Efficient sentinel surveillance strategies for preventing epidemics on networks

Abstract

Surveillance plays a crucial role in preventing emerging infectious diseases from becoming epidemic. In circumstances where it is possible to monitor the infection status of certain people, transport hubs, or hospitals, early detection of the disease allows interventions to be implemented before most of the damage can occur, or at least its impact can be mitigated. This paper addresses the question of which nodes we should select in a network of individuals susceptible to some infectious disease in order to minimize the number of casualties. By simulating disease outbreaks on a collection of empirical and synthetic networks we show that the best strategy depends on topological characteristics of the network. For highly modular or spatially embedded networks it is better to place the sentinels on nodes distributed across different regions. However, if the degree heterogeneity is high, then a strategy that targets network hubs is preferred. We further consider the consequences of having an incomplete sample of the network and demonstrate that the value of new information diminishes as more data is collected. Finally we find further marginal improvements using two heuristics informed by known results in graph theory that exploit the fragmented structure of sparse network data.

Outbreak detection for temporal contact data

“…Moreover, the degree heuristic has been shown to perform well by Holme ( 2018 ) for small outbreaks and by Colman et al. ( 2019 ) for static networks with small modularity and high degree heterogeneity. This brings us to another important implication of the greedy approach.…”

“…Colman et al. ( 2019 ) assert that a strategy based on selecting high-degree nodes for outbreak detection can be suboptimal if a (static) network is highly modular and exhibits a rather low degree heterogeneity because those central nodes may be topologically close. Therefore, they propose strategies that select high-degree nodes in different parts of the network (modules or spatial regions).…”

“…The reader should bear in mind that this paper does not aim to examine a fully realistic disease spreading scenario. Instead, we use a simple SIR model which makes this paper comparable to many other works in the field of optimal surveillance and control (Bajardi et al 2012;Holme 2018;Colman et al 2019;Schirdewahn et al 2017). However, as will become clear later on, the greedy approach is model-agnostic and can be applied to any spreading model that can be simulated on networks.…”

Optimising risk-based surveillance for early detection of invasive plant pathogens

“…By instead focussing on the probability of detection before a prespecified prevalence threshold is reached, we are better able to explore the impact of epidemiological and diagnostic parameters on the optimal deployment of surveillance resources for early detection. This allows us to draw valuable insights into surveillance strategies, unencumbered by the influence of other factors such as control costs, and thus bears similarities with studies of how to improve sentinel surveillance strategies in networks [33][34][35][36][37] (which, similarly, generally do not consider costs explicitly). On a practical level, our current approach also fits in well with the concept of 'maximum prevalence thresholds' commonly specified when planning conventional regulatory surveillance for regulated pathogens [50,51], making it valuable in a practical context.…”

“…By linking spatial and/or temporal simulation models that replicate the spread of the pest or pathogen to computational optimisation routines to identify particular sampling patterns, precise surveillance and/or control strategies that minimise the impact of the pest or pathogen can be identified [26]. Although much work to date has focused on identifying how best to conduct surveys in order to achieve certain disease management or mitigation objectives whilst considering spatial spread of a pest or pathogen [27][28][29][30][31][32] and on the value of different network metrics for identifying hosts to target for surveillance [33][34][35][36][37], there has been little work on the optimal deployment of surveillance resources for early pest or pathogen detection that explicitly considers the spread of the agent through a real-world landscape. No previous study has addressed the pivotal question: where exactly should surveillance resources be located to maximise the probability to detect an invading pathogen before it reaches a certain prevalence?…”