scholarly journals Using network properties to predict disease dynamics on human contact networks

2011 ◽  
Vol 278 (1724) ◽  
pp. 3544-3550 ◽  
Author(s):  
Gregory M. Ames ◽  
Dylan B. George ◽  
Christian P. Hampson ◽  
Andrew R. Kanarek ◽  
Cayla D. McBee ◽  
...  
Keyword(s):  
Degree Distribution ◽  
Stochastic Simulations ◽  
Disease Spread ◽  
Contact Structures ◽  
Contact Network ◽  
Contact Networks ◽  
Human Contact ◽  
Differential Equation Models ◽  
Network Properties ◽  
Intermediate Density

Recent studies have increasingly turned to graph theory to model more realistic contact structures that characterize disease spread. Because of the computational demands of these methods, many researchers have sought to use measures of network structure to modify analytically tractable differential equation models. Several of these studies have focused on the degree distribution of the contact network as the basis for their modifications. We show that although degree distribution is sufficient to predict disease behaviour on very sparse or very dense human contact networks, for intermediate density networks we must include information on clustering and path length to accurately predict disease behaviour. Using these three metrics, we were able to explain more than 98 per cent of the variation in endemic disease levels in our stochastic simulations.

scholarly journals Identification of effective spreaders in contact networks using dynamical influence

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Ruaridh A. Clark ◽  
Malcolm Macdonald
Keyword(s):  
Disease Transmission ◽  
Average Rate ◽  
Laplacian Matrix ◽  
Disease Spread ◽  
Eigenvector Centrality ◽  
Contact Networks ◽  
Human Contact ◽  
Spread Dynamics ◽  
Definition Of ◽  
High Degree

AbstractContact networks provide insights on disease spread due to the duration of close proximity interactions. For systems governed by consensus dynamics, network structure is key to optimising the spread of information. For disease spread over contact networks, the structure would be expected to be similarly influential. However, metrics that are essentially agnostic to the network’s structure, such as weighted degree (strength) centrality and its variants, perform near-optimally in selecting effective spreaders. These degree-based metrics outperform eigenvector centrality, despite disease spread over a network being a random walk process. This paper improves eigenvector-based spreader selection by introducing the non-linear relationship between contact time and the probability of disease transmission into the assessment of network dynamics. This approximation of disease spread dynamics is achieved by altering the Laplacian matrix, which in turn highlights why nodes with a high degree are such influential disease spreaders. From this approach, a trichotomy emerges on the definition of an effective spreader where, for susceptible-infected simulations, eigenvector-based selections can either optimise the initial rate of infection, the average rate of infection, or produce the fastest time to full infection of the network. Simulated and real-world human contact networks are examined, with insights also drawn on the effective adaptation of ant colony contact networks to reduce pathogen spread and protect the queen ant.

scholarly journals Revealing mechanisms of infectious disease spread through empirical contact networks

2021 ◽  
Vol 17 (12) ◽  
pp. e1009604
Author(s):  
Pratha Sah ◽  
Michael Otterstatter ◽  
Stephan T. Leu ◽  
Sivan Leviyang ◽  
Shweta Bansal
Keyword(s):  
Infectious Disease ◽  
Incomplete Data ◽  
Model Comparison ◽  
Transmission Rate ◽  
Disease Spread ◽  
Contact Network ◽  
Statistical Tool ◽  
Contact Networks ◽  
Crithidia Bombi ◽  
Transmission Pathways

The spread of pathogens fundamentally depends on the underlying contacts between individuals. Modeling the dynamics of infectious disease spread through contact networks, however, can be challenging due to limited knowledge of how an infectious disease spreads and its transmission rate. We developed a novel statistical tool, INoDS (Identifying contact Networks of infectious Disease Spread) that estimates the transmission rate of an infectious disease outbreak, establishes epidemiological relevance of a contact network in explaining the observed pattern of infectious disease spread and enables model comparison between different contact network hypotheses. We show that our tool is robust to incomplete data and can be easily applied to datasets where infection timings of individuals are unknown. We tested the reliability of INoDS using simulation experiments of disease spread on a synthetic contact network and find that it is robust to incomplete data and is reliable under different settings of network dynamics and disease contagiousness compared with previous approaches. We demonstrate the applicability of our method in two host-pathogen systems: Crithidia bombi in bumblebee colonies and Salmonella in wild Australian sleepy lizard populations. INoDS thus provides a novel and reliable statistical tool for identifying transmission pathways of infectious disease spread. In addition, application of INoDS extends to understanding the spread of novel or emerging infectious disease, an alternative approach to laboratory transmission experiments, and overcoming common data-collection constraints.

scholarly journals The network of sheep movements within Great Britain: network properties and their implications for infectious disease spread

2006 ◽  
Vol 3 (10) ◽  
pp. 669-677 ◽  
Author(s):  
Istvan Z Kiss ◽  
Darren M Green ◽  
Rowland R Kao
Keyword(s):  
Great Britain ◽  
Disease Transmission ◽  
Foot And Mouth Disease ◽  
Small World ◽  
Disease Spread ◽  
Contact Networks ◽  
Geographical Regions ◽  
Network Properties ◽  
Path Lengths ◽  
Intense Activity

During the 2001 foot and mouth disease epidemic in the UK, initial dissemination of the disease to widespread geographical regions was attributed to livestock movement, especially of sheep. In response, recording schemes to provide accurate data describing the movement of large livestock in Great Britain (GB) were introduced. Using these data, we reconstruct directed contact networks within the sheep industry and identify key epidemiological properties of these networks. There is clear seasonality in sheep movements, with a peak of intense activity in August and September and an associated high risk of a large epidemic. The high correlation between the in and out degree of nodes favours disease transmission. However, the contact networks were largely dissasortative: highly connected nodes mostly connect to nodes with few contacts, effectively slowing the spread of disease. This is a result of bipartite-like network properties, with most links occurring between highly active markets and less active farms. When comparing sheep movement networks (SMNs) to randomly generated networks with the same number of nodes and node degrees, despite structural differences (such as disassortativity and higher frequency of even path lengths in the SMNs), the characteristic path lengths within the SMNs are close to values computed from the corresponding random networks, showing that SMNs have ‘small-world’-like properties. Using the network properties, we show that targeted biosecurity or surveillance at highly connected nodes would be highly effective in preventing a large and widespread epidemic.

scholarly journals Modifying the network-based stochastic SEIR model to account for quarantine: an application to COVID-19

2021 ◽  
Vol 10 (s1) ◽  
Author(s):  
Chris Groendyke ◽  
Adam Combs
Keyword(s):  
Network Model ◽  
Transmission Rate ◽  
Disease Spread ◽  
Contact Network ◽  
Model Parameters ◽  
Seir Model ◽  
Disease States ◽  
Potential Contact ◽  
The Mean ◽  
Infectious State

Abstract Objectives: Diseases such as SARS-CoV-2 have novel features that require modifications to the standard network-based stochastic SEIR model. In particular, we introduce modifications to this model to account for the potential changes in behavior patterns of individuals upon becoming symptomatic, as well as the tendency of a substantial proportion of those infected to remain asymptomatic. Methods: Using a generic network model where every potential contact exists with the same common probability, we conduct a simulation study in which we vary four key model parameters (transmission rate, probability of remaining asymptomatic, and the mean lengths of time spent in the exposed and infectious disease states) and examine the resulting impacts on various metrics of epidemic severity, including the effective reproduction number. We then consider the effects of a more complex network model. Results: We find that the mean length of time spent in the infectious state and the transmission rate are the most important model parameters, while the mean length of time spent in the exposed state and the probability of remaining asymptomatic are less important. We also find that the network structure has a significant impact on the dynamics of the disease spread. Conclusions: In this article, we present a modification to the network-based stochastic SEIR epidemic model which allows for modifications to the underlying contact network to account for the effects of quarantine. We also discuss the changes needed to the model to incorporate situations where some proportion of the individuals who are infected remain asymptomatic throughout the course of the disease.

Data Delivery Properties of Human Contact Networks

2011 ◽  
Vol 10 (6) ◽  
pp. 868-880 ◽  
Author(s):  
Nishanth Sastry ◽  
D. Manjunath ◽  
Karen Sollins ◽  
Jon Crowcroft
Keyword(s):  
Data Delivery ◽  
Contact Networks ◽  
Human Contact

scholarly journals Spatial analyses of wildlife contact networks

2015 ◽  
Vol 12 (102) ◽  
pp. 20141004 ◽  
Author(s):  
Stephen Davis ◽  
Babak Abbasi ◽  
Shrupa Shah ◽  
Sandra Telfer ◽  
Mike Begon
Keyword(s):  
Critical Role ◽  
Host Density ◽  
Spatial Arrangement ◽  
Spatial Proximity ◽  
Contact Network ◽  
Spatial Constraints ◽  
Contact Networks ◽  
Proximity Graphs ◽  
Contact Rates ◽  
Pathogen Persistence

Datasets from which wildlife contact networks of epidemiological importance can be inferred are becoming increasingly common. A largely unexplored facet of these data is finding evidence of spatial constraints on who has contact with whom, despite theoretical epidemiologists having long realized spatial constraints can play a critical role in infectious disease dynamics. A graph dissimilarity measure is proposed to quantify how close an observed contact network is to being purely spatial whereby its edges are completely determined by the spatial arrangement of its nodes. Statistical techniques are also used to fit a series of mechanistic models for contact rates between individuals to the binary edge data representing presence or absence of observed contact. These are the basis for a second measure that quantifies the extent to which contacts are being mediated by distance. We apply these methods to a set of 128 contact networks of field voles ( Microtus agrestis ) inferred from mark–recapture data collected over 7 years and from four sites. Large fluctuations in vole abundance allow us to demonstrate that the networks become increasingly similar to spatial proximity graphs as vole density increases. The average number of contacts, , was (i) positively correlated with vole density across the range of observed densities and (ii) for two of the four sites a saturating function of density. The implications for pathogen persistence in wildlife may be that persistence is relatively unaffected by fluctuations in host density because at low density is low but hosts move more freely, and at high density is high but transmission is hampered by local build-up of infected or recovered animals.

scholarly journals Household members do not contact each other at random: implications for infectious disease modelling

2018 ◽  
Vol 285 (1893) ◽  
pp. 20182201 ◽  
Author(s):  
Nele Goeyvaerts ◽  
Eva Santermans ◽  
Gail Potter ◽  
Andrea Torneri ◽  
Kim Van Kerckhove ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Social Contact ◽  
Household Contact ◽  
Epidemic Models ◽  
Disease Spread ◽  
Contact Density ◽  
Contact Networks ◽  
Household Transmission ◽  
Household Members ◽  
Contact Survey

Airborne infectious diseases such as influenza are primarily transmitted from human to human by means of social contacts, and thus easily spread within households. Epidemic models, used to gain insight into infectious disease spread and control, typically rely on the assumption of random mixing within households. Until now, there has been no direct empirical evidence to support this assumption. Here, we present the first social contact survey specifically designed to study contact networks within households. The survey was conducted in Belgium (Flanders and Brussels) from 2010 to 2011. We analysed data from 318 households totalling 1266 individuals with household sizes ranging from two to seven members. Exponential-family random graph models (ERGMs) were fitted to the within-household contact networks to reveal the processes driving contact between household members, both on weekdays and weekends. The ERGMs showed a high degree of clustering and, specifically on weekdays, decreasing connectedness with increasing household size. Furthermore, we found that the odds of a contact between older siblings and between father and child are smaller than for any other pair. The epidemic simulation results suggest that within-household contact density is the main driver of differences in epidemic spread between complete and empirical-based household contact networks. The homogeneous mixing assumption may therefore be an adequate characterization of the within-household contact structure for the purpose of epidemic simulations. However, ignoring the contact density when inferring based on an epidemic model will result in biased estimates of within-household transmission rates. Further research regarding the implementation of within-household contact networks in epidemic models is necessary.

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