Interacting Spreading Processes in Multilayer Networks: A Systematic Review

© 2013 IEEE. The world of network science is fascinating and filled with complex phenomena that we aspire to understand. One of them is the dynamics of spreading processes over complex networked structures. Building the knowledge-base in the field where we can face more than one spreading process propagating over a network that has more than one layer is a challenging task, as the complexity comes both from the environment in which the spread happens and from characteristics and interplay of spreads' propagation. As this cross-disciplinary field bringing together computer science, network science, biology and physics has rapidly grown over the last decade, there is a need to comprehensively review the current state-of-the-art and offer to the research community a roadmap that helps to organise the future research in this area. Thus, this survey is a first attempt to present the current landscape of the multi-processes spread over multilayer networks and to suggest the potential ways forward

Supporting Large Scale Communication Systems on Infrastructureless Networks Composed of Commodity Mobile Devices: Practicality, Scalability, and Security.

Infrastructureless Delay Tolerant Networks (DTNs) composed of commodity mobile devices have the potential to support communication applications resistant to blocking and censorship, as well as certain types of surveillance. In this thesis we study the utility, practicality, robustness, and security of these networks. We collected two sets of wireless connectivity traces of commodity mobile devices with different granularity and scales. The first dataset is collected through active installation of measurement software on volunteer users' own smartphones, involving 111 users of a DTN microblogging application that we developed. The second dataset is collected through passive observation of WiFi association events on a university campus, involving 119,055 mobile devices. Simulation results show consistent message delivery performances of the two datasets. Using an epidemic flooding protocol, the large network achieves an average delivery rate of 0.71 in 24 hours and a median delivery delay of 10.9 hours. We show that this performance is appropriate for sharing information that is not time sensitive, e.g., blogs and photos. We also show that using an energy efficient variant of the epidemic flooding protocol, even the large network can support text messages while only consuming 13.7% of a typical smartphone battery in 14 hours. We found that the network delivery rate and delay are robust to denial-of-service and censorship attacks. Attacks that randomly remove 90% of the network participants only reduce delivery rates by less than 10%. Even when subjected to targeted attacks, the network suffered a less than 10% decrease in delivery rate when 40% of its participants were removed. Although structurally robust, the openness of the proposed network introduces numerous security concerns. The Sybil attack, in which a malicious node poses as many identities in order to gain disproportionate influence, is especially dangerous as it breaks the assumption underlying majority voting. Many defenses based on spatial variability of wireless channels exist, and we extend them to be practical for ad hoc networks of commodity 802.11 devices without mutual trust. We present the Mason test, which uses two efficient methods for separating valid channel measurement results of behaving nodes from those falsified by malicious participants.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120779/1/liuyue_1.pd

Digital proximity tracing on empirical contact networks for pandemic control

Digital contact tracing is a relevant tool to control infectious disease outbreaks, including the COVID-19 epidemic. Early work evaluating digital contact tracing omitted important features and heterogeneities of real-world contact patterns influencing contagion dynamics. We fill this gap with a modeling framework informed by empirical high-resolution contact data to analyze the impact of digital contact tracing in the COVID-19 pandemic. We investigate how well contact tracing apps, coupled with the quarantine of identified contacts, can mitigate the spread in real environments. We find that restrictive policies are more effective in containing the epidemic but come at the cost of unnecessary large-scale quarantines. Policy evaluation through their efficiency and cost results in optimized solutions which only consider contacts longer than 15-20 minutes and closer than 2-3 meters to be at risk. Our results show that isolation and tracing can help control re-emerging outbreaks when some conditions are met: (i) a reduction of the reproductive number through masks and physical distance; (ii) a low-delay isolation of infected individuals; (iii) a high compliance. Finally, we observe the inefficacy of a less privacy-preserving tracing involving second order contacts. Our results may inform digital contact tracing efforts currently being implemented across several countries worldwide. Digital contact tracing is increasingly considered as one of the tools to control infectious disease outbreaks, in particular the COVID-19 epidemic. Here, the authors present a modeling framework informed by empirical high-resolution contact data to analyze the impact of digital contact tracing apps

Stochastic spatial models of plant diseases

I present three models of plant--pathogen interactions. The models are stochastic and spatially explicit at the scale of individual plants. For each model, I use a version of pair approximation or moment closure along with a separation of timescales argument to determine the effects of spatial clustering on threshold structure. By computing the spatial structure early in an invasion, I find explicit corrections to mean field theory. In the first chapter, I present a lattice model of a disease that is not directly lethal to its host, but affects its ability to compete with neighbors. I use a type of pair approximation to determine conditions for invasions and coexistence. In the second chapter, I study a basic SIR epidemic point process in continuous space. I implement a multiplicative moment closure scheme to compute the threshold transmission rate as a function of spatial parameters. In the final chapter, I model the evolution of pathogen resistance when two plant species share a pathogen. Evolution may lead to non--resistance by a host that finds the disease to be a useful weapon. I use a lattice model with the ordinary pair approximation assumption to study phenotypic evolution via repeated invasions by novel strains.Comment: Ph.D. Dissertation; 140 pages; 75 EPS figures; Latex2

Challenges for modelling interventions for future pandemics

Funding: This work was supported by the Isaac Newton Institute (EPSRC grant no. EP/R014604/1). MEK was supported by grants from The Netherlands Organisation for Health Research and Development (ZonMw), grant number 10430022010001, and grant number 91216062, and by the H2020 Project 101003480 (CORESMA). RNT was supported by the UKRI, grant number EP/V053507/1. GR was supported by Fundação para a Ciência e a Tecnologia (FCT) project reference 131_596787873. and by the VERDI project 101045989 funded by the European Union. LP and CO are funded by the Wellcome Trust and the Royal Society (grant 202562/Z/16/Z). LP is also supported by the UKRI through the JUNIPER modelling consortium (grant number MR/V038613/1) and by The Alan Turing Institute for Data Science and Artificial Intelligence. HBS is funded by the Wellcome Trust and Royal Society (202562/Z/16/Z), and the Alexander von Humboldt Foundation. DV had support from the National Council for Scientific and Technological Development of Brazil (CNPq - Refs. 441057/2020-9, 424141/2018-3, 309569/2019-2). FS is supported by the UKRI through the JUNIPER modelling consortium (grant number MR/V038613/1). EF is supported by UKRI (Medical Research Council)/Department of Health and Social Care (National Insitute of Health Research) MR/V028618/1. JPG's work was supported by funding from the UK Health Security Agency and the UK Department of Health and Social Care.Mathematical modelling and statistical inference provide a framework to evaluate different non-pharmaceutical and pharmaceutical interventions for the control of epidemics that has been widely used during the COVID-19 pandemic. In this paper, lessons learned from this and previous epidemics are used to highlight the challenges for future pandemic control. We consider the availability and use of data, as well as the need for correct parameterisation and calibration for different model frameworks. We discuss challenges that arise in describing and distinguishing between different interventions, within different modelling structures, and allowing both within and between host dynamics. We also highlight challenges in modelling the health economic and political aspects of interventions. Given the diversity of these challenges, a broad variety of interdisciplinary expertise is needed to address them, combining mathematical knowledge with biological and social insights, and including health economics and communication skills. Addressing these challenges for the future requires strong cross-disciplinary collaboration together with close communication between scientists and policy makers.Publisher PDFPeer reviewe

Variation in host home range size decreases rabies vaccination effectiveness by increasing the spatial spread of rabies virus

Animal movement influences the spatial spread of directly transmitted wildlife disease through host-host contact structure. Wildlife disease hosts vary in home range- associated foraging and social behaviours, which may increase the spread and intensity of disease outbreaks. The consequences of variation in host home range movement and space use on wildlife disease dynamics are poorly understood, but could help to predict disease spread and determine more effective disease management strategies. We developed a spatially explicit individual-based model to examine the effect of spatiotemporal variation in host home range size on the spatial spread rate, persistence and incidence of rabies virus (RABV) in raccoons (Procyon lotor). We tested the hypothesis that variation in home range size increases RABV spread and decreases vaccination effectiveness in host populations following pathogen invasion into a vaccination zone. We simulated raccoon demography and RABV dynamics across a range of magnitudes and variances in weekly home range size for raccoons. We examined how variable home range size influenced the relative effectiveness of three components of oral rabies vaccination (ORV) programmes targeting raccoons—timing and frequency of bait delivery, width of the ORV zone and proportion of hosts immunized. Variability in weekly home range size increased RABV spread rates by 1.2-fold to 5.2-fold compared to simulations that assumed a fixed home range size. More variable host home range sizes decreased relative vaccination effectiveness by 71% compared to less variable host home range sizes under conventional vaccination conditions. We found that vaccination timing was more influential for vaccination effectiveness than vaccination frequency or vaccination zone width. Our results suggest that variation in wildlife home range movement behaviour increases the spatial spread and incidence of RABV. Our vaccination results underscore the importance of prioritizing individual-level space use and movement data collection to understand wildlife disease dynamics and plan their effective control and elimination

Variation in host home range size decreases rabies vaccination effectiveness by increasing the spatial spread of rabies virus

Animal movement influences the spatial spread of directly transmitted wildlife disease through host-host contact structure. Wildlife disease hosts vary in home range- associated foraging and social behaviours, which may increase the spread and intensity of disease outbreaks. The consequences of variation in host home range movement and space use on wildlife disease dynamics are poorly understood, but could help to predict disease spread and determine more effective disease management strategies. We developed a spatially explicit individual-based model to examine the effect of spatiotemporal variation in host home range size on the spatial spread rate, persistence and incidence of rabies virus (RABV) in raccoons (Procyon lotor). We tested the hypothesis that variation in home range size increases RABV spread and decreases vaccination effectiveness in host populations following pathogen invasion into a vaccination zone. We simulated raccoon demography and RABV dynamics across a range of magnitudes and variances in weekly home range size for raccoons. We examined how variable home range size influenced the relative effectiveness of three components of oral rabies vaccination (ORV) programmes targeting raccoons—timing and frequency of bait delivery, width of the ORV zone and proportion of hosts immunized. Variability in weekly home range size increased RABV spread rates by 1.2-fold to 5.2-fold compared to simulations that assumed a fixed home range size. More variable host home range sizes decreased relative vaccination effectiveness by 71% compared to less variable host home range sizes under conventional vaccination conditions. We found that vaccination timing was more influential for vaccination effectiveness than vaccination frequency or vaccination zone width. Our results suggest that variation in wildlife home range movement behaviour increases the spatial spread and incidence of RABV. Our vaccination results underscore the importance of prioritizing individual-level space use and movement data collection to understand wildlife disease dynamics and plan their effective control and elimination

Tackling complexity in biological systems: Multi-scale approaches to tuberculosis infection

Tuberculosis is an ancient disease responsible for more than a million deaths per year worldwide, whose complex infection cycle involves dynamical processes that take place at different spatial and temporal scales, from single pathogenic cells to entire hosts' populations. In this thesis we study TB disease at different levels of description from the perspective of complex systems sciences. On the one hand, we use complex networks theory for the analysis of cell interactomes of the causative agent of the disease: the bacillus Mycobacterium tuberculosis. Here, we analyze the gene regulatory network of the bacterium, as well as its network of protein interactions and the way in which it is transformed as a consequence of gene expression adaptation to disparate environments. On the other hand, at the level of human societies, we develop new models for the description of TB spreading on complex populations. First, we develop mathematical models aimed at addressing, from a conceptual perspective, the interplay between complexity of hosts' populations and certain dynamical traits characteristic of TB spreading, like long latency periods and syndemic associations with other diseases. On the other hand, we develop a novel data-driven model for TB spreading with the objective of providing faithful impact evaluations for novel TB vaccines of different types

On The Application Of Computational Modeling To Complex Food Systems Issues

Transdisciplinary food systems research aims to merge insights from multiple fields, often revealing confounding, complex interactions. Computational modeling offers a means to discover patterns and formulate novel solutions to such systems-level problems. The best models serve as hubs—or boundary objects—which ground and unify a collaborative, iterative, and transdisciplinary process of stakeholder engagement. This dissertation demonstrates the application of agent-based modeling, network analytics, and evolutionary computational optimization to the pressing food systems problem areas of livestock epidemiology and global food security. It is comprised of a methodological introduction, an executive summary, three journal-article formatted chapters, and an overarching discussion section. Chapter One employs an agent-based computer model (RUSH-PNBM v.1.1) developed to study the potential impact of the trend toward increased producer specialization on resilience to catastrophic epidemics within livestock production chains. In each run, an infection is introduced and may spread according to probabilities associated with the various modes of contact between hog producer, feed mill, and slaughter plant agents. Experimental data reveal that more-specialized systems are vulnerable to outbreaks at lower spatial densities, have more abrupt percolation transitions, and are characterized by less-predictable outcomes; suggesting that reworking network structures may represent a viable means to increase biosecurity. Chapter Two uses a calibrated, spatially-explicit version of RUSH-PNBM (v.1.2) to model the hog production chains within three U.S. states. Key metrics are calculated after each run, some of which pertain to overall network structures, while others describe each actor’s positionality within the network. A genetic programming algorithm is then employed to search for mathematical relationships between multiple individual indicators that effectively predict each node’s vulnerability. This “meta-metric” approach could be applied to aid livestock epidemiologists in the targeting of biosecurity interventions and may also be useful to study a wide range of complex network phenomena. Chapter Three focuses on food insecurity resulting from the projected gap between global food supply and demand over the coming decades. While no single solution has been identified, scholars suggest that investments into multiple interventions may stack together to solve the problem. However, formulating an effective plan of action requires knowledge about the level of change resulting from a given investment into each wedge, the time before that effect unfolds, the expected baseline change, and the maximum possible level of change. This chapter details an evolutionary-computational algorithm to optimize investment schedules according to the twin goals of maximizing global food security and minimizing cost. Future work will involve parameterizing the model through an expert informant advisory process to develop the existing framework into a practicable food policy decision-support tool

Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: Special Report of the Intergovernmental Panel on Climate Change

This Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX) has been jointly coordinated by Working Groups I (WGI) and II (WGII) of the Intergovernmental Panel on Climate Change (IPCC). The report focuses on the relationship between climate change and extreme weather and climate events, the impacts of such events, and the strategies to manage the associated risks. The IPCC was jointly established in 1988 by the World Meteorological Organization (WMO) and the United Nations Environment Programme (UNEP), in particular to assess in a comprehensive, objective, and transparent manner all the relevant scientific, technical, and socioeconomic information to contribute in understanding the scientific basis of risk of human-induced climate change, the potential impacts, and the adaptation and mitigation options. Beginning in 1990, the IPCC has produced a series of Assessment Reports, Special Reports, Technical Papers, methodologies, and other key documents which have since become the standard references for policymakers and scientists.This Special Report, in particular, contributes to frame the challenge of dealing with extreme weather and climate events as an issue in decisionmaking under uncertainty, analyzing response in the context of risk management. The report consists of nine chapters, covering risk management; observed and projected changes in extreme weather and climate events; exposure and vulnerability to as well as losses resulting from such events; adaptation options from the local to the international scale; the role of sustainable development in modulating risks; and insights from specific case studies