ArticlePhysical review. E2018
Objective measures for sentinel surveillance in network epidemiology.
Article in Physical review. E, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- An integrated framework for modelling respiratory disease transmission and designing surveillance networks using a sentinel index.Royal Society open science · 2025Article
- Article
- Article
- Outbreak detection for temporal contact data.Applied network science · 2021Article
- COVID-19 and Networks.New generation computing · 2021Article
- Optimising risk-based surveillance for early detection of invasive plant pathogens.PLoS biology · 2020Article
- Global Cropland Connectivity: A Risk Factor for Invasion and Saturation by Emerging Pathogens and Pests.Bioscience · 2020Article
- Efficient sentinel surveillance strategies for preventing epidemics on networks.PLoS computational biology · 2019Article
- Modeling Epidemics in Seed Systems and Landscapes To Guide Management Strategies: The Case of Sweet Potato in Northern Uganda.Phytopathology · 2019Article
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Authors and funding
1 author.
Funding
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Abstract
Assume one has the capability of determining whether a node in a network is infectious or not by probing it. 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, i.e., that depends both on the data set and the SIR parameter values.
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