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COVID-19 in Brazil
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Spring, 2020: in the midst of on-line courses, due to the pandemic of Covid-19.

With the onset of the Covid-19 coronavirus crisis, we focus on SIRD models, which might realistically model the course of the disease.

We start with an SIR model, such as that featured in the MAA model featured in
https://www.maa.org/press/periodicals/loci/joma/the-sir-model-for-spread-of-disease-the-differential-equation-model

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.

Resources:
  1. http://www.nku.edu/~longa/classes/2020spring/mat375/mathematica/SIRModel-MAA.nb
  2. https://www.maa.org/press/periodicals/loci/joma/the-sir-model-for-spread-of-disease-the-differential-equation-model
Coronavirus: A Simple SIR (Susceptible, Infected, Recovered) with death
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Pemodelan COVID-19
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This model shows an SIR model of COVID-19 infection in the Philippines. The data used in this model are recent data from COVID-19 statistics reports this 2022. The format of this Philippine COVID-19 model is guided by an Infection Model developed by martin.
Ph_Covid19SDM_Lilang, Rebekah
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Here we have a basic SEIR model and we will investigate what changes would be appropriate for modelling the 2019 Coronavirus.

We add simple containment meassures that affect two paramenters, the Susceptible population and the rate to become infected.

The initial parametrization is based on the suggested current data. The initial population is set for Catalonia.

The questions that we want to answer in this kind of models are not the shape of the curves, that are almost known from the beginning, but, when this happens, and the amplitude of the shapes. This is crucial, since in the current circumstance implies the collapse of certain resources, not only healthcare.

The validation process hence becomes critical, and allows to estimate the different parameters of the model from the data we obtain. This simulation approach allows to obtain somethings that is crucial to make decisions, the causality. We can infer this from the assumptions that are implicit on the model, and from it we can make decisions to improve the system behavior.

Yes, simulation works with causality and Flows diagrams is one of the techniques we have to draw it graphically, but is not the only one. On https://sdlps.com/projects/documentation/1009 you can review soon the same model but represented in Specification and Description Language.

SEIRD 02: COVID-19 spread with containment measures
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SD MODEL COVID-19
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Simulación Covid-19
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An Agent Based Model of Covid-19. Disease Dynamics of Agent Based Model, aims to present the Covid-19 status


Ph_Covid19ABM_Shanea Betorin
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Check how different times of recovery and deths in cases of covid-19 infulence 2 key mortality indicators:
Overall mortalityr ate (ratio of all deaths to all cases)
Resolved cases mortality rate (ratio of all deaths to recovered cases)

Assumed delays are:
5 weeks for recovery cases
2 weeks for death cases
Delays are built into conveyor stocks, so cannot be adjusted by slider

keep in mind Insigth uses similar but made-up numbers and linear flow of new cases (in opposition to exponential in real world)  
Understanding Covid-19 mortality
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Assignment 3 Norway Covid-19
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Das SEIRS(D)-Modell zum Simulieren der COVID-19 - Epidemie.
SEIR - COVID-19 (v.1) von Remigiusz Kinas
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Simulation of how a virus infects after entering the body, how it replicates inside living cells, and how the body's immune system responds towards the virus
System Dynamic Model 1b (Previously-infected individual)
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Demo_Group3_COVID-19
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COVID-19 в Бразилии за 2020-2024 года (динамика заболеваний)
3 months ago
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HW5 Version 1: Spread of COVID-19 in Cameroon
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COVID-19 Kazakstan Abdrakhman
10 months ago
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SIR Modeling of Covid-19 in Cameroon
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School project for data modell of the COVID-19 Virus
Corona - DE
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An SIR model for Covid-19

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:
  1. Wear a good mask: https://www.cdc.gov/coronavirus/2019-ncov/your-health/effective-masks.html
  2. Gotta catch those sneezes: https://www.dailymail.co.uk/sciencetech/article-8221773/Video-shows-26-foot-trajectory-coronavirus-infected-sneeze.html

MAT115 Covid Simulation
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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.
COVID Model
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Coronavirus Simulation
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As of May 2022, this model depicts the System Dynamics of COVID-19 cases in the Philippines. This model depicts the progression of the COVID-19 virus from susceptible to infected, recovered, and death.
Ph_Covid19SDM_EmrysJoyC.Olbinado