Parameterisation of serial interval distribution.

Accurate real-time estimation of the effective reproduction number (Rt) is critical for infectious disease surveillance and response. In vector-borne diseases like dengue, temperature strongly influences disease transmission by affecting generation times. However, most existing Rt estimation methods assume a fixed generation interval, leading to biased estimates and unreliable assessments of transmission risk in settings with fluctuating temperatures. In this study, we proposed and evaluated a novel framework to estimate a temperature-dependent reproduction number (td-Rt) that dynamically updates the generation interval distribution based on observed temperature data. We obtained real-time estimates of td-Rt through an adapted Bayesian recursive filtering process. Using real and simulated data for a temperature-sensitive disease (dengue), we evaluated the performance of td-Rt against the typically used temperature-independent reproduction number (ti-Rt) and angular reproduction number (), which does not require specification of the generation interval. Simulated data was generated under varying patterns of underlying Rt and temperature datasets. Performance was evaluated by classification accuracy, defined by the proportion of instances where estimated Rt correctly identified whether the true Rt was above or below 1. We found that td-Rt generally outperformed ti-Rt and in classifying periods of epidemic growth. td-Rt achieved the highest classification accuracy in 54 of 72 simulation scenarios, with accuracy ranging from 37.1%–95.9%. td-Rt accuracy was highest in scenarios with greater temperature variability, surpassing other methods by up to 20%. With Singapore dengue case data, td-Rt and signals showed 75% similarity, highlighting ’s potential as a complementary measure that is less sensitive to model assumptions. These findings highlight the importance of accounting for temperature in real-time transmissibility estimates, as temperature-driven variations in generation time can introduce model misspecification and bias. Incorporating temperature is especially crucial for climate-sensitive diseases like dengue. Future work could extend this framework to other pathogens and additional transmission-relevant covariates.