Scientific evidence suggests that nonpharmaceutical interventions (NPIs) effectively curb the spread of COVID-19 before a pharmaceutical solution. Implementing these interventions also significantly affects regular socioeconomic activities and practices of social, racial, and political justice. Local governments often face conflicting goals during policymaking. Striking a balance among competing goals during a global pandemic is a fine science of governance. How well state governments consume the scientific evidence and maintain such a balance remains less understood. This study employs a set of Bayesian hierarchical models to evaluate how state governments in the United States use scientific evidence to balance the fighting against the spread of COVID-19 disease and socioeconomic, racial, social justice, and other demands. We modeled the relationships between five NPI strategies and COVID-19 caseload information and used the modeled result to perform a balanced governance evaluation. The results suggest that governmental attitude and guidance effectively guide the public to fight back against a global pandemic. The more detailed spatiotemporally varying coefficient process model produces 612,000 spatiotemporally varying coefficients, suggesting all measures sometimes work somewhere. Summarized results indicate that states emphasizing NPIs fared well in curbing the spread of COVID-19. With over 1 million deaths due to COVID-19 in the United States, we feel the balance scale likely needs to tip toward preserving human lives. Our evaluation of governance policies is hence based on such an argument. This study aims to provide decision support for policymaking during a national emergency.