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3-бөлім, өзіндік жұмыс. COVID 19 Қытайда

2 months ago

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.

Clone of SEIRD 01: COVID-19 spread

Max Mira

Wolstenholme & Moya in COVID-19 reducing infection

MOYA. JOSEPH. NKOSI

Group_3_7BUIS021W_MODELS-Impact of COVID-19 on civil aviation_CausalLoop

Dharani Goli

COVID-19 Systemigram

Madison Fernandez

This diagram will map out the spread of the Coronavirus (SAR-CoV-2) and its complexities of health care.

Covid-19

Taleisha Y. Alcorn

COVID-19 Group 3

Meg Fredericks

covid-19

Luis Alberto Bravo Cordova

Modèle simple de causalité entre mesures et impact

COVID-19

Philippe Vallat

This model is to explain the COVID-19 outbreak in Brunie Island, Tasmania, Australia, and the relationship between it and the government policies , also with the local economy.

This model is upgraded on the basis of the SIR model and adds more variables.

A large number of COVID-19 cases will have a negative impact on the local economy. But if the number of cases is too small, it will have no impact on the macro economy

Government policy will help control the growth of COVID-19 cases by getting people tested.

BMA708 Model of COVID-19 Outbreak in Burnie island. Ming Liu 501335

Ming Liu

Spring, 2020:

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=1.27x10-6, we recover 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

* http://www.nku.edu/~longa/classes/2020spring/mat375/mathematica/SIRModel-MAA-with-Flattening.nb

Key for Lab SIR 2 -- Coronavirus: A Simple SIR (Susceptible, Infected, Recovered) Model for Coronavirus

Andrew E Long

Simplified Model_v2

Sage Gibbons

3

Rashid_COVID-19 crisis

Md Mamunur Rashid

COVID-19 model

Takafumi Ogura

Simulation einer Pandemie (Corona) am Beispiel der Bevölkerungssituation in Hamburg (1,9mio Einwohner, variabel)

Simulation einer Pandemie (Inf S2)

Jamil Osoria

This model demonstrates the relationship between the covid-19 outbreak, government policy, and economic impacts. This model was developed based on SIR model (Susceptible, Infection, Recovery). The model also outlines the policies been implemented by the government to cope with Covid-19 pandemic and it also indicate its economic impact.

Interesting insights

This model indicates the government policies have had positive influence on economic impact and it reduce its negative effects on the economy.

The model of COVID-19 outbreak in Burnie Tasmania

Anni Chen

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.

Covid-19 policy evaluation

ZHENGHUA YANG

Simulation of how a virus infects after entering the body, how it replicates inside living cells, and how the body's immune system responds towards the virus

System Dynamic Model 1b (Previously-infected individual)

Hazwan Hafiz

3

COVID-19

Daudi Keane

Whitney_6550_Covid-19_GlobalCrisis

Nora Whitney

4 months ago

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.

Week-12-Practice

Sai Nagavelli

6

The Binary Adder:

Andy Long

Spring, 2020 - Year of Covid-19

Having constructed a working example of a finite state machine (FSM), from Gersting's 7th edition (p. 730, Example 29), I decided to create a more useful one -- a binary adder (p. 732). It works!

Subject to these rules:

Your two binary numbers should start off the same length -- pad with zeros if necessary. Call this length L.
Now pad your two binary numbers with three extra 0s at the end; this lets the binary-to-decimal conversion execute.
numbers are entered from ones place (left to right).
In Settings, choose "simulation start" as 1, your "simulation length" as L+2 -- two more than the length of your initial input number vectors. (I wish that the Settings issues could be set without having to explicitly change it each time -- maybe it can, but I don't know how.)

Be attentive to order -- start with 1s place, 2s place, 4s, place, etc., and your output answer will be read in the same order.

To understand why we need three additional inputs of 0s:

For the useless first piece of output -- so n -> n+1
For the possibility of adding two binary numbers and ending up with an additional place we need to force out: 111 + 111 = 0 1 1 1
For the delay in computing the decimal number: it reads the preceding output to compute the decimal value.

Mat385 Finite State Machine (Binary Adder)

Andrew E Long

Pada Tugas mata kuliah Metode Penyelesaian Masalah dan Pemodelan , ditugaskan untuk membuat pemodelan penyebaran COVID-19 di negara yang dipilih, dan pada simulasi ini merupakan negara Indonesia

Dosen Pengampu :

Simulasi Pemodelan Penyebaran COVID-19 di Indonesia di Indonesia Dari Data Vaksinasi

Ngaliman Abu Ibrahim

This model is developed to simulate how Burnie can deal with a new outbreak of COVID-19 considering health and economic outcomes. The time limit of the simulation is 100 days when a stable circumstance is reached.

Stocks

There are four stocks involved in this model. Susceptible represents the number of people that potentially could be infected. Infected refers to the number of people infected at the moment. Recovered means the number of people that has been cured, but it could turn into susceptible given a specific period of time since the immunity does not seem everlasting. Death case refers to the total number of death since the beginning of outbreak. The sum of these four stocks add up to the initial population of the town.

Variables

There are four variables in grey colour that indicate rates or factors of infection, recovery, death or economic outcomes. They usually cannot be accurately identified until it happen, therefore they can be modified by the user to adjust for a better simulation outcome.

Immunity loss rate seems to be less relevant in this case because it is usually unsure and varies for individuals, therefore it is fixed in this model.

The most interesting variable in green represents the government policy, which in this situation should be shifting the financial resources to medical resources to control infection rate, reduce death rate and increase recovery rate. It is limited from 0 to 0.8 since a government cannot shift all of the resources. Bigger scale means more resources are shifted. The change of government policy will be well reflected in the economic outcome, users are encouraged to adjust it to see the change.

The economic outcome is the variable that roughly reflects the daily income of the whole town, which cannot be accurate but it does indicate the trend.

Assumptions:

The recovery of the infected won't happen until 30 days later since it is usually a long process. And the start of death will be delayed for 14 days considering the characteristic of the virus.

Economic outcome will be affected by the number of infected since the infected cannot normally perform financial activities.

Immunity effect is fixed at 30 days after recovery.

Interesting Insights:

In this model it is not hard to find that extreme government policy does not result in the best economic outcome, but the values in-between around 0.5 seems to reach the best financial outcome while the health issues are not compromised. That is why usually the government need to balance health and economic according to the circumstance.

New outbreak of COVID-19 in Burnie

Hank