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COVID-19 in France

Gulsezim

Covid-19 sim

Alexander Shaw

AT-3

Xuanze Liang

4

SEIR Model for COVID-19 in Italy

Artyom Zainulin

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

LILI LIU

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

Infectious Disease Model (Version 4.0)

Eleanor Jeram

9

Os dados utilizados tem como fonte o artigo: https://arxiv.org/pdf/2004.05699.pdf

Modelo cinético do COVID-19

Milena Lauschner Lopes

2 өзіндік жұмыс

Anarali Abdisattarov

4

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

Steven D'Alessandro

SYSTEMS DYNAMICS OF COVID-19

AL JOHN D. CENA

Covid-19

My Insight Gauhar

Gauhar Sisembek

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

Dafei Meng

Dinâmica epidemiológica do Covid-19 (SD)

Carlo Ralph De Musis

COVID-19 Indonesia

Wahyu Nur Hidayatun Nisa

Системная динамика COVID-19 в Китае

Madina Muradova

Covid-19

Tamires Gomes

Өздік жұмыс(жүйелік модельдеу)

Еркетай Томирис

ABM of COVID-19 cases in PUERTO PRINCESA CITY

Pia Mae Palay

==edited by Prasiantoro Tusono and Rio Swarawan Putra==

Spring, 2020: in the midst of on-line courses, due to the pandemic of Covid-19.

With the onset of the Covid-19 coronavirus crisis, we focus on SIRD models, which might realistically model the course of the disease.

We start with an SIR model, such as that featured in the MAA model featured in

https://www.maa.org/press/periodicals/loci/joma/the-sir-model-for-spread-of-disease-the-differential-equation-model

Without mortality, with time measured in days, with infection rate 1/2, recovery rate 1/3, and initial infectious population I_0=1.27x10-4, we reproduce their figure

With a death rate of .005 (one two-hundredth of the infected per day), an infectivity rate of 0.5, and a recovery rate of .145 or so (takes about a week to recover), we get some pretty significant losses -- about 3.2% of the total population.

Resources:

http://www.nku.edu/~longa/classes/2020spring/mat375/mathematica/SIRModel-MAA.nb
https://www.maa.org/press/periodicals/loci/joma/the-sir-model-for-spread-of-disease-the-differential-equation-model

Coronavirus: A Simple SIR (Susceptible, Infected, Recovered) with death - based on Andrew E Long