ABSTRACT
With the global outbreak of coronavirus disease 2019 (COVID-19), public health has received unprecedented attention. The cultivation of emergency and compound professionals is the general trend through public health education. However, current public health education is limited to traditional teaching models that struggle to balance theory and practice. Fortunately, the development of artificial intelligence (AI) has entered the stage of intelligent cognition. The introduction of AI in education has opened a new era of computer-assisted education, which brought new possibilities for teaching and learning in public health education. AI-based on big data not only provides abundant resources for public health research and management but also brings convenience for students to obtain public health data and information, which is conducive to the construction of introductory professional courses for students. In this review, we elaborated on the current status and limitations of public health education, summarized the application of AI in public health practice, and further proposed a framework for how to integrate AI into public health education curriculum. With the rapid technological advancements, we believe that AI will revolutionize the education paradigm of public health and help respond to public health emergencies.
Subject(s)
COVID-19 , Humans , COVID-19/epidemiology , Artificial Intelligence , Curriculum , Health EducationABSTRACT
With the global outbreak of coronavirus disease 2019 (COVID-19), public health has received unprecedented attention. The cultivation of emergency and compound professionals is the general trend through public health education. However, current public health education is limited to traditional teaching models that struggle to balance theory and practice. Fortunately, the development of artificial intelligence (AI) has entered the stage of intelligent cognition. The introduction of AI in education has opened a new era of computer-assisted education, which brought new possibilities for teaching and learning in public health education. AI-based on big data not only provides abundant resources for public health research and management but also brings convenience for students to obtain public health data and information, which is conducive to the construction of introductory professional courses for students. In this review, we elaborated on the current status and limitations of public health education, summarized the application of AI in public health practice, and further proposed a framework for how to integrate AI into public health education curriculum. With the rapid technological advancements, we believe that AI will revolutionize the education paradigm of public health and help respond to public health emergencies.
ABSTRACT
BACKGROUND: The recurrence of positive SARS-CoV-2 RT-PCR is frequently found in discharged COVID-19 patients but its clinical significance remains unclear. The potential cause, clinical characteristics and infectiousness of the recurrent positive RT-PCR patients need to be answered. METHODS: A single-centered, retrospective study of 51 discharged COVID-19 patients was carried out at a designated hospital for COVID-19. The demographic data, clinical records and laboratory findings of 25 patients with recurrent positive RT-PCR from hospitalization to follow-up were collected and compared to 26 patients with negative RT-PCR discharged regularly during the same period. Discharged patients' family members and close contacts were also interviewed by telephone to evaluate patients' potential infectiousness. RESULTS: The titer of both IgG and IgM antibodies was significantly lower (p = 0.027, p = 0.011) in patients with recurrent positive RT-PCR. Median duration of viral shedding significantly prolonged in patients with recurrent positive RT-PCR (36.0 days vs 9.0 days, p = 0.000). There was no significant difference in demographic features, clinical features, lymphocyte subsets count and inflammatory cytokines levels between the two groups of patients. No fatal case was noted in two groups. As of the last day of follow-up, none of the discharged patients' family members or close contact developed any symptoms of COVID-19. CONCLUSIONS: Patients with low levels of IgG and IgM are more likely to have recurrent positive SARS-CoV-2 RT-PCR results and lead to a prolonged viral shedding. The recurrent positive of SARS-CoV-2 RT-PCR may not indicate the recurrence or aggravation of COVID-19. The detection of SARS-CoV-2 by RT-PCR in the patients recovered from COVID-19 is not necessarily correlated with the ability of transmission.