scholarly journals Collecting close-contact social mixing data with contact diaries: reporting errors and biases

2011 ◽  
Vol 140 (4) ◽  
pp. 744-752 ◽  
Author(s):  
T. SMIESZEK ◽  
E. U. BURRI ◽  
R. SCHERZINGER ◽  
R. W. SCHOLZ
Keyword(s):  
Study Design ◽  
Close Contact ◽  
Disease Spread ◽  
Research Groups ◽  
Contact Networks ◽  
Reporting Accuracy ◽  
Complete Network ◽  
Social Mixing ◽  
Diary Studies ◽  
Contact Data

SUMMARYThe analysis of contact networks plays a major role to understanding the dynamics of disease spread. Empirical contact data is often collected using contact diaries. Such studies rely on self-reported perceptions of contacts, and arrangements for validation are usually not made. Our study was based on a complete network study design that allowed for the analysis of reporting accuracy in contact diary studies. We collected contact data of the employees of three research groups over a period of 1 work week. We found that more than one third of all reported contacts were only reported by one out of the two involved contact partners. Non-reporting is most frequent in cases of short, non-intense contact. We estimated that the probability of forgetting a contact of ⩽5 min duration is greater than 50%. Furthermore, the number of forgotten contacts appears to be proportional to the total number of contacts.

scholarly journals Infectious disease transmission and contact networks in wildlife and livestock

2015 ◽  
Vol 370 (1669) ◽  
pp. 20140107 ◽  
Author(s):  
Meggan E. Craft
Keyword(s):  
Infectious Disease ◽  
Disease Transmission ◽  
Wild Animal ◽  
Disease Spread ◽  
Farmed Fish ◽  
Infectious Disease Transmission ◽  
Network Modelling ◽  
Flexible Tool ◽  
Contact Networks ◽  
Contact Data

The use of social and contact networks to answer basic and applied questions about infectious disease transmission in wildlife and livestock is receiving increased attention. Through social network analysis, we understand that wild animal and livestock populations, including farmed fish and poultry, often have a heterogeneous contact structure owing to social structure or trade networks. Network modelling is a flexible tool used to capture the heterogeneous contacts of a population in order to test hypotheses about the mechanisms of disease transmission, simulate and predict disease spread, and test disease control strategies. This review highlights how to use animal contact data, including social networks, for network modelling, and emphasizes that researchers should have a pathogen of interest in mind before collecting or using contact data. This paper describes the rising popularity of network approaches for understanding transmission dynamics in wild animal and livestock populations; discusses the common mismatch between contact networks as measured in animal behaviour and relevant parasites to match those networks; and highlights knowledge gaps in how to collect and analyse contact data. Opportunities for the future include increased attention to experiments, pathogen genetic markers and novel computational tools.

scholarly journals Investigating the potential spread of infectious diseases of sheep via agricultural shows in Great Britain

2005 ◽  
Vol 134 (1) ◽  
pp. 31-40 ◽  
Author(s):  
C. R. WEBB
Keyword(s):  
Great Britain ◽  
Infectious Diseases ◽  
Close Contact ◽  
Farm Animal ◽  
Disease Spread ◽  
Infectious Period ◽  
Contact Network ◽  
Spatial Spread ◽  
Animal Populations ◽  
Contact Data

SUMMARYThe rate at which infectious diseases spread through farm animal populations depends both on individual disease characteristics and the opportunity for transmission via close contact. Data on the relationships affecting the contact structure of farm animal populations are, therefore, required to improve mathematical models for the spatial spread of farm animal diseases. This paper presents data on the contact network for agricultural shows in Great Britain, whereby a link between two shows occurs if they share common competitors in the sheep class. Using the network, the potential for disease spread through agricultural shows is investigated varying both the initial show infected and the infectious period of the disease. The analysis reveals a highly connected network such that diseases introduced early in the show season could present a risk to sheep at the majority of subsequent shows. This data emphasizes the importance of maintaining rigorous showground and farm-level bio-security.

scholarly journals Social mixing patterns for transmission models of close contact infections: exploring self-evaluation and diary-based data collection through a web-based interface

2006 ◽  
Vol 134 (6) ◽  
pp. 1158-1166 ◽  
Author(s):  
P. BEUTELS ◽  
Z. SHKEDY ◽  
M. AERTS ◽  
P. VAN DAMME
Keyword(s):  
Close Contact ◽  
Social Contact ◽  
Convenience Sample ◽  
Web Based ◽  
Self Evaluation ◽  
Social Mixing ◽  
Contact Data ◽  
Mixing Patterns ◽  
The Difference ◽  
Transmission Models

Although mixing patterns are crucial in dynamic transmission models of close contact infections, they are largely estimated by intuition. Using a convenience sample (n=73), we tested self-evaluation and prospective diary surveys with a web-based interface, in order to obtain social contact data. The number of recorded contacts was significantly (P<0·01) greater on workdays (18·1) vs. weekend days (12·3) for conversations, and vice versa for touching (5·4 and 7·2 respectively). Mixing was highly assortative with age for both (adults contacting other adults vs. 0- to 5-year-olds, odds ratio 8·9–10·8). Respondents shared a closed environment significantly more often with >20 other adults than with >20 children. The difference in number of contacts per day was non-significant between self-evaluation and diary (P=0·619 for conversations, P=0·125 for touching). We conclude that self-evaluation could yield similar results to diary surveys for general or very recent mixing information. More detailed data could be collected by diary, at little effort to respondents.

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 Characteristics of human encounters and social mixing patterns relevant to infectious diseases spread by close contact: a survey in Southwest Uganda

2018 ◽  
Vol 18 (1) ◽  
Author(s):  
O. le Polain de Waroux ◽  
S. Cohuet ◽  
D. Ndazima ◽  
A. J. Kucharski ◽  
A. Juan-Giner ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Close Contact ◽  
Social Mixing ◽  
Mixing Patterns

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.

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 Extracting Data from Disparate Sources for Agent-Based Disease Spread Models

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
M. Laskowski ◽  
B. C. P. Demianyk ◽  
J. Benavides ◽  
M. R. Friesen ◽  
R. D. McLeod ◽  
...  
Keyword(s):  
Demographic Data ◽  
Population Level ◽  
Real Data ◽  
Smartphone Application ◽  
Geographic Area ◽  
Disease Spread ◽  
Telecommunication Service ◽  
Interaction Patterns ◽  
Agent Based ◽  
Contact Data

This paper presents a review and evaluation of real data sources relative to their role and applicability in an agent-based model (ABM) simulating respiratory infection spread a large geographic area. The ABM is a spatial-temporal model inclusive of behavior and interaction patterns between individual agents. The agent behaviours in the model (movements and interactions) are fed by census/demographic data, integrated with real data from a telecommunication service provider (cellular records), traffic survey data, as well as person-person contact data obtained via a custom 3G smartphone application that logs Bluetooth connectivity between devices. Each source provides data of varying type and granularity, thereby enhancing the robustness of the model. The work demonstrates opportunities in data mining and fusion and the role of data in calibrating and validating ABMs. The data become real-world inputs into susceptible-exposed-infected-recovered (SEIR) disease spread models and their variants, thereby building credible and nonintrusive models to qualitatively model public health interventions at the population level.

scholarly journals A systematic review of social contact surveys to inform transmission models of close contact infections

2018 ◽  
Author(s):  
Thang Van Hoang ◽  
Pietro Coletti ◽  
Alessia Melegaro ◽  
Jacco Wallinga ◽  
Carlos Grijalva ◽  
...  
Keyword(s):  
Systematic Review ◽  
Close Contact ◽  
Social Contact ◽  
Physical Contact ◽  
Disease Spread ◽  
Sampling Schemes ◽  
Contact Patterns ◽  
Wide Range ◽  
Practice Approach ◽  
Definition Of

AbstractSocial contact data are increasingly being used to inform models for infectious disease spread with the aim of guiding effective policies on disease prevention and control. In this paper, we undertake a systematic review of the study design, statistical analyses and outcomes of the many social contact surveys that have been published. Our primary focus is to identify the designs that have worked best and the most important determinants and to highlight the most robust findings.Two publicly accessible online databases were systematically searched for articles regarding social contact surveys. PRISMA guidelines were followed as closely as possible. In total, 64 social contact surveys were identified. These surveys were conducted in 24 countries, and more than 80% of the surveys were conducted in high-income countries. Study settings included general population (58%), schools/universities (37%) and health care/conference/research institutes (5%). The majority of studies did not focus on a specific age group (38%), whereas others focused on adults (32%) or children (19%). Retrospective and prospective designs were used mostly (45% and 41% of the surveys, respectively) with 6% using both for comparison purposes. The definition of a contact varied among surveys, e.g. a non-physical contact may require conversation, close proximity or both. Age, time schedule (e.g., weekday/weekend) and household size were identified as relevant determinants for contact pattern across a large number of studies. The surveys present a wide range of study designs. Throughout, we found that the overall contact patterns were remarkably robust for the study details. By considering the most common approach in each aspect of design (e.g., sampling schemes, data collection, definition of contact), we could identify a common practice approach that can be used to facilitate comparison between studies and for benchmarking future studies.

scholarly journals Community health and human-animal contacts on the edges of Bwindi Impenetrable National Park, Uganda

2021 ◽  
Author(s):  
Renata Lara Muylaert ◽  
Ben Davidson ◽  
Alex Ngabirano ◽  
Gladys Kalema-Zikusoka ◽  
Hayley MacGregor ◽  
...  
Keyword(s):  
Direct Contact ◽  
National Park ◽  
Close Contact ◽  
Domestic Animals ◽  
Emerging Diseases ◽  
Pathogen Transmission ◽  
Disease Spread ◽  
Mountain Gorilla ◽  
Wild Animals ◽  
Bwindi Impenetrable National Park

Cross-species transmission of pathogens is intimately linked to human and environmental health. With limited healthcare and challenging living conditions, people living in poverty may be particularly susceptible to endemic and emerging diseases. Similarly, wildlife is impacted by human influences, including pathogen sharing, especially for species in close contact with people and domesticated animals. Here we investigate human and animal contacts and human health in a community living around the Bwindi Impenetrable National Park (BINP), Uganda. We used contact and health survey data to identify opportunities for cross-species pathogen transmission, focusing mostly on people and the endangered mountain gorilla. We conducted a survey with background questions and self-reported diaries to investigate 100 participants' health, such as symptoms and behaviours, and contact patterns, including direct contacts and sightings over a week. Contacts were revealed through networks, including humans, domestic, peri-domestic, and wild animals for 1) network of contacts seen in the week of background questionnaire completion, 2) network of contacts seen during the diary week. Participants frequently felt unwell during the study, reporting from one to 10 disease symptoms at different intensity levels (maximum of seven symptoms in one day), with severe symptoms comprising 6.4% of the diary records and tiredness and headaches the most common symptoms. Besides human-human contacts, direct contacts with livestock and peri-domestic animals were the most common. Wildlife contacts were the rarest, including one direct contact with gorilla with a concerning timeline of reported symptoms. The contact networks were moderately connected and revealing a preference in contacts within the same species or taxon and within their groups. Despite sightings of wildlife being much more common than touching, one participant declared direct contact with a mountain gorilla during the week. Gorillas were seen very close to six animal taxa (including themselves) considering all interaction types, mostly seen closer to other gorillas, but also people and domestic animals. Our findings reveal a local human population with recurrent symptoms of illness in a location with intense exposure to factors that can increase pathogen transmission, such as direct contact with domestic and wild animals and proximity among animal species. Despite significant biases and study limitations, the information generated here can guide future studies, such as models for disease spread and One Health interventions.

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