This three-essay dissertation examines how and why U.S. state governments change the stringency of COVID-19 policies under uncertainty and urgency. The three essays explore the applicability of three theoretical lens - policy diffusion, policy learning, and policy termination - in explaining policy change decisions. The first essay examines how two distinct policy diffusion mechanisms, namely regional emulation and lesson-drawing, shape the initial policy lift decisions during the early stage of the pandemic response. The second essay investigates the role of instrumental and political learning in explaining stringency changes in two directions: expansion and relaxation, during the middle stage of the pandemic response when states began to perceive the pandemic as a new normal. Drawing from the politics-science debate, the third essay investigates how states’ termination decisions regarding the face-mask policy are influenced by political and scientific considerations in the later response stage. By utilizing the fuzzy-set and multi-value Qualitative Comparative Analysis (QCA), the findings from the three essays reveal complex rationales behind policy change decisions. This knowledge is valuable for state policymakers as they navigate the complexity of balancing public health concerns, political interests, and socio-economic goals. Overall, this dissertation aligns with the growing interest among policy scholars and practitioners in enhancing policy response strategies in the face of novel crises. The implications derived from this research are particularly relevant in contexts where urgent and frequent policy adjustments are required to address the ever-changing and creeping nature of the crisis.