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.
Ausbreitung von SARS-CoV-19 in verschiedenen Ländern - bitte passen Sie die Variablen über die Schieberegler weiter unten entsprechend an
Italien
ältere Bevölkerung (>65): 0,228 Faktor der geschätzten unentdeckten Fälle: 0,6 Ausgangsgröße der Bevölkerung: 60 000 000 hoher Blutdruck: 0,32 (gbe-bund) Herzkrankheit: 0,04 (statista) Anzahl der Intensivbetten: 3 100
Deutschland
ältere Bevölkerung (>65): 0,195 (bpb) geschätzte unentdeckte Fälle Faktor: 0,2 (deutschlandfunk) Ausgangsgröße der Bevölkerung: 83 000 000 hoher Blutdruck: 0,26 (gbe-bund) Herzkrankheit: 0,2-0,28 (Herzstiftung) Anzahl der Intensivbetten: 5 880
Frankreich
ältere Bevölkerung (>65): 0,183 (statista) Faktor der geschätzten unentdeckten Fälle: 0,4 Ausgangsgröße der Bevölkerung: 67 000 000 Bluthochdruck: 0,3 (fondation-recherche-cardio-vasculaire) Herzkrankheit: 0,1-0,2 (oecd) Anzahl der Intensivbetten: 3 000
Je nach Bedarf:
Anzahl der Begegnungen/Tag: 1 = Quarantäne, 2-3 = soziale Distanzierung , 4-6 = erschwertes soziales Leben, 7-9 = überhaupt keine Einschränkungen // Vorgabe 2 Praktizierte Präventivmassnahmen (d.h. sich regelmässig die Hände waschen, das Gesicht nicht berühren usw.): 0.1 (niemand tut etwas) - 1 (sehr gründlich) // Vorgabe 0.8 Aufklärung durch die Regierung: 0,1 (sehr schlecht) - 1 (sehr transparent und aufklärend) // Vorgabe 0,9 Immunitätsrate (aufgrund fehlender Daten): 0 (man kann nicht immun werden) - 1 (wenn man es einmal hatte, wird man es nie wieder bekommen) // Vorgabe 0,4
Schlüssel
Anfällige: Menschen sind nicht mit SARS-CoV-19 infiziert, könnten aber infiziert werden Infizierte: Menschen sind infiziert worden und haben die Krankheit COVID-19 Geheilte: Die Menschen haben sich gerade von COVID-19 erholt und können es in diesem Stadium nicht mehr bekommen Tote: Menschen starben wegen COVID-19 Immunisierte: Menschen wurden immun und können die Krankheit nicht mehr bekommen Kritischer Prozentsatz der Wiederherstellung: Überlebenschance ohne spezielle medizinische Behandlung
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.
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.
This model can be used to investigate how government interventions affect transmission and mortality associated with COVID-19 during an outbreak, and how these interventions impact on the economic activities in Burnie, Tasmania.
Assumptions can be made that effective government intervention can reduce the number of people infected, whereas the local economy is severely impacted.
Insights:
1. When COVID-19 case are more than 10, government policy will be triggered.
2. Testing rate is very crucial to understanding the spread of the pandemic and responding appropriately.
This model calculates and demonstrates the possible spread of COVID-19 through an agent-based map. It shows the timeline of a healthy individual being infected to recovery.
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.
This is a system dynamics model of COVID-19 outbreak in Burnie which shows the process of infections and how government responses, impact on the local economy.
First part is outbreak model, we can know that when people is infected, there are two situations. One is that he recovers from treatment, but even if he recovered, the immunity loss rate increase, makes him to become infected again. The other situation is death. In this outbreak, the government's health policies (ban on non-essential trips, closure of non-essential retailers, limits on public gatherings and quarantine ) help to reduce the spread of the COVID-19 new cases. Moreover, government legislation is dependent on number of COVID-19 cases and testing rates.
Second part: the model of Govt legislation and economic impact. Gov policy can help to reduce infection rate and local economy at same way. The increase of number of COVID-19 cases has a negative impact on local Tourism industry and economic growth rate. On the other hand, Govt legislation also can be change when reported COVID-19 case are less or equal to 10.
This Model was developed from the SEIR model (Susceptible, Enposed, Infected, Recovered). It was designed to explore relationships between the government policies regarding the COVID-19 and its impact upon the economy as well as well-being of residents.
Assumptions:
Government policies will be triggered when reported COVID-19 case are 10 or less;
Government Policies affect the economy and the COV-19 infection negatively at the same time;
Government Policies can be divided as 4 categories, which are Social Distancing, Business Restrictions, Lock Down, Travel Ban, and Hygiene Level, and they represented strength of different aspects;
Parameters:
Policies like Social Distancing, Business Restrictions, Lock Down, Travel Ban all have different weights and caps, and they add up to 1 in total;
There are 4 cases on March 9th;
Ro= 5.7 Ro is the reproduction number, here it means one person with COVID-19 can potentially transmit the coronavirus to 5 to 6 people;
Interesting Insights:
Economy will grow at the beginning few weeks then becoming stagnant for a very long time;
Exposed people are significant, which requires early policies intervention such as social distancing.
The model is built to demonstrates how Burnie Tasmania can deal with a new COVID-19 outbreaks, taking government policies and economic effects into account.
The susceptible people are the local Burnie residents. If residents were infected, they would either recovered or dead. However, even they do recover, there is a chance that they will get infected again if immunity loss occurs.
From the simulation result we can see that with the implementation of local government policies including travel ban and social distancing, the number of infected people will decrease. The number of recovered people will increase in the first 5 weeks but then experience a decrease.
In addition, with the implementation of local government policy, the economic environment in Burnie will be relatively stable when the number of COVID-19 cases is stable.
![لطفا برای بزرگنمایی و مشاهده مدل بر روی دکمه [Explore The Model] کلیک کنید شگفتی سازهای تاثیر گذار بر محورمقاومت در جهان پساکرونا چه خواهند بود؟ لطفا نظرات خود را با من در میان بگذارید: motealle@gmail.com](https://insightmakercloud-files.storage.googleapis.com/51bfb573-9601-4a80-a239-cdb0426de311/images/cover.jpg?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=insightmakercloud-file-access%40insightmakercloud.iam.gserviceaccount.com%2F20241230%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20241230T172802Z&X-Goog-Expires=3601&X-Goog-SignedHeaders=host&X-Goog-Signature=9bb973e9b9d9f0cb7c1a6470dcb46343d221d2bc1638733efeff7ccd032e60d5ab88f53ab7be1059b16dc6123a7fb74b72bcb7d99fbb50fd2c626ad2a484dc22acc5721ed37ac561c8f7b4d62dc4378b084eacecd0cfa364a47947751d0fc17f534dc37189bf95c3ae22e5fe8f492c8b876fb686241abe3089a51428f1601af30f05f180784dd4bf8bf52f51ff841aecdc98e5e5780ba9bd9f7520e6938cb5b94d348526e165fb2218eb58e30cf7620b85130eb992e192ae3d27b772ff1298c36c12d1d0260c02193ceb9e20ab05fb58031a50e601749e2a06ae70ff98b3f7599c4b0803d31f9e3be861fe42f9f8df729a5cdd16a657fc6c1da52b0b9a8c3eda)
![Initial data from: Italian data [ link ], as of Mar 28 Incubation estimation [ link ] Model focuses on outbreak dynamics and control, this version ignores symptom onset to hospital admission and the rest of recovery dynamics.](https://insightmakercloud-files.storage.googleapis.com/a945023c-0f59-409e-b0a3-460d30361ee8/images/cover.jpg?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=insightmakercloud-file-access%40insightmakercloud.iam.gserviceaccount.com%2F20241230%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20241230T172802Z&X-Goog-Expires=3601&X-Goog-SignedHeaders=host&X-Goog-Signature=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)
