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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

Butcher/Student Check of Final Version of Italian COVID-19 outbreak

Andrew E Long

Propagación y recuperación Covid-19

kevin Francia

Қазақстандағы Ковид 19

Aruna Serikzhan

This is a complex model of COVID-19 outbreak in Burnie Tasmania. It show the effect of government policy to local economic and the impact of Covid-19.

Assumptions

Government policy can reduce the number of infected, however also would reduce the economic growth.

Interesting insights

Based on changing the value of government policy, it show that the policy can help to reduce on the number of death and infection.

Covid-19 Out break

Wing Ki Ng

системное Америка

Shirin

COVID-19 Outbreak in Burnie Tasmania Simulation Model

Introduction:

This model simulates the COVID-19 outbreak situation in Burnie and how the government responses impact local economy. The COVID-19 pandemic spread is influenced by several factors including infection rate, recovery rate, death rate and government's intervention policies.Government's policies reduce the infection spread and also impact economic activities in Burnie, especially its tourism and local businesses.

Assumptions:

- This model was built based on different rates, including infection rate, recovery rate, death rate, testing rate and economic growth rate. There can be difference between

this model and reality.

- This model considers tourism and local business are the main industries influencing local economy in Burnie.

- Government's intervention policies will positive influence on local COVID-19 spread but also negative impact on local economic activity.

- When there are more than 10 COVID-19 cases confirmed, the government policies will be triggered, which will brings effects both restricting the virus spread and reducing local economic growth.

- Greater COVID-19 cases will negatively influence local economic activities.

Interesting Insights:

Government's vaccination policy will make a important difference on restricting the infection spread. When vaccination rate increase, the number of deaths, infected people and susceptible people all decrease. This may show the importance of the role of government's vaccination policy.

When confirmed cases is more than 10, government's intervention policies are effective on reducing the infections, meanwhile local economic activities will be reduced.

BMA708-Tian Liang-586868-Model of COVID-19 Outbreak in Burnie, Tasmania

Өзіндік жұмыс

covid-19

Covid-19 SSM

COVID-19 в Китае

Tugas Pemodelan Transportasi Laut

Memodelkan persebaran pandemik covid-19 menggunakan insightmaker

Dosen pembimbing : Dr-Ing Ir. Setyo Nugroho

Pandemic Covid-19 Simulation

Rizka Oktaviana

Covid-19 in Belarus

юлия

7 months ago

Өзіндік жұмыс агент

Алтынай Садир

5

Variant of the model "COVID-19 spread" made by Anxo-Lois Pereira and Miquel Martínez de Morentin, including reinfection, permanent immunity and Vaccines. Made for the subject of TAED.

COVID-19 spread with reinfeccion, permanent immunity and vaccines

Miquel Mdm

COVID-19

Shalah Thomas

Disease Dynamics

Sim

COVID-19 Indonesia

Wahyu Nur Hidayatun Nisa

Динамикалық өзіндік жұмыс

Zholdasbay Asel

O presente Insight engloba diversos tipos de modelos compartimentais.
Pra visualizar alguns deles, procure testar os seguintes valores:

SI: S=995, I=5, β=0.1
SIS: S=980, I=20, β=0.1 e δ = 0.01
SIR: S=995, I=5, β=0.35 e γ=0.035
SIRS: S=995, I=5, β=0.4, γ=0.2 e μ=0.005
SEIR: S=995, I=5, β=0.5, ω=0.1 e γ=0.1
SEIRS: S=995, I=5, β=0.5, ω=0.1, γ=0.1 e μ=0.03.
SIRV: S=995, I=5, β=0.35, γ=0.035 e ν=0.01

Note que este é um Insight que pode ser modificado para mostrar cada um desses modelos e o usuário deverá tornar alguns fluxo nulos afim de manter apenas as conexões essenciais para cada sistema.

Compartmental models in epidemiology

Gabriel Rampeloti

Brief of the model:

The model predicts the outbreak of COVID-19 in the Burnie, Tasmania area. It is imperative to clarify that this model was developed from the SEIR model (Susceptible, Infected, Infected, Recovered). The spread of this pandemic is driven by a combination of infection rates, mortality rates, and recovery rates from the virus itself, as well as government policies.

For COVID-19 itself, vaccination directly reduces the infection rate, thereby reducing the mortality rate of COVID-19 patients and the reduction of confirmed cases. In other words, if the local population is adequately vaccinated, everyday life, shopping, tourism, and even national borders will be open rather than in a closed border situation.

Assumption of the model:

The model simulated based on different rates, including Infecting rate, Death Rate, Test Rate, Immunity Loss Rate and Recovery Rate. And, this model lists six elements of government policy, which including border closure, travel ban, social distancing, business restriction, self-quarantine, and vaccination schedule.

Besides, the model considers three economic entities in the Burnie area, one in the brick-and-mortar industry and online business industry. Government policies have somewhat reduced COVID-19 infections. Still, they have also at the same time, online businesses played an essential role in stimulating local economic activity during the pandemic. At the same time, however, online businesses played an indispensable role in promoting regional economic activity during the pandemic.

The prediction model is for reference only, and there may be differences between the actual cases and the model.

Insights of the model:

Due to the high infection and low recovery rates and timely government policy interventions, the number of susceptible individuals changes dramatically in the first four weeks. However, the number of sensitive individuals continues to decline after this period, but the decline is not significant. Secondly, with the implementation of government policies, the number of suspected patients who tested negative for medical follow-up continued to rise, implying that government policy interventions directly affect COVID-19.

BMA708_Model of COVID-19 in Burnie_Yuanyuan Liao

Yuanyuan Liao

COVID-19 Systemigram

Madison Fernandez

Covid-19

Марина Якимова

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.

covid-19 in USA

Denis

2 months ago

COVID-19 Vaccination of indigenous West Australians

Elizabeth McLachlan