The spread of infection can be simulated using System Dynamics (SD) models such as the SIR model. A simulator, based on this type of simplified models (cf. citation below), is provided at this link. Although very basic, the ‘sim’ can provide useful insights into the dynamics of Covid-19 propagation.
“Epidemic modelling is extremely complex, and small changes in the way it’s done can have huge impacts on the results.” — Kathryn Snow
Visualizing the impact of social distancing measures
Countries around the world are implementing social distancing measures to keep people from mixing and to prevent the collapse of the health system. Using the ‘sim’, you can attempt to “flatten the curve” by reducing the average number of people contacted per person per day, dividing it by 4, for example, as shown in the figure below.
Need for Hospital Beds
When hospitals are overwhelmed, the mortality due to SARS-CoV-2 is likely to increase (non-Covid-19 patients can also be affected, if the medical services, ordinarily available for these patients, are redirected to those who suffer from the virus). You can see this effect when using the ‘sim’: when the hospitals’ bed occupancy rate reaches 100%, a sudden change in the Covid-19 mortality rate causes a two-fold increase in the number of deaths at the end of the simulation.
Structure of the System Dynamics (SD) model
It consists of 6 “Nodes” (where “Stocks” accumulate) and 7 “Junctions” (circulating “Flows” between “Nodes”). Their characteristics are provided on the ‘sim’ page.
Modelling Covid-19’s Spread