Objective measures for sentinel surveillance in network epidemiology

TL;DR

This paper compares three objective measures for sentinel surveillance in network epidemiology, showing that their effectiveness varies with network structure and disease parameters, emphasizing the importance of choosing the right measure for specific scenarios.

Contribution

It systematically evaluates three different objective measures for sentinel surveillance on static and temporal networks, highlighting their differing rankings and scenario-dependent effectiveness.

Findings

Different measures can rank nodes very differently.

No single network structure predicts the best measure.

Scenario-specific measure selection is crucial.

Abstract

Assume one has the capability of determining whether a node in a network is infectious or not by probing them. Then problem of optimizing sentinel surveillance in networks is to identify the nodes to probe such that an emerging disease outbreak can be discovered early or reliably. Whether the emphasis should be on early or reliable detection depends on the scenario in question. We investigate three objective measures from the literature quantifying the performance of nodes in sentinel surveillance -- the time to detection or extinction, the time to detection, and the frequency of detection. As a basis for the comparison, we use the susceptible-infectious-recovered model on static and temporal networks of human contacts. We show that, for some regions of parameter space, the three objective measures can rank the nodes very differently. This means sentinel surveillance is a class of problems, and solutions need to chose an objective measure for the particular scenario in question. As opposed to other problems in network epidemiology, we draw similar conclusions from the static and temporal networks. Furthermore, we do not find one type of network structure that predicts the objective measures -- that depends both on the data set and the SIR parameter values.