CHARACTERISTICS AND COVID-19 INFECTION OUTCOME AMONG RURAL POPULATION IN NEGERI SEMBILAN, MALAYSIA

Coronavirus Disease 2019 (COVID-19) pandemic has still affected many countries around the world. While several studies have been published substantially regarding the risk factors of severe COVID-19 infection, less is known about the factors associated with COVID-19 infection among rural populations. This study aims to determine the characteristics and COVID-19 infection outcome in the rural population in Negeri Sembilan, Malaysia. Cross-sectional study was conducted in year 2021 among 3004 Laboratory-Confirmed COVID-19 cases in rural areas in Negeri Sembilan, Malaysia. The data was collected using proforma from list of COVID-19 cases in district health office and has been analyzed using SPSS version 25. Analysis shows that 884 (29.4%) out of 3004 Laboratory-Confirmed COVID-19 cases are classified as moderate-severe. The significant characteristics for moderate-severe COVID-19 infections were age (age 60 and above: aOR 2.00, 95% CI 1.474 – 2.715), gender (female: aOR 1.20, 95% CI 1.014-1.430), ethnicity (Chinese: aOR 3.09, 95% CI 1.746-5.454), employment status (employed: aOR 1.40, 95% CI 1.115-1.714), and chronic diseases (having chronic diseases: aOR 1.70, 95% CI 1.350-2.133). Early recognition of these characteristics among COVID-19 patients and prompt management of these cases might help to reduce COVID-19 severity in future. © 2022, Malaysian Journal of Public Health Medicine. All Rights Reserved.

Malaysian Enhanced Movement Control Order (EMCO): a unique and impactful approach to combating pandemic COVID-19

Purpose - The paper highlights the process-handling during the Enhanced Movement Control Order (EMCO) in combating pandemic COVID-19 in Malaysia. Design/methodology/approach - Malaysia first issued an EMCO following a cluster that involved a religious gathering. The EMCO was issued to lockdown the area, undertake screening, treat positive cases and quarantine their close contacts. Active case detection and mass sampling were the main activities involving the population in both zones. Findings - One hundred ninety-three confirmed COVID-19 cases were identified from the total population of 2,599. Of these cases, 99.5% were Malaysians, 31.7% were aged >60 years and all four deaths (Case Fatality Rate, 2.1%) were elderly people with comorbidities. One hundred and one cases (52.3%) were asymptomatic, of which 77 (77%) were detected during mass sampling. The risk factors contributing to the outbreak were contacts that had attended the religious gathering, regular mosque congregants, wedding ceremony attendees and close household contacts. Malaysia implemented an effective measure in the form of the EMCO to contain the COVID-19 outbreak, where the last cases were reported 16 days before the EMCO was lifted. Originality/value - The residents' compliance and inter-agency cooperation were essential elements to the success of the EMCO. A targeted approach using an EMCO should be implemented in a future pandemic.

Community-acquired Covid-19 Cluster among Non-Clinical Staff in a Teaching Hospital, Malaysia

A novel coronavirus, COVID-19 is responsible for the current outbreak of pneumonia. As of 16 September 2021, a total of 2,049,750 COVID-19 cases were recorded in Malaysia. In this study, we aimed to describe a cluster of COVID-19 cases among non-clinical staff in a teaching hospital in Kuala Lumpur Malaysia. We obtained epidemiological and clinical data from patients with confirmed COVID-19, via phone interviews and patient medical records. We performed chronological mapping of the cases to identify the possible period, mode of transmission, and cluster pattern. Three laboratory-confirmed COVID-19 cases were identified from the teaching hospital cluster. These three cases were linked epidemiologically to the religious gathering cluster in Malaysia. Seventy-four close contacts consisting of teaching hospital staff were quarantined. The median incubation period was four days (IQR 1-7). The need for intensified surveillance and a high level of alertness is vital to minimise the risk of widespread transmission in Malaysia. © 2022. IIUM Medical Journal Malaysia. All Rights Reserved.

Malaysia's Experience Navigating Colorectal Cancer Screening in the COVID-19 Pandemic and Opportunities to Build Back Better

The downstream effect of the pandemic on global cancer prevention and control efforts is wide-ranging, especially for lower and middle-income countries (LMICs), including Malaysia. This paper explores the performance of the colorectal cancer screening programme in Malaysia for the years 2019 and 2020, This is followed by evidence-based recommendations for building back a better cancer control programme in Malaysia. Malaysia screened a total of 31,529 eligible candidates in 2019 and 42,554 in 2020. A total of 2,668 (8.46%) and 2767 (6.50%) individuals tested positive for the immunochemical faecal occult blood test (iFOBT) in 2019 and 2020 respectively. Of these numbers, only 1454 (54.49%) of those who tested positive underwent colonoscopy in 2019 and this proportion reduced to 1148 (41.48%) in 2020. This analysis also shows a drop in the number of screenings in the second quarter of 2020. This drop coincides with the announcement of Malaysia's first Movement Control Order. Existing challenges exacerbated by pandemic restrictions have possibly led to a decreased colonoscopy attendance rate in 2020. To build back a better cancer control programme, better governance, and political will, coupled with improved financing, sustainable partnerships, improved service delivery, and a robust monitoring and evaluation mechanism is vital. © 2022 UPM Press. All rights reserved.

A Decision-Level Fusion Method for COVID-19 Patient Health Prediction

With the continuous attempts to develop effective machine learning methods, information fusion approaches play an important role in integrating data from multiple sources and improving these methods' performance. Among the different fusion techniques, decision-level fusion has unique advantages to fuse the decisions of various classifiers and getting an effective outcome. In this paper, we propose a decision-level fusion method that combines three well-calibrated ensemble classifiers, namely, a random forest (RF), gradient boosting (GB), and extreme gradient boosting (XGB) methods. It is used to predict the COVID-19 patient health for early monitoring and efficient treatment. A soft voting technique is used to generate the final decision result from the predictions of these calibrated classifiers. The method uses the COVID-19 patient's health information, travel demographic, and geographical data to predict the possible outcome of the COVID-19 case, recovered, or death. A different set of experiments is conducted on a public novel Corona Virus 2019 dataset using a different ratio of test sets. The experimental results show that the proposed fusion method achieved an accuracy of 97.24% and an F1-score of 0.97, which is higher than the current related work that has an accuracy of 94% and an F1-score 0.86, on 20% test set taken from the dataset. (C) 2021 Elsevier Inc. All rights reserved.

Impact of social distancing on covid-19 and other related infectious disease transmission: A systematic review

BACKGROUND: Similar to other coronaviruses, COVID-19 is transmitted mainly by droplets and is highly transmissible through close proximity or physical contact with an infected person. Countries across the globe have implemented public health control measures to prevent onward transmission and reduce burden on health care settings. Social or physical distancing was found to be one of appropriate measure based on previous experience with epidemic and pandemic contagious diseases. AIM: This study aims to review the latest evidence of the impact of social or physical distancing implemented during COVID-19 pandemic toward COVID-19 and other related infectious disease transmission. METHODOLOGY: The study uses PRISMA review protocol and formulation of research question was based on PICO. The selected databases include Ovid MEDLINE and Scopus. Thorough identification, screening and eligibility process were done, revealed selected 8 articles. The articles then ranked in quality through Mixed Method Appraisal Tool. RESULTS: A total of eight papers included in this analysis. Five studies (USA, Canada, South Korea and the United Kingdom) showed physical distancing had resulted in a reduction in Covid-19 transmission. In comparison, three other studies (Australia, South Korea and Finland) showed a similar decline on other infectious diseases (Human Immunodeficiency Virus, other sexually transmitted infections, influenza, respiratory syncytial virus, and Vaccine-Preventive Disease. The degree of the distancing policy implemented differs between strict and lenient, with both result in effectiveness in reducing transmission of infectious disease. CONCLUSION: Physical or social distancing may come in the form of extreme or lenient measure in effectively containing contagious disease such as COVID-19, however the stricter the measure will give more proportionate impact toward the economy, education, mental health issues, morbidity and mortality of non-COVID-19 diseases. Since we need this measure to ensure the reduction of infectious diseases transmission to help flattening the curve which allow much needed time for healthcare system to prepare adequately to response, “Precision physical distancing” can be implemented which will have more benefit toward the survival of the community as a whole. © 2021 Nor Rumaizah Mohd Nordin, Fadly Syah Arsad, Puteri Sofia Nadira Megat Kamaruddin, Muhammad Hilmi, Mohd Faizal Madrim, Mohd Rohaizat Hassan, Syed Sharizman Syed Abdul Rahim, Mohammad Saffree Jeffree, Abdul Rahman Ramdzan, Azman Atil, Khalid Mokti, Muhammad Aklil Abd. Rahim, Zulkhairul Naim Sidek Ahmad.

B5G-Enabled Distributed Artificial Intelligence on Edges for COVID-19 Pandemic Outbreak Prediction

In this study, we leverage the fusion of edge computing, artificial intelligence (AI) methods, and facilities provided by B5G to build a heterogeneous set of AI techniques for COVID-19 outbreak prediction. Advancement in the areas of AI, edge computing, the Internet of Things (IoT), and fast communication networks provided by beyond 5G (B5G) networks has opened doors for new possibilities by fusing these technologies and techniques. In a pandemic outbreak, such as COVID-19, the need for rapid analysis, decision making, and prediction of future trends becomes paramount. On a global map, the distributed processing and analysis of data at the source is now possible and much more efficient. With the features provided by B5G, such as low latency, larger area coverage, higher data rate, and realtime communication, building new intelligent and efficient frameworks is becoming easier. In this study, our aim is to achieve higher accuracy in prediction by fusing multiple AI methods and leveraging the B5G communication architecture. We propose a distributed architecture for training AI methods on edge devices, with the results of edge-trained models then propagated to a central cloud AI method, which then combines all the received edge-trained models into a global and final prediction model. The experimental results of five countries (United States, India, Italy, Bangladesh, and Saudi Arabia) show that the proposed distributed AI on edges can predict COVID-19 outbreak better than that of each individual AI method in terms of correlation coefficient scores.

Managing COVID-19 pandemic in teaching hospitals: An early preparedness setting

The World Health Organization (WHO) declared the 2019-20 coronavirus disease (COVID-19) outbreak a pandemic on 11th of March 2020. The Ministry of Health, Malaysia has made preparations for the involvement of all government hospitals, including some teaching hospitals. This report elaborates and discusses the early establishment of the Hospital Canselor Tuanku Muhriz Crisis Preparedness and Response Centre (HCTM CPRC), highlighting how teaching hospitals function in handling the clinical and epidemiological management of COVID-19 among hospital staff. The setting comprises of four critical functions of the HCTM CPRC, namely case investigation, close contact tracing, surveillance for data reporting and risk communication. This report highlighted that a CPRC in teaching hospitals benefits not only the patients and the hospital administration but also all hospital staff, especially in managing COVID-19 pandemic emergency crisis. © 2021 Penerbit Universiti Kebangsaan Malaysia. All rights reserved.

Epidemiological characteristics of covid-19 in Seremban, Negeri Sembilan, Malaysia

BACKGROUND: Coronavirus disease (COVID)-19 has become a global pandemic with an increasing burden on healthcare. Early recognition of the trend and pattern of the chain of transmission is necessary to slow down the spread. AIM: Therefore, the study aimed to describe the epidemiology of COVID-19 at a local setting. METHODS: A retrospective cross-sectional study was done to all COVID-19 cases registered in Seremban Health District. Statistical analysis, using Chi-square test, was employed to compare the sociodemographic characteristic of COVID-19 patients between the red zone area and the non-red zone area in Seremban. RESULTS: As of April, a total of 214 number of COVID-19 cases reported in Seremban district alone. The trend of cases registered has changed as more asymptomatic infection outnumbered patients with clinical symptoms from the aggressive active case detection (ACD) activity. Majority of the cases affecting Malay ethnicity were due to a large religious gathering event held 1 month earlier than subsequently spread the infection within the community. CONCLUSION: The first wave of COVID-19 cases in Seremban was sudden and unexpected, with a skewed distribution affecting a particular race group regardless red zone area and non-red zone area. Therefore, identifying the pattern of infection in the local community is important for a focused intervention strategy. ACD strategy, isolation of patients, quarantine the exposed, tracking down the close contact, and continuous health promotion and education will ultimately break the chain of transmission.

Mitigation strategies to fight the COVID-19 pandemic—present, future and beyond

Purpose: The latest novel coronavirus disease 2019 (COVID-19) pandemic continues to have a significant social and financial impact globally. It is very essential to study, categorize and systematize published research on mitigation strategies adopted during previous pandemic scenario that could provide an insight into improving the current crisis. The goal of this paper is to systematize and identify gaps in previous research and suggest potential recommendations as a conceptual framework from a strategic point of view. Design/methodology/approach: A systematic review of Scopus and Web of Science (WoS) core collection databases was performed based on strict keyword search selections followed by a bibliometric meta-analysis of the final dataset. Findings: This study indicated that the traditional mitigation techniques adopted during past pandemics are in place but are not capable of managing the transmission capability and virulence of COVID-19. There is a greater need for rethinking and re-engineering short and long-term approaches to prevent, control and contain the current pandemic situation. Practical implications: Integrating various mitigation approaches shall assist in flattening the pandemic curve and help in the long run. Originality/value: Articles, conference proceedings, books, book chapters and other references from two extensive databases (Scopus and WoS) were purposively considered for this study. The search was confined to the selected keywords outlined in the methodology section of this paper. © 2020, Ahmed Zainul Abideen, Fazeeda Binti Mohamad and Mohd Rohaizat Hassan.