ABSTRACT
Purpose: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading worldwide, causing mental health concerns among people. People's perceptions of the disease affect their psychological adaptation and health outcomes. In this study, we present people's perceptions of coronavirus disease 2019 (COVID-19), level of government trust, and their psychological distress during the pandemic for examining the impact of peoples' COVID-19 perceptions on their mental health. Patients and Methods: This cross-sectional study was conducted through a telephone survey in Taiwan in April 2020. Participants were randomly selected for telephone screening using a computer-assisted telephone interviewer system. A total of 1098 participants aged more than 20 years participated in the survey. Results: The mean age of participants was 47.7 ± 16.4 years. After controlling for covariates, participants who were worried about contracting COVID-19, those who believed that they had a chance of being infected with COVID-19, those who were reluctant to visit the hospital for fear of contracting the virus, those who felt that the pandemic had affected their daily life, and those with low levels of trust in the government's capacity to manage the pandemic had anxiety, hostility, depression, interpersonal sensitivity/inferiority, and psychological symptoms. Conclusion: People's perception of COVID-19 and public's trust in the government's ability to respond to the pandemic are related to psychological distress. Although the Taiwanese government may have undertaken effective epidemic control measures to address with the COVID-19 pandemic, this crisis may have still caused mental health problems in the general population. Health professionals and policy makers should pay more attention to high-risk groups among those at risk for developing mental health problems.
ABSTRACT
Emerging infectious diseases (EIDs), including the latest COVID-19 pandemic, have emerged and raised global public health crises in recent decades. Without existing protective immunity, an EID may spread rapidly and cause mass casualties in a very short time. Therefore, it is imperative to identify cases with risk of disease progression for the optimized allocation of medical resources in case medical facilities are overwhelmed with a flood of patients. This study has aimed to cope with this challenge from the aspect of preventive medicine by exploiting machine learning technologies. The study has been based on 83,227 hospital admissions with influenza-like illness and we analysed the risk effects of 19 comorbidities along with age and gender for severe illness or mortality risk. The experimental results revealed that the decision rules derived from the machine learning based prediction models can provide valuable guidelines for the healthcare policy makers to develop an effective vaccination strategy. Furthermore, in case the healthcare facilities are overwhelmed by patients with EID, which frequently occurred in the recent COVID-19 pandemic, the frontline physicians can incorporate the proposed prediction models to triage patients suffering minor symptoms without laboratory tests, which may become scarce during an EID disaster. In conclusion, our study has demonstrated an effective approach to exploit machine learning technologies to cope with the challenges faced during the outbreak of an EID.