal, PhD Department of Electrical Engineering, Qatar University, Doha-2713, Qatar Nasser Al-Emadi, PhD Department of Electrical Engineering, Qatar University, Doha-2713, Qatar Prof. Mamun Bin Ibne Reaz. PhD Department of Electrical, Electronic & Systems Engineering, Universiti Kebangsaan Malaysia, Bangi, Selangor 43600, Malaysia

****Contribution**** - We have developed the database of COVID-19 x-ray images from the Italian Society of Medical and Interventional Radiology (SIRM) COVID-19 DATABASE [1], Novel Corona Virus 2019 Dataset developed by Joseph Paul Cohen and Paul Morrison, and Lan Dao in GitHub [2] and images extracted from 43 different publications. References of each image are provided in the metadata. Normal and Viral pneumonia images were adopted from the Chest X-Ray Images (pneumonia) database [3].

Image Formats - All the images are in Portable Network Graphics (PNG) file format and the resolution are 299*299 pixels.

Objective - Researchers can use this database to produce useful and impactful scholarly work on COVID-19, which can help in tackling this pandemic.

Citation - Please cite these papers if you are using it for any scientific purpose: -M.E.H. Chowdhury, T. Rahman, A. Khandakar, R. Mazhar, M.A. Kadir, Z.B. Mahbub, K.R. Islam, M.S. Khan, A. Iqbal, N. Al-Emadi, M.B.I. Reaz, M. T. Islam, “Can AI help in screening Viral and COVID-19 pneumonia?” IEEE Access, Vol. 8, 2020, pp. 132665 - 132676. Paper link -Rahman, T., Khandakar, A., Qiblawey, Y., Tahir, A., Kiranyaz, S., Kashem, S.B.A., Islam, M.T., Maadeed, S.A., Zughaier, S.M., Khan, M.S. and Chowdhury, M.E., 2020. Exploring the Effect of Image Enhancement Techniques on COVID-19 Detection using Chest X-ray Images. Paper Link

Acknowledgments Thanks to the Italian Society of Medical and Interventional Radiology (SIRM) for publicly providing the COVID-19 Chest X-Ray dataset [3], Valencia Region Image Bank (BIMCV) padchest dataset [1] and would like to thank J. P. Cohen for taking the initiative to gather images from articles and online resources [5]. Finally to the Chest X-Ray Images (pneumonia) database in Kaggle and Radiological Society of North America (RSNA) Kaggle database for making a wonderful X-ray database for normal, lung opacity, viral, and bacterial pneumonia images [8-9]. Also, a big thanks to our collaborators! DATA ACCESS AND USE: Academic/Non-Commercial Use References: [1]https://bimcv.cipf.es/bimcv-projects/bimcv-covid19/#1590858128006-9e640421-6711 [2]https://github.com/ml-workgroup/covid-19-image-repository/tree/master/png [3]https://sirm.org/category/senza-categoria/covid-19/ [4]https://eurorad.org [5]https://github.com/ieee8023/covid-chestxray-dataset [6]https://figshare.com/articles/COVID-19_Chest_X-Ray_Image_Repository/12580328 [7]https://github.com/armiro/COVID-CXNet
[8]https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/data [9] https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia

As of July 13, 2022, 84 percent of Malaysian respondents stated that they were wearing face masks when in public places during the COVID-19 outbreak, up from 55 percent on Feb 24, 2020. Malaysia has vaccinated more than 80 percent of its adult population. However, due to the highly infectious type of Omicron, the country is still expecting an increase in the number of confirmed daily cases of COVID-19 infections.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

As of July 13, 2022, 76 percent of Malaysian respondents stated that they feared contracting the novel coronavirus and the infection caused by it, COVID-19. 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.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Authors of the Dataset:

Pratik Bhowal (B.E., Dept of Electronics and Instrumentation Engineering, Jadavpur University Kolkata, India) [LinkedIn], [Github] Subhankar Sen (B.Tech, Dept of Computer Science Engineering, Manipal University Jaipur, India) [LinkedIn], [Github], [Google Scholar] Jin Hee Yoon (faculty of the Dept. of Mathematics and Statistics at Sejong University, Seoul, South Korea) [LinkedIn], [Google Scholar] Zong Woo Geem (faculty of College of IT Convergence at Gachon University, South Korea) [LinkedIn], [Google Scholar] Ram Sarkar( Professor at Dept. of Computer Science Engineering, Jadavpur Univeristy Kolkata, India) [LinkedIn], [Google Scholar]

Overview The authors have created a new dataset known as Novel COVID-19 Chestxray Repository by the fusion of publicly available chest-xray image repositories. In creating this combined dataset, three different datasets obtained from the Github and Kaggle databases,created by the authors of other research studies in this field, were utilized.In our study,frontal and lateral chest X-ray images are used since this view of radiography is widely used by radiologist in clinical diagnosis.In the following section, authors have summarized how this dataset is created.

COVID-19 Radiography Database: The first release of this dataset reports 219 COVID-19,1345 viral pneumonia and 1341 normal radiographic chest X-ray images. This dataset was created by a team of researchers from Qatar University, Doha, Qatar, and the University of Dhaka, Bangladesh in collaboration with medical doctors and specialists from Pakistan and Malaysia.This database is regularly updated with the emergence of new cases of COVID-19 patients worldwide.Related Paper:https://arxiv.org/abs/2003.13145

COVID-Chestxray set:Joseph Paul Cohen and Paul Morrison and Lan Dao have created a public image repository on Github which consists both CT scans and digital chest x-rays.The data was collected mainly from retrospective cohorts of pediatric patients from Guangzhou Women and Children’s medical center.With the aid of metadata information provided along with the dataset,we were able to extract 521 COVID-19 positive,239 viral and bacterial pneumonias;which are of the following three broad categories:Middle East Respiratory Syndrome (MERS),Severe Acute Respiratory Syndrome (SARS), and Acute Respiratory Distress syndrome (ARDS);and 218 normal radiographic chest X-ray images of varying image resolutions. Related Paper: https://arxiv.org/abs/2006.11988

Actualmed COVID chestxray dataset:Actualmed-COVID-chestxray-dataset comprises of 12 COVID-19 positive and 80 normal radiographic chest x-ray images.

The combined dataset includes chest X-ray images of COVID-19,Pneumonia and Normal (healthy) classes, with a total of 752, 1584, and 1639 images respectively. Information about the Novel COVID-19 Chestxray Database and its parent image repositories is provided in Table 1.

Table 1: Dataset Description | Dataset| COVID-19 |Pneumonia | Normal | | ------------- | ------------- | ------------- | -------------| | COVID Chestxray set | 521 |239|218| | COVID-19 Radiography Database(first release) | 219 |1345|1341| | Actualmed COVID chestxray dataset| 12 |0|80| | Total|752|1584|1639|

DATA ACCESS AND USE: Academic/Non-Commercial Use Dataset License : Database: Open Database, Contents: Database Contents

Chest X-ray images were selected from a database of chest X-ray images for COVID-19 positive cases along with normal and viral pneumonia images which were collated by researchers from Qatar University and the University of Dhaka along with collaborators from Pakistan and Malaysia and some medical doctors. In their current release, there are 219 COVID-19 positive images, 1341 normal images and 1345 viral pneumonia images (Chowdhury et al., 2020). To ensure multiple representations, the dataset of chest X-ray images for both COVID-19 and normal cases were also selected from Mendeley dataset repository (El-Shafai, 2020) which contains 5500 Non-COVID X-ray images and 4044 COVID-19 X-ray images. This study, therefore, adopted these multi source datasets. Due to limited computing resources, in this study, 1,300 images were selected from each category. That is, 1,300 images of COVID-19 positive cases, 1,300 Normal images and 1,300 images of viral pneumonia cases, totaling 3,900 images in all. It is noted here that further descriptions of the datasets were not provided by the authors of the sources of the datasets.El-Shafai, Walid; Abd El-Samie, Fathi (2020), “Extensive COVID-19 X-Ray and CT Chest Images Dataset”, Mendeley Data, V3, doi: 10.17632/8h65ywd2jr.3Chowdhury, M. E., Rahman, T., Khandakar, A., Mazhar, R., Kadir, M. A., Mahbub, Z. B., . . . Reaz, M. B. (2020, March 29). Can AI help in screening Viral and COVID-19 pneumonia? Retrieved from https://arxiv.org/abs/2003.13145; https://www.kaggle.com/tawsifurrahman/covid19-radiography-database

As of May 2, 2023, the outbreak of the coronavirus disease (COVID-19) had been confirmed in almost every country in the world. The virus had infected over 687 million people worldwide, and the number of deaths had reached almost 6.87 million. The most severely affected countries include the U.S., India, and Brazil.

COVID-19: background information COVID-19 is a novel coronavirus that had not previously been identified in humans. The first case was detected in the Hubei province of China at the end of December 2019. The virus is highly transmissible and coughing and sneezing are the most common forms of transmission, which is similar to the outbreak of the SARS coronavirus that began in 2002 and was thought to have spread via cough and sneeze droplets expelled into the air by infected persons.

Naming the coronavirus disease Coronaviruses are a group of viruses that can be transmitted between animals and people, causing illnesses that may range from the common cold to more severe respiratory syndromes. In February 2020, the International Committee on Taxonomy of Viruses and the World Health Organization announced official names for both the virus and the disease it causes: SARS-CoV-2 and COVID-19, respectively. The name of the disease is derived from the words corona, virus, and disease, while the number 19 represents the year that it emerged.

As of March 30, 2020, 78 percent of Malaysian respondents stated that they would support the government to stop inbound international flights from countries with confirmed cases of COVID-19, up from 47 percent on Feb 24, 2020. On March 18, Malaysia imposed the Movement Control Order (MCO) to slow down the spread of the novel coronavirus infection, COVID-19. This restricted the movement of people, closing down non-essential businesses and educational institutions. The MCO would be lifted on April 28, 2020. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

IntroductionHealthcare workers (HCWs) have been continually exposed to patients with COVID-19 and are at higher risk of contracting the disease. Their psychological health is important for overall wellbeing and productivity, which could lead to a reduction in human errors during the pandemic crisis. This study aimed to measure the level of concerns, work practices, adequacy of preventive measures among HCWs, and the impacts on their life and work, including mental health status during the second wave of the COVID-19 pandemic in Malaysia.MethodsAn online questionnaire was distributed randomly to 1,050 HCWs from the Ministry of Health facilities in the Klang Valley who were involved directly in managing or screening COVID-19 cases from May to August 2020. The questionnaire was divided into five domains, which were concerns, impact on life and work, practice, perceived adequacy of preventive measures, and Revised Impact of Event Scale (IES-R). Logistic regression was used to identify sociodemographic predictors of the five domains.ResultsA total of 907 respondents (86.4%) participated in this survey. Approximately half of the respondents had a low concern (50.5%), most of them had a good practice (85.1%), with 67.5% perceiving there were adequate preventive measures, and they perceived the outbreak had a low impact (92%) on their life and work. From the IES-R domain, 18.6% of respondents potentially suffered from post-traumatic stress disorder (PTSD).ConclusionDuring the second wave of the COVID-19 outbreak in Malaysia, HCWs practiced high levels of precautions and preventive measures because they were aware of the risk of infection as an occupational hazard. With the adequate implementation of policy and control measures, the psychological wellbeing of the majority HCWs remained well and adequately supported.