## 1 Introduction  
SARS-CoV-2 is similar to SARS-CoV-1 and MERS-CoV in that all three are members of the Betacoronavirus genus, but they are quite different in their incubation period, symptom severity, and transmissibility [1, 2]. The shorter incubation period, greater symptom severity, and lower transmissibility of both SARS-CoV-1 and MERS-CoV resulted in quick containment, and thus a higher fatality rate (deaths/cases) for both viruses. The longer incubation, lesser symptom severity (mild to none), and greater transmissibility for SARS-CoV-2 have resulted in a lower fatality for SARS-CoV-2. Like many other countries, the United States (federal, state, and local governments) has been uneven in the enforcement of lockdowns [3, 4]. Massachusetts, New York State, and Washington State had different dynamics. Washington was hit earliest as SARS-CoV-2 spread from Wuhan, China, whereas New York was hit second, likely by people entering New York City from all over the world. Rhode Island had strong border restrictions imposed by its governor, who used the national guard to divert travel from neighboring states [5]. State governors have varied in their inclination to create and enforce public health policies. Unsurprisingly, variation in compliance with public health directives has occurred. Individual freedom and economic activity have been constrained in the short-term for the long-term benefit of the public, and exacerbated by the federal government when it sent mixed messages [6]. Consequently, state governors have varied widely in their declarations of lockdowns (We use the term lockdowns rather than voluntary stay-at-home or mandatory shelter-in-place order for brevity, while acknowledging that governors in different US states have implemented them with varying degrees of strictness). In early March 2020, many governors decided it was time to impose state-wide closures of schoolsand non-essential businesses. The decisions were made at the state level by the governors, but a rapid cascading of lockdowns spread across the country. A substantial shift in day-to-day life began for many people. Many adults and children started to work/study primarily or exclusively from home. Epidemiologists typically make assumptions about person-to-person contact and infer \(\mathrm{R}_{0}\), the spread rate of a virus, in a Susceptible-Exposed-Infective-Recovered model [7]. Other researchers have modeled the pandemic with different methods and assumptions, depending on their analytical perspective. Operations researchers and others have used mathematical models with \(\mathrm{R}_{0}\) to model the spread of the coronavirus [8; 9]. Sociologists and psychologists have used survey data models [10], archival data repositories [11], meta-analyses [12], or qualitative methods [13] to provide guidance for individual and collective decision-making. In this paper, we take a different approach. We do not make assumptions about \(\mathrm{R}_{0}\). Instead, we apply machine learning at the US state level of analysis, where there is significant variation in terms of public health capabilities and policies, healthcare quality, and population health. There are also geopolitical variations in terms of population density and climate, which may make people more or less inclined to venture outdoors. Finally, political proclivities vary across the country, with different attitudes toward government, in general, and health advisories in particular [3]. We look at state characteristics as well as aggregate human behavior, including movement shown by smartphone geolocation. Our target variable for all models is the number of COVID-19 cases, i.e., patients diagnosed and under some kind of treatment. In this paper, we analyze the spread of COVID-19 cases in the United States from 13 March 2020 to 31 May 2020. We divide this period into two regimes to reflect lockdown (13 March-15 April) and reopen (16 April-31 May). We observe this division to recognize two distinct regimes of American life.