Poster: Understanding Human Mobility during COVID-19 using Cellular Network Traffic

In this paper, our goal is to analyze and compare cellular network usage data from Rio de Janeiro 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 to evaluate the effect of lockdowns on mobility. Our analysis reveals that human mobility increases significantly even before lockdown restrictions are eased, with the trend continuing in the post-lockdown period. We also observe that the day of week has a significant impact on mobility of individuals, with the overall mobility on Fridays increasing over time possibly due to people self-relaxing restrictions and engaging in social activities on Friday evenings. We also design an interactive tool that showcases mobility patterns in different granularities and can potentially help people and government officials understand the mobility of individuals and the number of COVID-19 cases in a particular neighborhood.

Mobility-aware COVID-19 Case Prediction using Cellular Network Logs

In this paper, our goal is to model the aggregate mobility of individuals in a city by analyzing cellular network connections, and then leverage the designed mobility model to model and predict the number of COVID-19 infections in future. We analyze cellular network connections from 973 antennas for all users in the city of Rio de Janeiro from April 5, 2020 to July 2, 2020. We design a Markovian model that captures the mobility across municipalities. We then combine the transition probabilities of the Markov chain with the number of COVID-19 cases in a municipality during a particular week in the design of our mobility-aware COVID-19 case prediction models to predict the number of cases for the following week. Our experiments demonstrate that our mobility-aware models significantly outperform a baseline mobility-agnostic linear regression model in terms of metrics such as Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE).

Understanding the Issues Surrounding COVID-19 Vaccine Roll Out via User Tweets

Vaccinations have emerged as one of the key tools to combat the COVID-19 pandemic, reduce infections and to enable safe re-opening of societies. Vaccinating the entire world population is a challenging undertaking and with demand far exceeding supply in the world, it is expected that topics surrounding vaccinations generate a wide array of discussions. Therefore, in this paper, we collect data from Twitter during the early days of the COVID-19 vaccination program and adopt a linguistic approach to better understand and appreciate peoples’ concerns and opinions with regards to the roll out of the vaccines. We begin by studying the term frequencies (i.e., unigrams and bigrams) and observe discussions around vaccination doses, receiving doses, vaccine supply, scheduling appointments and wearing masks as the vaccination efforts get underway. We then adopt a seeded topic modeling approach to automatically identify the main topics of discussion in the tweets and the main issues being discussed in each topic. We observe that our dataset has nine distinct topics. For example, we observe topics related to vaccine distribution, eligibility, scheduling and COVID variants. We then study the sentiment of the tweets with respect to each of the nine topics and observe that the overall sentiment is negative for most of the topics. We only observe a higher percentage of positive sentiment for topics related to obtaining information and schools. Our research lays the foundation to conduct a more fine-grained analysis of the various issues faced by the people as the pandemic recedes over the course of the next few years. © 2021, Springer Nature Switzerland AG.

Hierarchical Models for Detecting Mobility Clusters during COVID-19

Analyzing and developing mobility models that accurately capture human mobility is critical for combating the COVID-19 pandemic and minimizing the spread of the disease. In this paper, we design a two-layer hierarchical mobility model to model user mobility in the city of Rio de Janeiro and its suburbs by analyzing cellular network connectivity data during the COVID-19 pandemic. To this end, we collaborate with one of the main network providers in Brazil, TIM Brazil, to collect user connectivity logs from April 5th, 2020 to July 2nd, 2020, which we use to generate mobility graphs. We adopt the Louvain community detection algorithm in the first layer of our hierarchical model to detect the main communities in the city from the mobility graphs. Our model then uses the KMeans++ and Agglomerative clustering methods in the second layer to identify high, medium, and low mobility clusters within each community. Via extensive experiments, we show that the Louvain, and the Kmeans++ and Agglomerative algorithms outperform traditional clustering approaches in the first and second layers, respectively. Our results also demonstrate that our hierarchical model is able to pinpoint main mobility locations within each community and can be used by authorities to implement partial lockdown measures in place of widely unpopular complete lockdowns. © 2021 ACM.

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 lock-down, and post-lockdown phases surrounding the COVID-19 pandemic to understand and model human mobility patterns during the pandemic. To this end, we 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 user mobility starts increasing around 3 weeks before the end of lockdown, with the trend continuing into the post-lockdown period. We also design an interactive tool that showcases mobility patterns in different granularities and can help government officials take informed actions to control the spread of the disease. © 2021 IEEE.

Understanding the Societal Disruption due to COVID-19 via User Tweets

In this paper, we collect data from Twitter and conduct a linguistic analysis of the user tweets to understand the social and economic disruption caused by the COVID-19 pandemic. To better appreciate peoples' opinions and concerns with regards to the socio-economic conditions of addiction, mental health, unemployment and immigration, we collect data for a period of approximately 3 months in the beginning of the pandemic. We analyze the term and co-occurrence frequencies to identify the most commonly occurring words and bigrams in the discussion for each of the four categories. We conduct semantic role labeling to determine the action words in each category and then adopt a LSTM-based dependency parsing model to identify the main nouns linked with these action words. We then adopt a seeded topic modeling approach to automatically identify the main topics of discussion in each category. We finally conclude with a sentiment analysis of the tweets in each category to determine the overall sentiment associated with each category. Our fine-grained linguistic study unearths the difficulties experienced by the people (e.g., action verb need associated with nouns such as aid and assistance in the unemployment category). We also observe that the overall sentiment in the tweets is negative, driven by people experiencing the pains of job loss, deportation, and the difficulty in accessing programs and treatments related to addiction. Our analysis highlights the main challenges experienced by the people during the start of the COVID-19 crisis and lays the foundation for recognizing and developing the most pertinent public and social policies so as to minimize peoples' suffering in case of a future pandemic. © 2021 IEEE.

Poster: COVID-19 Case Prediction using Cellular Network Traffic

In this paper, our goal is to leverage cellular network traffic data to model and forecast the number of COVID-19 infections in the future. To this end, we partner with one of the main cellular network providers in Brazil, TIM Brazil, and collect and analyze cellular network connections from 973 antennas for all users in the city of Rio de Janeiro and its suburbs. We develop a Markovian model that captures the mobility of individuals across municipalities of the city. The transition probabilities of the Markov chain are determined by analyzing user-level mobility events between antennas from the cellular network connectivity logs. We combine the aggregate mobility characteristics across municipalities as evidenced from the transition probabilities with the number of reported COVID-19 cases in a municipality during a particular week to design mobility-aware COVID-19 case prediction models that predict the number of cases for the following week. Our experiments demonstrate that our mobility-aware models significantly outperform a baseline mobility-agnostic linear regression model in terms of metrics such as Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). © 2021 IFIP.

Analyzing societal impact of COVID-19: A study during the early days of the pandemic

In this paper, we collect and study Twitter communications 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 an 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. © 2020 IEEE.