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3бөлім өзіндік

Gauhar Mukhameddamin

This is a mitigated model showing the potential spread of COVID-19 across the healthcare system.

COVID phased community model (Eastern)

David Spencer

The Infection / Recovery & Immune / Rate of a Global a Population of 8.2 Billion People.

The Pandemic Management of COVID-19 - Richard A. Estefan

Richard A. Estefan

Group_3_7BUIS021W_MODELS-Impact of COVID-19 on civil aviation_CausalLoop

Dharani Goli

Tugas 3 Pemodelan Transportasi Laut

STUDI KASUS : Simulasi Penyebaran Virus Corona atau COVID-19 di Indonesia dengan aplikasi Insight Maker

TUGAS 3_IGedeBagusIndraDanendra_04411740000036_Pemodelan Transportasi Laut

I Gede Bagus Indra Danendra

COVID-19 Model

Marcus Yates

This insight began as a March 22nd Clone of "Italian COVID 19 outbreak control"; thanks to Gabo HN for the original insight. The following links are theirs:

Initial data from:

Italian data [link] (Mar 4)

Incubation estimation [link]

Andy Long

Northern Kentucky University

May 2nd, 2020

This is an update of our model from April 9th, 2020. As we prepare for our final exam, I read a story in The Guardian about Italy's struggle to return to normalcy. The final paragraphs:

During the debate in the Senate on Thursday, the opposition parties grilled Conte. Ex-prime minister Matteo Renzi, who has called for less restraint in the reopening, remarked, “The people in Bergamo and Brescia who are gone, those who died of the virus, if they could speak, they’d tell us to relaunch the country for them, in their honour.”

Renzi’s controversial statement was harshly criticised by doctors who warned that the spread of the disease, which, as of Thursday, had killed almost 30,000 people in the country and infected more than 205,000 [ael: my emphasis], was not over and that a misstep could take the entire country back to mid-March coronavirus levels.

“We risk a new wave of infections and outbreaks if we’re not careful,” said Tullio Prestileo, an infectious diseases specialist at Palermo’s Benefratelli Hospital. “If we don’t realise this, we could easily find ourselves back where we started. In that case, we may not have the strength to get back up again.”

I have since updated the dataset, to include total cases from February 24th to May 2nd. I went to Harvard's Covid-19 website for Italy and and then to their daily updates, available at github. I downloaded the regional csv file for May 2nd, which had regional totals (21 regions); I grabbed the column "totale_casi" and did some processing to get the daily totals from the 24th of February to the 2nd of May.

The cases I obtained in this way matched those used by Gabo HN.

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

You can get a good fit to the data through April 9th 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.

The question is: how well did we do at modeling this epidemic through May 2nd (day 60)? And how can we change the model to do a better job of capturing the outbreak from March 3rd until May 2nd?

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

Clone of Clone of Final Version of Italian COVID-19 outbreak

Adheke Silas

COVID-19 model with hospitalizations and deaths

Guy Katriel

COVID-19

Jasurbek Abdalimov

My Insight

Каракат Алдажарова

Model description:

This model is designed to simulate the outbreak of Covid-19 in Burnie in Tasmania. It also tell us the impact of economic policies on outbreak models and economic growth.

Variables:

The simulation takes into account the following variables and its adjusting range:

On the left of the model, the variables are: infection rate( from 0 to 0.25), recovery rate( from 0 to 1), death rate( from 0 to 1), immunity loss rate( from 0 to 1), test rate ( from 0 to 1), which are related to Covid-19.

In the middle of the model, the variables are: social distancing( from 0 to 0.018), lock down( from 0 to 0.015), quarantine( from 0 to 0.015), vaccination promotion( from 0 to 0.019), border restriction( from 0 to 0.03), which are related to governmental policies.

On the right of the model, the variables are: economic growth rate( from 0 to 0.3), which are related to economic growth.

Assumptions:

(1) The model is influenced by various variables and can produce different results. The following values based on the estimation, which differ from actual values in reality.

(2) Here are just five government policies that have had an impact on infection rates in epidemic models. On the other hand, these policies will also have an impact on economic growth, which may be positive or negative.

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

(4) This model lists two typical economic activities, namely e-commerce and physical stores. Government policies affect these two types of economic activity separately. They together with economic growth rate have an impact on economic growth.

Enlightening insights:

(1) In the first two weeks, the number of susceptible people will be significantly reduced due to the high infection rate, and low recovery rate as well as government policies. The number of susceptible people fall slightly two weeks later. Almost all declines have a fluctuating downward trend.

(2) Government policies have clearly controlled the number of deaths, suspected cases and COVID-19 cases.

(3) The government's restrictive policies had a negative impact on economic growth, but e-commerce economy, physical stores and economic growth rate all played a positive role in economic growth, which enabled the economy to stay in a relatively stable state during the epidemic.

Model of COVID-19 Outbreak in Burnie, Tasmania

Cameroon COVID-19

System Dynamics

SD 2 COVID-19 Policy

Tugas 3_Thamara Shaifa Anwar_0441174000035_Pemodelan Transportasi Laut

Dosen Pengampu : Dr-Ing Ir. Setyo Nugroho

Clone of Pemodelan COVID-19 di Indonesia

Tisa Monita

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