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Covid -19 Nurym

3

This is a Simple Agent Based Model of Covid 19 cases in Barangay Cabayugan, Puerto Princesa City. The model has three classes of people: Susceptible, Infected, and Recovered .

Agent Based Covid 19 Model

Carlos Miguel Aniar

Covid-19 in Italy

Нуркасым Нурумкан

The model here shows the COVID-19 outbreaks in Burnie Tasmania, which has impacted in the local economy. the relationship between COVID-19 and economic situation has been shown in the graph. Based on the susceptible analysis, people who usual go out are might have chance to meet susceptible people and have a high rate to be infected. The period of spreading can be controlled by keeping social distance and Government lockdown policy.

Susceptible can be exposed by go out. resident has a possibility to infect and be infected by others. people who might be die due to the lack of immunity. and others would recover and get the immune.

Beside, the economy situation is proportionate to the recovery rate. If there are more recovery rate from the pandemic, the employment rate will be increased and the economy situation will recover as well.

COVID-19 outbreak in Burnie, TAS. BMA708 Assignment 3

Yifei MAO

Statistics of how covid affected employment

Insight Maker Model COVID

Bryan Ramos

This Model was developed from the SEIR Model (Susceptible, Exposed, Infected, Recovered) and it predicts the COVID-19 outbreak in Burnie, Tasmania. This pandemic outbreak contributes to diverse rates including infection rate, death rates and recovery rate, government policies and its economic impacts.

Assumptions:

This model is driven by its determined rates, e.g., incubation rate, morality rate, test rate and immunity loss rate and its recovery rate.

Government policies are involved in fully vaccination rate, social distance, national border closure, travel, and business restriction which effect Burnie’s economy.

There are three economic entities dimensions in Burnie Island, we can tell that the pandemic has negative impact on Brick-and-Mortar enterprises and tourism business to some extent, whereas, e commercial business plays a crucial role to stimulate the regional economic activities during the COVID-19 period.

Interesting Insights:

The figure of susceptible changes significantly during the initial 3 weeks because of low recovery rate and high infection rate. On the other hand, the implementation and interventions of government policies is effective, because the number of patients who tested negative is increased and the majority of them release and go back home after medical follow-up.

Xueli Huang 501514, BMA708 Model of COVID-19 Outbreak in Burnie, Tasmania

XUELI HUANG

Agent-Based Model Covid-19

Pearl Angeli Padul

57

Covid-19 in Germany

Мадияр Сейтмуратов

Santa Maria Covid-19

Paulo Villela

13

2Бөлім Өзіндік

Gauhar Mukhameddamin

4

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

Модель системной динамики по COVID-19 в Германии

Sid Kageno

A simple Susceptible - Infected - Recovered disease model.

covid 19

Maverick Labrador

Introduction:

This simulation model demonstrates the outbreak of Covid-19 in Burnie, Tasmania and how the corresponding government’s responses affect the spreading of Covid-19. Meanwhile, this model also shows how the economy in Burnie is influenced by the impacts of both Covid-19 and government policies.

Variables:

This simulation contains some relevant variables as follow:

Variables in Covid-19 outbreaks: (1) Infection rate, (2) Recovery rate, (3) Death rate, (4) Immunity loss rate

Variables in Government policies: (1) Vaccination rate, (2) Lockdown, (3) Travel ban, (4)Quarantine

Variables in Economy: (1) E-commerce business, (2) Unemployment rate, (3) Economic growth rate.

Assumption:

Government responses would be triggered when reported Covid-19 cases are at least 10.

The government policies reduce the spreading of Covid-19, but they would also limit economic development at the same time due to the negative impact of the policies on the economy is greater than the positive impact.

The increase in reported Covid-19 cases would negatively affect economic growth.

Interesting Insights:

The first finding is that the death number would keep increasing even though the infection rate has decreased, but with stronger government policies (such as implementing a coefficient over 25%), no more death numbers will occur caused by Covid-19.

The second finding is that as government policies limit business activities, with the increasing number of reported Covid-19 cases, economic growth will suffer a severe blow even if e-commerce grows, it can’t make up for this economic loss.

BMA 708 assignment 3 - simulation model of Covid-19 Outbreak in Burnie, Tasmania