The Nature, Cause and Consequence of COVID-19 Panic among Social Media Users in India

Aims The recent pandemic of COVID-19 has not only shaken the healthcare but also economic structure around the world. In addition to these direct effects, it has also brought in some indirect difficulties owing to the information epidemic on social media. As India experienced a later outbreak of COVID-19 and a prolonged uninterrupted lockdown, we aimed to understand the nature of panic social media users in India are experiencing due to the flow of (mis)information. We further extend this investigation to other countries. Methods We performed a cross-sectional study by conducting survey on multiple social media platforms. We received 1075 responses (sex ratio 2:1) through opportunity sampling from social media users of 30 different countries (between April 11, 2020 and May 15, 2020). We performed both quantitative and qualitative analyses on the 935 respondents from India. Several hypotheses are statistically tested on them and are further examined on rest of the 140 social media users from 29 other countries. We also performed a separate Twitter hashtag analysis and sentiment analysis on the responses. We applied a citizen science approach to involve the respondents in the analysis pipeline after the survey. Results This cross-sectional study on 1075 social media users from India and 29 other countries revealed a significant increase of social media usage and rise of panic over time in India. Middle-aged people and female exhibit a higher panic in India. The amount of panic was independent of the nature of association with COVID-19. The change of mental health was associated with panic level and productivity. Further qualitative analysis highlights the occurrences of information panic, economic panic, moral panic and spiritual panic, among other causes. Conclusions Several panic behaviors are unique to social media users in India possibly because COVID-19 broke out relatively later in comparison with the other countries and the uninterrupted lockdown prolonged for a long time. The amount of social media usage might not be causal but has a significant role in generating panic among the people in India. A significantly higher level of panic among the middle-aged people can be attributed to their higher amount of responsibility. The popularity of different hashtags, including the names of drugs under trial for COVID-19, in limited countries highlight that the causes of panic are not the same everywhere. As some of the respondents took part as citizen scientists a robust perspective to the outcome is obtained

Supervised Machine Learning Enables Geospatial Microbial Provenance

The recent increase in publicly available metagenomic datasets with geospatial metadata has made it possible to determine location-specific, microbial fingerprints from around the world. Such fingerprints can be useful for comparing microbial niches for environmental research, as well as for applications within forensic science and public health. To determine the regional specificity for environmental metagenomes, we examined 4305 shotgun-sequenced samples from the MetaSUB Consortium dataset—the most extensive public collection of urban microbiomes, spanning 60 different cities, 30 countries, and 6 continents. We were able to identify city-specific microbial fingerprints using supervised machine learning (SML) on the taxonomic classifications, and we also compared the performance of ten SML classifiers. We then further evaluated the five algorithms with the highest accuracy, with the city and continental accuracy ranging from 85–89% to 90–94%, respectively. Thereafter, we used these results to develop Cassandra, a random-forest-based classifier that identifies bioindicator species to aid in fingerprinting and can infer higher-order microbial interactions at each site. We further tested the Cassandra algorithm on the Tara Oceans dataset, the largest collection of marine-based microbial genomes, where it classified the oceanic sample locations with 83% accuracy. These results and code show the utility of SML methods and Cassandra to identify bioindicator species across both oceanic and urban environments, which can help guide ongoing efforts in biotracing, environmental monitoring, and microbial forensics (MF)