OBJECTIVE: This paper aimed to describe the development process of the COVID-19 Symptom Monitoring System (CoSMoS), which consists of a self-monitoring, algorithm-based Telegram bot and a teleconsultation system. We describe all the essential steps from the clinical perspective and our technical approach in designing, developing, and integrating the system into clinical practice during the COVID-19 pandemic as well as lessons learned from this development process.
METHODS: CoSMoS was developed in three phases: (1) requirement formation to identify clinical problems and to draft the clinical algorithm, (2) development testing iteration using the agile software development method, and (3) integration into clinical practice to design an effective clinical workflow using repeated simulations and role-playing.
RESULTS: We completed the development of CoSMoS in 19 days. In Phase 1 (ie, requirement formation), we identified three main functions: a daily automated reminder system for patients to self-check their symptoms, a safe patient risk assessment to guide patients in clinical decision making, and an active telemonitoring system with real-time phone consultations. The system architecture of CoSMoS involved five components: Telegram instant messaging, a clinician dashboard, system administration (ie, back end), a database, and development and operations infrastructure. The integration of CoSMoS into clinical practice involved the consideration of COVID-19 infectivity and patient safety.
CONCLUSIONS: This study demonstrated that developing a COVID-19 symptom monitoring system within a short time during a pandemic is feasible using the agile development method. Time factors and communication between the technical and clinical teams were the main challenges in the development process. The development process and lessons learned from this study can guide the future development of digital monitoring systems during the next pandemic, especially in developing countries.
OBJECTIVE: Therefore, this research aims to create a flexible mental health care architecture that leverages data-driven methodologies and ensemble machine learning models. The objective is to proficiently structure, process, and present data for positive computing. The adaptive data-driven architecture facilitates customized interventions for diverse mental disorders, fostering positive computing. Consequently, improved mental health care outcomes and enhanced accessibility for individuals with varied mental health conditions are anticipated.
METHOD: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, the researchers conducted a systematic literature review in databases indexed in Web of Science to identify the existing strengths and limitations of software architecture relevant to our adaptive design. The systematic review was registered in PROSPERO (CRD42023444661). Additionally, a mapping process was employed to derive essential paradigms serving as the foundation for the research architectural design. To validate the architecture based on its features, professional experts utilized a Likert scale.
RESULTS: Through the review, the authors identified six fundamental paradigms crucial for designing architecture. Leveraging these paradigms, the authors crafted an adaptive data-driven architecture, subsequently validated by professional experts. The validation resulted in a mean score exceeding four for each evaluated feature, confirming the architecture's effectiveness. To further assess the architecture's practical application, a prototype architecture for predicting pandemic anxiety was developed.
METHODS: We downloaded COVID-19 outbreak data of the number of confirmed cases in all countries as of October 19, 2020. The IRT-based predictive model was built to determine the pandemic IP for each country. A model building scheme was demonstrated to fit the number of cumulative infected cases. Model parameters were estimated using the Solver add-in tool in Microsoft Excel. The absolute advantage coefficient (AAC) was computed to track the IP at the minimum of incremental points on a given ogive curve. The time-to-event analysis (a.k.a. survival analysis) was performed to compare the difference in IPs among continents using the area under the curve (AUC) and the respective 95% confidence intervals (CIs). An online comparative dashboard was created on Google Maps to present the epidemic prediction for each country.
RESULTS: The top 3 countries that were hit severely by COVID-19 were France, Malaysia, and Nepal, with IP days at 263, 262, and 262, respectively. The top 3 continents that were hit most based on IP days were Europe, South America, and North America, with their AUCs and 95% CIs at 0.73 (0.61-0.86), 0.58 (0.31-0.84), and 0.54 (0.44-0.64), respectively. An online time-event result was demonstrated and shown on Google Maps, comparing the IP probabilities across continents.
CONCLUSION: An IRT modeling scheme fitting the epidemic data was used to predict the length of IP days. Europe, particularly France, was hit seriously by COVID-19 based on the IP days. The IRT model incorporated with AAC is recommended to determine the pandemic IP.
METHODOLOGY: A retrospective cross-sectional study was employed to identify patients with positive AR bacteria between March 2019 and March 2022. The bacterial isolates and patients' data were identified from laboratory and medical records departments retrospectively. Binary logistic regression analysis was performed to identify the factors associated with AR and deaths. Multinominal logistic regression was applied to confirm the factors associated with AR classification.
RESULTS: AR Gram-negative bacteria decreased during and after the pandemic. However, S. aureus showed a negligible increase in resistance rate after pandemic, while E. faecium, recorded a higher-than-average resistance rate during the pandemic. The prevalence of pan drug resistance (PDR) during the pandemic (85.7%) was higher than before (0%) and after (14.3%), p = 0.001. The length of stay and time were significant predictors for AR classification. The odds of multi drug resistance (MDR) development to PDR during the pandemic were 6 times higher than before and after (OR = 6.133, CI =, p = 0.020). Age, nationality, COVID-19 infection, smoking, liver disease, and type and number of bacteria were associated with death of patients with positive AR.
CONCLUSIONS: Further studies are recommended to explore the prevalence of PDR and to justify the increased rates of E. faecium AR during the COVID-19 pandemic.
METHODS: A descriptive cross-sectional study was conducted on hospitalised COVID-19 patients from April 2021 to June 2021 in a tertiary care centre. Ethical approval was taken from the Institutional Review Committee (Reference number: 2078/79/05). The hospital data were collected in the proforma by reviewing the patient's medical records during the study period of 2 months. Convenience sampling was used. Point estimate and 95% Confidence Interval were calculated.
RESULTS: Among 106 hospitalised COVID-19 patients, the prevalence of antibiotic use was 104 (98.11%) (95.52-100, 95% Confidence Interval). About 74 (71.15%) of patients received multiple antibiotics. The most common classes of antibiotics used were cephalosporins, seen in 85 (81.73%) and macrolides, seen in 57 (54.81%) patients.
CONCLUSIONS: The prevalence of antibiotic use among hospitalised COVID-19 patients was found to be higher when compared to other studies conducted in similar settings.
KEYWORDS: antibiotics; bacterial infection; co-infection; COVID-19.
METHOD: Through an online survey, we used Coronavirus Anxiety Scale (CAS) to measure the level of anxiety associated with the COVID-19 crisis and Brief Coping Orientation to Problems Experienced (COPE) to assess the coping responses adopted to handle stressful life events. Coping strategies were classified as adaptive and maladaptive, for which the aggregate sores were calculated. Multiple linear regression was used to determine the predictors of anxiety adjusted for potentially confounding variables. Results from 434 participants were available for analysis.
RESULTS: The mean score (SD) of the CAS was 1.1 (1.8). The mean scores of adaptive and maladaptive coping strategies were 35.69 and 19.28, respectively. Multiple linear regression revealed that maladaptive coping [Adjusted B coefficient = 4.106, p-value < 0.001] and presence of comorbidities [Adjusted B coefficient = 1.376, p-value = 0.025] significantly predicted anxiety.
CONCLUSION: Maladaptive coping and presence of comorbidities were the predictors of coronavirus anxiety. The apparent lack of anxiety in relation to COVID-19 and movement restriction is reflective of the reported high level of satisfaction with the support and services provided during the COVID-19 outbreak in Malaysia. Adaptive coping strategies were adopted more frequently than maladaptive. Nevertheless, public education on positive coping strategies and anxiety management may be still be relevant to provide mental health support to address the needs of the general population.