Bias in Estimating Subcritical Reproduction Numbers Under Imperfect Observation and Overlapping Transmission Chains:...

Estimating transmission potential and risk factors for emerging infections often relies on incomplete surveillance data. When infections are missed or their sources are misclassified, estimates of the effective reproduction number (Rs) and associations between case attributes and infection source may be biased. We developed an analytic framework to quantify and correct these biases, treating imperfect observation and entanglement of transmission chains as forms of misclassification that distort estimates of transmission parameters. The bias depends on the probability of case observation (Pobs) and the extent of concurrent introductions (Rp). We illustrate this framework using surveillance data for mpox in the Democratic Republic of the Congo (1981-1986, 2013-2017), showing that incomplete observation leads to underestimation of Rs, whereas overlapping introductions can cause overestimation. Both types of bias also attenuate odds ratios for traits that distinguish primary (spillover) from secondary (human-tohuman) infections. Accounting for these biases provides context for the global emergence of mpox in 2022 and for how current transmission patterns differ from historical trends. These results demonstrate how standard epidemiologic concepts of misclassification and missingness can clarify bias in transmission studies and improve interpretation of surveillance data for emerging pathogens, including zoonoses and vaccine-preventable infections.