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Very basic LV model, looking at the relationship between COVID-19 mitigation behavior and COVID-19 cases
Lotka Volterra COVID Model
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COVID-19 in Japan СРС-1
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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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Simplified Model_v2
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Өзіндік жұмысы жүйелік
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covid-19
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Tugas Pemodelan Transportasi Laut

Memodelkan persebaran pandemik covid-19 menggunakan insightmaker

Dosen pembimbing : Dr-Ing Ir. Setyo Nugroho
Pandemic Covid-19 Simulation
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This model estimates the deaths due to COVID19 in Bangalore City. 
Assumptions:
City has a population = 8 Million
Initial infected population = 10
Probability of infection = 8%
Contact rate in population = 6
Average duration of recovery = 10 days
Death rate = 1%
Quarantine rate = 80%
Delay in quarantine = 5 days
COVID-19_SIR_MODEL_No_Quarantine
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COVID-19 outbreak model brief description

The model stimulated the COVID-19 outbreak at Burnie in Tasmania. The pandemic spread was driven by infection rate, death rate, recovery rate, and government policy.

The government policy reduces the infection in some way, but it also decreases the physical industry. Online industry plays a vital role during the pandemic and brings more opportunities to the world economy. 

The vaccination directly reduces the infection rate. The national border will open as long as residents have been fully vaccinated. 

Assumption: 
The model was created based on different rates, including infection rate, death rate, testing rate and recovered rate. There will be difference between the real cases and the model. 

The model only list five elements of government policies embracing vaccination rate, national border and state border restrictions, public health orders, and business restrictions. Public health order includes social distance and residents should wear masks in high spread regions. 

This model only consider two industries which are physical industry, like manufacturer, retailers, or hospitality industries, and online industry. During the pandemic, employees star to work from home and students can have online class. Therefore, the model consider the COVID-19 has positive impact on online industry. 

Interesting insights:
The susceptible will decrease dramatically in first two weeks due to high infection rate and low recovery rate and government policy. After that, the number of susceptible will have a slight decline. 

The death toll and recovery rate was increased significantly in the first two weeks due to insufficient healthy response. And the trend will become mild as government policy works. 



BMA708_DafeiMeng_567691_Model of COVID-19 Outbreak in Burnie, Tasmania
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The System Dynamics Model presents the the COVID-19 status in Puerto Princesa City
Ауру Динамикасы COVID-19
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Exemple de modélisation
https://youtu.be/Kas0tIxDvrg
Les chiffres 
https://www.worldometers.info/coronavirus/coronavirus-symptoms/
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Modélisation Covid-19 aka Coronavirus
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Өзіндік жұмыс
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Modèle simple de causalité entre mesures et impact
COVID-19
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Tugas Kelompok Teknik Pemodelan dan Simulasi
Самостоятельная работа Covid-19
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Covid-19
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fatality (death rate)= 2.5%
(as of November in U.S.; source oneworldindata.org)

reinfection rate- unknown, rare
(as of October; source:https://www.ajmc.com/view/first-case-of-covid-19-reinfection-detected-in-the-us)
Covid-19 model 2
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Covid-19 sim
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COVID-19 Disease model
COVID-19 Disease Model
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COVID-19 Systems Model
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Covid-19 in Belarus
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The Covid-19 pandemic has introduced a variety of novel and intense difficulties, from dealing with the production network for individual defensive gear (PPE) to changing labor force ability to adapting to monetary misfortune. Amidst these difficulties lies a chance for medical services pioneers to more readily position and change their associations for an eventual fate of unusual amazement. To oversee limit, monetary misfortune, and care overhaul, medical services associations have settled on the basic choice to deliver or lessen labor force or to move numerous representatives to far off work, incorporating clinicians working with telehealth advances. (www.catalyst.nejm.org)


Reference:
Begun, J.W. PhD, Jiang, J.H, PhD,. (2020, October 9). NEJM Catalyst/Innovations in Care Delivery. Health Care Management During Covid-19: Insights from Complexity Science. Retrieved from https://catalyst.nejm.org/doi/full/10.1056/CAT.20.0505

Clone of Covid-19 Health Care Complexities and Variables