General short-term load forecasting based on multi-task temporal convolutional network in COVID-19

The spread of the global COVID-19 epidemic has resulted in significant shifts in electricity consumption compared to regular days. It is unknown if standard single-task, single-indicator load forecasting algorithms can accurately reflect COVID-19 load patterns. Power practitioners urgently want a simple, efficient, and accurate solution for anticipating reliable load. In this paper, we first propose a unique collaborative TCN-LSTM-MTL short-term load forecasting model based on mobility data, temporal convolutional networks, and multi-task learning. The addition of the parameter sharing layers and the structure with residual convolution improves the data input diversity of the forecasting model and enables the model to obtain a wider time series receptive field. Then, to demonstrate the usefulness of the mobility optimized TCN-LSTM-MTL, tests were conducted in three levels and twelve base regions using 19 different benchmark models. It is capable of controlling predicting mistakes to within 1 % in the majority of tasks. Finally, to rigorously explain the model, the Shapley additive explanations (SHAP) visual model interpretation technology based on game theory is introduced. It examines the TCN-LSTM-MTL model's internal mechanism at various time periods and establishes the validity of the mobility indicators as well as the asynchronous relationship between indicator significance and real contribution.

Weibo users perception of the COVID-19 pandemic on Chinese social networking service (Weibo): sentiment analysis and fuzzy-c-means model

Background: Over the last decade, social media analysis tools have been used to monitor public sentiment and communication methods for public health emergencies such as the Ebola and Zika epidemics. Research articles have indicated that many outbreaks and pandemics could have been promptly controlled if experts considered social media data. With the World Health Organization (WHO) pandemic statement and various governments government action on the disease, various sentiments regarding coronavirus disease 2019 (COVID-19) have spread across the world. Therefore, sentiment analyses in studying pandemics, such as COVID-19, are important based on recent events. Methods: The Term Frequency-Inverse Document Frequency (TF-IDF) method was used to extract keywords from the 850,083 content of Weibo from January 24, 2020, to March 31, 2020. Then the Latent Dirichlet Allocation (LDA) was used to perform topic analysis on the keywords. Finally, the fuzzy-c-means method was used to divide the content of Weibo into seven categories of emotions: fear, happiness, disgust, surprise, sadness, anger, and good. And the changes in emotion were tracked over time. Results: The results indicated that people showed “surprise” overall (55.89%);however, with time, the “surprise” decreased. As the knowledge regarding the COVID-19 increased, the “surprise” of the citizens decreased (from 59.95% to 46.58%). Citizens’ feelings of “fear” and “good” increased as the number of deaths associated with COVID-19 increased (“fear”: from 15.42% to 20.95% “good”: 10.31% to 18.89%). As the number of infections was suppressed, the feelings of “fear” and “good” diminished (“fear”: from 20.95% to 15.79% “good”: from 18.89% to 8.46%). Conclusions: The findings of this study indicate that people’s feelings were analyzed regarding the COVID-19 pandemic in three stages over time. In the beginning, people’s emotions were primarily “surprised”;however after the outbreak, people’s “surprise” decreased with increasing knowledge. At the end of the phase, I of the COVID-19 pandemic, people’s “fear” and “good” feelings were diminished as the epidemic was suppressed. People’s interest shifted from China to other countries and their concern about the situation in other countries. © Journal of Medical Artificial Intelligence. All rights reserved.

Severe acute respiratory syndrome coronavirus 2 viral load in respiratory and feces specimens of children with coronavirus disease 2019

Background: The viral load kinetics of children with coronavirus disease 2019 is not clear. Materials & methods: The viral load of throat, nasal and feces specimens of 10 children with coronavirus disease 2019 were detected and analyzed. Results: The virus load of nasal and throat specimen decreased extremely and all respiratory specimens tested negative on the third week after they were admitted. All children showed positive PCR results in their feces. A total of 70% children showed positive results at the fourth week and 40% children showed positive results in their feces at the fifth week. All children tested negative on the sixth week. Conclusion: The positive rate of stool in children was higher than that in adults and the shedding time of stool was longer than that of respiratory specimen.