“COVOID”: A Flexible, Freely Available Stochastic Individual Contact Model for Exploring COVID-19 Intervention and Control Strategies
Background: Throughout March 2020, leaders in countries across the globe were making crucial decisions about how and when to implement public health interventions to combat COVID-19. They urgently needed tools to help them to explore what will work best in their specific circumstances of epidemic size and spread and feasible intervention scenarios.
Objective: We sought to rapidly develop a flexible, freely available simulation model for use by modellers and researchers to allow investigation of how various public health interventions implemented at various time points might change the shape of the COVID-19 epidemic curve.
Methods: “COVOID” (COVID-19 Open-source Infection Dynamics) is a stochastic individual contact model (ICM), which extends the ICMs provided by the open-source EpiModel package for the R statistical computing environment. To demonstrate its use and inform urgent decisions as at 30 March 2020, we modelled similar intervention scenarios to those reported by other investigators using various model types, as well as novel scenarios. The scenarios involved isolation of cases, moderate social distancing and stricter population “lock-downs” enacted over varying time periods, in a hypothetical population of 100,000 people. On 30 April 2020, we simulated the epidemic curve for the three contiguous local areas (population 287,344) in eastern Sydney, Australia, that recorded 5.3% of Australian cases of COVID-19 through to 30 April 2020, under five different intervention scenarios, and compared the modelled predictions with the observed epidemic curve for these areas.
Results: COVOID allocates each member of a population to one of seven compartments. The number of times individuals in the various compartments interact with each other and their probability of transmitting infection at each interaction can be varied to simulate the effects of interventions. Using COVOID on 30 March 2020, we were able to replicate the epidemic response patterns to specific social distancing intervention scenarios reported by others. The simulated curve for three local areas of Sydney from 1 March to 30 April 2020 was similar to the observed epidemic curve, in terms of peak numbers of cases, total numbers of cases and duration, under a scenario representing the public health measures that were actually enacted, including case isolation and ramp-up of testing and social distancing measures.
Conclusions: COVOID allows rapid modelling of many potential intervention scenarios, can be tailored to diverse settings and requires only standard computing infrastructure. It replicates the epidemic curves produced by other models that require highly detailed population-level data, and its predicted epidemic curve, using parameters simulating the public health measures that were enacted, was similar in form to that actually observed in Sydney, Australia. Our team and collaborators are currently developing an extended open-source COVOID package comprising a suite of tools to explore intervention scenarios using several categories of model.