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Covid-19 Modell

Andrea Peter

COVID-19 in Jakarta

Rizky Alif Ramadhan

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

covid-19

Luis Alberto Bravo Cordova

Өзіндік жұмыс агенттік

Исақ Назерке

7

SARS-CoV-19 spread in different countries

- please adjust variables accordingly

Italy

elderly population (>65): 0.228
estimated undetected cases factor: 4-11
starting population size: 60 000 000
high blood pressure: 0.32 (gbe-bund)
heart disease: 0.04 (statista)
free intensive care units: 3 100

Germany

elderly population (>65): 0.195 (bpb)
estimated undetected cases factor: 2-3 (deutschlandfunk)
starting population size: 83 000 000
high blood pressure: 0.26 (gbe-bund)
heart disease: 0.2-0.28 (herzstiftung)
free intensive care units: 5 880

France

elderly population (>65): 0.183 (statista)
estimated undetected cases factor: 3-5
starting population size: 67 000 000
high blood pressure: 0.3 (fondation-recherche-cardio-vasculaire)
heart disease: 0.1-0.2 (oecd)
free intensive care units: 3 000

As you wish

numbers of encounters/day: 1 = quarantine, 2-3 = practicing social distancing, 4-6 = heavy social life, 7-9 = not caring at all // default 2
practicing preventive measures (ie. washing hands regularly, not touching your face etc.): 0.1 (nobody does anything) - 1 (very strictly) // default 0.8
government elucidation: 0.1 (very bad) - 1 (highly transparent and educating) // default 0.9
Immunity rate (due to lacking data): 0 (you can't get immune) - 1 (once you had it you'll never get it again) // default 0.4

Key

Healthy: People are not infected with SARS-CoV-19 but could still get it
Infected: People have been infected and developed the disease COVID-19
Recovered: People just have recovered from COVID-19 and can't get it again in this stage
Dead: People died because of COVID-19
Immune: People got immune and can't get the disease again
Critical recovery percentage: Chance of survival with no special medical treatment

SARS-CoV-19 model

Lucia Vega Resto

82

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

A Susceptible-Infected-Recovered (SIR) disease model with waning immunity

COVID-19 Delta Variant Spread Among Emory Students

Natalia

covid 19 in china 1

Dina Kusalina

4 4 months ago

From Yuan Tian's 2024 paper Early COVID-19 Pandemic Preparedness: Informing Public Health Interventions and Hospital Capacity Planning Through Participatory Hybrid Simulation Modeling and PhD Dissertation 2025 USask Fig 5.1 p96

Hybrid model of early Covid-19

Geoff McDonnell

11 months ago

2бөлім 1 тапсырма

kuttykyz ergaliqyzy

3

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

Системная динамика COVID-19 в Китае

Madina Muradova

SYSTEMS DYNAMICS OF COVID-19

AL JOHN D. CENA

Dinâmica epidemiológica do Covid-19 (SD)

Carlo Ralph De Musis

2 өзіндік жұмыс

Anarali Abdisattarov

4

Covid-19-rise

Carrick M. Eggleston

COVID-19 in France

Gulsezim

COVID-19_Group3

Kathryn Liddle

In this activity show the agent based model or ABM, this activity represent the population and incubation percentage, and move the ink to incubation, included the person, vulnerable, incubation, infected, recuperation, and also recuperated. This model help us to identify where to quickly become infected.

ABM Covid-19

Nurhusin Dassan

INTRODUCTION

This is a balanced loop model that demonstrates how COVID 19 outbreak in Burnie and the response of the government (e.g. by enforcing health policies: Lockdown; quarantine, non-necessary business closure; border closure) affect the local economy. This model has 13 positive loops and seven negative loops. Government response is dependent on the number of reported COVID-19 cases which in turn thought to be dependent on the testing rates less those who recovered from COVID 19 and dead. Economic activity is dependent on the economic growth rate, increased in online shopping, increased in unemployment, number of people who do not obey the rules, COVID 19 cases and health policies.

ASSUMPTIONS

· Both infection and economic growth is reduced by enforcing government policies

· However, the negative effect of government policies is reduced by the number of people who do not obey government health policies

· Govt policies are enforced when the reported COVID-19 case are 10 or greater.

· Number of COVID cases reported is dependent on the testing rates less those who recovered and dead.

· The higher number of COVID-19 cases have a negative effect on local economy. This phenomena is known as negative signalling.

· Government policies have a negative effect on economic activity because health policies limit both social and economic activities which directly or indirectly affect the economy in Burnie .

· This negative effect is somewhat reduced by the increase in online shopping and the number of people who do not obey heath rules.

INTERESTING INSIGHTS

The test ratings seem to play a vital role in controlling COVID-19 outbreak. Higher Rates of COVID testings decrease the number of COVID 19 deaths and number of infected. This is because higher rates of testing accelerate the government involvement (as the government intervention is triggered earlier, 10 COVID cases mark is reached earlier). Delaying the government intervention by reducing the COVID testing rates increases the death rates and number of infected.

Increased testing rates allow the figures (deaths, susceptible, infected) to reach a plateau quickly.

BMA708- Shakila Bethmage- 548351 - COVID 19 Outbreak in Burnie