Malaysia is currently facing an outbreak of COVID-19. We aim to present the first study in Malaysia to report the reproduction numbers and develop a mathematical model forecasting COVID-19 transmission by including isolation, quarantine, and movement control measures. We utilized a susceptible, exposed, infectious, and recovered (SEIR) model by incorporating isolation, quarantine, and movement control order (MCO) taken in Malaysia. The simulations were fitted into the Malaysian COVID-19 active case numbers, allowing approximation of parameters consisting of probability of transmission per contact (β), average number of contacts per day per case (ζ), and proportion of close-contact traced per day (q). The effective reproduction number (Rt) was also determined through this model. Our model calibration estimated that (β), (ζ), and (q) were 0.052, 25 persons, and 0.23, respectively. The (Rt) was estimated to be 1.68. MCO measures reduce the peak number of active COVID-19 cases by 99.1% and reduce (ζ) from 25 (pre-MCO) to 7 (during MCO). The flattening of the epidemic curve was also observed with the implementation of these control measures. We conclude that isolation, quarantine, and MCO measures are essential to break the transmission of COVID-19 in Malaysia.

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Background: COVID-19 has rapidly spread across the globe. Critical to the control of COVID-19 is the characterisation of its epidemiology. Despite this, there has been a paucity of evidence from many parts of the world, including Malaysia. We aim to describe the epidemiology of COVID-19 in Malaysia to inform prevention and control policies better. Methods: Malaysian COVID-19 data was extracted from 16 March 2020 up to 31 May 2021. We estimated the following epidemiological indicators: 7-day incidence rates, 7-day mortality rates, case fatality rates, test positive ratios, testing rates and the time-varying reproduction number (Rt). Findings: Between 16 March 2020 and 31 May 2021, Malaysia has reported 571,901 cases and 2,796 deaths. Malaysia's average 7-day incidence rate was 26 • 6 reported infections per 10 0,0 0 0 population (95% CI: 17 • 8, 38 • 1). The average test positive ratio and testing rate were 4 • 3% (95% CI: 1 • 6, 10 • 2) and 0 • 8 tests per 1,0 0 0 population (95% CI: < 0 • 1, 3 • 7), respectively. The case fatality rates (CFR) was 0 • 6% (95% CI: < 0 • 1, 3 • 7). Among the 2,796 cases who died, 87 • 3% were ≥ 50 years. Interpretation: The public health response was successful in the suppression of COVID-19 transmission or the first half of 2020. However, a state election and outbreaks in institutionalised populations have been the catalyst for more significant community propagation. This rising community transmission has continued in 2021, leading to increased incidence and strained healthcare systems. Calibrating NPI based on epidemiological indicators remain critical for us to live with the virus. (243 words) Funding: This study is part of the COVID-19 Epidemiological Analysis and Strategies (CEASe) Project with funding from the Ministry of Science, Technology and Innovation (UM.0 0 0 0245/HGA.GV).

Introduction The reporting of Coronavirus Disease 19 (COVID-19) mortality among healthcare workers highlights their vulnerability in managing the COVID-19 pandemic. Some low- and middle-income countries have highlighted the challenges with COVID-19 testing, such as inadequate capacity, untrained laboratory personnel, and inadequate funding. This article describes the components and implementation of a healthcare worker surveillance programme in a designated COVID-19 teaching hospital in Malaysia. In addition, the distribution and characteristics of healthcare workers placed under surveillance are described. Material and methods A COVID-19 healthcare worker surveillance programme was implemented in University Malaya Medical Centre. The programme involved four teams: contact tracing, risk assessment, surveillance and outbreak investigation. Daily symptom surveillance was conducted over fourteen days for healthcare workers who were assessed to have low-, moderate- and high-risk of contracting COVID-19. A cross-sectional analysis was conducted for data collected over 24 weeks, from the 6th of March 2020 to the 20th of August 2020. Results A total of 1,174 healthcare workers were placed under surveillance. The majority were females (71.6%), aged between 25 and 34 years old (64.7%), were nursing staff (46.9%) and had no comorbidities (88.8%). A total of 70.9% were categorised as low-risk, 25.7% were moderate-risk, and 3.4% were at high risk of contracting COVID-19. One-third (35.2%) were symptomatic, with the sore throat (23.6%), cough (19.8%) and fever (5.0%) being the most commonly reported symptoms. A total of 17 healthcare workers tested positive for COVID-19, with a prevalence of 0.3% among all the healthcare workers. Risk category and presence of symptoms were associated with a positive COVID-19 test (p<0.001). Fever (p<0.001), cough (p = 0.003), shortness of breath (p = 0.015) and sore throat (p = 0.002) were associated with case positivity. Conclusion COVID-19 symptom surveillance and risk-based assessment have merits to be included in a healthcare worker surveillance programme to safeguard the health of the workforce.

DISCLAIMER This paper was submitted to the Bulletin of the World Health Organization and was posted to the COVID-19 open site, according to the protocol for public health emergencies for international concern as described in Vasee Moorthy et al. (

Dengue is an increasing threat in Malaysia, particularly in the more densely populated regions of the country. We present an Artificial Intelligence driven model in predicting Aedes outbreak, using predictors of weather variables and vector indices sourced from the Ministry of Health. Analysis and predictions to estimate Aedes populations were conducted, with its results being used to infer the possibility of dengue outbreaks at pre-determined localities around the Klang Valley, Malaysia. A Bayesian Network machine learning technique was employed, with the model being trained using predictor variables such as temperature, rainfall, date of onset and notification, and vector indices such as the Ae. albopictus count, Ae. aegypti count and larval count. The interfaces of the system were developed using the C# language for Server-side configuration and programming, and HTML, CSS and JavaScript for the Client Side programming. The model was then used to predict the population of Aedes at periods of 7, 14, and 30 days. Using the Bayesian Network technique utilising the above predictor variables we proposed a finalised model with predictive accuracy ranging from 79%-84%. This model was developed into a Graphical User Interface, which was purposed to assist and educate the general public of regions at risk of developing dengue outbreak. This remains a valuable case-study on the importance of public data in the context of combating a public health risk via the development of models for predicting outbreaks of dengue which will hopefully spur further sharing of data by all parties in combating public health threats.

Background Hospitals are vulnerable to COVID-19 outbreaks. Intrahospital transmission of the disease is a threat to the healthcare systems as it increases morbidity and mortality among patients. It is imperative to deepen our understanding of transmission events in hospital-associated cases of COVID-19 for timely implementation of infection prevention and control measures in the hospital in avoiding future outbreaks. We examined the use of epidemiological case investigation combined with whole genome sequencing of cases to investigate and manage a hospital-associated cluster of COVID-19 cases. Methods An epidemiological investigation was conducted in a University Hospital in Malaysia from 23 March to 22 April 2020. Contact tracing, risk assessment, testing, symptom surveillance, and outbreak management were conducted following the diagnosis of a healthcare worker with SARS-CoV-2 by real-time PCR. These findings were complemented by whole genome sequencing analysis of a subset of positive cases. Results The index case was symptomatic but did not fulfill the initial epidemiological criteria for routine screening. Contact tracing suggested epidemiological linkages of 38 cases with COVID-19. Phylogenetic analysis excluded four of these cases. This cluster included 34 cases comprising ten healthcare worker-cases, nine patient-cases, and 15 community-cases. The epidemic curve demonstrated initial intrahospital transmission that propagated into the community. The estimated median incubation period was 4.7 days (95% CI: 3.5–6.4), and the serial interval was 5.3 days (95% CI: 4.3–6.5). Conclusion The study demonstrated the contribution of integrating epidemiological investigation and whole genome sequencing in understanding disease transmission in the hospital setting. Contact tracing, risk assessment, testing, and symptom surveillance remain imperative in resource-limited settings to identify and isolate cases, thereby controlling COVID-19 outbreaks. The use of whole genome sequencing complements field investigation findings in clarifying transmission networks. The safety of a hospital population during this COVID-19 pandemic may be secured with a multidisciplinary approach, good infection control measures, effective preparedness and response plan, and individual-level compliance among the hospital population.

Malaysia has reported 2.75 million cases and 31,485 deaths as of 30 December 2021. Underestimation remains an issue due to the underdiagnosis of mild and asymptomatic cases. We aimed to estimate the burden of COVID-19 cases in Malaysia based on an adjusted case fatality rate (aCFR). Data on reported cases and mortalities were collated from the Ministry of Health official GitHub between 1 March 2020 and 30 December 2021. We estimated the total and age-stratified monthly incidence rates, mortality rates, and aCFR. Estimated new infections were inferred from the age-stratified aCFR. The total estimated infections between 1 March 2020 and 30 December 2021 was 9,955,000-cases (95% CI: 6,626,000–18,985,000). The proportion of COVID-19 infections in ages 0–11, 12–17, 18–50, 51–65, and above 65 years were 19.9% (n = 1,982,000), 2.4% (n = 236,000), 66.1% (n = 6,577,000), 9.1% (n = 901,000), 2.6% (n = 256,000), respectively. Approximately 32.8% of the total population in Malaysia was estimated to have been infected with COVID-19 by the end of December 2021. These estimations highlight a more accurate infection burden in Malaysia. It provides the first national-level prevalence estimates in Malaysia that adjusted for underdiagnosis. Naturally acquired community immunity has increased, but approximately 68.1% of the population remains susceptible. Population estimates of the infection burden are critical to determine the need for booster doses and calibration of public health measures.