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Collapse of the economy, not just recession, is now very likely. To give just one possible cause, in the U.S. the fracking industry is in deep trouble. It is not only that most fracking companies have never achieved a free cash flow (made a profit) since the fracking boom started in 2008, but that  an already very weak  and unprofitable oil industry cannot cope with extremely low oil prices. The result will be the imminent collapse of the industry. However, when the fracking industry collapses in the US, so will the American economy – and by extension, probably, the rest of the world economy. To grasp a second and far more serious threat it is vital to understand the phenomenon of ‘Global Dimming’. Industrial activity not only produces greenhouse gases, but emits also sulphur dioxide which converts to reflective sulphate aerosols in the atmosphere. Sulphate aerosols act like little mirrors that reflect sunlight back into space, cooling the atmosphere. But when economic activity stops, these aerosols (unlike carbon dioxide) drop out of the atmosphere, adding perhaps as much as 1° C to global average temperatures. This can happen in a very short period time, and when it does mankind will be bereft of any means to mitigate the furious onslaught of an out-of-control and merciless climate. The data and the unrelenting dynamic of the viral pandemic paint bleak picture.  As events unfold in the next few months,  we may discover that it is too late to act,  that our reign on this planet has, indeed,  come to an abrupt end?  
Covid 19 - irreversible and catastrophic consequences
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The System Dynamic Model represents the Covid19 cases in Brgy. Sicsican, Puerto Princesa City as of May 27,2022. 

Total population of Brgy. Sicsican - 22625
Total Covid19 cases as of May 27, 2022 - 250
Local transmission - 241
Imported transmission - 9
Recovery - 226
Death Due to Covid19 - 15
Ph_Covid19SDM_EvelynBacalla
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Model description:
This model is designed to simulate the outbreak of Covid-19 in Burnie in Tasmania, death cases, the governmental responses and Burnie local economy. 

More importantly, the impact of governmental responses to both Covid-19 infection and to local economy, the impact of death cases to local economy are illustrated. 

The model is based on SIR (Susceptible, Infected and recovered) model. 

Variables:
The simulation takes into account the following variables: 

Variables related to Covid-19: (1): Infection rate. (2): Recovery rate. (3): Death rate. (4): Immunity loss rate. 

Variables related to Governmental policies: (1): Vaccination mandate. (2): Travel restriction to Burnie. (3): Economic support. (4): Gathering restriction.

Variables related to economic growth: Economic growth rate. 

Adjustable variables are listed in the part below, together with the adjusting range.

Assumptions:
(1): Governmental policies are aimed to control(reduce) Covid-19 infections and affect (both reduce and increase) economic growth accordingly.

(2) Governmental policy will only be applied when reported cases are 10 or more. 

(3) The increasing cases will negatively influence Burnie economic growth.

Enlightening insights:
(1) Vaccination mandate, when changing from 80% to 100%, doesn't seem to affect the number of death cases.

(2) Governmental policies are effectively control the growing death cases and limit it to 195. 

Burnie Tasmania Covid - 19 outbreak simulation Model by Yankang Huang 541 277
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ABM of COVID-19 cases in PUERTO PRINCESA CITY
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Here we have a basic model and we will investigate what changes would be appropriate for modelling the 2019 Coronavirus.

We add simple containment meassures that affect different paramenters.

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

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.

COVID-19 spread with containment measures
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COVID-19 Group 3
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Covid-19 model
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Өзіндік жұмыс
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COVID-19 Модель
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COVID-19_Group3
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жүйелік бақылау
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Simulation (SIR) Covid-19
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SIMULASI COVID-19_AUGUSTO DA COSTA DOS SANTOS
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Өзіндік жұмыс 2
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In this activity show the agent based model or ABM, this activity represent the population and incubation percentage, and move the ink to incubation, included the person, vulnerable, incubation, infected, recuperation, and also recuperated. This model help us to identify where to quickly become infected.
ABM Covid-19
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Covid-19 in afrika Adil stom
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Os dados utilizados tem como fonte o artigo: https://arxiv.org/pdf/2004.05699.pdf
Modelo cinético do COVID-19
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COVID-19 in USA (dyn.m)
4 12 months ago
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Өзіндік жұмыс 1 германия Covid-19
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Model based on several references:
1. https://insightmaker.com/insight/4iVOp2JcrDSTBvqjER7pxM/TA-Pemsim-SEIR-Covid-19-Model
2. https://insightmaker.com/insight/5GiU0WZLpKCLGOoe6xeIhT/SEIR-COVID-19-New-Kl-1
3. https://insightmaker.com/insight/DaOeZ0N9RcgU1Q87ofIj8/COVID-19-SEIR-Model-for-Indonesia

Locus set on Indonesia, during 2021
SEIR Model for COVID-19 in Indonesia
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Covid-19 in USA
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COVID-19 Model
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Данная модель отражает распространение COVID-19 в России на основе статистики за 2020 год. Модель построена в среде Insight Maker по типу SEIRD (Susceptible–Exposed–Infected–Recovered–Dead), с упрощённой динамикой.
Основные параметры:
-Исходное население (масштабировано): 1000 человек
-Заражённые в начале: 2.12% → 21 человек
-Выздоровевшие (Recovery period): через 14 дней
-Смертность: 1.71% от заболевших
-Потеря иммунитета: не учитывается (0%)
-Exogenous (внешнее заражение): 2.12%
-Transmit: 0.3 (зависит от количества заражённых и восприимчивых)
covid-19 in russia