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26 results for “contact network”
Human contact network analytics and COVID-19 hospital incidence in France
<p>This data set contains COVID-19 hospital incidence, temperature and human mobility and contact data recorded between 2020-03-24 and 2021-03-30 used in the paper:</p> <p>Selinger et al. 2021: Predicting COVID-19 incidence in French hospitals using human contact network analytics. 10.1016/j.ijid.2021.08.029</p> <p>See methods in the article for detailed descriptions and the data curation process.</p> <p> </p> <p><strong>1) cov_mob_tst_national.csv contains national-level data</strong><br> </p> <p>The columns comprise:</p> <p>incid_hosp: hospital admission incidence </p> <p>incid_rea: ICU admission incidence</p> <p>incid_dc: hospital death incidence </p> <p>incid_rad: incidence of those returned home</p> <p>within_departement_colocation_X%: X%-quantile of colocation probabilities with départements</p> <p>between_departement_colocation_X%: X%-quantile of colocation probabilities between départements</p> <p>fb_population_coverage_X%: X%-quantile of ratio of fb_population over census population in département</p> <p>null_links_X%: X%-quantile of null links across départements</p> <p>clustering_X%: X%-quantile of clustering coefficients across départements</p> <p>ricci_X%: X%-quantile of curvature across départements</p> <p>ricci_min_X%: X%-quantile of minimum curvature across départements</p> <p>ricci_mean_X%: X%-quantile of average curvature across départements</p> <p>ricci_max_X%: X%-quantile of maximum curvature across départements</p> <p>strength_X%: X%-quantile of network strengths across départements</p> <p>betweenness_centrality_X%: X%-quantile of betweenness_centrality scores across départements</p> <p>positive_test_ratio_weekly: ratio of weekly cumulated positive tested over weekly cumulated tests</p> <p>retail_and_recreation_percent_change_from_baseline: Google Mobility Reports</p> <p>grocery_and_pharmacy_percent_change_from_baseline: Google Mobility Reports</p> <p>parks_percent_change_from_baseline: Google Mobility Reports </p> <p>transit_stations_percent_change_from_baseline: Google Mobility Reports</p> <p>workplaces_percent_change_from_baseline: Google Mobility Reports</p> <p>residential_percent_change_from_baseline: Google Mobility Reports</p> <p>mean_temperature_X%: X% quantile of mean daily temperatures averaged over the week across départements</p> <p>min_temperature_X%: X% quantile of minimum daily temperatures averaged over the week across départements</p> <p>max_temperature_X%: X% quantile of maximum daily temperatures averaged over the week across départements</p> <p> </p> <p> </p> <p><strong>2) cov_mob_dep.csv contains département-level data</strong></p> <p>The columns comprise:</p> <p>dep: département code</p> <p>incid_hosp: hospital admission incidence </p> <p>incid_rea: ICU admission incidence</p> <p>incid_dc: hospital death incidence </p> <p>incid_rad: incidence of those returned home</p> <p>week: week (matched to colocation data recording usually on Tuesdays)</p> <p>dep_name: name of the département</p> <p>null_links: number of null links</p> <p>betweenness_centrality: betweenness centrality</p> <p>clustering: clustering coefficient</p> <p>strength: network strength</p> <p>ricci_mean: minimum curvature among all edges incident to a département</p> <p>ricci_min: mean curvature across all edges incident to a département </p> <p>ricci_X%: X%-quantile curvature among all edges incident to a département</p> <p>fb_population: number of facebook users </p> <p>facebook_colocation_within_dep: colocation probability within département</p> <p>fb_population_coverage: ratio of fb_population over census population in département</p> <p>facebook_colocation_between_dep_X%: X%-quantile of facebook colocation among all edges incident to the département</p> <p>min_temperature: minimum daily temperature averaged over the week</p> <p>max_temperature: maximum daily temperature averaged over the week</p> <p>mean_temperature: mean daily temperature averaged over the week</p> <p>incid_hosp_Y: incidence of hospital admission from Ynd most colocated département</p> <p>incid_rea_Y: incidence of ICU admission from Ynd most colocated département</p> <p>incid_dc_Y: incidence of hospital deaths from Ynd most colocated département</p> <p>incid_rad_Y: incidence of returned home from Ynd most colocated département</p> <p> </p>
Social network structure is robust to parasite induced changes in contact behavior of domestic sheep
<p>Understanding how parasitism may affect social behavior and social networks is key to understanding the impact of infection on a population. Infection can disrupt social networks by altering the behavior of both infected individuals (e.g. by reducing activity) and the behavior of uninfected individuals (e.g. avoiding sick individuals), both of which can <span>have an impact on social group dynamics and parasite transmission</span>. Here we test experimentally how parasitism affects social contact behavior and social network structure using a common parasite infection of sheep. Three treatment groups, each with 4 replicate social groups were established (i) Parasitised; all lambs were infected with a parasitic nematode, (ii) Non-parasitised; all lambs remained uninfected (iii) Mixed; part of each group were infected, and part of the group remained uninfected. Contact behaviours of each individual were recorded using proximity loggers during four phases of infection (pre-parasite, pre-patent, patent-parasite, post-parasite). We found infected individuals in the parasitised and mixed groups reduced contact frequency following infection. Infected individuals in mixed groups however reduced contact frequency to a greater extent than infected animals in the fully parasitised group. D<span>espite the reduction in contacts between infected animals in the mixed group, the social network structure was unaffected, as non-infected individuals maintained pre-parasite levels of social interactions with their infected conspecifics. </span><span>These results demonstrate </span>how infection can impact the social behavior of all animals within a group, and how the expression of behavioral change may depend on the parasitic status of all group members and the response of uninfected conspecifics.</p>
Agta hunter-gatherer oral microbiomes are shaped by contact network structure
<p>Here we investigate the effects of extensive sociality and mobility on the oral microbiome of 138 Agta hunter-gatherers from the Philippines. Our comparisons of microbiome composition showed that the Agta are more similar to Central African Bayaka hunter-gatherers than to neighbouring farmers. We also defined the Agta social microbiome as a set of 137 oral bacteria (only 7% of 1980 amplicon sequence variants) significantly influenced by social contact (quantified through wireless sensors of short-range interactions). We show that large interaction networks including strong links between close kin, spouses, and even unrelated friends, can significantly predict bacterial transmission networks across Agta camps. Finally, we show that more central individuals to social networks are also bacterial supersharers. We conclude that hunter-gatherer social microbiomes are predominantly pathogenic and were shaped by evolutionary tradeoffs between extensive sociality and disease spread.<strong> </strong></p>
Social network structure is robust to parasite induced changes in contact behavior of domestic sheep
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Data from: Infection-induced behavioural changes reduce connectivity and the potential for disease spread in wild mice contact networks
Infection may modify the behaviour of the host and of its conspecifics in a group, potentially altering social connectivity. Because many infectious diseases are transmitted through social contact, social connectivity changes can impact transmission dynamics. Previous approaches to understanding disease transmission dynamics in wild populations were limited in their ability to disentangle different factors that determine the outcome of disease outbreaks. Here we ask how social connectivity is affected by infection and how this relationship impacts disease transmission dynamics. We experimentally manipulated disease status of wild house mice using an immune challenge and monitored social interactions within this free-living population before and after manipulation using automated tracking. The immune-challenged animals showed reduced connectivity to their social groups, which happened as a function of their own behaviour, rather than through conspecific avoidance. We incorporated these disease-induced changes of social connectivity among individuals into models of disease outbreaks over the empirically-derived networks. The models revealed that changes in host behaviour frequently resulted in the disease being contained to very few animals, as opposed to becoming widespread. Our results highlight the importance of considering the role that behavioural alterations during infection can have on social dynamics when evaluating the potential for disease outbreaks.
Data from: Contact networks structured by sex underpin sex-specific epidemiology of infection
Contact networks are fundamental to the transmission of infection and host sex often affects the acquisition and progression of infection. However, the epidemiological impacts of sex-related variation in animal contact networks have rarely been investigated. We test the hypothesis that sex-biases in infection are related to variation in multilayer contact networks structured by sex in a population of European badgers Meles meles naturally infected with Mycobacterium bovis. Our key results are that male-male and between-sex networks are structured at broader spatial scales than female-female networks and that in male-male and between-sex contact networks, but not female-female networks, there is a significant relationship between infection and contacts with individuals in other groups. These sex differences in social behaviour may underpin male-biased acquisition of infection and may result in males being responsible for more between-group transmission. This highlights the importance of sex-related variation in host behaviour when managing animal diseases.
Data from: High-resolution contact networks of free-ranging domestic dogs Canis familiaris and implications for transmission of infection
Contact patterns strongly influence the dynamics of disease transmission in both human and non-human animal populations. Domestic dogs Canis familiaris are a social species and are a reservoir for several zoonotic infections, yet few studies have empirically determined contact patterns within dog populations. Using high-resolution proximity logging technology, we characterised the contact networks of free-ranging domestic dogs from two settlements (n = 108 dogs, covering >80% of the population in each settlement) in rural Chad. We used these data to simulate the transmission of an infection comparable to rabies and investigated the effects of including observed contact heterogeneities on epidemic outcomes. We found that dog contact networks displayed considerable heterogeneity, particularly in the duration of contacts and that the network had communities that were highly correlated with household membership. Simulations using observed contact networks had smaller epidemic sizes than those that assumed random mixing, demonstrating the unsuitability of homogenous mixing models in predicting epidemic outcomes. When contact heterogeneities were included in simulations, the network position of the individual initially infected had an important effect on epidemic outcomes. The risk of an epidemic occurring was best predicted by the initially infected individual's ranked degree, while epidemic size was best predicted by the individual's ranked eigenvector centrality. For dogs in one settlement, we found that ranked eigenvector centrality was correlated with range size. Our results demonstrate that observed heterogeneities in contacts are important for the prediction of epidemiological outcomes in free-ranging domestic dogs. We show that individuals presenting a higher risk for disease transmission can be identified by their network position and provide evidence that observable traits hold potential for informing targeted disease management strategies.
Data from: Rate of inter-sex interactions affects injury likelihood in Tasmanian devil contact networks
Identifying the types of contacts that result in disease transmission is important for accurately modelling and predicting transmission dynamics and disease spread in wild populations. We investigated contacts within a population of adult Tasmanian devils (Sarcophilus harrisii) over a six-month period and tested whether individual-level contact patterns were correlated with accumulation of bite wounds. Bite wounds are important in the spread of devil facial tumour disease (DFTD), a clonal cancer cell line transmitted through direct inoculation of tumour cells when susceptible and infected individuals bite each other. We used multi-model inference and network autocorrelation models to investigate the effects of individual-level contact patterns, identities of interacting partners, and position within the social network on the propensity to be involved in bite-inducing contacts. We found that males were more likely to receive potentially disease-transmitting bite wounds than females, particularly during the mating season when males spend extended periods mate-guarding females. The number of bite wounds individuals received during the mating season was unrelated to any of the network metrics examined. Our approach illustrates the necessity for understanding which contact types spread disease in different systems to assist the management of this and other infectious wildlife diseases.
Data for "Evaluating disease surveillance strategies for early outbreak detection in contact networks with varying community structure"
<p>New York City contact network data used in the publication “<a href="https://doi.org/10.1016/j.socnet.2024.06.003">Evaluating disease surveillance strategies for early outbreak detection in contact networks with varying community structure</a>” (LA-UR-23-26868). This contact network comes in the form of a weighted edge list. Each row describes an edge, with the first and second column containing the labels of the nodes connected by the edge, and the third column contains the corresponding weight of the edge. In this network, an edge encodes an interaction between two individuals and the weight describes the duration of the interaction in seconds. In total the edge list describes 6,376,729,847 interactions among 6,813,615 individuals; the first 10 interactions are listed below as an example.</p> <p>2, 1, 84121<br>4, 3, 83654.4<br>5, 3, 79591.4<br>5, 4, 87642<br>6, 3, 79853<br>6, 4, 81604<br>6, 5, 79146<br>8, 7, 80604<br>10, 9, 84259.6<br>12, 11, 68990.8</p> <p> </p> <p>This work is approved for public distribution under LA-UR-24-25046.</p>
A Framework for Improving Social Inclusion using Network Analysis and IoT-based Contact Tracing, Dataset and Source Code
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Daily colony contact networks for the ant Camponotus fellah
<p>Each zip file contains 11 files.</p> <p>Each file gives the daily pairwise contact counts for a given pair of ants, represented as a weighted adjacency matrix.</p> <p>The diagonal is filled with zeros because there are no self-self contacts.</p> <p>The parameters defining the contacts are given in the Supporting Information of the original paper:</p> <p>Mersch, Danielle P., Alessandro Crespi, and Laurent Keller. "Tracking individuals shows spatial fidelity is a key regulator of ant social organization." <em>Science</em> 340, no. 6136 (2013): 1090-1093.</p>
Data from: Modeling multi-species and multi-mode contact networks: implications for persistence of bovine tuberculosis at the wildlife-livestock interface
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Data from: High-resolution contact networks of free-ranging domestic dogs Canis familiaris and implications for transmission of infection
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Data from: Rate of inter-sex interactions affects injury likelihood in Tasmanian devil contact networks
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Data from: Infection-induced behavioural changes reduce connectivity and the potential for disease spread in wild mice contact networks
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Data from: Contact networks structured by sex underpin sex-specific epidemiology of infection
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Data from: Raccoon contact networks predict seasonal susceptibility to rabies outbreaks and limitations of vaccination
1. Infectious disease transmission often depends on the contact structure of host populations. Although it is often challenging to capture the contact structure in wild animals, new technology has enabled biologists to obtain detailed temporal information on wildlife social contacts. In this study, we investigated the effects of raccoon contact patterns on rabies spread using network modelling. 2. Raccoons (Procyon lotor) play an important role in the maintenance of rabies in the United States. It is crucial to understand how contact patterns influence the spread of rabies in raccoon populations in order to design effective control measures and to prevent transmission to human populations and other animals. 3. We constructed a dynamic system of contact networks based on empirical data from proximity logging collars on a wild suburban raccoon population and then simulated rabies spread across these networks. Our contact networks incorporated the number and duration of raccoon interactions. We included differences in contacts according to sex and season, and both short-term acquaintances and long-term associations. Raccoons may display different behaviours when infectious, including aggression (furious behaviour) and impaired mobility (dumb behaviour); the network model was used to assess the impact of potential behavioural changes in rabid raccoons. We also tested the effectiveness of different vaccination coverage levels. 4. Our results demonstrate that when rabies enters a suburban raccoon population, the likelihood of a disease outbreak affecting the majority of the population is high. Both the magnitude of rabies outbreaks and the speed of rabies spread depend strongly on the time of year that rabies is introduced into the population. When there is a combination of dumb and furious behaviours in the rabid raccoon population, there are similar outbreak sizes and speed of spread to when there are no behavioural changes due to rabies infection. 5. By incorporating detailed data describing the variation in raccoon contact rates into a network modelling approach, we were able to show that suburban raccoon populations are highly susceptible to rabies outbreaks, that the risk of large outbreaks varies seasonally and that current vaccination target levels may be inadequate to prevent the spread of rabies within these populations. Our findings provide new insights into rabies dynamics in raccoon populations and have important implications for disease control.
Data from: Indirect interactions influence contact network structure and diffusion dynamics
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 SPST models for the study of various airborne diseases 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.
Social network community structure and the contact-mediated sharing of commensal E. coli among captive rhesus macaques (Macaca mulatta)
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Data associated with Balasubramaniam, Beisner et al. (PeerJ, 2016): "Social buffering and contact transmission: Network connections have beneficial and detrimental effects on Shigella infection risk among captive rhesus macaques"
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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