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Өзіндік жұмыс 2
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Агентская модель болезни COVID-19 в США
Агентская модель болезни COVID-19 в США
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Системная динамика COVID-19 в США
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Динамика
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covid-19
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Explanation of the Model

The sample model demonstrate the COVID-19 outbreak in Burnie, Tasmania appearing how the government reacts by executing important health approaches and the impacts on the economy of the region

Assumptions

The economic growth rate is subordinate on the extent of the populace who can be exposed. The number of COVID-19 cases adversely impacts the economy. The government arrangement is activated when the COVID-19 cases are 10 or above

Interesting Insights

1. There is a positive relationship between exposure to COVID- 19 and economic growth rate. Since the more individuals go out, the more trade activity takes place and that ultimately results economic growth

2. Expanding the testing rate results
- Higher cases being recognized
- Strict  government intervention
- Less deaths

BMA708_Assignment3_Md Shihabul Islam_548056
Insight diagram
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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Here we have a basic SEIR model and we will investigate what changes would be appropriate for modelling the 2019 Coronavirus 

SEIR Infectious Disease Model for COVID-19
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SIR Modeling of Covid-19 in Cameroon
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Өзіндік жұмыс Аида 1
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Пневмония в США
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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.

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

Самостоятельная работа COVID-19
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A sample model for class discussion modeling COVID-19 outbreaks and responses from government with the effect on the local economy.  Govt policy is dependent on reported COVID-19 cases, which in turn depend on testing rates less those who recover

Assumptions
The government has reduced both the epidemic and economic development by controlling immigration.




Yuhao c, BMA708_Marketing insights into Big Data.
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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.
Week-12-Practice
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Өздік жұмыс(жүйелік модельдеу)
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Air Quality and the Effects it has on Human Health in America Post COVID-19
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About the Model 
This model is a dynamic model which explains the relationship between the police of the government and the economy situation in Burnie Tasmania after the outbreak of Corona Virus.

This model is based on SIR model, which explains the dynamic reflection between the people who were susceptible, infected,deaths and recovered. 

Assumptions 
This model assumes that when the Covid-19 positive is equal or bigger than 10, the government policy can be triggered. This model assumes that the shopping rate in retail shops and the dining rates in the restaurants can only be influenced by the government policy.

Interesting Insights  

The government police can have negative influence on the infection process, as it reduced the possibility of people get infected in the public environments. The government policy has a negative effect on shopping rate in retail shops and the dining rate in the restaurants. 

However, the government policy would cause negative influence on economy. As people can not  shopping as normal they did, and they can not dinning in the restaurants. The retail selling growth rate and restaurant revenue growth rate would be reduced, and the economic situation would go worse. 
Corona virus outbreak in Burnie Tasmania (Xuexiao Zhang 538712)
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March 22nd Clone of "Italian COVID 19 outbreak control"; thanks to Gabo HN for the insight.

Initial data from:
Italian data [link] (Mar 4)
Incubation estimation [link]

Andy Long
April 9th, 2020

I have since updated the dataset, to include total cases from February 24th to April 9th.
I went to                                                                                                 
https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/KDFYZW                           
and downloaded the archive for April 9th:                                                                 
https://dataverse.harvard.edu/file.xhtml?persistentId=doi:10.7910/DVN/KDFYZW/C2HSTK&version=19.0          

I dug through the files, and found the file dpc-covid19-ita-regioni.csv, which had regional totals (21 regions); I grabbed the column "totale_casi" and used some lsp code to get the daily totals from the 24th of February til the 9th of April.

The good news is that the cases I obtained in this way matched those used by Gabo HN.

The initial data started on March 3rd (that's 0 in this Insight).

You can get a good fit to the data by choosing the following (and notice that I've short-circuited the process from the Infectious to the Dead and Recovered). I've also added the Infectious to the Total cases.

Incubation Rate:  .025
R0: 3
First Lockdown: IfThenElse(Days() == 5, 16000000, 0)
Total Lockdown: IfThenElse(Days() >= 7, 0.7,0)

(I didn't want to assume that the "Total Lockdown" wasn't leaky! So it gets successively tighter, but people are sloppy, so it simply goes to 0 exponentially, rather than completely all at once.)

deathrate: .01
recoveryrate: .03

"Death flow": [deathrate]*[Infectious]
"Recovery flow": [recoveryrate]*[Infectious]

Total Reported Cases: [Dead]+[Surviving / Survived]+[Infectious]



Resources:
  * https://annals.org/aim/fullarticle/2762808/incubation-period-coronavirus-disease-2019-covid-19-from-publicly-reported
MAT375 Version of Italian COVID 19 outbreak control
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Model di samping adalah model SEIR yang telah dimodifikasi sehingga dapat digunakan untuk menyimulasikan perkembangan penyebaran COVID-19.
SEIR Model for COVID-19 in Indonesia - v2
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системное Америка
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Агенттик моделирование. ковид Корея
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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)