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Objective To investigate the impact of non-pharmaceutical interventions (NPIs) on COVID-19 transmission in different pandemic stages across 12 Asian countries.Design This was an ecological study of publicly available data. This study used the Stringency Index from the Oxford COVID-19 Government Response Tracker (OxCGRT) as a composite measure of implementation strictness of non-pharmaceutical interventions.Setting Data were obtained from Our World in Data and OxCGRT (January 2021 to September 2022).Participants 12 countries were included in the study: Azerbaijan, Turkey, Bahrain, Israel, Lebanon, Japan, South Korea, Singapore, Malaysia, Thailand, Cambodia, and Indonesia.Main outcome measure The instantaneous reproduction number (Rt). Rt is defined as the expected number of secondary infections occurring at time t, divided by the number of infected individuals, each scaled by their relative infectiousness at time t (an individual’s relative infectiousness is based on the generation interval and time).Results Three different pandemic development patterns were identified: Cluster 1 countries (marked by distinct fluctuation), Cluster 2 countries (characterised by smaller fluctuation) and Cluster 3 countries (featuring a peak between July and September). An increase in the Stringency Index was associated with a significant decrease in Rt during warmer seasons in both Cluster 1 and 2 (both p values < 0.05). For Cluster 1, the accumulated local effects (ALEs) of the Stringency Index reached a maximum magnitude of approximately 1.80 during April–June, declining from 1.55 (95% CI 1.36 to 1.75) to −0.25 (95% CI −0.33 to −0.17). This was followed by a secondary ALE magnitude of about 0.47 during July–September, decreasing from 0.15 (95% CI 0.12 to 0.18) to −0.32 (95% CI −0.39 to −0.25). Similarly, in Cluster 2, the ALE of the Stringency Index peaked at a magnitude of about 1.25 during July–September, dropping from 0.65 (95% CI 0.54 to 0.76) to −0.60 (95% CI −0.70 to −0.51). In Cluster 3, during July–September, once the Stringency Index reached a certain threshold, the Rt value initially declined but subsequently increased again.Conclusion This study demonstrates that the effectiveness of NPIs varies with seasonal changes and pandemic patterns. Therefore, to improve the efficiency of public health responses, policymakers should tailor NPI strategies based on seasonal variations and local socio-structural factors. The findings provide new insights for future research on the impact of NPI implementation during pandemics, which plays a critical role in pandemic management.