ABSTRACT
COVID-19 is a global public health problem that causes severe acute respiratory syndrome (SARS). It is also extremely contagious with rapidly increasing death rates. In this paper, we propose an optimal control model with SIRS (Susceptible-Infected-Recovered-Susceptible) kinetics to examine the effects of several intervention measures (e.g., vaccination and treatment) under the limited medical resources scenarios. This model is also employed to investigate the possibility of reinfection because of the fading of immunity problem. As a case study, the modeling framework is parametrised using COVID-19 daily confirmed and recovered cases in Malaysia. The parameters have been approximated by relying on the model's best fit to actual data published by the Malaysian Ministry of Health (MOH). Our numerical simulation results show that the inclusion of optimal control components with vaccination and treatment strategies would dramatically reduce the number of active cases even in the presence of reinfection forces. Regardless of the relative weightage (or costs) of vaccination and treatment, as well as the possibility of reinfection, it is critical to plan effective COVID-19 control measures by vaccinating as many people as possible (and as early as possible). Overall, these insights help explore the importance of intervention measures and the allocation of medical resources to control the severity of this pandemic.
ABSTRACT
Objective: The rising burden of stroke in Malaysia has prompted attention on the primary caregivers of stroke survivors. Caregiving burden following stroke is a significant health care concern, however, the assessment of caregiving burden is understudied in Malaysia. This study aims to measure the informal stroke caregivers' burden one month after the patients were discharged from the stroke care. Methods: Acute stroke survivors and their informal caregivers were recruited from three major hospitals in the northeast of Peninsula Malaysia. To minimize the risk of direct contact during the COVID-19 pandemic, the caregivers who fulfilled study criteria were interviewed by phone. Information obtained included caregiver's demographic, self-reported caregiver burden measured using two commonly used tools, namely the Malay version Zarit Burden Interview (ZBI) and The Malay version Caregiver Assessment of Function and Upset (CAFU). ZBI consists of 22 questions and CAFU has a 15-item multidimensional measure of dependence. We measured ZBI and CAFU scores twice, one at baseline (post-discharge) and another one month later. The scores were presented as mean and standard deviation (SD). Results: A total of 93 informal caregivers were recruited with 66% of them being female. Generally, the caregiving burden was reduced from discharge (baseline) to one-month post-discharge. The overall mean (SD) scores for ZBI reduced from 23.7 (SD=14.45) to 19.9 (SD=12.67). The overall mean (SD) for CAFU dependency scores reduced from 43.5 (SD=12.65) to 39.4 (SD=15.19), for CAFU upset scores reduced from 5.3 (SD=7.69) to 3.9 (SD=5.47). Between male and female caregiver, the mean ZBI reduced for 13.2% and 23.1% while the CAFU dependency score reduced from 41.7 to 39.4 and 46.4 to 39.4 respectively. Conclusion: The burden felt by the caregivers was initially high but reduced significantly even after 1- month. Any psychosocial support or intervention aimed to ease caregiver burden should be started early.
ABSTRACT
COVID-19 is a major health threat across the globe, which causes severe acute respiratory syndrome, and it is highly contagious with significant morbidity and mortality. In this paper, we examine the feasibility and implications of several phases of Movement Control Order (MCO) and some non-pharmaceutical intervention (NPI) strategies implemented by Malaysian government in the year 2020 using a mathematical model with SIR-neural network approaches. It is observed that this model is able to mimic the trend of infection trajectories of COVID-19 pandemic and, Malaysia had succeeded to flatten the infection curve at the end of the Conditional MCO (CMCO) period. However, the signs of 'flattening' with R-0 of less than one had been taken as a signal to ease up on some restrictions enforced before. Though the government has made compulsory the use of face masks in public places to control the spread of COVID-19, we observe a contrasting finding from our model with regards to the impacts of wearing mask policies in Malaysia on R0 and the infection curve. Additionally, other events such as the Sabah State Election at the end of third quarter of 2020 has also imposed a dramatic COVID-19 burden on the society and the healthcare systems.