The question we ask is whether online echo-chambers on social media networks enhance the anxiety and depression of individuals during the COVID19 outbreak. More specifically we want to measure the intensity of the communication about COVID-19 within the echo-chamber of individuals on Twitter and investigate the impact on their subsequent tweets in terms of the level of anxiety and signs of depressive language in their Tweets. We measure echo-chambers by the number of users in the social network that tweeted about COVID-19. We build on an extensive dataset of Twitter users for whom we have identified a large number of demographic and geographic variables (such as the gender, age, ethnicity, location by state, political affiliation) as well as their social network.

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I am conducting studies on lifestyle behaviors, in particular diet, sleep behaviors, and circadian rest-activity rhythms in relation to cardiometabolic outcomes (hypertension, type 2 diabetes, and obesity). Sample sizes of my studies range from n=100 to n=16,000.

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The PHIA project is a multi-country population-based HIV Impact Assessment survey which has interviewed and tested for HIV over 450,000 people of all ages in Africa. We are also currently conducting a second round of surveys in many countries, and hope to use best practices in big data management to generate a combined dataset across all countries. We want to combine this data with environmental, mobility and social media data and then use machine learning to identify trends in HIV incidence, treatment disruption and risk factors. We would also be interested in looking at other ways to use environmental data to predict potential zoonotic outbreaks.

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