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COVID-19 in USA

Марина Якимова

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

77

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

Atakan Han 150501024

During the Covid-19 Outbreak Model

Atakan

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

The government has reduced both the epidemic and economic development by controlling immigration.

Yuhao c, BMA708_Marketing insights into Big Data.

Yuhao Chen

Tugas Kelompok Teknik Pemodelan dan Simulasi

Самостоятельная работа Covid-19

ERA

Develop a basic Systemigram / Rich Picture to tell the story of covid 19 mitigation

Systemigram Covid-19

Diana A.

Данная модель отражает распространение COVID-19 в России на основе статистики за 2020 год. Модель построена в среде Insight Maker по типу SEIRD (Susceptible–Exposed–Infected–Recovered–Dead), с упрощённой динамикой.

Основные параметры:

-Исходное население (масштабировано): 1000 человек

-Заражённые в начале: 2.12% → 21 человек

-Выздоровевшие (Recovery period): через 14 дней

-Смертность: 1.71% от заболевших

-Потеря иммунитета: не учитывается (0%)

-Exogenous (внешнее заражение): 2.12%

-Transmit: 0.3 (зависит от количества заражённых и восприимчивых)

covid-19 in russia

Аяна Нукеева

3 months ago

Exemple de modélisation

https://youtu.be/Kas0tIxDvrg

Les chiffres

https://www.worldometers.info/coronavirus/coronavirus-symptoms/

Modélisation Covid-19 aka Coronavirus

Erwin Hämmerli

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

COVID-19 Delta Variant Spread Among Emory Students

Natalia

Өзіндік жұмыс агенттік

Исақ Назерке

5 10 months ago

The System Dynamics Of Covid-19 Pandemic at Puerto Princesa City Palawan

Bea Kylah A. Buñag

COVID-19 Vaccination of indigenous West Australians

Elizabeth McLachlan

COVID-19 Systemigram

Madison Fernandez

COVID-19 in Iran 2 - ө. ж.

Арайлым Есләмбек

COVID-19 Indonesia

Wahyu Nur Hidayatun Nisa

Atakan Han 150501024

After the Covid-19 Outbreak Model

Atakan

Covid-19 Storytelling

Angely Ferrer

Covid-19

Марина Якимова

A systems model of the relationships amongst economic situation, health situations and Covid-19 in Burnie, Tasmania.

Health situation

According to exposed and go out population decreases, the population of infected decreases after a stable high cases period.

Economic situation

When the infected population decreases, the population economic recovery increases over time, then become stable after a period of time.

BMA708 Assessment 3 Complex system

Jingyi Qiu

Whitney_6550_Covid-19_GlobalCrisis

Nora Whitney

COVID-19

Gabriel Quevedo

COVID-19

Shalah Thomas

This model shows an SIR model of COVID-19 infection in the Philippines. The data used in this model are recent data from COVID-19 statistics reports this 2022. The format of this Philippine COVID-19 model is guided by an Infection Model developed by martin.

Ph_Covid19SDM_Lilang, Rebekah

Aikah Lilang