Sampling of temporal networks: methods and biases

Temporal networks have been increasingly used to model a diversity of systems that evolve in time; for example, human contact structures over which dynamic processes such as epidemics take place. A fundamental aspect of real-life networks is that they are sampled within temporal and spatial frames. Furthermore, one might wish to subsample networks to reduce their size for better visualization or to perform computationally intensive simulations. The sampling method may affect the network structure and thus caution is necessary to generalize results based on samples. In this paper, we study four sampling strategies applied to a variety of real-life temporal networks. We quantify the biases generated by each sampling strategy on a number of relevant statistics such as link activity, temporal paths and epidemic spread. We find that some biases are common in a variety of networks and statistics, but one strategy, uniform sampling of nodes, shows improved performance in most scenarios. Given the particularities of temporal network data and the variety of network structures, we recommend that the choice of sampling methods be problem oriented to minimize the potential biases for the specific research questions on hand. Our results help researchers to better design network data collection protocols and to understand the limitations of sampled temporal network data

Statistical modeling of disease emergence

Infectious diseases seem to be appearing at an unprecedented rate: within the last few years alone, a sequence of novel diseases like MERS-CoV, Chikungunya, and Zika have emerged. Concurrently, a number of previously known diseases have re-emerged like the 2009 H1N1 pandemic and the 2014 Ebola epidemic. While these known and unknown emergence events have all begun with a wildlife or livestock spillover transmission event into humans, they each present unique subsequent public health challenges. Quantitative prediction of either the re-emergence of a known disease or potential for global spread of a novel disease can help optimize public health responses and resource allocation, but these events are usually analyzed in retrospect. In this dissertation, I developed quantitative frameworks that can be used in real-time for predicting disease emergence risk. In Chapter 2, I identified a seasonal trend to pandemic influenza emergence events, and proposed a hypothesis to explain the seasonal patterns and predict pandemic emergence risk for seasonal flu data. In Chapter 3, I developed a framework to both predict the number of imported Zika cases into a region, and subsequently assist public health decision-making during an uncertain outbreak. Finally, in Chapter 4, I developed a method that can be used to update regional transmission risk estimates of a novel disease before transmission occurs. Altogether, the results presented in this dissertation suggest that statistical modeling can be an important tool to assist real-time public health predictions and responses.Ecology, Evolution and Behavio

Analysing livestock network data for infectious diseases control:an argument for routine data collection in emerging economies

Livestock movements are an important mechanism of infectious disease transmission. Where these are well recorded, network analysis tools have been used to successfully identify system properties, highlight vulnerabilities to transmission, and inform targeted surveillance and control. Here we highlight the main uses of network properties in understanding livestock disease epidemiology and discuss statistical approaches to infer network characteristics from biased or fragmented datasets. We use a ‘hurdle model’ approach that predicts (i) the probability of movement and (ii) the number of livestock moved to generate synthetic ‘complete’ networks of movements between administrative wards, exploiting routinely collected government movement permit data from northern Tanzania. We demonstrate that this model captures a significant amount of the observed variation. Combining the cattle movement network with a spatial between-ward contact layer, we create a multiplex, over which we simulated the spread of ‘fast’ (R0 = 3) and ‘slow’ (R0 = 1.5) pathogens, and assess the effects of random versus targeted disease control interventions (vaccination and movement ban). The targeted interventions substantially outperform those randomly implemented for both fast and slow pathogens. Our findings provide motivation to encourage routine collection and centralization of movement data to construct representative networks. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’

Analysing livestock network data for infectious disease control: an argument for routine data collection in emerging economies

Livestock movements are an important mechanism of infectious disease transmission. Where these are well recorded, network analysis tools have been used to successfully identify system properties, highlight vulnerabilities to transmission, and inform targeted surveillance and control. Here we highlight the main uses of network properties in understanding livestock disease epidemiology and discuss statistical approaches to infer network characteristics from biased or fragmented datasets. We use a ‘hurdle model’ approach that predicts (i) the probability of movement and (ii) the number of livestock moved to generate synthetic ‘complete’ networks of movements between administrative wards, exploiting routinely collected government movement permit data from northern Tanzania. We demonstrate that this model captures a significant amount of the observed variation. Combining the cattle movement network with a spatial between-ward contact layer, we create a multiplex, over which we simulated the spread of ‘fast’ (R0 = 3) and ‘slow’ (R0 = 1.5) pathogens, and assess the effects of random versus targeted disease control interventions (vaccination and movement ban). The targeted interventions substantially outperform those randomly implemented for both fast and slow pathogens. Our findings provide motivation to encourage routine collection and centralization of movement data to construct representative networks. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’

Modern temporal network theory: A colloquium

The power of any kind of network approach lies in the ability to simplify a complex system so that one can better understand its function as a whole. Sometimes it is beneficial, however, to include more information than in a simple graph of only nodes and links. Adding information about times of interactions can make predictions and mechanistic understanding more accurate. The drawback, however, is that there are not so many methods available, partly because temporal networks is a relatively young field, partly because it more difficult to develop such methods compared to for static networks. In this colloquium, we review the methods to analyze and model temporal networks and processes taking place on them, focusing mainly on the last three years. This includes the spreading of infectious disease, opinions, rumors, in social networks; information packets in computer networks; various types of signaling in biology, and more. We also discuss future directions.Comment: Final accepted versio

Developing methods and applications for the analysis of cetacean social networks

Cetaceans, the whales, dolphins, and porpoises, represent a taxon of intense interest for researchers studying non-human social structure. Social network analysis has become a central tool for studying these species, however the collection, analysis, and application of cetacean social network data comes with numerous challenges. In this thesis, I address key research gaps in the study of cetacean social networks, using the well-studied southern resident killer whale populations as my study system. In the first chapter, I present a systematic literature review on cetacean social networks, in order to identify open areas for future research and development. In Chapter 2, I address the question of social complexity and its quantification. Using mixture models, I develop and test measure of social complexity based on relationship diversity that can be derived from association networks. In Chapter 3, I demonstrate that a commonly used statistical procedure for regression in association networks does not specify a proper null hypothesis, and results in high type I error rates. In Chapter 4, I use unmanned aerial systems methods to measure association and interaction networks within a group of southern resident killer whales, finding important differences in the structure of these different networks. In Chapter 5, I use long-term photographic data to model the spread of a novel pathogen over the social network of the endangered southern resident killer whale community to assess overall risk and potential management strategies. In Chapter 6, I use a multi-decade dataset of social associations, survival, and fecundity to test the link between aspects of the social environment and fitness in the southern resident killer whale population. In the final chapter, I provide a general discussion and synthesis of my results, and suggest areas for future research, both generally and within the southern resident population specifically.Natural Environment Research Council (NERC

Burstiness in activity-driven networks and the epidemic threshold

We study the effect of heterogeneous temporal activations on epidemic spreading in temporal networks. We focus on the susceptible-infected-susceptible model on activity-driven networks with burstiness. By using an activity-based mean-field approach, we derive a closed analytical form for the epidemic threshold for arbitrary activity and inter-event time distributions. We show that, as expected, burstiness lowers the epidemic threshold while its effect on prevalence is twofold. In low-infective systems burstiness raises the average infection probability, while it weakens epidemic spreading for high infectivity. Our results can help clarify the conflicting effects of burstiness reported in the literature. We also discuss the scaling properties at the transition, showing that they are not affected by burstiness.INFN BIOPHYS project, Spanish Ministry of Science as well as the Agencia Espanola de Investigacion (AEI) for financial support under grant FIS2017-84256-P (FEDER funds)

Early detection and control of potential pandemics.

Over the centuries, human beings have been inflicted with a variety of contagious diseases, resulting in tens of millions of respiratory illnesses and deaths worldwide. Early detection of disease spread facilitates timely responses that can greatly reduce its impact on a population. Therefore, this early information is a major public health objective and is crucial for policy makers and public health officials responsible for protecting the public from the spread of contagious diseases. Current indicators of the spread of contagious outbreaks lag behind its actual spread, leaving no time for a planned response. The studies of Christakis et al. in 2010 have shown that social networks can provide more timely information for prediction. However, the reported social network methods used to monitor disease spread do not consider contact patterns of individuals over space and time, such as during their movement from place to place. In this dissertation we propose a more effective way to chart the spread of contagious outbreaks, in a spatio-temporal sense, using “contact networks”. This enables more effective control of the spread of contagious outbreaks in their early stages so as to “nip a potential pandemic in the bud.” In order to enhance the prediction model developed we introduce factors to consider the intensity of exposure to the disease, and the susceptibility of the individual. This would involve the consideration of both space and time factors, since diseases caused by either viruses or bacteria involve some type of contact, either direct (e.g. shaking hands) or through the atmosphere (e.g. coughing or sneezing) between the susceptible and infected individuals. In this dissertation, we apply data mining methodologies and predictive modeling technologies, such as logistic regression, decision trees and neural networks to estimate the infection risk based on an individual’s demographic information and health status. The information used in the models can be obtained from a wide variety of data sources, including historical medical records from hospitals and clinics. Early information on the presence of a potential disease outbreak can be obtained from sensors , such as, First Watch and EARS (Early Aberration Response Systems) and central individuals in “contact” networks