OpenABM-Covid19 - an agent-based model for non-pharmaceutical interventions against COVID-19 including contact tracing

Abstract

SARS-CoV-2 has spread across the world, causing high mortality and unprecedented restrictions on social and economic activity. Policymakers are assessing how best to navigate through the ongoing epidemic, with models being used to predict the spread of infection and assess the impact of public health measures. Here, we present OpenABM-Covid19: an agent-based simulation of the epidemic including detailed age-stratification and realistic social networks. By default the model is parameterised to UK demographics and calibrated to the UK epidemic, however, it can easily be re-parameterised for other countries. OpenABM-Covid19 can evaluate non-pharmaceutical interventions, including both manual and digital contact tracing. It can simulate a population of 1 million people in seconds per day allowing parameter sweeps and formal statistical model-based inference. The code is open-source and has been developed by teams both inside and outside academia, with an emphasis on formal testing, documentation, modularity and transparency. A key feature of OpenABM-Covid19 is its Python interface, which has allowed scientists and policymakers to simulate dynamic packages of interventions and help compare options to suppress the COVID-19 epidemic.

High‐Resolution Agent‐Based Modeling of COVID‐19 Spreading in a Small Town

“…Simulating truly random interactions using a contact network approach similar to Hinch et al. [ 24 ] may offer an alternative, which will be part of our future work. Along these lines, the impact of local travel and commute can further be included in the model by integrating traffic flow simulations.…”

“…When a symptomatic agent is confirmed COVID‐19 positive, they are assigned to one of the three formal treatments according to a probabilistic mechanism: home isolation, regular hospitalization, and hospitalization in an ICU. [ 22,24 ] Afterward, the agent can change treatment types depending on their recovery status and clinically observed COVID‐19 progression. The agent's initial treatment is chosen based on the probability of normal hospitalization and hospitalization in an ICU obtained from clinical data depending on their age.…”

“…formulated an open‐source agent‐based modeling framework to support the analysis of select non‐pharmaceutical interventions and contact tracing schemes. [ 24 ] The merits of ABMs have been recognized by a large number of studies, which have shed light on technical aspects of their implementation as well as their scalability across scenarios. [ 25–29 ]…”

A fair efficacy formula for assessing the effectiveness of contact tracing applications

“…100% . 0.9 (26) εT ukmax = 46.57% (27) The above formula therefore provides the final measure which can then be used with the Fraser et al paper and the Ferretti et al paper to plot a given protocols' contact tracing efficacy score and determine its controlling effect on a given infection's spread.…”

“…This paper?s efficacy results can be directly applied to the Oxford Big Data Institute (BDI) virus spread simulator, called OpenABM-COVID19 [27] enabling the evaluation of contact tracing protocols from an epidemiology perspective, e.g. predicting impact on infection and death rates, as well as demand on hospital beds.…”