## 1 Introduction
#### Literature Review  
Detecting a shift from one regime to another can be difficult [23] because a regime is, essentially, a context with a fundamentally different set of parameters. To fit a model to one regime and to persist in applying it after a shift to a new regime, instead of fitting a new model, would cause the model to start performing poorly. One reason why it is difficult to detect a regime change is that the change can be sudden/abrupt, incremental, gradual, or recurring/oscillating [24]. In our problem, however, it is simple to detect a switch in regime, because it pertains to state lockdowns. We take the midpoint of the time window in which governors declared lockdowns as the start of the lockdown regime and the midpoint of the time window in which governors lifted their lockdowns as the start of the reopen regime. According to theories of individual decision making and the healthcare literature, key variables to model the incidence of COVID-19 cases may include individual beliefs and perceptions, but also social factors, such as observations of other individuals, and influences from larger contexts: neighborhood norms, business environment, government restrictions, etc. [25; 26]. There are \"top-down\" forces applying constraints (business and school closures) and \"lateral forces\", i.e., person to person interactions (verbal, physical), as well as social comparisons and social mirroring, e.g., seeing others wearing a mask or maintaining a physical distance of 6+ feet. Some of these influences are explicit and processed deliberately, whereas others are cultural and largely unconscious. People generally try to behave consistent with their physical and social environments, minimizing their health risks while carrying on with their daily lives. Under the best of circumstances, we know that individuals are boundedly rational [27; 28]. They are embedded in nested and overlapping contexts of information, which attempt to empower them [29]. They need to filter out most information, which is challenging in an age of unregulated social media, or else they experience information overload. In addition, many sources of information from the media and social media contain misinformation, and many people are not able to filter the signal from the noise [30]. Software bots have been shown to create and amplify misinformation [31]. In addition, people in a pandemic may be in a sustained state of emotional distress, economic duress, and general anxiety. Overall, the assumption that people are rational decision-makers is untenable during a combination of the pandemic [20; 21] and \"info-demic\" [32], i.e., a syndemic [33]. Focusing on the individual's immediate environment, we note the Neighborhood Health framework [34; 35]. This framework includes factors that impact human health:health hazards or stressors, the built environment, housing quality, social environments (safety, social connections, local institutions, etc. ), local resources (food, exercise, recreation), and the aesthetic quality of natural spaces. All of these can have large or small but cumulative impacts. This framework is a large network of social and physical factors operating at multiple levels, with feedback loops that influence health outcomes. The Neighborhood Health framework highlights the simultaneous effects of neighborhoods on health. Neighborhoods are both physical and social environments that have resources (and constraints), and they set boundaries for physical health exposure and set norms for appropriate behavior. During a pandemic, if one leaves home, there will be visual and verbal reminders of what is considered a significant health risk, e.g., not wearing a mask. Neighborhoods are much smaller units than a geographic state, but state policies regarding public health influence local government policies as well as the behaviors of its citizens in a top-down fashion. The impact is not deterministic, however. If one does not identify with the culture or policies of one's state, one can choose not to comply.