OBJECTIVE: This paper presents a rescue framework for the transfusion of the best CP to the most critical patients with COVID-19 on the basis of biological requirements by using machine learning and novel MCDM methods.
METHOD: The proposed framework is illustrated on the basis of two distinct and consecutive phases (i.e. testing and development). In testing, ABO compatibility is assessed after classifying donors into the four blood types, namely, A, B, AB and O, to indicate the suitability and safety of plasma for administration in order to refine the CP tested list repository. The development phase includes patient and donor sides. In the patient side, prioritisation is performed using a contracted patient decision matrix constructed between 'serological/protein biomarkers and the ratio of the partial pressure of oxygen in arterial blood to fractional inspired oxygen criteria' and 'patient list based on novel MCDM method known as subjective and objective decision by opinion score method'. Then, the patients with the most urgent need are classified into the four blood types and matched with a tested CP list from the test phase in the donor side. Thereafter, the prioritisation of CP tested list is performed using the contracted CP decision matrix.
RESULT: An intelligence-integrated concept is proposed to identify the most appropriate CP for corresponding prioritised patients with COVID-19 to help doctors hasten treatments.
DISCUSSION: The proposed framework implies the benefits of providing effective care and prevention of the extremely rapidly spreading COVID-19 from affecting patients and the medical sector.
METHODS: Electronic databases and country-specific healthcare databases were searched to identify relevant studies/reports. The quality assessment of individual studies was conducted using the Newcastle-Ottawa Scale. Country-specific proportion of individuals with COVID-19 who developed ARDS and reported death were combined in a random-effect meta-analysis to give a pooled mortality estimate of ARDS.
RESULTS: The overall pooled mortality estimate among 10,815 ARDS cases in COVID-19 patients was 39% (95% CI: 23-56%). The pooled mortality estimate for China was 69% (95% CI: 67-72%). In Europe, the highest mortality estimate among COVID-19 patients with ARDS was reported in Poland (73%; 95% CI: 58-86%) while Germany had the lowest mortality estimate (13%; 95% CI: 2-29%) among COVID-19 patients with ARDS. The median crude mortality rate of COVID-19 patients with reported corticosteroid use was 28.0% (lower quartile: 13.9%; upper quartile: 53.6%).
CONCLUSIONS: The high mortality in COVID-19 associated ARDS necessitates a prompt and aggressive treatment strategy which includes corticosteroids. Most of the studies included no information on the dosing regimen of corticosteroid therapy, however, low-dose corticosteroid therapy or pulse corticosteroid therapy appears to have a beneficial role in the management of severely ill COVID-19 patients.
METHOD: A systematic review and metanalysis was conducted in accordance with the PRISMA criteria. The PubMed, Scopus, Science direct, Web of science, CINHAL, Medline, and Google Scholar databases were searched with no lower time-limt and until 24 June 2020. The heterogeneity of the studies was measured using I2 test and the publication bias was assessed by the Egger's test at the significance level of 0.05.
RESULTS: The I2 test was used to evaluate the heterogeneity of the selected studies, based on the results of I2 test, the prevalence of sleep disturbances in nurses and physicians is I2: 97.4% and I2: 97.3% respectively. After following the systematic review processes, 7 cross-sectional studies were selected for meta-analysis. Six studies with the sample size of 3745 nurses were examined in and the prevalence of sleep disturbances was approximated to be 34.8% (95% CI: 24.8-46.4%). The prevalence of sleep disturbances in physicians was also measured in 5 studies with the sample size of 2123 physicians. According to the results, the prevalence of sleep disturbances in physicians caring for the COVID-19 patients was reported to be 41.6% (95% CI: 27.7-57%).
CONCLUSION: Healthcare workers, as the front line of the fight against COVID-19, are more vulnerable to the harmful effects of this disease than other groups in society. Increasing workplace stress increases sleep disturbances in the medical staff, especially nurses and physicians. In other words, increased stress due to the exposure to COVID-19 increases the prevalence of sleep disturbances in nurses and physicians. Therefore, it is important for health policymakers to provide solutions and interventions to reduce the workplace stress and pressures on medical staff.
OBJECTIVE: Thus, this study aimed to evaluate their perception of face mask wearing during COVID-19 and its contributing factors.
METHODOLOGY: A total of 1024 respondents, aged ≥ 18 years, participated in this online cross-sectional survey from October 2021 to December 2021. The Face Mask Perception Scale (FMPS) was used to measure their perceptions.
RESULTS: Most of the respondents perceived wearing a face mask as uncomfortable. Our findings also revealed statistically significant differences and a small effect (f2 = 0.04) in which respondents who were concerned about being infected by the virus perceived face mask wearing appearance positively (B = - 0.09 units of log-transformed, 95% CI = - 0.15, - 0.04), whereas married respondents perceived it negatively (B = 0.07 units of log-transformed, 95% CI = 0.03, 0.09). There were no statistically significant differences in other domains of FMPS.
CONCLUSION: In conclusion, discomfort was a major complaint. Marital status and fear of COVID-19 infection affected their perceptions. The public health implications of these findings highlight the importance of addressing discomfort and societal perceptions, particularly those influenced by factors such as marital status and COVID-19 experience, to promote widespread acceptance and consistent usage of face masks, which is crucial in mitigating the spread of COVID-19.
OUTBREAK SITUATION: A stringent screening process at all airports in Malaysia was enforced after the first case outside China was reported in Thailand. Up to April 14, 2020, Malaysia had reported two waves of COVID-19 cases, with the first wave ending successfully within less than 2 months. In early March 2020, the second wave occurred, with worrying situations.
ACTIONS TAKEN: The Government of Malaysia enforced a Movement Control Order starting on March 18, 2020 to break the chain of COVID-19. The media actively spread the hashtag #stayhome. Non-governmental organizations, as well as prison inmates, started to produce personal protective equipment for frontliners. Various organizations hosted fundraising events to provide essentials mainly to hospitals. A provisional hospital was set up and collaborations with healthcare service providers were granted, while additional laboratories were assigned to enhance the capabilities of the Ministry of Health.
ECONOMIC DOWNTURN: An initial financial stimulus amounting to RM 20.0 billion was released in February 2020, before the highlighted PRIHATIN Package, amounting to RM 250 billion, was announced. The PRIHATIN Package has provided governmental support to society, covering people of various backgrounds from students and families to business owners.
METHODOLOGY: This study was conducted using daily confirmed cases of COVID-19 collected from the official Ministry of Health, Malaysia (MOH) and John Hopkins University websites. An Autoregressive Integrated Moving Average (ARIMA) model was fitted to the training data of observed cases from 22 January to 31 March 2020, and subsequently validated using data on cases from 1 April to 17 April 2020. The ARIMA model satisfactorily forecasted the daily confirmed COVID-19 cases from 18 April 2020 to 1 May 2020 (the testing phase).
RESULTS: The ARIMA (0,1,0) model produced the best fit to the observed data with a Mean Absolute Percentage Error (MAPE) value of 16.01 and a Bayes Information Criteria (BIC) value of 4.170. The forecasted values showed a downward trend of COVID-19 cases until 1 May 2020. Observed cases during the forecast period were accurately predicted and were placed within the prediction intervals generated by the fitted model.
CONCLUSIONS: This study finds that ARIMA models with optimally selected covariates are useful tools for monitoring and predicting trends of COVID-19 cases in Malaysia.