Exploring the influence of COVID-19 on the spread of hand, foot, and mouth disease with an automatic machine learning prediction model.

Hand, foot, and mouth disease (HFMD) is an important public health problem and has received concern worldwide. Moreover, the coronavirus disease 2019 (COVID-19) epidemic also increases the difficulty of understanding and predicting the prevalence of HFMD. The purpose is to prove the usability and applicability of the automatic machine learning (Auto-ML) algorithm in predicting the epidemic trend of HFMD and to explore the influence of COVID-19 on the spread of HFMD. The AutoML algorithm and the autoregressive integrated moving average (ARIMA) model were applied to construct and validate models, based on the monthly incidence numbers of HFMD and meteorological factors from May 2008 to December 2019 in Henan province, China. A total of four models were established, among which the Auto-ML model with meteorological factors had minimum RMSE and MAE in both the model constructing phase and forecasting phase (training set: RMSE = 1424.40 and MAE = 812.55; test set: RMSE = 2107.83, MAE = 1494.41), so this model has the best performance. The optimal model was used to further predict the incidence numbers of HFMD in 2020 and then compared with the reported cases. And, for analysis, 2020 was divided into two periods. The predicted incidence numbers followed the same trend as the reported cases of HFMD before the COVID-19 outbreak; while after the COVID-19 outbreak, the reported cases have been greatly reduced than expected, with an average of only about 103 cases per month, and the incidence peak has also been delayed, which has led to significant changes in the seasonality of HFMD. Overall, the AutoML algorithm is an applicable and ideal method to predict the epidemic trend of the HFMD. Furthermore, it was found that the countermeasures of COVID-19 have a certain influence on suppressing the spread of HFMD during the period of COVID-19. The findings are helpful to health administrative departments.

Ultrasensitive detection of SARS-CoV-2 S protein with aptamers biosensor based on surface-enhanced Raman scattering.

A rapid and accurate diagnostic modality is essential to prevent the spread of SARS-CoV-2. In this study, we proposed a SARS-CoV-2 detection sensor based on surface-enhanced Raman scattering (SERS) to achieve rapid and ultrasensitive detection. The sensor utilized spike protein deoxyribonucleic acid aptamers with strong affinity as the recognition entity to achieve high specificity. The spherical cocktail aptamers-gold nanoparticles (SCAP) SERS substrate was used as the base and Au nanoparticles modified with the Raman reporter molecule that resonates with the excitation light and spike protein aptamers were used as the SERS nanoprobe. The SCAP substrate and SERS nanoprobes were used to target and capture the SARS-CoV-2 S protein to form a sandwich structure on the Au film substrate, which can generate ultra-strong "hot spots" to achieve ultrasensitive detection. Analysis of SARS-CoV-2 S protein was performed by monitoring changes in SERS peak intensity on a SCAP SERS substrate-based detection platform. This assay detects S protein with a LOD of less than 0.7 fg mL-1 and pseudovirus as low as 0.8 TU mL-1 in about 12 min. The results of the simulated oropharyngeal swab system in this study indicated the possibility of it being used for clinical detection, providing a potential option for rapid and accurate diagnosis and more effective control of SARS-CoV-2 transmission.

A data-driven interpretable ensemble framework based on tree models for forecasting the occurrence of COVID-19 in the USA.

This prevalence of coronavirus disease 2019 (COVID-19) has become one of the most serious public health crises. Tree-based machine learning methods, with the advantages of high efficiency, and strong interpretability, have been widely used in predicting diseases. A data-driven interpretable ensemble framework based on tree models was designed to forecast daily new cases of COVID-19 in the USA and to determine the important factors related to COVID-19. Based on a hyperparametric optimization technique, we developed three machine learning algorithms based on decision trees, including random forest (RF), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), and three linear ensemble models were used to integrate these outcomes for better prediction accuracy. Finally, the SHapley Additive explanation (SHAP) value was used to obtain the feature importance ranking. Our outcomes demonstrated that, among the three basic machine learners, the prediction accuracy was the following in descending order: LightGBM, XGBoost, and RF. The optimized LAD ensemble was the most precise prediction model that reduced the prediction error of the best base learner (LightGBM) by approximately 3.111%, while vaccination, wearing masks, less mobility, and government interventions had positive effects on the control and prevention of COVID-19.

Rapid Point-of-Care Assay by SERS Detection of SARS-CoV-2 Virus and Its Variants.

Addressing the spread of coronavirus disease 2019 (COVID-19) has highlighted the need for rapid, accurate, and low-cost diagnostic methods that detect specific antigens for SARS-CoV-2 infection. Tests for COVID-19 are based on reverse transcription PCR (RT-PCR), which requires laboratory services and is time-consuming. Here, by targeting the SARS-CoV-2 spike protein, we present a point-of-care SERS detection platform that specifically detects SARS-CoV-2 antigen in one step by captureing substrates and detection probes based on aptamer-specific recognition. Using the pseudovirus, without any pretreatment, the SARS-CoV-2 virus and its variants were detected by a handheld Raman spectrometer within 5 min. The limit of detection (LoD) for the pseudovirus was 124 TU µL-1 (18 fM spike protein), with a linear range of 250-10,000 TU µL-1. Moreover, this assay can specifically recognize the SARS-CoV-2 antigen without cross reacting with specific antigens of other coronaviruses or influenza A. Therefore, the platform has great potential for application in rapid point-of-care diagnostic assays for SARS-CoV-2.

The correlation between temperature and the incidence of COVID-19 in four first-tier cities of China: a time series study.

The COVID-19 outbreak emerged in Wuhan, China, and was declared a global pandemic in March 2020. This study aimed to explore the association of daily mean temperature with the daily counts of COVID-19 cases in Beijing, Shanghai, Guangzhou, and Shenzhen, China. Data on daily confirmed cases of COVID-19 and daily mean temperatures were retrieved from the 4 first-tier cities in China. Distributed lag nonlinear models (DLNMs) were used to assess the association between daily mean temperature and the daily cases of COVID-19 during the study period. After controlling for the imported risk index and long-term trends, the distributed lag nonlinear model showed that there were nonlinear and lag relationships. The daily cumulative relative risk decreased for every 1.0 °C change in temperature in Shanghai, Guangzhou, and Shenzhen. However, the cumulative relative risk increased with a daily mean temperature below - 3 °C in Beijing and then decreased. Moreover, the delayed effects of lower temperatures mostly occurred within 6-7 days of exposure. There was a negative correlation between the cumulative relative risk of COVID-19 incidence and temperature, especially when the temperature was higher than - 3 °C. The conclusions from this paper will help government and health regulators in these cities take prevention and protection measures to address the COVID-19 crisis and the possible collapse of the health system in the future.

Spatiotemporal cluster analysis of COVID-19 and its relationship with environmental factors at the city level in mainland China.

This study sought to identify the spatial, temporal, and spatiotemporal clusters of COVID-19 cases in 366 cities in mainland China with the highest risks and to explore the possible influencing factors of imported risks and environmental factors on the spatiotemporal aggregation, which would be useful to the design and implementation of critical preventative measures. The retrospective analysis of temporal, spatial, and spatiotemporal clustering of COVID-19 during the period (January 15 to February 25, 2020) was based on Kulldorff's time-space scanning statistics using the discrete Poisson probability model, and then the logistic regression model was used to evaluate the impact of imported risk and environmental factors on spatiotemporal aggregation. We found that the spatial distribution of COVID-19 cases was nonrandom; the Moran's I value ranged from 0.017 to 0.453 (P < 0.001). One most likely cluster and three secondary likely clusters were discovered in spatial cluster analysis. The period from February 2 to February 9, 2020, was identified as the most likely cluster in the temporal cluster analysis. One most likely cluster and seven secondary likely clusters were discovered in spatiotemporal cluster analysis. Imported risk, humidity, and inhalable particulate matter PM2.5 had a significant impact on temporal and spatial accumulation, and temperature and PM10 had a low correlation with the spatiotemporal aggregation of COVID-19. The information is useful for health departments to develop a better prevention strategy and potentially increase the effectiveness of public health interventions.

Nursing Scheduling Mode and Experience from the Medical Teams in Aiding Hubei Province During the COVID-19 Outbreak: A Systematic Scoping Review of 17 Studies.

INTRODUCTION: Due to COVID-19 outbreak, since January 24, 2020, national medical teams from across the country and the armed forces have been dispatched to aid Hubei. The present review was designed to timely summarize the existing frontline information about nursing scheduling mode with special focus on the length of shifts with the aim to contribute to improve the nurses' job satisfaction and the quality of nursing services. METHODS: Articles from Jan 2020 to October 2020 were retrieved from China National Knowledge Infrastructure, Wanfang Data and Weipu Information, with the terms "COVID-19", "designated hospital", "Hubei-assisted", "makeshift hospital", "nursing", "nursing shift", "whole-system takeover" and variations of these, in the title and abstract fields and the Boolean combinations of these words as the retrieval strategy. RESULTS: Seventeen journal articles have been included in the target field, from the nurses in aiding Hubei Province, four kinds of shift length, 2-hour (h), 3-h, 4-h and 6-h shift have been considered, the main nursing scheduling mode adopted in designated hospitals for COVID-19 patients was dynamic scheduling based on workload, flexible scheduling based on working hours, workload and the number of critically ill patients admitted, humanized scheduling based on the daily reported health status of the nurses, and professional-integrated scheduling according to the professional distribution of nurses on the basis of four-hour shift length, and in makeshift hospitals for mild patients, the scheduling mode was 6-h based correspondingly. CONCLUSION: The descriptive results of the present systematic review shed light on the challenges and practical solutions of nursing scheduling mode in the context of cross-regional medical assistance. Additionally, the present systematic review could provide the academic community of nurses, nurse managers and administrators with baseline information and scientific productions from the content's points of view in the target field.