The model here shows the COVID-19 outbreaks in Burnie Tasmania, which has impacted in the local economy. the relationship between COVID-19 and economic situation has been shown in the graph. Based on the susceptible and exposed rate, the period of spreading can be controlled by lockdown policy. 
The model here shows the COVID-19 outbreaks in Burnie Tasmania, which has impacted in the local economy. the relationship between COVID-19 and economic situation has been shown in the graph. Based on the susceptible and exposed rate, the period of spreading can be controlled by lockdown policy. 

Susceptible can be exposed by go out.  resident has a possibility to infect and be infected by others. The infection rate, new cases, immunity rate as well as doing exercise can effect the recovery rate. The economy situation is proportionate to the recovery rate. If there are more recovery rate from the pandemic, the economy situation will recover as well.   


 Recently, a new article published on <Science> explores the feasibility of living with the current Coronavirus in the long-term through mathematical modeling. Since either complete eradication or herd immunity is difficult to achieve in the short term, this work may provide useful and helpful

Recently, a new article published on <Science> explores the feasibility of living with the current Coronavirus in the long-term through mathematical modeling. Since either complete eradication or herd immunity is difficult to achieve in the short term, this work may provide useful and helpful public health policy implications in real environment.


Based on the model developed in the article, I translate it into a dynamic model here, so you may gain useful insights or check your own assumptions when simulating.

 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.

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.

 Modelling the demand for health and care resources resulting from the Covid-19 outbreak using an SEIR model.
Modelling the demand for health and care resources resulting from the Covid-19 outbreak using an SEIR model.

Cálculo de Número de Infectados do COVID-19 Cálculo de Ocupação de Leitos de UTI
Cálculo de Número de Infectados do COVID-19
Cálculo de Ocupação de Leitos de UTI
 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

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

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:
 The model here shows the COVID-19 outbreaks in Burnie Tasmania, which has impacted in the local economy. the relationship between COVID-19 and economic situation has been shown in the graph. Based on the susceptible analysis, people who usual go out are might have chance to meet susceptible people
The model here shows the COVID-19 outbreaks in Burnie Tasmania, which has impacted in the local economy. the relationship between COVID-19 and economic situation has been shown in the graph. Based on the susceptible analysis, people who usual go out are might have chance to meet susceptible people and have a high rate to be infected. The period of spreading can be controlled by keeping social distance and Government lockdown policy. 

Susceptible can be exposed by go out.  resident has a possibility to infect and be infected by others. people who might be die due to the lack of immunity. and others would recover and get the immune. 

Beside, the economy situation is proportionate to the recovery rate. If there are more recovery rate from the pandemic, the employment rate will be increased and the economy situation will recover as well.   
A simple ABM example illustrating how the SEIR model works. It can be a basis for experimenting with learning the impact of human behavior on the spread of a virus, e.g. COVID-19.
A simple ABM example illustrating how the SEIR model works. It can be a basis for experimenting with learning the impact of human behavior on the spread of a virus, e.g. COVID-19.