This model calculates and demonstrates the possible spread of COVID-19 through an agent-based map. It shows the timeline of a healthy individual being infected to recovery.
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 can be used to investigate how government interventions affect transmission and mortality associated with COVID-19 during an outbreak, and how these interventions impact on the economic activities in Burnie, Tasmania.
Assumptions can be made that effective government intervention can reduce the number of people infected, whereas the local economy is severely impacted.
Insights:
1. When COVID-19 case are more than 10, government policy will be triggered.
2. Testing rate is very crucial to understanding the spread of the pandemic and responding appropriately.
This stock-flow simulation model is to show Covid-19 virus spread rate, sources of spreading and safety measures followed by all the countries affected around the world.
The simulation also aims at predicting for how much more period of time the virus will persist, how many people could recover at what kind of rate and also about the virus toughness dependence based on its excessive speed, giving rise to bigger numbers day-by-day.
Villela, Paulo (2020) paulo.villela@engenharia.ufjf.br
This model is based on Crokidakis, Nuno. (2020). Data analysis and modeling of the evolution of COVID-19 in Brazil. For more details see full paper here.
Without mortality, with time measured in days, with infection rate 1/2, recovery rate 1/3, and initial infectious population I_0=1.27x10-4, we reproduce their figure
With a death rate of .005 (one two-hundredth of the infected per day), an infectivity rate of 0.5, and a recovery rate of .145 or so (takes about a week to recover), we get some pretty significant losses -- about 3.2% of the total population.
This Model was developed from the SEIR model (Susceptible, Enposed, Infected, Recovered). It was designed to explore relationships between the government policies regarding the COVID-19 and its impact upon the economy as well as well-being of residents.
Assumptions:
Government policies will be triggered when reported COVID-19 case are 10 or less;
Government Policies affect the economy and the COV-19 infection negatively at the same time;
Government Policies can be divided as 4 categories, which are Social Distancing, Business Restrictions, Lock Down, Travel Ban, and Hygiene Level, and they represented strength of different aspects;
Parameters:
Policies like Social Distancing, Business Restrictions, Lock Down, Travel Ban all have different weights and caps, and they add up to 1 in total;
There are 4 cases on March 9th;
Ro= 5.7 Ro is the reproduction number, here it means one person with COVID-19 can potentially transmit the coronavirus to 5 to 6 people;
Interesting Insights:
Economy will grow at the beginning few weeks then becoming stagnant for a very long time;
Exposed people are significant, which requires early policies intervention such as social distancing.
![Initial data from: Italian data [ link ], as of Mar 28 Incubation estimation [ link ] Model focuses on outbreak dynamics and control, this version ignores symptom onset to hospital admission and the rest of recovery dynamics.](https://insightmakercloud-files.storage.googleapis.com/a945023c-0f59-409e-b0a3-460d30361ee8/images/cover.jpg?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=insightmakercloud-file-access%40insightmakercloud.iam.gserviceaccount.com%2F20240419%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20240419T135821Z&X-Goog-Expires=3601&X-Goog-SignedHeaders=host&X-Goog-Signature=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)
