Abstract
We utilize functional data analysis techniques to investigate patterns of COVID-19 positivity and mortality in the US and their associations with Google search trends for COVID-19-related symptoms. Specifically, we represent state-level time series data for COVID-19 and Google search trends for symptoms as smoothed functional curves. Given these functional data, we explore the modes of variation in the data using functional principal component analysis (FPCA). We also apply functional clustering analysis to identify patterns of COVID-19 confirmed case and death trajectories across the US. Moreover, we quantify the associations between Google COVID-19 search trends for symptoms and COVID-19 confirmed case and death trajectories using dynamic correlation. Finally, we examine the dynamics of correlations for the top nine Google search trends of symptoms commonly associated with COVID-19 confirmed case and death trajectories. Our results reveal and characterize distinct patterns for COVID-19 spread and mortality across the US. The dynamics of these correlations suggest the feasibility of using Google queries to forecast COVID-19 cases and mortality for up to three weeks in advance. Our results and analysis framework set the stage for the development of predictive models for forecasting COVID-19 confirmed cases and deaths using historical data and Google search trends for nine symptoms associated with both outcomes.
Highlights
In December 2019, an outbreak of the coronavirus disease (COVID-19) caused by the spread of the 2019 novel coronavirus (SARS-CoV-2) originated in the city of Wuhan inChina
We utilized different functional data techniques (including functional principalprincipal component analysis, analysis, dynamic dynamic correlations, and analysis techniques to analyze and categorize different patterns of the timetions, and functional canonical correlation) to analyze and categorize different patterns of dynamics of COVID-19 confirmed and death cases andcases to identify between the time-dynamics of COVID-19 confirmed and death and toassociations identify associations trajectories of COVID-19 symptoms search trends and COVID-19 trajectories of between trajectories of Google COVID-19 symptoms search trends and COVID-19 trajecconfirmed and death cases
COVID-19 confirmed cases and deaths time-series as well as selected Google search trends for symptoms related to COVID-19 infection
Summary
In December 2019, an outbreak of the coronavirus disease (COVID-19) caused by the spread of the 2019 novel coronavirus (SARS-CoV-2) originated in the city of Wuhan inChina. Due to the exponential worldwide spread of the virus, the World Health Organization (WHO) declared COVID-19 a pandemic on 11 March 2020. //coronavirus.jhu.edu/map.html (accessed on 11 January 2021), the number of COVID-19 worldwide confirmed cases reached more than 90 million (including more than 22 million cases in the US) and the number of global deaths reached more than 1.9 million (~376,000 in the US). The COVID-19 pandemic has caused a huge negative global impact on the economy [1], health [2,3], and education [4,5,6], with the US as one of the top countries affected by this pandemic. Tang et al [7] applied functional principal component analysis (FPCA) to examine the modes of variation for COVID-19 confirmed case trajectories for 50 US states, quantified the correlations between confirmed case and death trajectories using functional canonical correlation analysis (FCCA), and grouped the 50 states into five subgroups where states shared similar COVID-19 spread
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