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• Collected papers on estimations of four epidemiological parameters regarding COVID-19. • Quantitatively compared and synthesized preprints and peer-reviewed publications. • Verified the validity of the COVID-19 parameter estimations of the preprints. • Synthesized parameters can effectively capture the patterns of COVID-19. We aimed to explore the collective wisdom of preprints related to COVID-19 by comparing and synthesizing them with results of peer-reviewed publications. PubMed, Google Scholar, medRxiv, bioRxiv, arXiv, and SSRN were searched for papers regarding the estimation of four epidemiological parameters of COVID-19: the basic reproduction number, incubation period, infectious period, and case-fatality-rate. Distributions of parameters and timeliness of preprints and peer-reviewed papers were compared. Four parameters in two groups were synthesized by bootstrapping, and their validities were evaluated by simulated cumulative cases of the susceptible-exposed-infectious-recovered-dead-cumulative (SEIRDC) model. A total of 106 papers were included for analysis. The distributions of four parameters in two literature groups were close, and the timeliness of preprints was better. Synthesized estimates of the basic reproduction number (3.18, 95% CI 2.85–3.53), incubation period (5.44 days, 95% CI 4.98–5.99), infectious period (6.25 days, 95% CI 5.09–7.51), and case-fatality-rate (4.51%, 95% CI 3.41%–6.29%) were obtained. Simulated cumulative cases of the SEIRDC model matched well with the onset cases in China. The validity of the COVID-19 parameter estimations of the preprints was on par with that of peer-reviewed publications, and synthesized results of literatures could reduce the uncertainty and be used for epidemic decision-making. [ABSTRACT FROM AUTHOR]

1 Transmission chain of COVID-19 outbreak in Beijing was reconstructed. 2 The Bayesian data augmentation approaches account for uncertain data. 3 The risk of infection among different populations was quantified. 4 The source and captured transmission patterns of COVID-19 were effectively traced. The aim of the study was to reconstruct the complete transmission chain of the COVID-19 outbreak in Beijing's Xinfadi Market using data from epidemiological investigations, which contributes to reflecting transmission dynamics and transmission risk factors. We set up a transmission model, and the model parameters are estimated from the survey data via Markov chain Monte Carlo sampling. Bayesian data augmentation approaches are used to account for uncertainty in the source of infection, unobserved onset, and infection dates. The rate of transmission of COVID-19 within households is 9.2%. Older people are more susceptible to infection. The accuracy of our reconstructed transmission chain was 67.26%. In the gathering place of this outbreak, the Beef and Mutton Trading Hall of Xinfadi market, most of the transmission occurs within 20 m, only 19.61% of the transmission occurs over a wider area (>20 m), with an overall average transmission distance of 13.00 m. The deepest transmission generation is 9. In this outbreak, there were 2 abnormally high transmission events. The statistical method of reconstruction of transmission trees from incomplete epidemic data provides a valuable tool to help understand the complex transmission factors and provides a practical guideline for investigating the characteristics of the development of epidemics and the formulation of control measures. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, online social media platforms such as Twitter facilitate the exchange of information among people. However, the prevalence of "infodemic" such as online hate speech has exacerbated social rifts, discrimination, prejudice and even hate crimes. Timely and effective detection of the hate speech will help create a healthy public opinion environment. Most of the current COVID-19-related hate speech research focuses on a single language, such as English. In this paper, we introduce a cross-lingual transfer learning method, aiming to contribute to hate speech detection in low-resource languages. We propose a deep learning based model to classify hate speech with a pre-trained language model for multilingual text embedding. Data augmentation and cross-lingual contrastive learning are then utilized to further improve the performance of cross-lingual knowledge transfer. To evaluate our method, we collected three publicly available annotated COVID-19-related hate speech datasets on Twitter, i.e., two in English and one in German. Furthermore, a Chinese dataset based on Weibo is constructed to expand multilingual data. The experimental results across three languages illustrate the effectiveness of our method for cross-lingual hate speech detection. Test F1-scores of our method for English, Chinese, German as transfer target languages can reach up to 0.728, 0.799 and 0.612 respectively, which are on average better than other baselines. • Suggested a cross-lingual COVID-19-related hate speech detection problem. • Introduced cross-lingual contrastive learning. • Validated model performance on datasets in three languages. [ABSTRACT FROM AUTHOR]

Falls are an important factor in significantly deteriorating quality of life of older adults, consequently leading to both physical and psychological harm. A wearable-based fall-related recognition system (WFRS) indeed facilitates the prediction, detection, and classification of fall events in helping fallers. Previous studies have provided a relatively comprehensive introduction to WFRSs from the perspective of sensor types and recognition algorithms. However, while these studies provide a clear technical direction, how to choose the appropriate technology for each phase of the experiment is a stumbling block for newly interested researchers. Accordingly, a comprehensive review article covering the mainstream technologies of WFRSs is imperative and meaningful. This review analyzes 48 state-of-the-art researches in WFRSs from three databases (i.e., IEEE Explorer, ScienceDirect, and MDPI) and introduces the pipeline techniques that consist of data acquisition, preprocessing, feature extraction, model training, and evaluation. Specifically, we first analyze the pros and cons of the use of different number of sensors for data collection. We then introduce the widely used preprocessing techniques including filtering and data augmentation. Afterwards, we detail the extraction of various features and illustrate methods for the selection, training, and evaluation of fall recognition models. We finally discuss factors affecting the overall performance of a model and offer suggestions for future research. • A comprehensive review of Wearable-based Fall-related Recognition Systems. • Summarize and discuss the techniques of data acquisition, preprocessing and augmentation in FRSs. • Investigate FRSs research that implemented threshold, machine learning and deep learning algorithms. • Discuss factors determining WFRSs' overall performance and show future directions. [ABSTRACT FROM AUTHOR]

Abstract: The objective of this research was to design and develop the Epidemiological Investigation and Intelligent Analytical System for foodborne disease to improve the ability to control foodborne disease events in field epidemiological investigations. The system is composed of standard electronic investigation questionnaires for collecting data from field investigations, a statistical analysis module integrated with statistically programmed methods for calculating odds and risk ratios for risk factors, an intelligent auxiliary diagnosis module featuring a feedback function able to enhance diagnostic accuracy, and a knowledge database for acquiring information in areas related to foodborne disease. The system has been provided to the People’s Liberation Army Emergency Response Team for public health events. In field experiments, the system was very effective and improved work efficiency in epidemiological investigations of foodborne disease. [Copyright &y& Elsevier]