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.
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.
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.
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 is a simple example of an SIR model for my Mathematics for Liberal Arts classes at Northern Kentucky University, Spring of 2022.
Let's think about things on the scale of a week. What happens over a week?
With an Ro of 2 (2 people infected for each infected individual, over the course of a week); recovery rate of 1 (every infected person loses their infectiousness after a week), and resusceptible rate of .05 (meaning .05, or a twentieth of the recovered lose their immunity each week), the disease peaks -- does the wave, then waves again before the year is out, then ultimately becomes
"endemic" (that is, it's never going away, which is clear after two years -- that is, a time of 104 weeks). This is like our seasonal flu (only the disease in this simulation doesn't illustrate seasonality -- that requires a more complicated model).
With an Ro of .9, recovery rate of 1, and resusceptible rate of .05, the disease is eliminated.
Masking, social distancing (including quarantining following contact), and quarantines all serve to reduce infectivity. And if we can drive infectivity down far enough, the disease can be eliminated. Other things that help is slowing down the resusceptibility, by vaccinating. Vaccines (in general) impart an immune response that reduces -- or even eliminates -- your susceptibility. We are still learning the extent to which these vaccines impart long-term immunity.
Other tools at our disposal include Covid-19 treatments, which increase the recovery rate, and vaccinations, which reduce the resusceptible rate. These can also serve to help us eradicate a disease, so that it doesn't become endemic (and so plague us forever).
Andy Long
Mathematics and Statistics
Some resources:
Wear a good mask: https://www.cdc.gov/coronavirus/2019-ncov/your-health/effective-masks.html
Gotta catch those sneezes: https://www.dailymail.co.uk/sciencetech/article-8221773/Video-shows-26-foot-trajectory-coronavirus-infected-sneeze.html
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.
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.
![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%2F20240425%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20240425T043916Z&X-Goog-Expires=3600&X-Goog-SignedHeaders=host&X-Goog-Signature=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)
