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Objective: Compared with other regions in the world, the transmission characteristics of the COVID-19 epidemic in Africa are more obvious, has a unique transmission mode in this region; At the same time, the data related to the COVID-19 epidemic in Africa is characterized by low data quality and incomplete data coverage, which makes the prediction method of COVID-19 epidemic suitable for other regions unable to achieve good results in Africa. In order to solve the above problems, this paper proposes a prediction method that nests the in-depth learning method in the mechanism model. From the experimental results, it can better solve the above problems and better adapt to the transmission characteristics of the COVID-19 epidemic in African countries. Methods: Based on the SIRV model, the COVID-19 transmission rate and trend from September 2021 to January 2022 of the top 15 African countries (South Africa, Morocco, Tunisia, Libya, Egypt, Ethiopia, Kenya, Zambia, Algeria, Botswana, Nigeria, Zimbabwe, Mozambique, Uganda, and Ghana) in the accumulative number of COVID-19 confirmed cases was fitted by using the data from Worldometer. Non-autoregressive (NAR), Long-short term memory (LSTM), Autoregressive integrated moving average (ARIMA) models, Gaussian and polynomial functions were used to predict the transmission rate β in the next 7, 14, and 21 days. Then, the predicted transmission rate βs were substituted into the SIRV model to predict the number of the COVID-19 active cases. The error analysis was conducted using root-mean-square error (RMSE) and mean absolute percentage error (MAPE). Results: The fitting curves of the 7, 14, and 21 days were consistent with and higher than the original curves of daily active cases (DAC). The MAPE between the fitted and original 7-day DAC was only 1.15% and increased with the longer of predict days. Both the predicted β and DAC of the next 7, 14, and 21 days by NAR and LSTM nested models were closer to the real ones than other three ones. The minimum RMSEs for the predicted number of COVID-19 active cases in the next 7, 14, and 21 days were 12,974, 14,152, and 12,211 people, respectively when the order of magnitude for was 106, with the minimum MAPE being 1.79%, 1.97%, and 1.64%, respectively. Conclusion: Nesting the SIRV model with NAR, LSTM, ARIMA methods etc. through functionalizing β respectively could obtain more accurate fitting and predicting results than these models/methods alone for the number of confirmed COVID-19 cases in Africa in which nesting with NAR had the highest accuracy for the 14-day and 21-day predictions. The nested model was of high significance for early understanding of the COVID-19 disease burden and preparedness for the response. [ABSTRACT FROM AUTHOR]

Background: Epidemics or pandemics, such as the current Coronavirus Disease 2019 (COVID-19) crisis, pose unique challenges to healthcare professionals (HCPs). Caring for patients during an epidemic/pandemic may impact negatively on the mental health of HCPs. There is a lack of evidence-based advice on what would be effective in mitigating this impact.Objectives: This rapid review synthesizes the evidence on the psychological impact of pandemics/epidemics on the mental health of HCPs, what factors predict this impact, and the evidence of prevention/intervention strategies to reduce this impact.Method: According to rapid review guidelines, systematic searches were carried out in Embase.com , PubMed, APA PsycINFO-Ovid SP, and Web of Science (core collection). Searches were restricted to the years 2003 or later to ensure inclusion of the most recent epidemic/pandemics, such as Severe Acute Respiratory Syndrome (SARS). Papers written in French or English, published in peer-reviewed journals, and of quantitative design using validated measures of mental health outcomes were included. Of 1308 papers found, 50 were included. The full protocol for this rapid review was registered with Prospero (reg.no. CRD42020175985).Results: Results show that exposed HCPs working with patients during an epidemic/pandemic are at heightened risk of mental health problems in the short and longer term, particularly: psychological distress, insomnia, alcohol/drug misuse, and symptoms of posttraumatic stress disorder (PTSD), depression, anxiety, burnout, anger, and higher perceived stress. These mental health problems are predicted by organizational, social, personal, and psychological factors and may interfere with the quality of patient care. Few evidence-based early interventions exist so far.Discussion: HCPs need to be provided with psychosocial support to protect their mental wellbeing if they are to continue to provide high quality patient care. Several recommendations relevant during and after an epidemic/pandemic, such as COVID-19, and in preparation for a future outbreak, are proposed. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic has become a global concern. Iran is one of the countries affected most by the SARS-CoV-2 outbreak. As a result, the use of information technology (IT) has a variety of applications for pandemic management. The purpose of this study was to develop a conceptual framework for responding to the COVID-19 pandemic via IT management, based on extensive literature review and expert knowledge.Methods: The conceptual framework is developed in three stages: (1) a literature review to gather practical experience with IT applications for managing the COVID-19 pandemic, (2) a study of Iranian documents and papers that present Iran's practical experience with COVID-19, and (3) developing a conceptual framework based on the previous steps and validating it through a Delphi approach in two rounds, and by 13 experts.Results: The proposed conceptual framework demonstrates that during pandemics, 22 different types of technologies were used for various purposes, including virtual education, early warning, rapid screening and diagnosis of infected individuals, and data management. These objectives were classified into six categories, with the following applications highlighted: (1) Prevention (M-health, Internet search queries, telehealth, robotics, Internet of things (IoT), Artificial Intelligence (AI), big data, Virtual Reality (VR), social media); (2) Diagnosis (M-health, drones, telehealth, IoT, Robotics, AI, Decision Support System (DSS), Electronic Health Record (EHR)); (3) Treatment (Telehealth, M-health, AI, Robotic, VR, IoT); (4) Follow-up (Telehealth, M-health, VR), (5) Management & planning (Geographic information system, M-health, IoT, blockchain), and (6) Protection (IoT, AI, Robotic and automatic vehicles, Augmented Reality (AR)). In Iran, the use of IT for prevention has been emphasized through M-health, internet search queries, social media, video conferencing, management and planning objectives using databases, health information systems, dashboards, surveillance systems, and vaccine coverage.Conclusions: IT capabilities were critical during the COVID-19 outbreak. Practical experience demonstrates that various aspects of information technologies were overlooked. To combat this pandemic, the government and decision-makers of this country should consider strategic planning that incorporates successful experiences against COVID-19 and the most advanced IT capabilities. [ABSTRACT FROM AUTHOR]