Materials and methods: Multiple interviews and an entomological survey were conducted to elucidate the possible mechanism of infection in this patient.
Results: Findings point to locally-transmitted malaria, likely introduced by a patient from an endemic region in Tawau. Transmission via this route is rare, and has never before been reported in our setting.
Conclusions: This rare case highlights the need for constant vigilance in malaria control and elimination, especially when the target of country-wide elimination is close.
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 100,000 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 1,000 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.0000245/HGA.GV).
METHODS: COVID-19 data on cases, deaths, testing, and vaccinations were extracted from the Our World in Data (OWID) COVID-19 data repository for all the ten ASEAN countries. Comparative time-trends of the epidemiology of COVID-19 using the incidence rate, cumulative case fatality rate (CFR), delay-adjusted case fatality rate, cumulative mortality rate (MR), test positivity rate (TPR), cumulative testing rate (TR) and vaccination rate was carried out.
RESULTS: Over the study period, a total of 12,720,661 cases and 271,475 deaths was reported within the ASEAN region. Trends of daily per capita cases were observed to peak between July and September 2021 for the ASEAN region. The cumulative case fatality rate (CFR) in Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, and Vietnam, was of 0.9% (N=68), 2.2% (N=2,610), 3.5% (N=142,889), 0.1% (N=36), 1.2% (N=27,700), 4.0% (N=18,297), 1.6% (N=40,424), 0.1% (N=215), 1.7% (N=18,123), and 2.6% (N=21,043), respectively. CFR was consistently highest between January-June 2020. The cumulative mortality rate (MR) was 9.5, 13.7, 51.4, 0.2, 80.3, 32.4, 34.5, 1.6, 23.9 and 19.7 per 100,000 population, respectively. The cumulative test positivity rate (TPR) was 8.4%, 16.9%, 4.6%, 7.5%, 11.1%, 12.9%, 0.5%, 11.7%, and 3.6%, with the cumulative testing rate (TR) at 25.0, 90.1, 27.4, 917.7, 75.8, 177.8, 3303.3, 195.2, and 224.9 tests per 1,000 population in Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, and Vietnam, respectively. The percentage of population that completed vaccinations (VR) was 44.5%, 65.3%, 18.5%, 28.2%, 61.8%, 6.8%, 19.2%, 76.8%, 22.7%, and 10% in Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, and Vietnam, respectively.
CONCLUSION: In 2020, most countries in ASEAN had higher case fatality rates but lower mortalities per population when compared to the third quarter of 2021 where higher mortalities per population were observed. Low testing rates have been one of the factors leading to high test positivity rates. Slow initiation of vaccination programs was found to be the key factor leading to high incidence and case fatality rate in most countries in ASEAN. Effective public health measures were able to interrupt the transmission of this novel virus to some extent. Increasing preparedness capacity within the ASEAN region is critical to ensure that any future similar outbreaks can be dealt with collectively.
METHODS: This study proposed a deterministic, compartmental model with contact tracing and vaccination components. We defined contact tracing effectiveness as the proportion of contacts of a positive case that was successfully traced and the vaccination rate as the proportion of daily doses administered per population in Malaysia. Sensitivity analyses on the untraced and infectious populations were conducted.
RESULTS: At a vaccination rate of 1.4%, contact tracing with an effectiveness of 70% could delay the peak of untraced asymptomatic cases by 17 d and reduce it by 70% compared with 30% contact tracing effectiveness. A similar trend was observed for symptomatic cases when a similar experiment setting was used. We also performed sensitivity analyses by using different combinations of contact tracing effectiveness and vaccination rates. In all scenarios, the effect of contact tracing on COVID-19 incidence persisted for both asymptomatic and symptomatic cases.
CONCLUSIONS: While vaccines are progressively rolled out, efficient contact tracing must be rapidly implemented concurrently to reach, find, test, isolate and support the affected populations to bring COVID-19 under control.
METHODS: A five-tiered workflow of data acquisition; processing; databasing, sharing, version control; visualisation; and monitoring was used. COVID-19 data were initially collated from press releases and then transitioned to official sources.
RESULTS: Key COVID-19 indicators were tabulated and visualised, deployed using open-source hosting in October 2022. The system demonstrated high performance, handling extensive data volumes, with a 92.5% user conversion rate, evidencing its value and adaptability.
CONCLUSION: This cost-effective, scalable solution aids health specialists and authorities in tracking disease burden, particularly in low-resource settings. Such innovations are critical in health crises like COVID-19 and adaptable to diverse health scenarios.
PATIENTS AND METHODS: The dataset encompassed patient data from a tertiary cardiothoracic center in Malaysia between 2011 and 2015, sourced from electronic health records. Extensive preprocessing and feature selection ensured data quality and relevance. Four machine learning algorithms were applied: Logistic Regression, Gradient Boosted Trees, Support Vector Machine, and Random Forest. The dataset was split into training and validation sets and the hyperparameters were tuned. Accuracy, Area Under the ROC Curve (AUC), precision, F-measure, sensitivity, and specificity were some of the evaluation criteria. Ethical guidelines for data use and patient privacy were rigorously followed throughout the study.
RESULTS: With the highest accuracy (88.66%), AUC (94.61%), and sensitivity (91.30%), Gradient Boosted Trees emerged as the top performance. Random Forest displayed strong AUC (94.78%) and accuracy (87.39%). In contrast, the Support Vector Machine showed higher sensitivity (98.57%) with lower specificity (59.55%), but lower accuracy (79.02%) and precision (70.81%). Sensitivity (87.70%) and specificity (87.05%) were maintained in balance via Logistic Regression.
CONCLUSION: These findings imply that Gradient Boosted Trees and Random Forest might be an effective method for identifying patients who would develop AKI following heart surgery. However specific goals, sensitivity/specificity trade-offs, and consideration of the practical ramifications should all be considered when choosing an algorithm.
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.
METHODS: This study uses a mixed methods design. It focuses primarily on qualitative data to understand processes and strategies and to identify specific areas that can be improved through stakeholder engagement in the screening program. Quantitative data play a dual role in supporting the selection of participants for the qualitative study based on program monitoring data and assessing inequalities in screening and program implementation in healthcare facilities in Malaysia. Meanwhile, literature review identifies existing strategies to improve colorectal cancer screening. Additionally, the knowledge-to-action framework is integrated to ensure that the research findings lead to practical improvements to the colorectal cancer screening program.
DISCUSSION: Through this complex mix of qualitative and quantitative methods, this study will explore the complex interplay of population- and systems-level factors that influence screening rates. It involves identifying barriers to effective colorectal cancer screening in Malaysia, comparing current strategies with international best practices, and providing evidence-based recommendations to improve the local screening program.