Lessons Learned from the Policies Developed for the Management of the COVID-19 Pandemic in Northern Cyprus: A Mixed-Methods Study.

Background/Objectives: The COVID-19 (Coronavirus Disease, 2019) pandemic affected all countries in a variety of ways, and forced policymakers to adapt national health infrastructure. In this context, the strategic adaptation and policy evolution of small island states are understudied. Therefore, the objective of this study was to quantitatively analyse the relationship between confirmed COVID-19 cases and health policy decisions in Northern Cyprus. We also examined the shifting management strategies employed during the pandemic using a replicable statistical analysis framework. Methods: In this mixed-methods study, we used systematic thematic analysis to categorise official policy decisions from March 2020 to December 2022. Yearly linear regression models using SPSS and Python correlated the monthly number of decisions with the number of confirmed COVID-19 cases. The analyses included R2 values, p-values, and visualisations with 95% confidence intervals. Results: The findings of this study highlight a three-phase strategic period. In 2020, the results (R2 = 0.03, p = 0.63) showed no significant relationship, indicating initial uncertainty. The results (R2 = 0.60, p = 0.003) indicate a strong negative correlation in 2021, which reflects the consistency of the proactive suppression strategies adopted. Conversely, for 2022, the results (R2 = 0.79, p < 0.001) show a strong positive correlation representing the shift to a reactive mitigation strategy, in which the government responded based on case peaks. Conclusions: This study's primary finding is that strategic agility was key to managing the pandemic. For small island states in particular, the effectiveness of geographic advantages like border control depends on a coherent strategy that transcends initial uncertainty. Our data-driven framework provides a tool for analysing this strategic evolution and guiding responses to future pandemics.