The COVID-19 pandemic has propagated the globe over with uneering ease. It has been no different in Malaysia. Despite being able to stem the propagation of two-waves to date, the Malaysian people are now in the midst of a third wave. This dashboard aims to track and leverage on important parameters of disease propagation and control across the country during this third wave of infection as a means of supplementing the decision-making process within the country. As such it present important parameters over the course of the last 90-days only.
This dashboard has been conceptualized, developed and maintained by a team from the Department of Social and Preventive Medicine, lead by Prof Dr Sanjay Rampal, assisted by Prof Dr Victor Hoe Chee Wai and Dr Vivek Jason Jayaraj.
The data being used within this dashboard has tracked from several different sources:
Case date– new cases, mortalities and discharges Director General of Malaysia, daily Press conferences
Health capacity data – Several important notes on the data used for the Health capacity visualisations :
For Malaysia, Sabah, W.P. Labuan, Selangor and W.P. Kuala Lumpur
- Health capacity data (number of beds, ICU beds and ventilators) at the national level as well as Sabah and W.P. Labuan were extricated from numbers which were released to the media during daily press conferences as well as from the official twitter profile by the Director General of Malaysia, daily Press conferences from 24th October to 29th October.
- Subsequently the Director General of Malaysia, daily Press conferences also provided health capacity figures for Selangor, and W.P. Kuala Lumpur as well in the 18th November press release. Then Malaysian number of beds was also added based on the increase quoted for Selangor and W.P. Kuala Lumpur.
- A third update was provided on the 9th of December- regarding the re-opening of the MAEPS field hospital with an addition of 1000 beds.
- A fourth update was provided by the Minister of Health on 25th December on bed capacity across Malaysia. The Health DG provided a further update on 28th December on bed capacities in Selangor and W.P. Kuala Lumpur.
- The latest update was provided by the Health DG on 30th May 2021 on national health capacity.
For all other states and Federal territories
- Health capacity data for all other states were extricated from a series of surveys and reports conducted by the MOH including:
- Kementerian Kesihatan Malaysia. (2020). Health Indicators 2019
- Ling, T. L., Har, L. C., Nor, M. R. M., Ismail, N. I., & Ismail, W. N. W. (2017). Malaysian Registry of Intensive Care 2017 Report
- National Healthcare Statistics Initiative. (2011). National Healthcare Establishments & Workforce Statistics (Hospital) 2012-2013
- Only Government beds (MOH beds, special institute beds & non-MOH beds) were considered. Of this we assumed that approximately 20% of beds could be made available at any one time (does not inculde surge capacity low-risk bed). The figure of 20% corresponds to the number of government beds currently available for COVID-19 in the state of Sabah.
Update (1/6/2021): As of the 1st of June the MOH provided a detailed list of all low-risk beds available across the country and their occupancy. We summed these low risk beds together with 20% of all hospital beds (government, non-government, private) collated from the abovementioned resources and classified these as an estimated number of available non-ICU beds for COVID-19 within a state or Federal territory.
- Bed occupancy percentages for each state requires active cases to tabulate. These are not provided during the daily press conferences. As such we initially used a median 5-day period (IQR:3-9) of admission for all cases outside China as has been reported by Rees, E. M., Nightingale, E. S., Jafari, Y., Waterlow, N. R., Clifford, S., Carl, C. A., … Knight, G. M. (2020, September 3). COVID-19 length of hospital stay: A systematic review and data synthesis. BMC Medicine, Vol. 18, p. 270.. A percentage can then be tabulated (No of Active cases/No Of estimated beds available)*100. These estimates are an approximate and may overestimate health capacity especially for states where actual health capacity estimates are not available.
- However, we noted that several state Crisis Preparedness and Response Centers have provided active cases by state although these were scattered and difficult to access. These figures appear to follow closely with a median admission of 9-days (and in most instances appears to be an underestimation of active cases) as opposed to the previously used 5-day duration of admission. This suggests a larger proportion of asymptomatic cases (from Active Cases Detection) being admitted to healthcare facilities and serving out the full duration of 10-days as is explained within the the COVID-19 Management Guidelines in Malaysia No.5. As such, beginning 24th November 2020, we estimate bed capacity percentages based on a median admission of 9 days- the upper quartile from the above article.
Serial interval data– literature review
- Bi, Q., Wu, Y., Mei, S., Ye, C., Zou, X., Zhang, Z., … Feng, T. (2020). Epidemiology and Transmission of COVID-19 in Shenzhen China: Analysis of 391 cases and 1,286 of their close contacts. MedRxiv, 2020.03.03.20028423.
- Ganyani, T., Kremer, C., Chen, D., Torneri, A., Faes, C., Wallinga, J., & Hens, N. (2020). Estimating the generation interval for COVID-19 based on symptom onset data. MedRxiv, 2020.03.05.20031815.
- He, X., Lau, E. H. Y., Wu, P., Deng, X., Wang, J., Hao, X., … Leung, G. M. (2020). Temporal dynamics in viral shedding and transmissibility of COVID-19. Nature Medicine, 1–4.
- Tindale, L., Coombe, M., Stockdale, J. E., Garlock, E., Lau, W. Y. V., Saraswat, M., … Colijn, C. (2020). Transmission interval estimates suggest pre-symptomatic spread of COVID-19.MedRxiv, 2020.03.03.20029983.
- Xu, X.-K., Liu, X.-F., Wang, L., Taslim Ali, S., Du, Z., Bosetti, P., … Ka Shing, L. (2020). Household transmissions of SARS-CoV-2 in the time of unprecedented travel 1 lockdown in China 2 3. MedXriv.
- Zhang, J., Litvinova, M., Wang, W., Wang, Y., Deng, X., Chen, X., … Yu, H. (2020). Evolving epidemiology and transmission dynamics of coronavirus disease 2019 outside Hubei province, China: a descriptive and modelling study. The Lancet. Infectious Diseases
- Percentage change of Grocery and Pharmacy visits,
- Percentage change in Retail and recreation,
- Percentage change in Park visits
A further two were visualised on their own.
- Percentage change in transit station visits,
- Percentage change in workplace visit
- Dashboard interface – the flexdashboard package.
- Visualization – the plotly and ggplot2 packages
- Data manipulation – dplyr, tidyr, and purrr packages
- Estimation of the Time varying reproductive number- EpiEstim
Deployment and reproducibly
This dashboard has been deployed and maintained on the website for the Department of Social and Preventive Medicine, Faculty of Medicine, University Malaya. For any questions, feedback or request for the source code can be made by email to the department or by filling the attached feedback form herewith.
Last updated: Tuesday, 24th November 2020