Inference of causality in epidemics on temporal contact networks

Braunstein, Alfredo; Ingrosso, Alessandro

doi:10.1038/srep27538

Cited by 21 publications

(12 citation statements)

References 15 publications

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“…Some of the model parameters, such as incubation period, follow a distribution based on the uncertainty in the characteristics of diseases. We assumed at least one infected student was present in the classroom on the first day in order to address the patient zero problem [ 28 ]. As a result, all simulations would contain a form of disease propagation.…”

Section: Methodsmentioning

confidence: 99%

Assessing school-based policy actions for COVID-19: An agent-based analysis of incremental infection risk

Zafarnejad

Griffin

2021

Computers in Biology and Medicine

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Many schools and universities have seen a significant increase in the spread of COVID-19. As such, a number of non-pharmaceutical interventions have been proposed including distancing requirements, surveillance testing, and updating ventilation systems. Unfortunately, there is limited guidance for which policy or set of policies are most effective for a specific school system. We develop a novel approach to model the spread of SARS-CoV-2 quanta in a closed classroom environment that extends traditional transmission models that assume uniform mixing through air recirculation by including the local spread of quanta from a contagious source. In addition, the behavior of students with respect to guideline compliance was modeled through an agent-based simulation. Estimated infection rates were on average lower using traditional transmission models compared to our approach. Further, we found that although ventilation changes were effective at reducing mean transmission risk, it had much less impact than distancing practices. Duration of the class was an important factor in determining the transmission risk. For the same total number of semester hours for a class, delivering lectures more frequently for shorter durations was preferable to less frequently with longer durations. Finally, as expected, as the contact tracing level increased, more infectious students were identified and removed from the environment and the spread slowed, though there were diminishing returns. These findings can help provide guidance as to which school-based policies would be most effective at reducing risk and can be used in a cost/comparative effectiveness estimation study given local costs and constraints.

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Section: Methodsmentioning

confidence: 99%

Assessing school-based policy actions for COVID-19: An agent-based analysis of incremental infection risk

Zafarnejad

Griffin

2021

Computers in Biology and Medicine

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“…There is very strong evidence that these networks play a critical role in large and damaging epidemics, including the 2009 H1N1 influenza pandemic [10] and the 2001 British foot-and-mouth disease epidemic [28]. Because of the key importance of timing in these networks to their capacity to spread disease, methods to assess the susceptibility of temporal graphs and networks to disease incursion have recently become an active area of work within network epidemiology in general, and within livestock network epidemiology in particular [9,41,47,48].…”

Section: Definition 1 (Temporal Graph)mentioning

confidence: 99%

Deleting edges to restrict the size of an epidemic in temporal networks

Enright

Meeks

Mertzios

et al. 2021

Journal of Computer and System Sciences

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Spreading processes on graphs are a natural model for a wide variety of real-world phenomena, including information or behaviour spread over social networks, biological diseases spreading over contact or trade networks, and the potential flow of goods over logistical infrastructure. Often, the networks over which these processes spread are dynamic in nature, and can be modeled with graphs whose structure is subject to discrete changes over time, i.e. with temporal graphs. Here, we consider temporal graphs in which edges are available at specified timesteps, and study the problem of deleting edges from a given temporal graph in order to reduce the number of vertices (temporally) reachable from a given starting point. This could be used to control the spread of a disease, rumour, etc. in a temporal graph. In particular, our aim is to find a temporal subgraph in which a process starting at any single vertex can be transferred to only a limited number of other vertices using a temporally-feasible path (i.e. a path, along which the times of the edge availabilities increase). We introduce a natural deletion problem for temporal graphs and we provide positive and negative results on its computational complexity, both in the traditional and the parameterised sense (subject to various natural parameters), as well as addressing the approximability of this problem.

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“…The importance of these problems combined with their inherit temporal nature, made temporal graph theory a tool for analyzing epidemiology in (animal) networks (Braunstein and Ingrosso 2016;Enright and Meeks 2015;Enright and Kao 2018;Nöremark and Widgren 2014;Valdano et al 2015a;2015b). and ) studied how reachability sets on temporal graphs change, when the schedule of the edges can change according to specific operations.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Reachability Sets in Temporal Graphs by Delaying

Deligkas

Potapov

2020

AAAI

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A temporal graph is a dynamic graph where every edge is assigned a set of integer time labels that indicate at which discrete time step the edge is available. In this paper, we study how changes of the time labels, corresponding to delays on the availability of the edges, affect the reachability sets from given sources. The questions about reachability sets are motivated by numerous applications of temporal graphs in network epidemiology and scheduling problems in supply networks in manufacturing. We introduce control mechanisms for reachability sets that are based on two natural operations of delaying time events. The first operation, termed merging, is global and batches together consecutive time labels in the whole network simultaneously. This corresponds to postponing all events until a particular time. The second, imposes independent delays on the time labels of every edge of the graph. We provide a thorough investigation of the computational complexity of different objectives related to reachability sets when these operations are used. For the merging operation, we prove NP-hardness results for several minimization and maximization reachability objectives, even for very simple graph structures. For the second operation, we prove that the minimization problems are NP-hard when the number of allowed delays is bounded. We complement this with a polynomial-time algorithm for the case of unbounded delays.

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Inference of causality in epidemics on temporal contact networks

Cited by 21 publications

References 15 publications

Assessing school-based policy actions for COVID-19: An agent-based analysis of incremental infection risk

Assessing school-based policy actions for COVID-19: An agent-based analysis of incremental infection risk

Deleting edges to restrict the size of an epidemic in temporal networks

Optimizing Reachability Sets in Temporal Graphs by Delaying

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