Real-time Estimation of Epidemiologic Parameters from Contact Tracing Data During an Emerging Infectious Disease Outbreak

Background: Contact tracing can provide accurate information on relevant parameters of an ongoing emerging infectious disease outbreak. This is crucial to investigators seeking to control such an outbreak. However, crude contact tracing data are difficult to interpret and methods for analyzing these data are scarce. We present a method to estimate and visualize key outbreak parameters from contact tracing information in real time by taking into account data censoring. Methods/results: Exposure type-specific attack rates and the reproduction number R(t) are estimated from contact tracing data by using maximum likelihood estimation to account for censored data. The attack rates reflect, in the context of contact tracing, the specificity of the contact definition; a higher value indicates relatively efficient contact tracing. The evolution of R(t) over time provides information regarding the effectiveness of interventions. To allow a real-time overview of the outbreak, the attack rates and the evolution of R(t) over time are visualized together with the case-contact network and epicurve. We applied the method to a well-documented smallpox outbreak in The Netherlands to demonstrate the added value. Conclusion: Our method facilitates the analysis of contact tracing information by quickly turning it into accessible information, helping outbreak investigators to make real-time decisions to more effectively and efficiently control infectious disease outbreaks.