Affiliation:
1. Department of Philosophy of Sciences and Technology, University of Science and Technology of China, Hefei-Anhui, China
2. Department of Social Work, Chinese University of Hong Kong, Sha Tin, Hong Kong
3. Department of medicine, Hefei First People's Hospital, The Third Affiliated Hospital of Anhui Medical University, China
Abstract
Background Technological innovations gained momentum and supported COVID-19 intelligence surveillance among high-risk populations globally. We examined technology surveillance using mobile thermometer detectors (MTDs), knowledge of App, and self-efficacy as a means of sensing body temperature as a measure of COVID-19 risk mitigation. In a cross-sectional survey, we explored COVID-19 risk mitigation, mobile temperature detectable by network syndromic surveillance mobility, detachable from clinicians, and laboratory diagnoses to elucidate the magnitude of community monitoring. Materials and Methods In a cross-sectional survey, we create in-depth comprehension of risk mitigation, mobile temperature Thermometer detector, and other variables for surveillance and monitoring among 850 university students and healthcare workers. An applied structural equation model was adopted for analysis with Amos v.24. We established that mobile usability knowledge of APP could effectively aid in COVID-19 intelligence risk mitigation. Moreover, both self-efficacy and mobile temperature positively strengthened data visualization for public health decision-making. Results The algorithms utilize a validated point-of-center test to ascertain the HealthCode scanning system for a positive or negative COVID-19 notification. The MTD is an alternative personal self-testing procedure used to verify temperature rates based on previous SARS-CoV-2 and future mobility digital health. Personal self-care of MTD mobility and knowledge of mHealth apps can specifically manage COVID-19 mitigation in high or low terrestrial areas. We found mobile usability, mobile self-efficacy, and app knowledge were statistically significant to COVID-19 mitigation. Additionally, interaction strengthened the positive relationship between self-efficacy and COVID-19. Data aggregation is entrusted with government database agencies, using natural language processing and machine learning mechanisms to validate and analyze. Conclusion The study shows that temperature thermometer detectors, mobile usability, and knowledge of App enhanced COVID-19 risk mitigation in a high or low-risk environment. The standardizing dataset is necessary to ensure privacy and security preservation of data ethics.
Subject
Health Information Management,Computer Science Applications,Health Informatics,Health Policy
Reference57 articles.
1. SARS-CoV-2 getting into the brain; neurological phenotype of COVID-19, and management by nano-biotechnology
2. World Health Organization (WHO). Critical Preparedness, Readiness and Response Actions for COVID-19: Interim Guidance. World Health Organization. [Internet]. Vol. 2, WHO Global site. 2021. Available from: WHO/2019-nCoV/Community_Actions/2020.3.
3. The International Health Regulations (2005)
4. Department SR. Number of mobile cell phone subscriptions in China from February 2020 to February 2021 [Internet]. Technology & Telecommunication. 2020 [cited 2021 Jun 14]. Available from: https://www.statista.com/statistics/278204/china-mobile-users-by-month.
5. A Smartphone-Driven Thermometer Application for Real-time Population- and Individual-Level Influenza Surveillance