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 out-perform a baseline mobility-agnostic linear regression model in terms of metrics such as Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE).

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.

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).

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.