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In the real world, the spread of various epidemics can be modelled using the SEIR disease transmission model. Early detection and prevention of susceptible individuals is an effective method of controlling the spread of infectious viruses. This paper presents a study that detects susceptible community on a hospital contact network, which encompasses patients, nurses, doctors and managers. The goal of our work is to identify susceptible communities with patients and healthcare workers, and analyse the independent contact networks of various roles to determine the high-influence nodes within the hospital contact network. If these high-influence nodes are part of the susceptible community, they should be the focus of observation to prevent the spread of the virus. The proposed model combines social network analysis method and machine learning method with the disease transmission model. This study employs the classic overlapping community detection method CPM for community detection and the PageRank algorithm to rank node influence. The experimental results, obtained from real-world hospital contact networks over a 4-day period, demonstrate the effectiveness of the model. [ABSTRACT FROM AUTHOR]

Background: The purpose of this paper is to study how the Delta variant spread in a China city, and to what extent the non-pharmaceutical prevention measures of local government be effective by reviewing the contact network of COVID-19 cases in Xi'an, China. Methods: We organize the case reports of the Shaanxi Health Commission into a database by text coding and convert them into a network matrix. Then we construct a dynamic contact network for the corresponding analysis and calculate network indicators. we analyze the cases' dynamic contact network structure and intervals between diagnosis time and isolation time by using data visualization, network analysis method, and Ordinary Least Square (OLS) regression. Results: The contact network for this outbreak in Xi'an is very sparse, with a density of less than 0.0001. The contact network is a scale-free network. The average degree centrality is 0.741 and the average PageRank score is 0.0005. The network generated from a single source of infection contains 1371 components. We construct three variables of intervals and analyze the trend of intervals during the outbreak. The mean interval (interval 1) between case diagnosis time and isolation time is − 3.9 days. The mean of the interval (interval 2) between the infector's diagnosis time and the infectee's diagnosis time is 4.2 days. The mean of the interval (interval 3) between infector isolation time and infectee isolation time is 2.9 days. Among the three intervals, only interval 1 has a significant positive correlation with degree centrality. Conclusions: By integrating COVID-19 case reports of a Chinese city, we construct a contact network to analyze the dispersion of the outbreak. The network is a scale-free network with multiple hidden pathways that are not detected. The intervals of patients in this outbreak decreased compared to the beginning of the outbreak in 2020. City lockdown has a significant effect on the intervals that can affect patients' network centrality. Our study highlights the value of case report text. By linking different reports, we can quickly analyze the spread of the epidemic in an urban area. [ABSTRACT FROM AUTHOR]

Fabric of a granular assembly represents the topology of the contact network. This paper investigates the evolution of contact anisotropy (fabric) and average coordination number for a granular assembly subjected to uniaxial compression through the Discrete Element Method (DEM). A monosize three-dimensional random close-packed granular assembly with periodic boundary conditions under uniaxial compression is considered in this work. The fabric evolution is studied by post-processing the output data of the DEM simulation. The influence of cyclic loading, strain rate, and Young's modulus on the evolution of contact anisotropy and average coordination number is presented. The Young's modulus of the particle shows a significant influence on the particle contact creation during compression of the granular assembly with high strain rate. Effect of inertia on the contact anisotropy is observed to be significant during the compression of granular assemblies with different Young's modulus under high strain rate. The paper concludes with a semi-empirical model to predict the evolution of contact anisotropy as a function of the macroscopic stress state of the assembly during quasi-static uniaxial compaction. The model also introduces two microscopic non-dimensional parameters that are independent of friction between the particles and can be used to relate the macroscopic stresses with the contact anisotropy. [ABSTRACT FROM AUTHOR]

The effective degree SIR model describes the dynamics of diseases with lifetime acquired immunity on a static random contact network. It is typically modeled as a system of ordinary differential equations describing the probability distribution of the infection status of neighbors of a susceptible node. Such a construct may not be used to study networks with an infinite degree distribution, such as an infinite scale-free network. We propose a new generating function approach to rewrite the effective degree SIR model as a nonlinear transport type partial differential equation. We show the existence and uniqueness of the solutions the are biologically relevant. In addition we show how this model may be reduced to the Volz model with the assumption that the infection statuses of the neighbors of an susceptible node are initially independent to each other. This paper paves the way to study the stability of the disease-free steady state and the disease threshold of the infinite dimensional effective degree SIR models. [ABSTRACT FROM AUTHOR]

Base pairing in RNA are significantly rich and versatile due to the potential non-canonical base pairing amongst nucleotides. Not only that, one base in RNA can pair with more than one bases simultaneously. This opens up a new dimension of research to detect such types of base-base pair networks in RNA and to analyze them. Even if a base do not form a pair, it may have significant extent of π - π stacking overlap that can stabilize the structures. In this work, we report a software tool, called BPNet, that accepts a mmCIF or PDB file and computes the base-pair/ π - π contact network components using graph formalism. The software can run on Linux platform in both serial and parallel modes. It generates several information in suitable file formats for visualization of the networks. This paper describes the BPNet software and also presents some interesting results obtained by analyzing several RNA structures by the software to show its effectiveness. [ABSTRACT FROM AUTHOR]

The paper examines the mechanics and physics of granular material responses at the macroscopic and microscopic levels during both monotonic and cyclic loadings. A numerical analysis referring to a long retaining wall is conducted using a two-dimensional discrete element model representing a granular system with a free top surface. On one of the lateral boundaries referring to the retaining wall, both active and passive loadings were applied monotonically as well as cyclically. First, the development of sheared zones and classic failure wedges resulting from active and passive monotonic displacements are discussed with respect to Rankine's and Roscoe's solution angles. Then, a series of loading cycles were performed using slow small-amplitude displacements at different stress states chosen before the occurrence of failure along the passive monotonic stress response curve. Particular interest is focused on the ultimate asymptotic cyclic response of the granular system, the occurrence of a high-mobility (convective) zone and a detailed macroscopic and microscopic analysis. Finally, major kinematical features that are displayed during cyclic loading from different starting stresses to eventually reach the same asymptotic state were elucidated through particle vortex-like flux formations, including contact rotations. The change in material stiffness was also investigated based on the evolution of strong and weak contact networks, together with the analysis of fabric anisotropy within the entire domain, including the high-mobility zone considered separately. [ABSTRACT FROM AUTHOR]

Background: Studies measuring contact networks have helped to improve our understanding of infectious disease transmission. However, several methodological issues are still unresolved, such as which method of contact measurement is the most valid. Further, complete network analysis requires data from most, ideally all, members of a network and, to achieve this, acceptance of the measurement method. We aimed at investigating measurement error by comparing two methods of contact measurement - paper diaries vs. wearable proximity sensors - that were applied concurrently to the same population, and we measured acceptability.Methods: We investigated the contact network of one day of an epidemiology conference in September 2014. Seventy-six participants wore proximity sensors throughout the day while concurrently recording their contacts with other study participants in a paper-diary; they also reported on method acceptability.Results: There were 329 contact reports in the paper diaries, corresponding to 199 contacts, of which 130 were noted by both parties. The sensors recorded 316 contacts, which would have resulted in 632 contact reports if there had been perfect concordance in recording. We estimated the probabilities that a contact was reported in a diary as: P = 72 % for <5 min contact duration (significantly lower than the following, p < 0.05), P = 86 % for 5-15 min, P = 89 % for 15-60 min, and P = 94 % for >60 min. The sets of sensor-measured and self-reported contacts had a large intersection, but neither was a subset of the other. Participants' aggregated contact duration was mostly substantially longer in the diary data than in the sensor data. Twenty percent of respondents (>1 reported contact) stated that filling in the diary was too much work, 25 % of respondents reported difficulties in remembering contacts, and 93 % were comfortable having their conference contacts measured by sensors.Conclusion: Reporting and recording were not complete; reporting was particularly incomplete for contacts <5 min. The types of contact that both methods are capable of detecting are partly different. Participants appear to have overestimated the duration of their contacts. Conducting a study with diaries or wearable sensors was acceptable to and mostly easily done by participants. Both methods can be applied meaningfully if their specific limitations are considered and incompleteness is accounted for. [ABSTRACT FROM AUTHOR]

The understanding of naturally occurring materials such as clay, sand, hard and soft rocks under a common theoretical framework has been a topic of persistent research interest. Over the past few decades, various sample reconstitution techniques have been developed in the literature to mimic in situ conditions, and to parse carefully the influence of various components in a cohesive-frictional geomaterial such that their behavior can be folded into the broad ambit of a continuum mechanics framework. The initial fabric of natural rock specimens is compared with reconstituted cemented sand samples using X-ray computed tomography (XRCT) scans. The efficacy of laboratory reconstitution techniques in replicating the initial microstructural features of natural rocks is evaluated here. Additionally, discrete element method (DEM) protocols which are often employed in generating cohesive granular ensembles are employed here and compared against the naturally occurring and artificially reconstituted fabric. A significant difference is observed in the grain boundaries of reconstituted and naturally occurring rocks. Additionally, the arrangement of particles, the orientation of grain contacts, and their coordination number are examined to assess the efficacy of laboratory-reconstituted specimens at micro-length scale. [ABSTRACT FROM AUTHOR]

Background: With increasing interest in animal social networks, field biologists have started exploring the use of advanced tracking technologies for mapping social encounters in free-ranging subjects. Proximity logging, which involves the use of animal-borne tags with the capacity for two-way communication, has attracted particular attention in recent years. While the basic rationale of proximity logging is straightforward, systems generate very large datasets which pose considerable challenges in terms of processing and visualisation. Technical aspects of data handling are crucial for the success of proximity-logging studies, yet are only rarely reported in full detail. Here, we describe the procedures we employed for mining the data generated by a recent deployment of a novel proximity-logging system, "Encounternet", to study social-network dynamics in tool-using New Caledonian crows. Results: Our field deployment of an Encounternet system produced some 240,000 encounter logs for 33 crows over a 19-day study period. Using this dataset, we illustrate a range of procedures, including: examination of tag reciprocity (i.e. whether both tags participating in an encounter detected the encounter and, if so, whether their records differed); filtering of data according to a predetermined signal-strength criterion (to enable analyses that focus on encounters within a particular distance range); amalgamation of temporally clustered encounter logs (to remove data artefacts and to enable robust analysis of biological patterns); and visualisation of dynamic network data as timeline plots (which can be used, among other things, to visualise the simulated diffusion of information). Conclusions: Researchers wishing to study animal social networks with proximity-logging systems should be aware of the complexities involved. Successful data analysis requires not only a sound understanding of hardware and software operation, but also bioinformatics expertise. Our paper aims to facilitate future projects by explaining in detail some of the subtleties that are easily overlooked in irst-pass analyses, but are key for reaching valid biological conclusions. We hope that this work will prove useful to other researchers, especially when read in conjunction with three recently published companion papers that report aspects of system calibration and key results. [ABSTRACT FROM AUTHOR]

An important characteristic of influenza A is its ability to escape host immunity through antigenic drift. A novel influenza A strain that causes a pandemic confers full immunity to infected individuals. Yet when the pandemic strain drifts, these individuals will have decreased immunity to drifted strains in the following seasonal epidemics. We compute the required decrease in immunity so that a recurrence is possible. Models for influenza A must make assumptions on the contact structure on which the disease spreads. By considering local stability of the disease free equilibrium via computation of the reproduction number, we show that the classical random mixing assumption predicts an unrealistically large decrease of immunity before a recurrence is possible. We improve over the classical random mixing assumption by incorporating a contact network structure. A complication of contact networks is correlations induced by the initial pandemic. We provide a novel analytic derivation of such correlations and show that contact networks may require a dramatically smaller loss of immunity before recurrence. Hence, the key new insight in our paper is that on contact networks the establishment of a new strain is possible for much higher immunity levels of previously infected individuals than predicted by the commonly used random mixing assumption. This suggests that stable contacts like classmates, coworkers and family members are a crucial path for the spread of influenza in human populations. [ABSTRACT FROM AUTHOR]

Mesoscale contact networks for asphalt mixtures play a crucial role in load resistance. However, there is a lack of quantitative characterization methods for contact networks. Topology can describe the contact relationships between multiple objects through points and lines. In this study, aggregate information was extracted by industrial computed tomography (CT) and digital image processing technology (DIP). The classification of disruption aggregates was determined by a two-dimensional (2D) topological contact model. Then, the correlation mechanism between topological indices and topologies was established based on the topological matrix. The algebraic connectivity (La) of nine different asphalt mixture disruption aggregates was analyzed. The results revealed that each type of asphalt mixture disruption aggregate was dominated by a certain La with higher frequency. As the nominal maximum size (NMAS) increased, there was a shift in the dominant topologies of different asphalt mixture disruption aggregates from those associated with lower La to those linked to higher La. Based on the higher frequency of La, there were 8 topologies with frequencies exceeding 20%; 5 with frequencies surpassing 10%; and 14 and 17 with frequencies greater than 5% and 2%, respectively. Furthermore, it was observed that the topologies of disruption aggregates predominantly consisted of simple topologies. Additionally, the frequency of complex topologies decreased as the number of nodes and connectivity increased. [ABSTRACT FROM AUTHOR]

Using the technique of edge-based compartmental modelling (EBCM) for the spread of susceptible-infected-recovered (SIR) diseases in networks, in a recent paper (PloS One, 8(2013), e69162), Miller and Volz established an SIR disease network model with heterogeneous infectiousness and susceptibility. The authors provided a numerical example to demonstrate its validity but they did not perform any mathematical analysis of the model. In this paper, we resolve this problem. Using the nature of irreducible cooperative system in the theory of monotonic dynamical system, we prove that the dynamics of the model are completely determined by a critical value ρ0: When ρ0 > 0, the disease persists in a globally stable outbreak equilibrium; while when ρ0 < 0, the disease dies out in the population and the disease free equilibrium is globally stable.

The development of computational intelligence based approaches for the compression of graphs is an under-explored area of research. Further, compression of weighted graphs is significantly more complicated than compression of unweighted graphs. In this paper a multi-objective approach using NSGA-II is applied to the problem of weighted graph compression. The approach is designed to find a balance between the level of compression and the distortion created by the compression. Distortion is measured using two fitness functions that each evaluate changes both in graph structure and in edge weights. The methodology is applied to three weighted contact networks with differing characteristics. It was found that the multi-objective approach is useful in identifying suitable compression ratios based upon defined levels of acceptable distortion, with a single-objective genetic algorithm then applied to focus on this target compression ratio to further reduce distortion. • A multi-objective approach is applied to the problem of weighted graph compression. • The approach helps find a balance between the levels of compression and distortion. • Two fitness functions were applied to three weighted contact networks. • One of the fitness functions enables easier selection of a target compression ratio. [ABSTRACT FROM AUTHOR]

Mathematical models play an important role in management of outbreaks of acute respiratory infections (ARI). While such models are generally used to study the spread of a solitary virus, in reality multiple viruses co-circulate in the population. These viruses have been studied in detail, including the course of infection and immune defense mechanisms. We developed an agent-based model, called ABM-ARI, assimilating heterogeneous data and theoretical knowledge into a biologically motivated system, that allows to reproduce the seasonal patterns of ARI incidence and simulate interventions. ABM-ARI uses city-specific data to create a synthetic population and to construct realistic contact networks in different activity settings. Characteristics of infection, immune protection and non-specific resistance were varied between individuals to account for the population heterogeneity. For the calibration, we minimised the normalised mean absolute error between simulated and observed epidemic curves. ABM-ARI was built based on the quantitative assessment of features of predominant respiratory viruses and epidemiological characteristics of the population. It provides a good fit to the observed epidemic curves for different age groups and viruses. We also simulated one-week school closures when student absences were at or above 10%, 20% or 30% and found that only 10% and 20% thresholds resulted in a reduction of the incidence. ABM-ARI has a great potential in tackling the challenge of emerging infections by simulating and evaluating the effectiveness of various interventions. [ABSTRACT FROM AUTHOR]

To effectively mitigate the spread of communicable diseases, it is necessary to understand the interactions that enable disease transmission among individuals in a population; we refer to the set of these interactions as a contact network. The structure of the contact network can have profound effects on both the spread of infectious diseases and the effectiveness of control programs. Therefore, understanding the contact network permits more efficient use of resources. Measuring the structure of the network, however, is a challenging problem. We present a Bayesian approach to integrate multiple data sources associated with the transmission of infectious diseases to more precisely and accurately estimate important properties of the contact network. An important aspect of the approach is the use of the congruence class models for networks. We conduct simulation studies modeling pathogens resembling SARS‐CoV‐2 and HIV to assess the method; subsequently, we apply our approach to HIV data from the University of California San Diego Primary Infection Resource Consortium. Based on simulation studies, we demonstrate that the integration of epidemiological and viral genetic data with risk behavior survey data can lead to large decreases in mean squared error (MSE) in contact network estimates compared to estimates based strictly on risk behavior information. This decrease in MSE is present even in settings where the risk behavior surveys contain measurement error. Through these simulations, we also highlight certain settings where the approach does not improve MSE. [ABSTRACT FROM AUTHOR]

The perceived infection risk changes individual behaviors, which further affects the disease dynamics. This perception is influenced by social communication, including surveying their social network neighbors about the fraction of infected neighbors and averaging their neighbors' perception of the risk. We model the interaction of disease dynamics and risk perception on a two-layer random network that combines a social network layer with a contact network layer. We found that if information spreads much faster than disease, then all individuals converge on the true prevalence of the disease. On the other hand, if the two dynamics have comparable speeds, the risk perception still converges to a value uniformly on the network. However, the perception lags behind the true prevalence and has a lower peak value. We also study the behavior change caused by the perception of infection risk. This behavior change may affect the disease dynamics by reducing the transmission rate along the edges of the contact network or by breaking edges and isolating the infectious individuals. The effects on the basic reproduction number, the peak size, and the final size are studied. We found that these two effects give the same basic reproduction number. We find edge-breaking has a larger effect on reducing the final size, while reducing the transmission rate has a larger effect on reducing the peak size, which is true for both scalefree and Poisson networks. [ABSTRACT FROM AUTHOR]

Abstract Background The purpose of this paper is to study how the Delta variant spread in a China city, and to what extent the non-pharmaceutical prevention measures of local government be effective by reviewing the contact network of COVID-19 cases in Xi’an, China. Methods We organize the case reports of the Shaanxi Health Commission into a database by text coding and convert them into a network matrix. Then we construct a dynamic contact network for the corresponding analysis and calculate network indicators. we analyze the cases’ dynamic contact network structure and intervals between diagnosis time and isolation time by using data visualization, network analysis method, and Ordinary Least Square (OLS) regression. Results The contact network for this outbreak in Xi’an is very sparse, with a density of less than 0.0001. The contact network is a scale-free network. The average degree centrality is 0.741 and the average PageRank score is 0.0005. The network generated from a single source of infection contains 1371 components. We construct three variables of intervals and analyze the trend of intervals during the outbreak. The mean interval (interval 1) between case diagnosis time and isolation time is − 3.9 days. The mean of the interval (interval 2) between the infector’s diagnosis time and the infectee’s diagnosis time is 4.2 days. The mean of the interval (interval 3) between infector isolation time and infectee isolation time is 2.9 days. Among the three intervals, only interval 1 has a significant positive correlation with degree centrality. Conclusions By integrating COVID-19 case reports of a Chinese city, we construct a contact network to analyze the dispersion of the outbreak. The network is a scale-free network with multiple hidden pathways that are not detected. The intervals of patients in this outbreak decreased compared to the beginning of the outbreak in 2020. City lockdown has a significant effect on the intervals that can affect patients’ network centrality. Our study highlights the value of case report text. By linking different reports, we can quickly analyze the spread of the epidemic in an urban area.

In the design of surveillance, there is often a desire to target high risk herds. Such risk-based approaches result in better allocation of resources and improve the performance of surveillance activities. For many contagious animal diseases, movement of live animals is a main route of transmission, and because of this, herds that purchase many live animals or have a large contact network due to trade can be seen as a high risk stratum of the population. This paper presents a new method to assess herd disease risk in animal movement networks. It is an improvement to current network measures that takes direction, temporal order, and also movement size and probability of disease into account. In the study, the method was used to calculate a probability of disease ratio (PDR) of herds in simulated datasets, and of real herds based on animal movement data from dairy herds included in a bulk milk survey for Coxiella burnetii . Known differences in probability of disease are easily incorporated in the calculations and the PDR was calculated while accounting for regional differences in probability of disease, and also by applying equal probability of disease throughout the population. Each herd's increased probability of disease due to purchase of animals was compared to both the average herd and herds within the same risk stratum. The results show that the PDR is able to capture the different circumstances related to disease prevalence and animal trade contact patterns. Comparison of results based on inclusion or exclusion of differences in risk also highlights how ignoring such differences can influence the ability to correctly identify high risk herds. The method shows a potential to be useful for risk-based surveillance, in the classification of herds in control programmes or to represent influential contacts in risk factor studies. [ABSTRACT FROM AUTHOR]

Since their establishment, the movement of objects and information from communities of origin into European museums has typically been one-way. Over the last decade this has shifted with digital images and information connected to objects increasingly travelling outwards. As Museums engage with the expanding open-web economy they face pressure to embrace exchange and dialogue with diverse publics. Using the examples of digital research network projects at the University of Cambridge Museum of Archaeology and Anthropology and the British Museum this paper examines how in practice issues surrounding online interactions between community groups, objects and the museum have been played out past and present. Recent theorization has created a binary opposition between the real object and its digital representation. In this article we critique this separation and in doing so reveal the potential for contact networks, as an alternative reciprocal model of engaging with things to move us into a new realm of digital possibilities for object-based engagement. [ABSTRACT FROM AUTHOR]

The article focuses on the development of a complex network-based contact network model in analyzing the epidemics of H1N1 influenza. It states that a contact tracing mechanism is utilized in studying H1N1 virus transmission dynamics and in analyzing the data of H1N1 influenza provided by Beijing Health Bureau in Beijng, China. It mentions that the peak value impact of new infections is examined based on contact tracing coefficient and random checking coefficient.

As the COVID-19 vaccination has been quickly rolling out around the globe, the evaluation of the effects of vaccinating populations for the safe reopening of schools has become a focal point for educators, decision-makers, and the general public. Within this context, we develop a contact network agent-based model (CN-ABM) to simulate on-campus disease transmission scenarios. The CN-ABM establishes contact networks for agents based on their daily activity patterns, evaluates the agents' health status change in different activity environments, and then simulates the epidemic curve. By applying the model to a real-world campus environment, we identify how different community risk levels, teaching modalities, and vaccination rates would shape the epidemic curve. The results show that without vaccination, retaining under 50% of on-campus students can largely flatten the curve, and having 25% on-campus students can achieve the best result (peak value < 1%). With vaccination, having a maximum of 75% on-campus students and at least a 45% vaccination rate can suppress the curve, and a 65% vaccination rate can achieve the best result. The developed CN-ABM can be employed to assist local government and school officials with developing proactive intervention strategies to safely reopen schools. [ABSTRACT FROM AUTHOR]

The contact network, defined by a fabric tensor, influences phenomena such as force or heat transfer in a granular assembly. The correlation between fabric, stress, and conductivity has, however, been least explored. Furthermore, a link between these quantities may help to build a benchmark method to assist experiments substantially. The present work bridges the gap between the macroscopic quantities, such as stress and conductivity tensor, using its microscopic connection and the fabric tensor. The study presents a few interesting functional forms and non-dimensional macroscopic quantities that couple conductivity, stress, and fabric. The main feature of these functional forms is that they are independent of friction. These functional forms will provide a good estimate of fabric and conductivity which can be used real-time during the experimental investigations as well. [ABSTRACT FROM AUTHOR]

• The behavior of epidemic on six benchmark networks is simulated. •.The geometric network was the best fit for contacts pattern in Qazvin province of Iran. • The effect of crucial parameters on behavior of the epidemic is discussed. • The effect of soft seesaw scenarios for lockdown is analyzed. • Soft seesaw lockdown scenarios are suitable alternatives in poor societies. The ongoing COVOD-19(SARS-CoV-2) outbreak has had a devastating impact on the economy, education and businesses. In this paper, the behavior of an epidemic is simulated on different contact networks. Herein, it is assumed that the infection may be transmitted at each contact from an infected person to a susceptible individual with a given probability. The probability of transmitting the disease may change due to the individuals' social behavior or interventions prescribed by the authorities. We utilized simulation on the contact networks to demonstrate how seesaw scenarios of lockdown can curb infection and level the pandemic without maximum pressure on the poor societies. Soft scenarios consist of closing businesses 2, 3, and 4 days in between with four levels of lockdown respected by 25%, 50%, 75%, and 100% of the population. The findings reveal that the outbreak can be flattened under softer alternatives instead of a doomsday scenario of complete lockdown. More specifically, it is turned out that proposed soft lockdown strategies can flatten up to 120% of the pandemic course. It is also revealed that transmission probability has a crucial role in the course of the infection, growth rate of the infection, and the number of infected individuals. [ABSTRACT FROM AUTHOR]

Measuring close proximity interactions between individuals can provide key information on social contacts in human communities and related behaviours. This is even more essential in rural settings in low- and middle-income countries where there is a need to understand contact patterns for the implementation of strategies for social protection interventions. We report the quantitative assessment of contact patterns in a village in rural Malawi, based on proximity sensors technology that allows for high-resolution measurements of social contacts. Our results revealed that the community structure of the village was highly correlated with the household membership of the individuals, thus confirming the importance of the family ties within the village. Social contacts within households occurred mainly between adults and children, and adults and adolescents and most of the inter-household social relationships occurred among adults and among adolescents. At the individual level, age and gender social assortment were observed in the inter-household network, and age disassortativity was instead observed in intra-household networks. Moreover, we obtained a clear trend of the daily contact activity of the village. Family members congregated in the early morning, during lunch time and dinner time. In contrast, inter-household contact activity displayed a growth from the morning, reaching a maximum in the afternoon. The proximity sensors technology used in this study provided high resolution temporal data characterized by timescales comparable with those intrinsic to social dynamics and it thus allowed to have access to the level of information needed to understand the social context of the village. [ABSTRACT FROM AUTHOR]

This paper describes the R package EpiILMCT, which allows users to study the spread of infectious disease using continuous time individual level models (ILMs). The package provides tools for simulation from continuous time ILMs that are based on either spatial demographic, contact network, or a combination of both of them, and for the graphical summarization of epidemics. Model fitting is carried out within a Bayesian Markov Chain Monte Carlo framework. The continuous time ILMs can be implemented within either susceptible-infected-removed (SIR) or susceptible-infected-notified-removed (SIN R) compartmental frameworks. As infectious disease data is often partially observed, data uncertainties in the form of missing infection times - and in some situations missing removal times - are accounted for using data augmentation techniques. The package is illustrated using both simulated and an experimental data set on the spread of the tomato spotted wilt virus disease.

Infectious disease transmission between individuals in a heterogeneous population is often best modelled through a contact network. This contact network can be spatial in nature, with connections between individuals closer in space being more likely. However, contact network data are often unobserved. Here, we consider the fit of an individual level model containing a spatially-based contact network that is either entirely, or partially, unobserved within a Bayesian framework, using data augmented Markov chain Monte Carlo (MCMC). We also incorporate the uncertainty about event history in the disease data. We also examine the performance of the data augmented MCMC analysis in the presence or absence of contact network observational models based upon either knowledge about the degree distribution or the total number of connections in the network. We find that the latter tend to provide better estimates of the model parameters and the underlying contact network. [ABSTRACT FROM AUTHOR]

Respiratory pathogens are expected to spread through social contacts, but outbreaks often occur quickly and unpredictably, making it challenging to simultaneously record social contact and disease incidence data, especially in wildlife. Thus, the role of social contacts in the spread of infectious disease is often treated as an assumption in disease simulation studies, and few studies have empirically demonstrated how pathogens spread through social networks. In July–August 2015, an outbreak of respiratory disease was observed in a wild chimpanzee community in Kibale National Park, Uganda, during an ongoing behavioral study of male chimpanzees, offering a rare opportunity to evaluate how social behavior affects individual exposure to socially transmissible diseases. From May to August 2015, we identified adult and adolescent male chimpanzees displaying coughs and rhinorrhea and recorded 5-m proximity data on males (N = 40). Using the network k-test, we found significant relationships between male network connectivity and the distribution of cases within the network, supporting the importance of short-distance contacts for the spread of the respiratory outbreak. Additionally, chimpanzees central to the network were more likely to display clinical signs than those with fewer connections. Although our analyses were limited to male chimpanzees, these findings underscore the value of social connectivity data in predicting disease outcomes and elucidate a potential evolutionary cost of being social. [ABSTRACT FROM AUTHOR]

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Point data obtained from real‐time location systems (RTLSs) can be processed into animal contact networks, describing instances of interaction between tracked individuals. Proximity‐based definitions of interanimal "contact," however, may be inadequate for describing epidemiologically and sociologically relevant interactions involving body parts or other physical spaces relatively far from tracking devices. This weakness can be overcome by using polygons, rather than points, to represent tracked individuals and defining "contact" as polygon intersections.We present novel procedures for deriving polygons from RTLS point data while maintaining distances and orientations associated with individuals' relocation events. We demonstrate the versatility of this methodology for network modeling using two contact network creation examples, wherein we use this procedure to create (a) interanimal physical contact networks and (b) a visual contact network. Additionally, in creating our networks, we establish another procedure to adjust definitions of "contact" to account for RTLS positional accuracy, ensuring all true contacts are likely captured and represented in our networks.Using the methods described herein and the associated R package we have developed, called contact, researchers can derive polygons from RTLS points. Furthermore, we show that these polygons are highly versatile for contact network creation and can be used to answer a wide variety of epidemiological, ethological, and sociological research questions.By introducing these methodologies and providing the means to easily apply them through the contact R package, we hope to vastly improve network‐model realism and researchers' ability to draw inferences from RTLS data. [ABSTRACT FROM AUTHOR]

Many parasites infect multiple species and persist through a combination of within- and between-species transmission. Multispecies transmission networks are typically constructed at the species level, linking two species if any individuals of those species interact. However, generalist species often consist of specialized individuals that prefer different subsets of available resources, so individual- and species-level contact networks can differ systematically. To explore the epidemiological impacts of host specialization, we build and study a model for pollinator pathogens on plant-pollinator networks, in which individual pollinators have dynamic preferences for different flower species. We find that modeling and analysis that ignore individual host specialization can predict die-off of a disease that is actually strongly persistent and can badly over- or underpredict steady-state disease prevalence. Effects of individual preferences remain substantial whenever mean preference duration exceeds half of the mean time from infection to recovery or death. Similar results hold in a model where hosts foraging in different habitats have different frequencies of contact with an environmental reservoir for the pathogen. Thus, even if all hosts have the same long-run average behavior, dynamic individual differences can profoundly affect disease persistence and prevalence. [ABSTRACT FROM AUTHOR]

In many epidemiologic models, a disease is assumed to spread along a contact network. We aim to infer this network, in addition to the epidemiologic model parameters, from the binary status of individuals observed throughout time. We perform an exact evaluation of the probability for each edge to be part of the network by using the matrix-tree theorem on the set of vertices made of the individual status at all times. This leads to a computational complexity of order O (m n 2) , where n is the number of individuals and m the length of the time series. Simulations are provided to demonstrate the efficiency of the proposed method, and it is applied on data concerning seed choices by farmers in India and on data on a measles outbreak. [ABSTRACT FROM AUTHOR]