## 3 Results  
### Lagged Correlations for Google Trend and Twitter  
We first looked at COVID-19 infections, Twitter tweets and Google search data for all 50 U.S. states in early 2020 when the pandemic first appeared in the U.S. Specifically, we gathered state-level daily COVID-19 confirmed cases from 20 January 2020 to 30 May 2020. For the same period, we quantified the number of COVID-19 related tweets in each individual state by analyzing the geographic information from an open COVID-19 Twitter chatter dataset [15] (see Methods for detail). For Google Trends, we searched the data within a state by using a combination of one of three keywords ('coronavirus', 'COVID' and 'COVID-19') and the state's full name as an integrated search input (e.g., 'coronavirus Massachusetts'). To capture the baseline interest before the pandemic, we set the starting time two weeks earlier than the COVID-19 outbreak date in a given state. It is known that the novel coronavirus was identified as the cause of this disease in early January and was then officially named novel coronavirus pneumonia by the NHC of China and later 'coronavirus disease 2019' (usually abbreviated as COVID-19, COVID) by the WHO in February. Therefore, we believe the selected three keywords 'coronavirus', 'COVID' and 'COVID-19' are the most likely search terms for individuals and thus are sufficient to include most Internet content related to COVID-19 in our search period. We used the combination of keywords and states' names because residents are usually more concerned about their local situation of the pandemic, and thus, such an integrated search term more reasonably reflects the local Internet interest for COVID-19. We find that without adding the state's full name, the search trends with the above keywords would become too similar in all 50 states (see SI Figure S1), which is undesirable to investigate the differences in state-by-state interest. In Figure 1, we illustrate the comparison among the daily confirmed cases, the number of COVID-19 related tweets and the Google Trends indexes (with different search terms) in Oregon, Massachusetts, Iowa and California up to 30 May 2020 (see Methods for details of data processing). One can observe that the overall graph patterns are different between states. We then investigated the relationship between the COVID-19 pandemic spreading and the Internet data in all 50 U.S. states. Figure 2 shows the lagged Spearman correlation between the Internet data from Twitter and Google Trends and the reported COVID-19 cases for the selected four states. To quantify the correlation strength of the tweeting behavior and the search activity for an individual state, we denote \(c^{*}\) as the highest correlation coefficient. In principle, a larger \(c^{*}\) indicates a higher accuracy in correlating with the state-level pandemic. Besides, we define \(l^{*}\) as the optimal lag achieving \(c^{*}\). In other words, when the curve of Internet data is right-shifted by \(l^{*}\) days, it will most closely resemble the curve of COVID-19 daily infection. Therefore, \(l^{*}\) itself is a direct metric to measure the distance between the two'spikes' of two curves, i.e., the time of the outbreak of the Internet's collective awareness before the outbreak of epidemic. A larger \(l^{*}\) corresponds to an earlier peak of Internet searches and tweeting about COVID-19, indicating that residents start being active on the Internet earlier. An additional advantage of using \(l^{*}\) is that it can be automatically quantified through the lagged correlation analysis. In other words, it is parameter-free and is an intrinsic quantity expressed purely from the observed data. We find that \(c^{*}\) and \(l^{*}\) are quite different among different states and between Google Trends and Twitter (see Figure 2). For instance, for Massachusetts, \(c^{*}\) is 0.80 with \(l^{*}=27\) using the Twitter data and is up to 0.90 with \(l^{*}=17\) for tracking 'COVID Massachusetts' on GoogleTrends.