Nesting the SIRV model with NAR, LSTM and statistical methods to fit and predict COVID-19 epidemic trend in Africa.

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 ß in the next 7, 14, and 21 days. Then, the predicted transmission rate ßs 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 ß 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 ß 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.

Advances in the use of nighttime light data to monitor and assess coastal fisheries under the impacts of human activities and climate and environmental changes: A case study in the Beibu Gulf

Nighttime light remote sensing has attracted increasing attention in fishery management due to its unique advantages in observing light fisheries. NASA's Black Marble product suite (VNP46A1) and Luojia 1–01 images are the latest nighttime light data with improved spatial and temporal resolutions. This paper developed an effective night-light fishing boat (NLFB) dataset to monitor and assess the coastal light fisheries in the Beibu Gulf. Daily NLFB data from 2018 to 2020 were used to map the spatiotemporal variations in fishing intensity, number of fishing boats and locations of core fishing areas. The influencing mechanisms of climate and environmental changes, fishery closures, COVID-19 and typhoons on the temporal and spatial variations in light fisheries were evaluated and discussed. Combined with the fishery boundaries defined by the China-Vietnam bilateral agreements, nighttime light remote sensing revealed the effective management of and restrictions on (such as fishery closures and lockdowns) fishing activities in the Chinese fishery area. The Vietnamese fishery area was under higher fishing pressure, especially in densely populated estuaries where the fish spawn and feed. Therefore, a cross-regional marine protected area (MPA) is recommended as a realistic strategy for the sustainable management of light fisheries in the Beibu Gulf. Nighttime light remote sensing provides a useful opportunity to combat illegal, unreported and unregulated (IUU) fishing in the future.

Post-infection cognitive impairments in a cohort of elderly patients with COVID-19.

BACKGROUND: Understanding the long-term effects of coronavirus disease 2019 (COVID-19) on cognitive function is essential for monitoring the cognitive decline in the elderly population. This study aims to assess the current cognitive status and the longitudinal cognitive decline in elderly patients recovered from COVID-19. METHODS: This cross-sectional study recruited 1539 COVID-19 inpatients aged over 60 years who were discharged from three COVID-19-designated hospitals in Wuhan, China, from February 10 to April 10, 2020. In total, 466 uninfected spouses of COVID-19 patients were selected as controls. The current cognitive status was assessed using a Chinese version of the Telephone Interview of Cognitive Status-40 (TICS-40) and the longitudinal cognitive decline was assessed using an Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE). Cognitive assessments were performed 6 months after patient discharge. RESULTS: Compared with controls, COVID-19 patients had lower TICS-40 scores and higher IQCODE scores [TICS-40 median (IQR): 29 (25 to 32) vs. 30 (26 to 33), p < 0.001; IQCODE median (IQR): 3.19 (3.00 to 3.63) vs. 3.06 (3.00 to 3.38), p < 0.001]. Severe COVID-19 patients had lower TICS-40 scores and higher IQCODE scores than non-severe COVID-19 patients [TICS-40 median (IQR): 24 (18 to 28) vs. 30 (26 to 33), p < 0.001; IQCODE median (IQR): 3.63 (3.13 to 4.31) vs. 3.13 (3.00 to 3.56), p < 0.001] and controls [TICS-40 median (IQR): 24 (18 to 28) vs. 30 (26 to 33), p < 0.001; IQCODE median (IQR) 3.63 (3.13 to 4.31) vs. 3.06 (3.00 to 3.38), p < 0.001]. Severe COVID-19 patients had a higher proportion of cases with current cognitive impairment and longitudinal cognitive decline than non-severe COVID-19 patients [dementia: 25 (10.50 %) vs. 9 (0.69 %), p < 0.001; Mild cognitive impairment (MCI): 60 (25.21 %) vs. 63 (4.84 %), p < 0.001] and controls [dementia: 25 (10.50 %) vs. 0 (0 %), p < 0.001; MCI: 60 (25.21 %) vs. 20 (4.29 %), p < 0.001)]. COVID-19 severity, delirium and COPD were risk factors of current cognitive impairment. Low education level, severe COVID-19, delirium, hypertension and COPD were risk factors of longitudinal cognitive decline. CONCLUSIONS: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is associated with an increased risk of long-term cognitive decline in elderly population. COVID-19 patients, especially severe patients, should be intensively monitored for post-infection cognitive decline.

Traditional Chinese medicine shenhuang granule in patients with severe/critical COVID-19: A randomized controlled multicenter trial.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is still a pandemic, with a high mortality rate in severe/critical cases. Therapies based on the Shenghuang Granule have proved helpful in viral infection and septic shock. HYPOTHESIS/PURPOSE: The objective of the current study was to compare the efficacy and safety of the traditional Chinese medicine, Shenhuang Granule, with standard care in hospitalized patients with severe/critical COVID-19. STUDY DESIGN AND METHODS: This was an open-label, multicenter, randomized, controlled clinical trial. At 4 medical centers, a total of 111 severe/critical patients were randomly assigned to receive Shenhuang Granule (SHG group) twice a day for 14 days, in addition to standard care, or to receive standard care alone (Control group). The maximal follow up time was 75 days. The clinical endpoint was clinical improvement and mortality. RESULTS: 54 patients were assigned to the control group and 57 to the SHG group. The overall mortality was 75.9% (41/54) in the control group, and 38.6% (22/57) in the SHG group (p < 0.01 vs. control). The post hoc analysis showed that in the severe category, the mortality of the control group vs. the SHG group was 58.8% (10/17) vs. 5.3% (1/19) (p < 0.01); while in the critical category, it was 83.8% (31/37) vs. 55.3% (21/38) (p < 0.05). In the severe category, the mortality of patients who eventually received an invasive ventilator in the control vs. the SHG group was 58.8% (10/17) vs. 0 (0/19) (p < 0.01). Administration of SHG was associated with increased lymphocytes and decreased adverse events. CONCLUSION: Shenhuang Granule is a promising integrative therapy for severe and critical COVID-19.