scholarly journals Indirect interactions influence contact network structure and diffusion dynamics

2019 ◽  
Vol 6 (8) ◽  
pp. 190845 ◽  
Author(s):  
Md Shahzamal ◽  
Raja Jurdak ◽  
Bernard Mans ◽  
Frank de Hoog
Keyword(s):  
Disease Transmission ◽  
Infected Individual ◽  
Indirect Interactions ◽  
Decay Rates ◽  
High Rate ◽  
Contact Network ◽  
Interaction Patterns ◽  
Contact Networks ◽  
Diffusion Dynamics ◽  
Airborne Disease

Interaction patterns at the individual level influence the behaviour of diffusion over contact networks. Most of the current diffusion models only consider direct interactions, capable of transferring infectious items among individuals, to build transmission networks of diffusion. However, delayed indirect interactions, where a susceptible individual interacts with infectious items after the infected individual has left the interaction space, can also cause transmission events. We define a diffusion model called the same place different time transmission (SPDT)-based diffusion that considers transmission links for these indirect interactions. Our SPDT model changes the network dynamics where the connectivity among individuals varies with the decay rates of link infectivity. We investigate SPDT diffusion behaviours by simulating airborne disease spreading on data-driven contact networks. The SPDT model significantly increases diffusion dynamics with a high rate of disease transmission. By making the underlying connectivity denser and stronger due to the inclusion of indirect transmissions, SPDT models are more realistic than same place same time transmission (SPST)-based models for the study of various airborne disease outbreaks. Importantly, we also find that the diffusion dynamics including indirect links are not reproducible by the current SPST models based on direct links, even if both SPDT and SPST networks assume the same underlying connectivity. This is because the transmission dynamics of indirect links are different from those of direct links. These outcomes highlight the importance of the indirect links for predicting outbreaks of airborne diseases.

scholarly journals Targeted pandemic containment through identifying local contact network bottlenecks

2021 ◽  
Vol 17 (8) ◽  
pp. e1009351
Author(s):  
Shenghao Yang ◽  
Priyabrata Senapati ◽  
Di Wang ◽  
Chris T. Bauch ◽  
Kimon Fountoulakis
Keyword(s):  
Disease Transmission ◽  
Network Flow ◽  
State Of The Art ◽  
Simulation Models ◽  
Simulation Modelling ◽  
Structural Features ◽  
Contact Network ◽  
Contact Networks ◽  
Edge Betweenness ◽  
Models Of Disease

Decision-making about pandemic mitigation often relies upon simulation modelling. Models of disease transmission through networks of contacts–between individuals or between population centres–are increasingly used for these purposes. Real-world contact networks are rich in structural features that influence infection transmission, such as tightly-knit local communities that are weakly connected to one another. In this paper, we propose a new flow-based edge-betweenness centrality method for detecting bottleneck edges that connect nodes in contact networks. In particular, we utilize convex optimization formulations based on the idea of diffusion with p-norm network flow. Using simulation models of COVID-19 transmission through real network data at both individual and county levels, we demonstrate that targeting bottleneck edges identified by the proposed method reduces the number of infected cases by up to 10% more than state-of-the-art edge-betweenness methods. Furthermore, the proposed method is orders of magnitude faster than existing methods.

Advances from the nexus of animal behaviour and pathogen transmission: new directions and opportunities using contact networks

Behaviour ◽  
2018 ◽  
Vol 155 (7-9) ◽  
pp. 567-583 ◽  
Author(s):  
Stephan T. Leu ◽  
Stephanie S. Godfrey
Keyword(s):  
High Resolution ◽  
Disease Transmission ◽  
Network Models ◽  
Population Level ◽  
Pathogen Transmission ◽  
Molecular Techniques ◽  
Gps Tracking ◽  
Contact Network ◽  
Computational Techniques ◽  
Contact Networks

Abstract Contact network models have enabled significant advances in understanding the influence of behaviour on parasite and pathogen transmission. They are an important tool that links variation in individual behaviour, to epidemiological consequences at the population level. Here, in our introduction to this special issue, we highlight the importance of applying network approaches to disease ecological and epidemiological questions, and how this has provided a much deeper understanding of these research areas. Recent advances in tracking host behaviour (bio-logging: e.g., GPS tracking, barcoding) and tracking pathogens (high-resolution sequencing), as well as methodological advances (multi-layer networks, computational techniques) started producing exciting new insights into disease transmission through contact networks. We discuss some of the exciting directions that the field is taking, some of the challenges, and importantly the opportunities that lie ahead. For instance, we suggest to integrate multiple transmission pathways, multiple pathogens, and in some systems, multiple host species, into the next generation of network models. Corresponding opportunities exist in utilising molecular techniques, such as high-resolution sequencing, to establish causality in network connectivity and disease outcomes. Such novel developments and the continued integration of network tools offers a more complete understanding of pathogen transmission processes, their underlying mechanisms and their evolutionary consequences.

scholarly journals Modeling workplace contact networks: The effects of organizational structure, architecture, and reporting errors on epidemic predictions

2015 ◽  
Vol 3 (3) ◽  
pp. 298-325 ◽  
Author(s):  
GAIL E. POTTER ◽  
TIMO SMIESZEK ◽  
KERSTIN SAILER
Keyword(s):  
Organizational Structure ◽  
Disease Transmission ◽  
Latent Variable ◽  
Respiratory Infections ◽  
Social Contact ◽  
Distance Measures ◽  
Contact Network ◽  
Variable Model ◽  
Contact Networks ◽  
Contact Patterns

AbstractFace-to-face social contacts are potentially important transmission routes for acute respiratory infections, and understanding the contact network can improve our ability to predict, contain, and control epidemics. Although workplaces are important settings for infectious disease transmission, few studies have collected workplace contact data and estimated workplace contact networks. We use contact diaries, architectural distance measures, and institutional structures to estimate social contact networks within a Swiss research institute. Some contact reports were inconsistent, indicating reporting errors. We adjust for this with a latent variable model, jointly estimating the true (unobserved) network of contacts and duration-specific reporting probabilities. We find that contact probability decreases with distance, and that research group membership, role, and shared projects are strongly predictive of contact patterns. Estimated reporting probabilities were low only for 0–5 min contacts. Adjusting for reporting error changed the estimate of the duration distribution, but did not change the estimates of covariate effects and had little effect on epidemic predictions. Our epidemic simulation study indicates that inclusion of network structure based on architectural and organizational structure data can improve the accuracy of epidemic forecasting models.

scholarly journals Predictors of free-roaming domestic dogs' contact network centrality and their relevance for rabies control

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Charlotte Warembourg ◽  
Guillaume Fournié ◽  
Mahamat Fayiz Abakar ◽  
Danilo Alvarez ◽  
Monica Berger-González ◽  
...  
Keyword(s):  
Disease Transmission ◽  
Small World ◽  
Domestic Dogs ◽  
Contact Network ◽  
Potential Contribution ◽  
Dog Ownership ◽  
Contact Patterns ◽  
Rabies Control ◽  
The Social ◽  
Study Sites

AbstractFree roaming domestic dogs (FRDD) are the main vectors for rabies transmission to humans worldwide. To eradicate rabies from a dog population, current recommendations focus on random vaccination with at least 70% coverage. Studies suggest that targeting high-risk subpopulations could reduce the required vaccination coverage, and increase the likelihood of success of elimination campaigns. The centrality of a dog in a contact network can be used as a measure of its potential contribution to disease transmission. Our objectives were to investigate social networks of FRDD in eleven study sites in Chad, Guatemala, Indonesia and Uganda, and to identify characteristics of dogs, and their owners, associated with their centrality in the networks. In all study sites, networks had small-world properties and right-skewed degree distributions, suggesting that vaccinating highly connected dogs would be more effective than random vaccination. Dogs were more connected in rural than urban settings, and the likelihood of contacts was negatively correlated with the distance between dogs’ households. While heterogeneity in dog's connectedness was observed in all networks, factors predicting centrality and likelihood of contacts varied across networks and countries. We therefore hypothesize that the investigated dog and owner characteristics resulted in different contact patterns depending on the social, cultural and economic context. We suggest to invest into understanding of the sociocultural structures impacting dog ownership and thus driving dog ecology, a requirement to assess the potential of targeted vaccination in dog populations.

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 Splatters and Aerosols Contamination in Dental Aerosol Generating Procedures

2021 ◽  
Vol 11 (4) ◽  
pp. 1914
Author(s):  
Pingping Han ◽  
Honghui Li ◽  
Laurence J. Walsh ◽  
Sašo Ivanovski
Keyword(s):  
Particle Size ◽  
Disease Transmission ◽  
High Speed ◽  
Low Speed ◽  
Dental Procedures ◽  
Dental Equipment ◽  
Produce Contamination ◽  
Fluorescein Dye ◽  
Filter Papers ◽  
Airborne Disease

Dental aerosol-generating procedures produce a large amount of splatters and aerosols that create a major concern for airborne disease transmission, such as COVID-19. This study established a method to visualise splatter and aerosol contamination by common dental instrumentation, namely ultrasonic scaling, air-water spray, high-speed and low-speed handpieces. Mock dental procedures were performed on a mannequin model, containing teeth in a typodont and a phantom head, using irrigation water containing fluorescein dye as a tracer. Filter papers were placed in 10 different locations to collect splatters and aerosols, at distances ranging from 20 to 120 cm from the source. All four types of dental equipment produced contamination from splatters and aerosols. At 120 cm away from the source, the high-speed handpiece generated the greatest amount and size (656 ± 551 μm) of splatter particles, while the triplex syringe generated the largest amount of aerosols (particle size: 1.73 ± 2.23 μm). Of note, the low-speed handpiece produced the least amount and size (260 ± 142 μm) of splatter particles and the least amount of aerosols (particle size: 4.47 ± 5.92 μm) at 120 cm. All four dental AGPs produce contamination from droplets and aerosols, with different patterns of distribution. This simple model provides a method to test various preventive strategies to reduce risks from splatter and aerosols.

scholarly journals Extended Lifetime of Respiratory Droplets in a Turbulent Vapor Puff and Its Implications on Airborne Disease Transmission

2021 ◽  
Vol 126 (3) ◽  
Author(s):  
Kai Leong Chong ◽  
Chong Shen Ng ◽  
Naoki Hori ◽  
Rui Yang ◽  
Roberto Verzicco ◽  
...  
Keyword(s):  
Disease Transmission ◽  
Airborne Disease ◽  
Respiratory Droplets

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 Automated monitoring of behavior reveals bursty interaction patterns and rapid spreading dynamics in honeybee social networks

2018 ◽  
Vol 115 (7) ◽  
pp. 1433-1438 ◽  
Author(s):  
Tim Gernat ◽  
Vikyath D. Rao ◽  
Martin Middendorf ◽  
Harry Dankowicz ◽  
Nigel Goldenfeld ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Interactions ◽  
Communication Networks ◽  
Disease Transmission ◽  
Large Scale ◽  
Reference Network ◽  
Interaction Patterns ◽  
Human Communication ◽  
Automated Monitoring ◽  
Phone Calls

Social networks mediate the spread of information and disease. The dynamics of spreading depends, among other factors, on the distribution of times between successive contacts in the network. Heavy-tailed (bursty) time distributions are characteristic of human communication networks, including face-to-face contacts and electronic communication via mobile phone calls, email, and internet communities. Burstiness has been cited as a possible cause for slow spreading in these networks relative to a randomized reference network. However, it is not known whether burstiness is an epiphenomenon of human-specific patterns of communication. Moreover, theory predicts that fast, bursty communication networks should also exist. Here, we present a high-throughput technology for automated monitoring of social interactions of individual honeybees and the analysis of a rich and detailed dataset consisting of more than 1.2 million interactions in five honeybee colonies. We find that bees, like humans, also interact in bursts but that spreading is significantly faster than in a randomized reference network and remains so even after an experimental demographic perturbation. Thus, while burstiness may be an intrinsic property of social interactions, it does not always inhibit spreading in real-world communication networks. We anticipate that these results will inform future models of large-scale social organization and information and disease transmission, and may impact health management of threatened honeybee populations.

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.

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