JMIR Publications

ABSTRACT

Background:

Leaders in countries and communities across the globe are making crucial decisions about how and when to implement public health interventions to combat COVID-19. They urgently need tools that will 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 develop a flexible, freely available simulation model that allows investigation of how various public health interventions, singly and in combination, and implemented at various time points 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, we modelled similar intervention scenarios to those recently reported by other investigators using various model types, as well as several novel strategies. The modelled strategies involve isolation of cases, moderate social distancing and stricter population “lock-downs” enacted over varying time periods, in a hypothetical population of 100,000 people.

Results:

COVOID allocates each member of its hypothetical 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, we were able to replicate the epidemic response patterns to specific social distancing intervention scenarios reported by other investigators. In six novel intervention scenarios, involving ramp-up of self-isolation and social distancing, the COVID-19 epidemic curve is both substantially flattened and “shrunk”. A scenario of early and rapid case finding and strict enforcement of isolation results in almost complete suppression of the epidemic. Scenarios of moderate social isolation and lockdown result in large reductions in cases and deaths, but “rebound” may occur after these measures are relaxed, unless intensive case-finding and strict enforcement of isolation are enacted.

Conclusions:

COVOID allows rapid modelling of many potential intervention scenarios, can be tailored to diverse settings and requires only standard computing infrastructure. Although the estimated case numbers it produces cannot yet be validated due to lack of data, it replicates the epidemic curves produced by other models that require highly detailed population-level data. It is freely available as a tool to support public health decision makers in the current COVID-19 crisis.