Automatic identification and explanation of root causes on COVID-19 index anomalies.

This paper reports a method for automatically identifying, analyzing and explaining anomalies in different indexes of COVID-19 crisis using Artificial Intelligence (AI) based techniques. With systematic application of News sensor, language detection & translation, Keyword-based extraction of COVID-19 indexes, Convolutional Neural Network (CNN) based anomaly detection, and Natural Language Processing (NLP) based explanation methods, this paper demonstrates a methodological solution for strategic decision makers to make evidence-based policy decisions on COVID-19 (in multiple dimensions like Travel, Vaccine, Medical etc.). Firstly, COVID-19 related News is fetched from multiple sources in multiple languages. Then, AI-based language detection and translation process automatically translates these News and posts in real-time. Next, COVID-19 related News and posts are segregated in multiple groups using pre-defined keywords for creation of multiple indexes. Lastly, CNN based anomaly detection identifies all the anomalies on multiple COVID-19 indexes with NLP-based explanations. A standalone decision support system was developed that implemented the presented method. This decision support system allows a strategic decision-maker to comprehend "when, where, and why there are fluctuations in COVID-19 related sentiments on a particular dimension". Method was validated with Tweets from 15/072021 to 24/05/2022 resulting in automated generation of 5 COVID-19 indexes and 69 anomalies with explanations. In summary, this method of anomaly detection on COVID-19 indexes presents:•An explicit, transferable and reproducible procedure for detecting anomalies on multiple indexes of COVID-19 in multiple languages•It helps a strategic decision maker to comprehend the root-causes of anomalies in COVID-19 related travel, vaccine, medical indexes•The solution developed using the presented method allows evidence-based strategic decision-making COVID-19 crisis using AI, Deep Learning and NLP.

Current Trend of Virology in the Post COVID-19 Era with Related Topic-Based Tweet Analysis

In light of current trends in virology, we performed social media analysis of 13 main topics in the area of virology and ranked these topics with metrics such as users, posts, engagement, and influence. These metrics were monitored against the 13 keywords on Twitter for the same period (i.e., from 27 November to 6 December 2021) for benchmarking purposes. The 13 main topics were "virological Science", " preventive vaccines", "therapeutic vaccines", "viral pathogenesis", "viral immunology", "antiviral strategies", "virus structure", "virus expression", "viral resistance", "emerging viruses", "interspecies transmission", "viruses and cancer" and " viral diseases". "viral diseases" recorded the highest number of users (i.e., 905 users) and the highest number of post (i.e., about 1K posts). The second-highest number of posts were monitored to be on "therapeutic vaccines" with 729 posts from 691 users. In terms of engagement, "viral diseases" (3.4 K) were found to be on the top followed by "viruses and cancer" (3.1K). Lastly, in terms of influence, "viral diseases" recorded 9.0 million influences followed by 6.6 million influences on "emerging viruses". In summary, "viral diseases" was found to be the most engaging and influential topic highest with the highest number of posts from most of the tweet users. In relation to trending hashtags in virology, #COVID19 recorded the highest number of hashtags, followed by # omicron, #sarscov2, #publichealth, #omicronvarient, #wuhan, #originofcovid, #fauci and #epidemiology. Word clouds showing the main area of discussion were also generated for these 13 main topics.