This article is an effort to isolate and estimate the impact of political party control of state government on the length of time it took U.S. states to issue shelter-in-place orders (SIPOs) in an effort to control the spread of COVID-19. We adopt a two-step process to isolate the effect of politics. First, we measure the number of days between the date that state-level cases first exceeded one case per 100,000 population and state issuance of a SIPO. This permits us to compare governor's choices in similar contextswhen the disease is known to be present in the community. Second, we use a statistical toolsurvival analysisto differentiate between state characteristics that might be associated with higher risks of uncontrolled spread and the impact of political party control. We find that the timing of disease progression was remarkably similar across states, suggesting that many states reached the 1:100,000 benchmark at about the same time. By contrast, we find that, even after controlling for a variety of factors, a Republican governor coupled with a Republican majority in the state senate predicts delays in the implementation of SIPOs delays on the order of two days for Republican states that did act, but no action by more than one-third of those states after more than two months above 1:100,000. In contrast, potentially important decision-making factors such as urbanization, elderly population, hospital beds, extent of chronic diseases, and budget capacity of the state government serve, at best, as poor predictors of SIPO timing.

The formation of a balanced system of parameters (budget; duration of implementation; customers and developers' satisfaction with the results of the project the course of the project) is a critical factor in the successful implementation of the project. Development of the formal models, which described the direct and cross-linking relationship between the input (budget, implementation time) and output (customer and developer satisfaction) parameters of the project creates the conditions for increasing the validity of decisions related to the project organization. The feature of empirical models constructing is the need to share actual (historical) data about budgets and the previously implemented projects duration, and subjective estimates (determined by experience) of consumers and developers. The paper considers the approach for the construction of parametric regression models based on the joint use of measured data and expert estimates. The additive or multiplicative form of interaction of direct and cross-linking relationship is substantiated, this depend on which target group (consumers or developers) are assigned the outputs of a multivariable object.

Many U.S. states changed election policies leading up to the November 3, 2020, general election to reduce the potential spread of COVID-19, and policy changes at the state level resulted in uneven access to voting options outside the polling site on the day of the election. This preliminary research examines the extent of in-person voting and other methods for voting as percentages of the overall population, separated by state, to determine if such policy changes helped reduce the spread of COVID-19. The data is correlated with the increase in the SARS-CoV-2 virus the week leading into the election compared to two weeks after the election. Political party in control of the state executive, urbanization, and the relative size of state government are also considered. While numerous court cases regarding the fairness of electoral methods were launched during the 2020 election cycle, the focus of this article is whether the percent of the population who voted in person on the day of the election may have differentially increased the spread of COVID-19 within the 50 U.S. states as well as the extent that the public service managed the election process in a safe manner by mitigating the risk of COVID-19.