ABSTRACT
Objectives: This study was aimed at exploring and analyzing the epidemiological profile, surveillance, and response to COVID-19, including transmission dynamics and cluster formation. Methodology: This was a retrospective analysis of surveillance data, including contact tracing, risk factors, and clinical information. Binary logistic regressions were used to assess the likelihood of admission, cluster formation, and of each individual being an index patient. Clusters were demonstrated through geographic data systems, network analysis, and visualization software. Results: A total of 1100 COVID-19 cases were diagnosed from 20 March to 7 June 2020, of which 144 (13.1%) were asymptomatic. The median time from symptom onset to admission was 7 days (IQR, 4.5-10), and the median symptom duration was 5 days (IQR, 3-9). Eighty-nine clusters containing 736 patients were identified. The surveillance and control actions were divided into three phases. Clusters began to form in phase 2 and became more pronounced in phase 3. Patients ≥50 years of age and patients presenting with fever had relatively higher odds of admission: OR = 12.85 (95% CI 5.13-32.19) and 2.53 (95% CI 1.24-5.17), respectively. Cluster formation was observed among females, asymptomatic patients, and people living in Awabi: OR = 2.3 (95% CI 1.7-3.1), 6.39 (95% CI 2.33-17.2), and 3.54 (95% CI 2.06-6.07), respectively. Patients working in the police and defense sectors had higher odds of being an index patient: OR = 7.88 (95% CI 3.35-18.52). Conclusion: Case-based interventions should be supported by population-wide measures, particularly movement restrictions. Establishing prevention teams or district units, or primary care will be crucial for the control of future pandemics. Prevention should always be prioritized for vulnerable populations.
ABSTRACT
Background: Covid-19 pandemic has left deep psychological impacts, especially among infected patients. It is extremely important to understand the extent of those effects, while improving the compliance with isolation measures at the same time. Objectives: To detect prevalence of stress using two psychological scales and examine the stress associated factors, also to identify self-isolation compliance rates among COVID-19 patients. Methods: Cross-sectional research was conducted from 15 November to 22 December 2020, involving 379 patient participants selected via systematic random sampling. Kessler 10 Psychological Distress (K10) and the impact of event scale-revised (IES-R) tests were used to ascertain the levels of distress. Results: K10 measure revealed elevated stress amongst 121 (31.9%) of participants, whereas IES_R indicated the level was 37.7%. Using the K10 indicated the multivariate analysis was significant for females (OR = 2.482, 95% CI: 1.532-4.021), patients with financial problems (OR = 2.332, 95% CI: 1.270-4.282) and patients experiencing shortages of essentials (OR = 4.920, 95% CI: 2.524-9.590). The IES-R scale indicated that only female and patients experiencing shortages scored significantly in multivariate analysis, (OR = 1.895, 95% CI: 1.1223-2.935) and (OR = 2.928, 95% CI: 1.1580-5.424), respectively. Those undergoing shorter isolation periods reported lower levels of stress on both K10, p=0.016 and IES-R, p=0.002. Approximately 90% of patients used their own towels during isolation. Moreover, 80.2% slept in separate rooms and 74% used masks in the presence of other family members. Essential supply shortages were reported by 14.2% of respondents. Conclusions: Self-compliance rates were not optimal, while psychological distress was more prevalent among some groups. Intervention is imperative to minimize stress and improve self-isolation compliance.
