Journal of Data Science

This study analyzes the impact of the COVID-19 pandemic on subjective well-being as measured through Twitter for the countries of Japan and Italy. In the first nine months of 2020, the Twitter indicators dropped by 11.7% for Italy and 8.3% for Japan compared to the last two months of 2019, and even more compared to their historical means. To understand what affected the Twitter mood so strongly, the study considers a pool of potential factors including: climate and air quality data, number of COVID-19 cases and deaths, Facebook COVID-19 and flu-like symptoms global survey data, coronavirus-related Google search data, policy intervention measures, human mobility data, macro economic variables, as well as health and stress proxy variables. This study proposes a framework to analyse and assess the relative impact of these external factors on the dynamic of Twitter mood and further implements a structural model to describe the underlying concept of subjective well-being. It turns out that prolonged mobility restrictions, flu and Covid-like symptoms, economic uncertainty and low levels of quality in social interactions have a negative impact on well-being.

The spreading pattern of COVID-19 in the early months of the pandemic differs a lot across the states in the US under different quarantine measures and reopening policies. We proposed to cluster the US states into distinct communities based on the daily new confirmed case counts from March 22 to July 25 via a nonnegative matrix factorization (NMF) followed by a k-means clustering procedure on the coefficients of the NMF basis. A cross-validation method was employed to select the rank of the NMF. The method clustered the 49 continental states (including the District of Columbia) into 7 groups, two of which contained a single state. To investigate the dynamics of the clustering results over time, the same method was successively applied to the time periods with an increment of one week, starting from the period of March 22 to March 28. The results suggested a change point in the clustering in the week starting on May 30, caused by a combined impact of both quarantine measures and reopening policies.

The coronavirus disease 2019 (COVID-19) pandemic caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has placed epidemic modeling at the center of attention of public policymaking. Predicting the severity and speed of transmission of COVID-19 is crucial to resource management and developing strategies to deal with this epidemic. Based on the available data from current and previous outbreaks, many efforts have been made to develop epidemiological models, including statistical models, computer simulations, mathematical representations of the virus and its impacts, and many more. Despite their usefulness, modeling and forecasting the spread of COVID-19 remains a challenge. In this article, we give an overview of the unique features and issues of COVID-19 data and how they impact epidemic modeling and projection. In addition, we illustrate how various models could be connected to each other. Moreover, we provide new data science perspectives on the challenges of COVID-19 forecasting, from data collection, curation, and validation to the limitations of models, as well as the uncertainty of the forecast. Finally, we discuss some data science practices that are crucial to more robust and accurate epidemic forecasting.