Estimating the strength of symptom propagation from synthetic data

PhoebeAsplin

University of Warwick

"Estimating the strength of symptom propagation from synthetic data"

Symptom propagation occurs when an individual’s symptom severity is correlated with the symptom severity of the individual who infected them. Determining whether - and to what extent - these correlations exist requires data-driven methods. In this study, we use synthetic data to determine the types of data required to estimate the strength of symptom propagation and investigate the effect of reporting bias on these estimates. We found that even a relatively small number of contact tracing data points was sufficient to gain a reasonable estimate for the strength of symptom propagation. Increasing the number of contact tracing data points further improved our estimates. In contrast, population incidence alone was insufficient to accurately estimate the symptom propagation parameters, even with a large number of data points. Nonetheless, concurrently using population incidence data with contact tracing data led to increased accuracy when estimating the overall disease severity. We then considered the effect of severe cases being more likely to be reported in the contact tracing data. When contact tracing data alone was used, we found that our estimates for the strength of symptom propagation were robust to all reporting bias scenarios considered. However, the reporting bias led us to overestimate the overall disease severity. Using population incidence data in addition to contact tracing data reduced the error in disease severity but at the cost of increasing the error in the strength of symptom propagation when reporting bias was in both primary and secondary cases. Consequently, these errors led to us sometimes finding support for symptom propagation, even when the synthetic data was generated without.
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