Simulating COVID-19 at large scale

EpiGraph is an agent-based parallel simulator that performs realistic stochastic simulations of the propagation of the COVID-19 virus across wide geographic expanses. EpiGraph was originally designed in the Computer Architecture research group of University Carlos III and later, developed further with the collaboration of Barcelona Supercomputing Center. EpiGraph’s team is currently providing support to the Spanish Ministry of Health by means of the evaluation of different vaccination scenarios.

The current implementation of EpiGraph includes functionalities for modeling via a realistic interconnection network based on actual individual interactions extracted from social networks and demographical data. This network includes the characteristics of each individual, their relationships at work, school, home and during leisure time, and a transportation model which simulates the spatial dynamics of the virus’ propagation over-large scale areas. EpiGraph also includes a model of the interaction between COVID-19 spread and climate and meteorological factors, such as temperature, atmospheric pressure and humidity levels.

EpiGraph has been recently upgraded with new features that include:

  • New social collectives including different professions (health, education, catering, etc.) and different elderly collectives (residing in nursing homes, living at home, etc.). Each collective has a particular social interaction pattern.
  • Social contact patterns based on contact matrices: the number and distribution of an individual’s contacts are age and profession-dependent.
  • Infectious agents: influenza or COVID-19, including its multiple COVID-19 variants (Wuhan, British strain, etc.).
  • Non-pharmacological interventions: use of different classes of face mask used by the whole population or specific collectives, and different social-distancing measures including school and work closures, lockdowns and travel restrictions.
  • COVID-19 vaccination: EpiGraph currently models and simulates the Pfizer-BioNTech, Moderna, Astra-Zeneca and  Janssen vaccines. 

Research areas

  • Analysis of the efficacy of different vaccination strategies.
  • Evaluation of the impact of propagation of the new COVID-19 strains taking into account different transmission rates and vaccine efficacies  for each variant.
  • Study of the efficiency of enforcement policies for slowing the spread of the epidemy.
  • Analysis of the impact of climate conditions on the epidemic outcome.
  • Assessment of different COVID-19 Testing Protocols.

Data management

Figure below shows the different data sources involved in a simulation. Epigraph consists of two main components: the scenario generation (upper part of the figure) that creates the scenarios and the simulator (lower part of the figure), that simulates the COVID-19 propagation on them. The input data sources used in the scenario generation are the city geolocation provided by web applications, that are used to identify the geographic coordinates of each city; its related NUTS code, as well as the distances between each pair of cities. The second data source are the Eurostat, and Spanish-equivalent INE, that provide the demographic data used by the simulator. This information includes -among other-, the population pyramid and the distribution of employment related to each city. Two different social-network graphs and contact matrices are used for generating the contact patterns of each individual.

Regarding the data sources involved in the simulation stage (lower part of the figure), the COVID-19 model parameters were taken from the existing literature. The non-pharmaceutical interventions (NPIs) applied in each region, the coronavirus incidence, and the vaccination data that are used to model the vaccine efficacy and the existing doses administrated in each region in Spain. This information was taken from the existing literature and government databases, respectively. Finally, the meteorological data consists of a collection of meteorological measurements.