Modeling of Disease Contagion Processes  (MDCP) Session 1

Abstract: Face-to-face contacts between individuals play an important role in social interactions and can also determine the potential transmission routes of infectious diseases, in particular of respiratory pathogens. An accurate description of these patterns is therefore of interest in order to identify contagion pathways, to inform models of epidemic spread, and to design and evaluate control measures such as the targeting of specific groups of individuals with appropriate prevention strategies or interventions.Many datasets describing contacts between individuals in different contexts have been obtained using either surveys or wearable sensors. Both techniques yield however incomplete or biased data, due on the one hand to population sampling (in both cases), and to underreporting or bad estimation of contact durations (for surveys). Here I will use data describing contacts gathered by both techniques and show how these issues might bias the outcome of simulation of spreading processes, leading to an underestimation of the epidemic risk. I will then describe methods to compensate for such biases. Close

Abstract: The growing complexity of global mobility is a key challenge for the understanding of the worldwide spread of emergent infectious diseases and the design of effective containment strategies. Despite global connectivity, containment policies are often based on national, regional and ?egocentric? assessments of outbreak situations that are no longer effective or meaningful, as demonstrated by 2014 Ebola outbreak in West Africa where months passed before a concerted, international effort followed. Despite the importance of the matter, optimal strategies in highly connected non-local settings are poorly understood. In this work we investigate a model for the optimal resource distribution for outbreak mitigation in a network of interacting countries. Each node can exercise a limited amount of resources among all nodes in the network to mitigate an outbreak. We define the cost of each node as a combination of invested resources and the cost incurred by the disease. In this game theoretical framework nodes explore the strategy space until the system reaches its equilibrium. Contrary to common belief, we observe that purely selfish and cooperative actions do not differ considerably in single outbreak scenarios, i.e. purely selfish behavior tends to invest resources at the outbreak location. However, in a scenario with multiple outbreak locations different patterns can be observed. When resources are abundant, we observe a behavior similar to single outbreak scenario, in which the network is split into two regions allocating the resources to the nearest infected node. In a setting where resources are limited a third region emerges in which the available resources are commited to the investing nodes themselves. In all cases the topological distance to the infected node determines the strategy: proximity to one of two infected nodes implies investment into the infected node whereas in an equidistant scenario, self-investment is rational on average. Inhomogenious distribution of the resources is not changing the findings described above. Furthermore, we find that the degree of the infected node affects the behavior of its neighbors in a non trivial manner. (Presenter: Olga Baranov) Close

Abstract: Network-based infectious disease models have been highly effective in elucidating the role of contact structure in the spread of infection. As such, pair- and neighbourhood-based approximation models have played a key role in linking findings from network simulations to standard (random-mixing) results. Recently, for SIR-type infections (that produce one epidemic in a closed population) on locally tree-like networks, these approximations have been shown to be exact. Network models are, however, ideally suited for Sexually Transmitted Infections (STIs) due to the greater level of detail available for sexual contact networks, and these diseases have SIS-type dynamics. Here, using the most testing network constructions, we consider the accuracy of three systematic approximations that can be applied to arbitrary disease dynamics (and in one case arbitrary network topology). By examining how and when these approximation models converge we generate insights into the role of network structure in the infection dynamics of STIs. Close

Abstract: Contagion processes in structured populations, on networks or in space have attracted much attention in the past. Yet, the great majority of studies focus on the spread of single agents, e.g. single pathogens in epidemic context, single pieces of information in social contagion or single species in coupled eco-systems. Situations in which multiple, interacting agents spread simultaneously are less well understood, particularly when two agents cooperate during the spreading process. We investigate contagion dynamics of two cooperatively interacting agents. We show that cooperation between transmissible agents changes the threshold behavior of the system in a substantial and qualitative way. We show that as a function of the basic reproduction ratio of the system, discontinuous transition to an endemic state emerge in which both agents prevail. Furthermore, the system exhibits hysteresis, i.e. threshold for outbreaks and elimination differ. Finally we discuss unexpected and counterintuitive properties of cooperatively interacting contagion processes in spatially extended systems. The presented work may serve as a starting point for further investigations of interacting contagion processes in populations. Close

Abstract: Most aspects of real world social networks, e.g., clustering [1,2] and community structure [3], and of human behaviour, e.g., social distancing and increased hygiene, will slow disease spread. Here, we consider a model where individuals with essential societal roles--such as teachers, first responders, health-care workers, etc.-- who fall ill are replaced with healthy individuals. We refer to this process as relational exchange. Relational exchange is also a behaviour, but one whose effect on disease transmission is less obvious. By incorporating this behaviour into a dynamic network model, we demonstrate that replacing individuals can accelerate disease transmission. Furthermore, we find that the effects of this process are trivial when considering a standard mass-action model, but dramatic when considering network structure: featuring accelerating spread, discontinuous transitions, and hysteresis loops. This result highlights another critical shortcoming in mass-action models, namely their inability to account for many behavioural processes. Lastly, using empirical data, we find that this mechanism parsimoniously explains observed patterns across 17 influenza outbreaks at the U.S.A. national-level, 25 years of influenza data at the U.S.A. state-level, and 19 years of dengue virus data from Puerto Rico. We anticipate that our findings will advance the emerging field of disease forecasting, improve our capacity to model the physics of complex behaviours on networks, and will better inform public health decision making during outbreaks. Close

Abstract: The health and treatment of patients in health care institutions is threatened by the spread of hospital-associated (HA) pathogens, particularly antimicrobial resistant micro-organisms, that take advantage of this susceptible population. Although the responsibility for preventing and controlling the spread of such pathogens lies with individual hospitals, they are by far isolated entities. Patients exchanged between hospitals offer pathogens the opportunity to spread from one hospital to another, thus forming an epidemiological contact between them. The complete hospital network formed by these exchanged patients influences the chance of a hospital encounter HA-pathogens as well as the within-hospital prevalence of such pathogens. However, patient movement patterns may change over time as a result of policy decisions. Hospitals may for instance be driven to specialise and stop offering certain procedures, forcing patients to seek medical treatment elsewhere. The hospital network may therefore change over time, which could have its bearing on the spread of HA-pathogens. Using data from the NHS hospital episode statistics, we tracked the changes in the hospital network in England between 2004 and 2014, and identified both global and local changes in the network that affected the networks susceptibility to the spread of HA-pathogens. Overall, the number of admissions and re-admissions increased, with a pronounced increase in the number of patients moving between hospitals. The increasing number of admissions seems to be driven by an aspiration to reduce the length of stay. Despite this reduced length of stay, the probability of pathogens spreading between hospitals increased over the years. Changes in the network were not uniform over all links, while some pairs of hospitals increased their number of exchanged patients, others almost severed their link completely. These preferential changes affected the community structure of the hospital network, while some of them moved the hospitals in the network closer together overall, increasing the risk of nation-wide outbreaks. Although some of the changes we observed could be the result of a natural evolution of the hospital network, for instance because of an ageing population, many of the observed changes seem to be policy driven. As these policies are often aiming for higher efficiency on the hospital level, the unintended consequences they have on the spread of HA-pathogens are often overseen. The extra cost required to control the spread of HA-pathogens, or treat the increasing number of affected patients, should be taken into account when considering the cost-effectiveness of changes in health care policy. Close

Abstract: Diseases affecting farmed cattle compromise both human and animal health and welfare, and represent a major cause of loss in economic revenue. Their spread is known to be driven, or at least facilitated, by animal displacements among livestock holdings, both within and across countries. As a result, studying the networks of animal movements is a key step in devising new prevention and containment strategies. Past works have already analyzed cattle networks in several European countries, highlighting complex interactions between topology, function and dynamics at different spatial and time resolutions. A comprehensive study, showing the impact of country-specific driving factors on network evolution and topology, is however still missing. By using data from several European countries, and focusing on the features relevant for the spread of infections, we perform a comparative analysis to highlight both general and country-specific patterns. We find that coarse-graining the structure into statistical distributions of centrality measures is an effective way to highlight the properties shared by all networks, which represent the fingerprint of a livestock market. The situation dramatically changes when we zoom in to the microscopic structure, as we find several country-specific characteristics, especially in temporal evolution. This twofold behavior suggests that on one hand it is possible to identify several global patterns in the ways animal disease spread, which can then be applied to countries for which data are unavailable, or incomplete. On the other hand, resolved country-specific data are needed when devising tailored and targeted intervention strategies. Close