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Assignment 3 Norway Covid-19

C. T

Somulacion clase 2, retroalimentación + y - , primer versión

Modelo Covid-19 Co

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An SIR model for Covid-19

This is a simple example of an SIR model for my Mathematics for Liberal Arts classes at Northern Kentucky University, Spring of 2022.

Let's think about things on the scale of a week. What happens over a week?

With an Ro of 2 (2 people infected for each infected individual, over the course of a week); recovery rate of 1 (every infected person loses their infectiousness after a week), and resusceptible rate of .05 (meaning .05, or a twentieth of the recovered lose their immunity each week), the disease peaks -- does the wave, then waves again before the year is out, then ultimately becomes

"endemic" (that is, it's never going away, which is clear after two years -- that is, a time of 104 weeks). This is like our seasonal flu (only the disease in this simulation doesn't illustrate seasonality -- that requires a more complicated model).

With an Ro of .9, recovery rate of 1, and resusceptible rate of .05, the disease is eliminated.

Masking, social distancing (including quarantining following contact), and quarantines all serve to reduce infectivity. And if we can drive infectivity down far enough, the disease can be eliminated. Other things that help is slowing down the resusceptibility, by vaccinating. Vaccines (in general) impart an immune response that reduces -- or even eliminates -- your susceptibility. We are still learning the extent to which these vaccines impart long-term immunity.

Other tools at our disposal include Covid-19 treatments, which increase the recovery rate, and vaccinations, which reduce the resusceptible rate. These can also serve to help us eradicate a disease, so that it doesn't become endemic (and so plague us forever).

Andy Long

Mathematics and Statistics

Some resources:

Wear a good mask: https://www.cdc.gov/coronavirus/2019-ncov/your-health/effective-masks.html
Gotta catch those sneezes: https://www.dailymail.co.uk/sciencetech/article-8221773/Video-shows-26-foot-trajectory-coronavirus-infected-sneeze.html

MAT115 Covid Simulation

Andrew E Long

COVID-19 outbreak in Burnie Tasmania Simulation Model

Introduction

This model simulates how COVID-19 outbreak in Burnie and how the government responses influence the economic community. Government responses are based on the reported COVID-19 cases amount, whcih is considered to be based on testing rate times number of people who are infected minus those recovered from COVID-19 and dead.

Government interventions include the implement of healthy policy, border surveillance, quarantine and travel restriction. After outbreak, economic activities are positively affected by the ecommerce channel development and normal economic grwoth, while the unemployement rate unfortunately increases as well.

Assumption

Enforcing government policies reduce both infection and economica growth.
When there are 10 or greater COVID-19 cases reported, the governmwnt policies are triggered.
Greater COVID-19 cases have negatively influenced the economic activities.
Government policies restict people's activities socially and economically, leading to negative effects on economy.
Opportunities for jobs are cut down too, making umemployment rate increased.
During the outbreak period, ecommerce has increased accordingly because people are restricted from going out.

Interesting insights

An increase in vaccination rate will make difference on reduing the infection. People who get vaccinated are seen to have higher immunity index to fight with COVID-19. Further research is needed.

Testing rate is considered as critical issue to reflect the necessity of government intervention. Higher testing rate seems to boost immediate intervention. Reinforced policies can then reduce the spread of coronvirus but absoluately have negative impacts on economy too.

Mengling Xue 561743 BMA708_Marketing insights into Big Data

Mengling Xue

An Agent Based Model of Covid-19. Disease Dynamics of Agent Based Model, aims to present the Covid-19 status

Ph_Covid19ABM_Shanea Betorin

Covid-19 in Germany

COVID-19

Covid-19 Pandemic

Озиндик жумыс, ковид Россия

Акбота Серікқызы

6 months ago

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

Model di samping adalah model SEIR yang telah dimodifikasi sehingga dapat digunakan untuk menyimulasikan perkembangan penyebaran COVID-19.

SEIR Model for COVID-19 in Indonesia - v2

Ngaliman Abu Ibrahim

Covid-19 Pandemic

Sneha Tirchy Shekar

Overview:

Overall, this analysis showed a COVID-19 outbreak in Burnie, the government policies to curtail that, and some of the impacts it is having on the Burnie economy.

Variables

The simulation made use of the variables such as; Covid-19: (1): Infection rate. (2): Recovery rate. (3): Death rate. (4): Immunity loss rate etc.

Assumptions:

From the model, it is apparent that government health policies directly affect the economic output of Burnie. A better health policy has proven to have a better economic condition for Burnie and verse versa.

In the COVID-19 model, some variables are set at fixed rates, including the immunity loss rate, recovery rate, death rate, infection rate, and case impact rate, as this is normally influenced by the individual health conditions and social activities.

Moving forward, we decided to set the recovery rate to 0.7, which is a rate above the immunity loss rate of 0.5, so, the number of susceptible could be diminished over time.

Step 1: Try to set all value variables at their lowest point and then stimulate.

Outcome: the number of those Infected are– 135; Recovered – 218; Cases – 597; Death – 18,175; GDP – 10,879.

Step 2: Try to increase the variables of Health Policy, Quarantine, and Travel Restriction to 0.03, others keep the same as step 1, and simulate

Outcome: The number of those Infected – 166 (up); Recovered – 249 (up); Cases – 554 (down); Death – 18,077 (down); GDP – 824 (down).

With this analysis, it is obvious that the increase of health policy, quarantine, and travel restriction will assist in increase recovery rate, a decrease in confirmed cases, a reduction in death cases or fatality rate, but a decrease in Burnie GDP.

Step 3: Enlarge the Testing Rate to 0.4, variable, others, maintain the same as step 2, and simulate

Outcome: It can be seen that the number of Infected is down to – 152; those recovered down to – 243; overall cases up to – 1022; those that died down to–17,625; while the GDP remains – 824.

In this step, it is apparent that the increase of testing rate will assist to increase the confirmed cases.

Step 4: Try to change the GDP Growth Rate to 0.14, then Tourism Growth Rate to 0.02, others keep the same as step 3, and then simulate the model

Outcome: what happens is that the Infected number – 152 remains the same; Recovered rate– 243 the same; Number of Cases – 1022 (same); Death – 17,625 (same); but the GDP goes up to– 6,632.

This final step made it obvious that the increase of GDP growth rate and tourism growth rate will help to improve the overall GDP performance of Burnie's economy.

The Recent COVID-19 Outbreak in Burnie Tasmania - Buchi Okafor 546792

Buchi Okafor

Covid-19 Model самостоятельная1

Kuanysheva Nurbanu

My Insight Covid-19

Жоха Бахти

A sample model for class discussion modeling COVID-19 outbreaks and responses from government with the effect on the local economy. Govt policy is dependent on reported COVID-19 cases, which in turn depend on testing rates less those who recover

Assumptions

Govt policy reduces infection and economic growth in the same way.

Govt policy is trigger when reported COVID-19 case are 10 or less.

A greater number of COVID-19 cases has a negative effect on the economy. This is due to economic signalling that all is not well.

Interesting insights

Higher testing rates seem to trigger more rapid government intervention, which reduces infectious cases. The impact on the economy though of higher detected cases though is negative.

Clone of Burnie COVID-19 outbreak demo model version 2

KV

Simulation of the spread of COVID-19 in Wuhan.

COVID-19

Chris Hitchin

COVID-19 modelling with SEIR(D) Model method to predict transmission of COVID-19.

SEIR(D) Model COVID-19

pemsim kelompok 3

Propagação COVID-19

Camila Silva

This System Model presents the cases of COVID-19 in Puerto Princesa City as of June 3, 2021

Insight Author: Pia Mae M. Palay

System Dynamic Model of COVID 19 in Puerto Princesa City

Pia Mae Palay

13

The Covid-19 pandemic has introduced a variety of novel and intense difficulties, from dealing with the production network for individual defensive gear (PPE) to changing labor force ability to adapting to monetary misfortune. Amidst these difficulties lies a chance for medical services pioneers to more readily position and change their associations for an eventual fate of unusual amazement. To oversee limit, monetary misfortune, and care overhaul, medical services associations have settled on the basic choice to deliver or lessen labor force or to move numerous representatives to far off work, incorporating clinicians working with telehealth advances. (www.catalyst.nejm.org)

Reference:

Begun, J.W. PhD, Jiang, J.H, PhD,. (2020, October 9). NEJM Catalyst/Innovations in Care Delivery. Health Care Management During Covid-19: Insights from Complexity Science. Retrieved from https://catalyst.nejm.org/doi/full/10.1056/CAT.20.0505

Covid-19 Health Care Complexities and Variables

Taleisha Y. Alcorn

Covid-19

Luis Felipe Dias Lopes

14

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

SD USA

Abdramanova Akmaral

BMA708_Assignment 3_Xiaoya Zuo

Xiaoya Zuo

3