A simple ABM example illustrating how the SEIR model works. It can be a basis for experimenting with learning the impact of human behavior on the spread of a virus, e.g. COVID-19.
This model was designed from the SIR model(susceptible, infected, recovered) to determine the effect of the covid-19 outbreak on economic outcomes via government policy.
Assumptions
The government policy is triggered when the number of infected is more than ten.
The government policies will take a negative effect on Covid-19 outbreaks and the financial system.
Adjusted parameter: Immunity loss rate can be changed from vaccination rate.
Government policy's parameters
Adjusted parameters: Testing rate(from 0.15 to 0.95), vaccination rate(from 0.3 to 1), travel ban(from 0 to 0.9), social distancing(from 0.1 to 0.8), Quarantine(from 0.1 to 0.9)
Economic's parameters
Fixed parameter: Tourism
Adjusted parameter: Economic growth rate(from 0.3 to 0.5)
Interesting insight
An increased vaccination rate and testing rate will decrease the number of infected cases and have a little more negative effect on the economic system. However, the financial system still needs a long time to recover in both cases.
This Model was first developed from the SIR model (Susceptible, Infected, Recovered). It was designed to explore relationship between the government policies regarding the COVID-19 and its influences on the economy as well as well-being of local residents.
Assumptions:
Government policies will be triggered when reported COVID-19 case are 10 or less;
Government policies reduces the infection and economic growth at the same time.
Interesting Insights:
In the first two weeks, the infected people showed an exponential growth, in another word, that’s the most important period to control the number of people who got affected.
Clone of Model of COVID-19 Outbreak in Burnie, Tasmania
