Measuring real-time disease transmissibility with temperature-dependent generation intervals

by Esther Li Wen Choo, Kris V. Parag, Jo Yi Chow, Jue Tao Lim

Accurate real-time estimation of the effective reproduction number (R_t) 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 R_t 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-R_t) that dynamically updates the generation interval distribution based on observed temperature data. We obtained real-time estimates of td-R_t through an adapted Bayesian recursive filtering process. Using real and simulated data for a temperature-sensitive disease (dengue), we evaluated the performance of td-R_t against the typically used temperature-independent reproduction number (ti-R_t) and angular reproduction number (Ωt), which does not require specification of the generation interval. Simulated data was generated under varying patterns of underlying R_t and temperature datasets. Performance was evaluated by classification accuracy, defined by the proportion of instances where estimated R_t correctly identified whether the true R_t was above or below 1. We found that td-R_t generally outperformed ti-R_t and Ωt in classifying periods of epidemic growth. td-R_t achieved the highest classification accuracy in 54 of 72 simulation scenarios, with accuracy ranging from 37.1%–95.9%. td-R_t accuracy was highest in scenarios with greater temperature variability, surpassing other methods by up to 20%. With Singapore dengue case data, td-R_t and Ωt signals showed 75% similarity, highlighting Ωt’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.