## 1 Introduction
To model them as one undivided timeframe would be to disregard the different day-to-day realities of physical movement and social interactions. Each regime was a different world of individual and collective behavior with different networks, perceptions of physical risk, and different trade-offs between physical safety and economic activity. In each regime, people were prevented or discouraged from a long list of specific activities and behaviors, e.g., going to gyms or bars. In each regime, some businesses were closed or curtailed to minimize person-to-person exposure. Exceptions in all states were made for businesses considered essential, such as grocery stores and hospitals. Each regime was a new environment in which different variables of the environment were relevant and influential. We have three general expectations based on human mobility, social distancing, and control measures (school closures, public gathering bans) implemented in China [14; 15; 16], as well as travel restrictions, social distancing, and home quarantine implemented in Australia [17]. All of these studies tracked, modeled, or simulated changes in COVID-19 within or between different cities, states, and regions. They generally found that restrictions, e.g., travel bans or lockdowns, were more helpful earlier in the pandemic and less helpful later in the pandemic for slowing the spread of the virus. Hence, we model the pandemic in different time regimes with location and mobility as core factors:  
1. We expect that the factors associated with COVID-19 cases vary substantially between the two time regimes. 2. We expect that state-by-state differences are a significant driver of cases. 3. We expect that human mobility is a significant driver of cases. To model the effects of human behaviors, we recognize that direct and indirect contact may increase the likelihood of the virus spreading. In addition, both linear and non-linear relationships may be in effect. Some model variables may be common to the two regimes, whereas others are likely to be different. The case numbers may change from regime to regime as new medical treatments are discovered. Machine learning methods may be highly effective at capturing these various relationships to predict our target variable, COVID-19 cases, in different models within the regimes. We assume that GPS data is a good metric for tracking individual movement because cell/mobile phones are nearly ubiquitous, and people rarely turn off their geolocation tracking service. Geolocation information can be aggregated and de-identified from passively collected mobile phone data, and it has been used to model the temporal and spatial dynamics of epidemics and pandemics, including malaria [18], cholera [19], measles [20], dengue [21] and Ebola [22]. It is thus an unobtrusive and non-invasive way to capture individual movement, aggregating to the state level, so that individual privacy rights are respected. During the Spring 2020 lockdowns, there was a change in the propensity of people to travel out of state. We also note that there seemed to be a difference based on the political inclination of the state. We, therefore, choose to include Political Color, a rough indicator of the state's governor stance on lockdowns, as one of our variables of interest in predicting COVID-19 cases. The rest of the paper is organized as follows. We first review some technical and behavioral streams of literature in the area of public health and analytics. We then describe our methods, including data sources for the variables of interest and descriptive statistics. We then describe the results of our machine learning models and discuss our interpretation of the best model. We conclude by describing the overall research contributions, as well as limitations and future directions.