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The emergence of COVID-19 in the United States resulted in a series of federal and state-level lock-downs and COVID-19 related health mandates to manage the spread of the virus. These policies may negatively impact the mental health state of the population. This study focused on the trends in mental health indicators following the COVID-19 pandemic amongst four United States geographical regions, and political party preferences. Indicators of interest included feeling anxious, feeling depressed, and worried about finances. Survey data from the Delphi Group at Carnegie Mellon University were analyzed using clustering algorithms and dynamic connectome obtained from sliding window analysis. Connectome refers to the description of connectivity on a network. United States maps were generated to observe spatial trends and identify communities with similar mental health and COVID-19 trends. Between March 3rd, 2021, and January 10th, 2022, states in the southern geographic region showed similar trends for reported values of feeling anxious and worried about finances. There were no identifiable communities resembling geographical regions or political party preference for the feeling depressed indicator. We observed a high degree of correlation among southern states as well as within Republican states, where the highest correlation values from the dynamic connectome for feeling anxious and feeling depressed variables seemingly overlapped with an increase in COVID-19 related cases, deaths, hospitalizations, and rapid spread of the COVID-19 Delta variant. 

The COVID-19 pandemic has had a devastating impact on the world at large with over 750 million cases and almost 7 million deaths reported thus far. Of those, over 100 million cases and 1 million deaths have occurred in the United States of America (USA). The mental health of the general population has been impacted by several aspects of the pandemic including lockdowns, media sensationalism, social isolation, and spread of the disease. In this paper, we examine the associations that social isolation and COVID-19 infection and related death had with the prevalence of anxiety and depression in the general population of the USA in a state-by-state multiple time-series analysis. Vector Error Correction Models are estimated and we subsequently evaluated the coefficients of the estimated models and calculated their impulse response functions for further interpretation. We found that COVID-19 incidence was positively associated with anxiety across the studied period for a majority of states. Variables related to social isolation had a varied effect depending on the state being considered.