Parameter estimation for contact tracing in graph-based models

TL;DR

This paper introduces a maximum-likelihood estimator for contact tracing parameters in a stochastic SIR model on trees, validated through simulations and applied to COVID-19 data from India.

Contribution

It develops a novel approximation-based estimator for contact tracing parameters that is effective even with partial data and small tracing probabilities.

Findings

Power-law and negative binomial distributions fit COVID-19 contact data well.

Tracing probability is estimated to be relatively large.

Estimates are robust to variations in the reproduction number.

Abstract

We adopt a maximum-likelihood framework to estimate parameters of a stochastic susceptible-infected-recovered (SIR) model with contact tracing on a rooted random tree. Given the number of detectees per index case, our estimator allows to determine the degree distribution of the random tree as well as the tracing probability. Since we do not discover all infectees via contact tracing, this estimation is non-trivial. To keep things simple and stable, we develop an approximation suited for realistic situations (contract tracing probability small, or the probability for the detection of index cases small). In this approximation, the only epidemiological parameter entering the estimator is $R_0$. The estimator is tested in a simulation study and is furthermore applied to covid-19 contact tracing data from India. The simulation study underlines the efficiency of the method. For the empirical covid-19 data, we compare different degree distributions and perform a sensitivity analysis. We find that particularly a power-law and a negative binomial degree distribution fit the data well and that the tracing probability is rather large. The sensitivity analysis shows no strong dependency of the estimates on the reproduction number. Finally, we discuss the relevance of our findings.