Cranic Computing

Context-Aware Realistic Epidemic Simulator: a data-driven approach to mimic real-world interactions and guide containment measures

The COVID-19 pandemic has highlighted the importance of timely interventions in the presence of an epidemics and raised the attention towards models able to illustrate and possibly predict the impact of mitigation strategies at different levels. In this context, we designed and developed CARES, a Context-Aware Realistic Epidemic Simulator that, based on the accurate representation of the interactions between individuals of the population under study, permits to replicate and compare multiple scenarios that would be prohibitive to study analytically.

CARES is a data-driven simulator of epidemic outbreaks within a synthetic population of interacting agents, designed so as to capture the peculiarities of specific places and amenities in order to understand their role in the epidemics and guide precise mitigation strategies. To this end, CARES incorporates the characteristics of locations, services and infrastructures (for example parks, hospitals, school, etc.) and allows to isolate the impact of simulated context-specific interactions with respect to a baseline of aggregated interactions modeled upon geographic and social constraints.

CARES' underlying model is based on the representation of social interactions by means of a graph whose vertices are the individuals and whose edges are decomposed into several layers:

The SB layer encodes information on the social fabric and allows to define realistic patterns of fruition of services (e.g., friends and relatives may go together to a place and are more likely to interact if they meet by chance), thus increasing the verisimilitude of the interactions simulated by the other layers. By being able to study the evolution of the epidemic for any combination of the defined layers, it will be possible to isolate the effect of each individual social context or of their combinations with respect to the baseline composed of all those interactions that are not directly measurable and/or controllable.

For the construction of the graph we integrate heterogeneous data from multiple sources. In addition to geo-referenced socio-demographic statistics, we use data extracted from regional and municipal geographic information systems, in order to model services and infrastructures with great precision. CARES is released as open-source software under the GPLv3.

The CARES project has been funded under the FISR 2020 COVID, the code of the simulator is available here

Inferring Urban Social Networks from Publicly Available Data

The definition of suitable generative models for synthetic yet realistic social networks is a widely studied problem in the literature. By not being tied to any real data, random graph models cannot capture all the subtleties of real networks and are inadequate for many practical contexts—including areas of research, such as computational epidemiology, which are recently high on the agenda. At the same time, the so-called contact networks describe interactions, rather than relationships, and are strongly dependent on the application and on the size and quality of the sample data used to infer them. To fill the gap between these two approaches, we present a data-driven model for urban social networks, implemented and released as open source software. By using just widely available aggregated demographic and social-mixing data, we are able to create, for a territory of interest, an age-stratified and geo-referenced synthetic population whose individuals are connected by “strong ties” of two types: intra-household (e.g., kinship) or friendship. While household links are entirely data-driven, we propose a parametric probabilistic model for friendship, based on the assumption that distances and age differences play a role, and that not all individuals are equally sociable. The demographic and geographic factors governing the structure of the obtained network, under different configurations, are thoroughly studied through extensive simulations focused on three Italian cities of different size.(Paper, pdf)

A Model for Urban Social Networks

Defining accurate and flexible models for real-world networks of human beings is instrumental to understand the observed properties of phenomena taking place across those networks and to support computer simulations of dynamic processes of interest for several areas of research – including computational epidemiology, which is recently high on the agenda. In this paper we present a flexible model to generate age-stratified and geo-referenced synthetic social networks on the basis of widely available aggregated demographic data and, possibly, of estimated age-based social mixing patterns. Using the Italian city of Florence as a case study, we characterize our network model under selected configurations and we show its potential as a building block for the simulation of infections’ propagation. A fully operational and parametric implementation of our model is released as open-source.(Paper)

Data-Driven Simulation of Contagions in Public Venues

The COVID-19 pandemic triggered a global research effort to define and assess timely and effective containment policies. Understanding the role that specific venues play in the dynamics of epidemic spread is critical to guide the implementation of fine-grained non-pharmaceutical interventions (NPIs). In this paper, we present a new model of context-dependent interactions that integrates information about the surrounding territory and the social fabric. Building on this model, we developed an open-source data-driven simulator of the patterns of fruition of specific gathering places that can be easily configured to project and compare multiple scenarios. We focused on the greatest park of the City of Florence, Italy, to provide experimental evidence that our simulator produces contact graphs with unique, realistic features, and that gaining control of the mechanisms that govern interactions at the local scale allows to unveil and possibly control non-trivial aspects of the epidemic.(Paper)

Epidemics in a Synthetic Urban Population with Multiple Levels of Mixing

Network–based epidemic models that account for heterogeneous contact patterns are extensively used to predict and control the diffusion of infectious diseases. We use census and survey data to reconstruct a geo–referenced and age–stratified synthetic urban population connected by stable social relations. We consider two kinds of interactions, distinguishing daily (household) contacts from other frequent contacts. Moreover, we allow any couple of individuals to have rare for- tuitous interactions. We simulate the epidemic diffusion on a synthetic urban network for a typical medium-size Italian city and characterize the outbreak speed, pervasiveness, and predictability in terms of the socio– demographic and geographic features of the host population. Introducing age–structured contact patterns results in faster and more pervasive outbreaks, while assuming that the interaction frequency decays with distance has only negligible effects. Preliminary evidence shows the existence of patterns of hierarchical spatial diffusion in urban areas, with two regimes for epidemic spread in low- and high-density regions.(Paper)

Epidemic risk assessment from geographic population density

The geographic distribution of the population on a region is a significant ingredient in shaping the spatial and temporal evolution of an epidemic outbreak. Heterogeneity in the population density directly impacts the local relative risk: the chances that a specific area is reached by the contagion depend on its local density and connectedness to the rest of the region. We consider an SIR epidemic spreading in an urban territory subdivided into tiles (i.e., census blocks) of given population and demographic profile. We use the relative attack rate and the first infection time of a tile to quantify local severity and timing: how much and how fast the outbreak will impact any given area. Assuming that the contact rate of any two individuals depends on their household distance, we identify a suitably defined geographical centrality that measures the average connectedness of an area as an efficient indicator for local riskiness. We simulate the epidemic under different assumptions regarding the socio-demographic factors that influence interaction patterns, providing empirical evidence of the effectiveness and soundness of the proposed centrality measure. (Paper, pdf)

The Fitness-Corrected Block Model, or how to create maximum-entropy data-driven spatial social networks

Models of networks play a major role in explaining and reproducing empirically observed patterns. Suitable models can be used to randomize an observed network while preserving some of its features, or to generate synthetic graphs whose properties may be tuned upon the characteristics of a given population. In the present paper, we introduce the Fitness-Corrected Block Model, an adjustable-density variation of the well-known Degree-Corrected Block Model, and we show that the proposed construction yields a maximum entropy model. When the network is sparse, we derive an analytical expression for the degree distribution of the model that depends on just the constraints and the chosen fitness-distribution. Our model is perfectly suited to define maximum-entropy data-driven spatial social networks, where each block identifies vertices having similar position (e.g., residence) and age, and where the expected block-to-block adjacency matrix can be inferred from the available data. In this case, the sparse-regime approximation coincides with a phenomenological model where the probability of a link binding two individuals is directly proportional to their sociability and to the typical cohesion of their age-groups, whereas it decays as an inverse-power of their geographic distance. We support our analytical findings through simulations of a stylized urban area.(Paper)