Insight diagram

Here we have a basic SEIR model and we will investigate what changes would be appropriate for modelling the 2019 Coronavirus 

Clone of SEIR Infectious Disease Model for COVID-19
Insight diagram
Tugas Permo
Clone of Tugas Permo_Corona
Insight diagram
This System Model presents the cases of COVID-19 in Puerto Princesa City as of June 3, 2021

Insight Author: Pia Mae M. Palay
Clone of System Dynamic Model of COVID 19 in Puerto Princesa City
Insight diagram

Here we have a basic SEIR model and we will investigate what changes would be appropriate for modelling the 2019 Coronavirus 

Clone of SEIR Infectious Disease Model for COVID-19
Insight diagram
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
Clone of SARS-CoV-19 model
Insight diagram
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
Clone of Clone of SARS-CoV-19 model
Insight diagram
A simple ABM example illustrating how the SEIR model works. It can be a basis for experimenting with learning the impact of human behavior on the spread of a virus, e.g. COVID-19.
Clone of SEIR ABM MODEL
Insight diagram
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
Insight diagram

INTRODUCTION

COVID-19

Coronavirus which was named COVID-19 is a respiratory disease which affects the lungs of the infected person and thus making such people vulnerable to other diseases such as pneumonia. It was first discovered in Wuhan China in December 2019 and since then has spread across the world affecting more than 40 million people from which over one million have died.

In the early discovery of the COVID-19, there were measures that were put in place with the help World Health Organization (WHO). They recommended a social distance of 1.5 meters to 2 meters to curb the spread since the scientist warned that COVID-19 can be carried in the droplets when someone breathes or cough. Another measure which was advised by WHO was wearing of mask, especially when people are in group. Wearing of mask would ensure that someone’s droplets do not leave their mouth or nose when they breathe or cough. It also help one from breathing in the virus which believed to be contagious and airborne.

The World Health Organization also advised on washing of the hand and avoiding frequent touching of the face. People mostly use their hand to touch surfaces which mad their hand the greatest harbor of the disease. Therefore, washing hands with soap will kill and wash away the virus from the hands. Avoiding touching of face also will prevent people from contracting the disease since the virus is believed to enter the body through openings such as eye, nose and mouth.

Another measure as a precaution from contracting the disease was to avoid hand shaking, hugging, kissing and any other thing which would bring people together. These were measures put to ensure that COVID-19 do not move from one person to another because of its airborne nature and the fact that it can be carried from the mouth or nose droplets.

Healthcare workers, in most of the countries, were provided with Personal Protective Equipment (PPEs) which helped them to protect themselves from contracting the virus. Healthcare workers were at the forefront in combating the disease since they were the people receiving the sick, including the ones with the virus. This exposed them to COVID-19 more than anyone hence more care was needed for them. Their PPEs comprised of white overall covering the whole body from head to toes. It also includes face mask and googles worn to prevent anything getting in their eyes. Their hands also were covered with gloves which were removed occasionally to avoid concentration of the virus on one glove.

COVID-19 affected many economies across the world as it greatly affected the human economic activities across the world. Due to the nature and how it spread, COVID-19 lead many countries to lockdown the country as we know it. Travelling was stopped as many countries feared the surge of the virus due to many people travelling form the countries which are already greatly affected. Another reason which travelling was hampered was due to the fact that the virus could spread among the travelers in an airplane. There were no proper measures to ensure social distance in the airplane and many people feared travelling from fear of contracting the disease.

This greatly affected the economy of many countries including great economies like USA. Tourism industry was the one affected the most as many country mostly depend on foreign travelers as their tourist. Many countries do not have proper domestic tourism structure and therefore depend on visitors who travels from foreign countries. Such countries have their economies greatly affected since the earnings from tourism either gone down or was not there at all.

Apart from locking down the country from foreigners, many major cities across the world were under lockdown. This means that even the citizens of the country were neither allowed in or out of the city. This restricted movement of people affecting greatly the human economic activities as many businesses were closed down especially transport businesses. The movement of goods from one places to another was affected making business difficult to carry out. Many people who dealt in perishable agricultural products count losses as their farm produced were destroyed because of lack of wider market. Some countries banned some businesses such as importing second hand clothes since it was believed that they could harbor the virus. Most of the meeting places such as sporting events and pubs were closed down affecting greatly the people who were involved in such businesses.

Across the world, schools were closed. Schools contain students in large numbers which could affect many students across the world. Learning was temporary stopped as different countries were finding ways of curbing the virus.

Scientist are busy like bees across the world to find the vaccine for the diseases that have ravage many countries and above all, they are trying to find the cure. Many countries have carried out their trial of vaccines with the hope to find an effective vaccine for the virus.

Meanwhile it is necessary to find ways by which the virus can be controlled so that it doesn’t spread to a point where it come out of control. Some of the measures put by the WHO has been highlighted above, but these measures need to be studied to ensure that measures which are more effective are affected at great heights. I therefore, have created a model in Insight Maker to check how these measures prove their effectiveness over time.

Clone of Acomplex systems model of the relationships among different players in the town of Burnie, Tasmania - Nguyen Dang Khoa 520572
Insight diagram
The System Dynamics Model presents the the COVID-19 status in Puerto Princesa City
Самостоятельная 1 Индия
Insight diagram
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:
  1. http://www.nku.edu/~longa/classes/2020spring/mat375/mathematica/SIRModel-MAA.nb
  2. https://www.maa.org/press/periodicals/loci/joma/the-sir-model-for-spread-of-disease-the-differential-equation-model
Clone of Coronavirus: A Simple SIR (Susceptible, Infected, Recovered) with death
Insight diagram
Coronavirus, COVID-19
Insight diagram
This model visualizes the Covid-19 hypothetical transmission at Barangay Busybees, Taytay, Palawan.
Hypothetical Situation: Barangay Busybees:
- Has a total population of 500 individual.
- Is a rural area.
- Strictly implemented health protocols.
Conclusion: Given the population and situation, transmission of Covid-19 is observed to be less in earlier stage but once transmission start it became rampant. Despite the rampant transmission, the strict implementation of protocols made recovery of people from Covid 19 possible with 92% recovered from the covid.

Model 2
Insight diagram

Here we have a basic SEIR model and we will investigate what changes would be appropriate for modelling the 2019 Coronavirus 

Clone of SEIR Infectious Disease Model for COVID-19
Insight diagram
Perkembangan Kasus PMK (Penyakit Mulut dan Kuku) pada Hewan Ternak di Pulau Lombok, Nusa Tenggara Barat
Insight diagram
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
Clone of SARS-CoV-19 model
Insight diagram
COVID-19 outbreak model brief description

The model stimulated the COVID-19 outbreak at Burnie in Tasmania. The pandemic spread was driven by infection rate, death rate, recovery rate, and government policy.

The government policy reduces the infection in some way, but it also decreases the physical industry. Online industry plays a vital role during the pandemic and brings more opportunities to the world economy. 

The vaccination directly reduces the infection rate. The national border will open as long as residents have been fully vaccinated. 

Assumption: 
The model was created based on different rates, including infection rate, death rate, testing rate and recovered rate. There will be difference between the real cases and the model. 

The model only list five elements of government policies embracing vaccination rate, national border and state border restrictions, public health orders, and business restrictions. Public health order includes social distance and residents should wear masks in high spread regions. 

This model only consider two industries which are physical industry, like manufacturer, retailers, or hospitality industries, and online industry. During the pandemic, employees star to work from home and students can have online class. Therefore, the model consider the COVID-19 has positive impact on online industry. 

Interesting insights:
The susceptible will decrease dramatically in first two weeks due to high infection rate and low recovery rate and government policy. After that, the number of susceptible will have a slight decline. 

The death toll and recovery rate was increased significantly in the first two weeks due to insufficient healthy response. And the trend will become mild as government policy works. 



BMA708_DafeiMeng_567691_Model of COVID-19 Outbreak in Burnie, Tasmania
Insight diagram
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:
  1. http://www.nku.edu/~longa/classes/2020spring/mat375/mathematica/SIRModel-MAA.nb
  2. https://www.maa.org/press/periodicals/loci/joma/the-sir-model-for-spread-of-disease-the-differential-equation-model
Clone of Coronavirus: A Simple SIR (Susceptible, Infected, Recovered) with death
Insight diagram
The SEIRS(D) model for the purpose of experimenting with the phenomena of viral spread. I use it for COVID-19 simulation.
Clone of SEIR - COVID-19 (v.1)
Insight diagram
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
Clone of SARS-CoV-19 model
Insight diagram
Explanation of the Model
This is a Model of COVID-19 outbreak in Burnie, Tasmania which shows the government actions in response to the pandemic COVID-19 and its affects on the Economy. The government health policy changes depending on the reported cases, which is a dependent upon the testing rate. 

Assumptions
Lockdown and travel ban were the main factor in government policy. It negatively impacts on the Economic growth as individuals are not going out which is directly affects the business around the world, in this insight 'Burnie'. This reduces the economic growth and the factors positively effecting economic growth such as Tourism.

Government policies has a negative impact on Exposer of individuals. Moreover, it also has a negative impact on chances of infection when exposed as well as other general infection rate.
 

Interesting Insight 
There is a significant impact of test rating on COVID-19 outbreak. Higher rates increases the government involvement, which decreases cases as well as the total death. 
In contrast, lower testing rates increase the death rate and cases. 

Tourism which plays a avital role in Tasmanian Economy greatly affects the Economic Growth. The decline of Tourism in parts of Tasmania such as Burnie, would directly decrease the economy of Tasmania.


  
BMA 708, Assessment Tast 3: Complex System, Burnie COVID-19 outbreak, Diprina Shakya-519673
Insight diagram

Here we have a basic SEIR model and we will investigate what changes would be appropriate for modelling the 2019 Coronavirus 

Clone of SEIR Infectious Disease Model for COVID-19
Insight diagram
The System Dynamic Model represents the Covid19 cases in Brgy. Sicsican, Puerto Princesa City as of May 27,2022. 

Total population of Brgy. Sicsican - 22625
Total Covid19 cases as of May 27, 2022 - 250
Local transmission - 241
Imported transmission - 9
Recovery - 226
Death Due to Covid19 - 15
Clone of Ph_Covid19SDM_EvelynBacalla
Insight diagram
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:
  1. Wear a good mask: https://www.cdc.gov/coronavirus/2019-ncov/your-health/effective-masks.html
  2. Gotta catch those sneezes: https://www.dailymail.co.uk/sciencetech/article-8221773/Video-shows-26-foot-trajectory-coronavirus-infected-sneeze.html

Clone of MAT115 Covid Simulation