FACS: A geospatial agent-based simulator for analysing COVID-19 spread and public health measures on local regions

ABSTRACT

The recent Covid-19 outbreak has had a tremendous impact on the world, and many countries are struggling to help incoming patients and at the same time, rapidly enact new public health measures such as lock downs. Many of these decisions are guided by the outcomes of so-called Susceptible-Exposed-Infectious-Recovered (SEIR) models that operate on a national level. Here we introduce the Flu And Coronavirus Simulator (FACS), a simulation tool that models the viral spread at the sub-national level, incorporating geospatial data sources to extract buildings and residential areas in a region. Using FACS, we can model Covid-19 spread at the local level, and provide estimates of the spread of infections and hospital arrivals for different scenarios. We validate the simulation results with the ICU admissions obtained from the local hospitals in the UK. Such validated models can be used to support local decision-making for an effective health care capability response to the epidemic.

KEYWORDS:

• Agent-based simulation
• COVID-19 Spread
• location graph
• lock down scenarios
• epidemiology
• model validation

Acknowledgments

We are grateful to the London North West University Healthcare NHS Trust for providing us with validation data, as well as valuable feedback during the development and validation stages of this project. The calculations were performed in Poznan Supercomputing and Networking Center.

Disclosure statement

The simulation results presented in this paper are for educational and research purpose only.

Notes

1. https://github.com/djgroen/facs/blob/master/README.md

2. https://github.com/djgroen/facs/blob/master/covid_data/disease_covid19.yml

3. https://facs.readthedocs.io/en/latest/preparation.html

4. YAML is a human friendly data serialisation standard for all programming languages https://yaml.org/

5. Note that hospitals have a reduced infection rate, due to the safety precautions taken there (e.g., the use of personal protective equipment (PPE) and effective quarantining of patients). We reflect this protective aspect using a multiplier that initially reduces the infection rate in hospitals by 50%, and that increases in value as the simulation progresses and hospitals adopt improved practices and have more PPE, to a final value of 92%.

Additional information

Funding

This work was supported by the HiDALGO and VECMA projects, which has received funding from the European Union Horizon 2020 research and innovation programme under grant agreement No 824115 and 800925.