COVID-19 Research Projects and Outcomes

A brief description of some of our ongoing projects related to COVID-19 is given below. Most of these research projects are a joint collaboration with Prof. Arti Ramesh . Our goal is to make our research findings readily accessible so that it can benefit the greater community.

Characterizing Human Mobility Patterns During COVID-19 using Cellular Network Data
In this paper, our goal is to analyze and compare cellular network usage data from pre-lockdown, during lockdown, and post-lockdown phases surrounding the COVID-19 pandemic to understand and model human mobility patterns during the pandemic, and evaluate the effect of lockdowns on mobility. To this end, we collaborate with one of the main cellular network providers in Brazil, and collect and analyze cellular network connections from 1400 antennas for all users in the city of Rio de Janeiro and its suburbs from March 1, 2020 to July 1, 2020. Our analysis reveals that the total number of cellular connections decreases to 78% during the lockdown phase and then increases to 85% of the pre-COVID era as the lockdown eases. We observe that as more people work remotely, there is a shift in the antennas incurring top 10% of the total traffic, with the number of connections made to antennas in downtown Rio reducing drastically and antennas at other locations taking their place. We also observe that while nearly 40-45% users connected to only 1 antenna each day during the lockdown phase indicating no mobility, there are around 4% users (i.e., 80K users) who connected to more than 10 antennas, indicating very high mobility. We also observe that the amount of mobility increases towards the end of the lockdown period even before the lockdown eases and the upward trend continues in the post-lockdown period. Finally, we design an interactive tool that showcases mobility patterns in different granularities that can potentially help people and government officials understand the mobility of individuals and the number of COVID cases in a particular neighborhood. Our analysis, inferences, and interactive showcasing of mobility patterns based on large-scale data can be extrapolated to other cities of the world and has the potential to help in designing more effective pandemic management measures in the future.
Necati A. Ayan, Nilson L. Damasceno, Sushil Chaskar, Peron R. de Sousa, Arti Ramesh, Anand Seetharam, Antonio A. de A. Rocha
Under Review
Paper Link

Investigating Societal Impact of COVID-19
In this paper, we collect and study Twitter com- munications to understand the societal impact of COVID-19 in the United States during the early days of the pandemic. With infections soaring rapidly, users took to Twitter asking people to self isolate and quarantine themselves. Users also demanded closure of schools, bars, and restaurants as well as lockdown of cities and states. We methodically collect tweets by identifying and tracking trending COVID-related hashtags. We first manually group the hashtags into six main categories, namely, 1) General COVID, 2) Quarantine, 3) Panic Buying, 4) School Closures, 5) Lockdowns, and 6) Frustration and Hope, and study the temporal evolution of tweets in these hashtags. We conduct a linguistic analysis of words common to all hashtag groups and specific to each hashtag group and identify the chief concerns of people as the pandemic gripped the nation (e.g., exploring bidets as an alternative to toilet paper). We conduct sentiment analysis and our investigation reveals that people reacted positively to school closures and negatively to the lack of availability of essential goods due to panic buying. We adopt a state-of-the-art semantic role labeling approach to identify the action words (e.g., fear, test), which capture the actions people are referring to in the tweets. We then leverage a LSTM-based dependency parsing model to analyze the context of the above- mentioned action words (e.g., verb deal is accompanied by nouns such as anxiety, stress, and crisis). Finally, we develop a scalable seeded topic modeling approach to automatically categorize and isolate tweets into hashtag groups and experimentally validate that our topic model provides a grouping similar to our manual grouping. Our study presents a systematic way to construct an aggregated picture of peoples’ response to the pandemic and lays the groundwork for future fine-grained linguistic and behavioral analysis.
Swaroop Gowdra, Anand Seetharam, Arti Ramesh
Accepted to SocialCom 2020
Paper Link

Ensemble Regression Models for Short-term Prediction of Confirmed COVID-19 Cases
Accurately predicting the number of new COVID-19 cases is critical to understanding and controlling the spread of the disease as well as effectively managing scarce resources (e.g., hospital beds, ventilators). To this end, we design a regression based ensemble learning model consisting of Linear regression, Ridge, Lasso, ARIMA, and SVR that takes the previous 14 days data into account to predict the number of new COVID-19 cases in the short-term. The ensemble model outputs the best performance by taking into account the performance of all the models. We consider data from top 50 countries around the world that have the highest number of confirmed cases between January 21, 2020 and April 30, 2020. Our results in terms of relative percentage error show that the ensemble method provides superior prediction performance for a vast majority of these countries with less than 10% error for 6 countries and less than 40% error for 27 countries.
Raushan Raj, Anand Seetharam, Arti Ramesh
In Proceedings of the Harvard CRCS Workshop on AI for Social Good 2020
Paper Link
Code

Understanding the Impact of COVID-19 on Education and Some Tips to Improve Online Teaching
Online education is here to stay-COVID-19 has mandated this. Therefore, in this paper, I discuss two important issues. Firstly, I discuss how the disease will disproportionately impact the teaching quality at various kinds of institutions (Research 1 (R1), Research 2 (R2), and Teaching). The main reason I say disproportionately and not just differentially is that even during normal times, resources available (e.g., grading or teaching support, technical support) per course offered is lower at small public schools in comparison to well-funded ones. The pandemic is likely to stretch the limited resources at small public schools thinner, thus worsening the situation for faculty at these universities. Secondly, given that faculty at all schools have to teach online in some form or the other, I provide some tips on designing high-quality educational videos from the comfort of one’s home.
Anand Seetharam
In Proceedings of the SIGCOMM Network Education Workshop 2020
Paper Link