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

Area of Science:

• Epidemiology
• Mathematical Biology
• Public Health

Background:

• Accurate real-time estimation of the effective reproduction number (Rt) is vital for infectious disease control.
• Temperature significantly impacts vector-borne disease transmission, affecting generation times.
• Existing Rt estimation methods often assume a fixed generation interval, leading to biased results in fluctuating temperatures.

Purpose of the Study:

• To propose and evaluate a novel framework for estimating a temperature-dependent reproduction number (td-Rt).
• To dynamically update the generation interval distribution using observed temperature data for more accurate Rt estimation.
• To assess the performance of td-Rt against temperature-independent (ti-Rt) and angular reproduction number ([Formula: see text]) methods.

Main Methods:

• Developed a temperature-dependent reproduction number (td-Rt) estimation framework.
• Employed an adapted Bayesian recursive filtering process for real-time td-Rt estimation.
• Utilized simulated and real-world dengue case data for performance evaluation against ti-Rt and [Formula: see text].

Main Results:

• td-Rt generally outperformed ti-Rt and [Formula: see text] in identifying epidemic growth periods.
• td-Rt achieved the highest classification accuracy in 54 of 72 simulation scenarios (37.1%-95.9%).
• td-Rt accuracy was highest in scenarios with greater temperature variability, improving by up to 20%.

Conclusions:

• Accounting for temperature-driven variations in generation time is crucial for accurate real-time transmissibility estimates.
• The td-Rt framework offers improved accuracy for climate-sensitive diseases like dengue.
• Incorporating temperature-dependent generation intervals enhances infectious disease surveillance and response strategies.