## 2 Materials and Methods  
COVID-19 Twitter Data. The COVID-19 tweets on Twitter are acquired from an open COVID-19 Twitter chatter dataset [15], which is a collection of the identifiers of tweets specifically using coronavirus-related keywords (coronavirus, 2019 nCoV, COVID19, CoronavirusPandemic, CoronaOutbreak, etc. ), starting from 27 January 2020. After hydrating the full JSON objects from these tweets' identifiers, we extract the daily number of tweets in the U.S. at the state level according to a tweet's location. This means that according to the Twitter developer API description, we lose 98% of the tweets, but we can be certain that they indeed come from a specific location. Specifically, we first identify all geo-located tweets (i.e., tweets associated with a geographic place), only retaining tweets with a location in the U.S. Then, we assign a tweet to a state using its specific location, such as city and town (see the heatmap of the number of geo-located tweets in 50 U.S. states until 30 September 2020 in the SI Figure S2). The study period and the smooth operation of the Twitter data are the same as that of COVID-19 infection cases. We would like to emphasize that the Twitter data used in this work are the raw count of tweets, i.e., the daily number of COVID-19 tweets in each state. In particular, the correlation quantities (i.e., \(c^{*}\) and \(l^{*}\) defined in this work) for each state can be obtained by looking at the curve pattern of COVID-19-related tweets. Therefore, as we iteratively correlate two curves, both representative of the size of a state, to obtain the best possible correlation value, there is no need to normalize it with the total number of tweets sent in a state. Google Trends and Keywords. As the most used search engine in the U.S., Google Trends ([[https://www.google.com/trends](https://www.google.com/trends)]([https://www.google.com/trends](https://www.google.com/trends)), accessed on 1 October 2020) provides an excellent proxy for Internet-search trends. The Google Trends index measures the search activity of a term compared to the most actively searched keyword for a selected region. For each individual state, we use a combination of a COVID-related keyword and the state's full name as an integrated search term throughout this paper. We also tried to use Google Trends at the state level; however, this led to very similar curves across all states. Specifically, for studying the U.S.' interest in the pandemic, we use the pytrends API to track three respective keywords, 'coronavirus', 'COVID' and 'COVID-19' on Google Trends among 50 U.S. states. For studying COVID testing, we select the corresponding keyword 'COVID testing' for querying Google Trends. After downloading the data, we smooth them with the same parameter used in the real data (i.e., 3 days-average for the three general keywords, 7 days average for 'COVID testing'). The time parameter is set to two weeks before the COVID-19 data in each state to fully capture the baseline interest. U.S. Interest in Google Trends. We did a cross-sectional comparison of several search interests within the U.S. region. Particularly, we downloaded the Google Trends index of 'coronavirus','sports','music', 'news' and 'weather' from 15 January 2020 to September 30, 2020, by setting the search area to the U.S. As a purely intuitive comparison, we did not apply a smoothing operation to the data. COVID-19 Testing Trends in U.S. We obtain the state-by-state testing trend through the COVID Tracking Project ([[https://coronavirus.jhu.edu/testing/individual-states](https://coronavirus.jhu.edu/testing/individual-states)]([https://coronavirus.jhu.edu/testing/individual-states](https://coronavirus.jhu.edu/testing/individual-states)), access on 1 October 2020), which is as far as we know the best source to cite for daily U.S. test numbers. We include the data from 1 February 2020 to 30 September 2020 in this study.