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
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:
Wear a good mask: https://www.cdc.gov/coronavirus/2019-ncov/your-health/effective-masks.html
Gotta catch those sneezes: https://www.dailymail.co.uk/sciencetech/article-8221773/Video-shows-26-foot-trajectory-coronavirus-infected-sneeze.html
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.
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.
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.
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.
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.
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 stock-flow simulation model is to show Covid-19 virus spread rate, sources of spreading and safety measures followed by all the countries affected around the world.
The simulation also aims at predicting for how much more period of time the virus will persist, how many people could recover at what kind of rate and also about the virus toughness dependence based on its excessive speed, giving rise to bigger numbers day-by-day.
Collapse of the economy, not just recession, is now very likely. To give just one possible cause,
in the U.S. the fracking industry is in deep trouble. It is not only that most
fracking companies have never achieved a free cash flow (made a profit)
since the fracking boom started in 2008, but that an already very weak and unprofitable oil industry cannot cope with
extremely low oil prices. The result will be the imminent collapse of the
industry. However, when the fracking industry collapses in the US, so will the American
economy – and by extension, probably, the rest of the world economy. To grasp a
second and far more serious threat it is vital to understand the phenomenon of ‘Global
Dimming’. Industrial activity not only produces greenhouse gases, but
emits also sulphur dioxide which converts to reflective sulphate aerosols in the
atmosphere. Sulphate aerosols act like little mirrors that reflect sunlight
back into space, cooling the atmosphere. But when economic activity stops, these
aerosols (unlike carbon dioxide) drop out of the atmosphere, adding perhaps as
much as 1° C to global average temperatures. This can happen in a very short period
time, and when it does mankind will be bereft of any means to mitigate the
furious onslaught of an out-of-control and merciless climate. The data and the unrelenting
dynamic of the viral pandemic paint bleak picture. As events unfold in the next few months, we may discover that it is too late to act, that our reign on this planet has, indeed, come to an abrupt end?
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.
![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%2F20241123%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20241123T080435Z&X-Goog-Expires=3600&X-Goog-SignedHeaders=host&X-Goog-Signature=b834006e0dd9cb6302696cfb3c3a948cac1899d3027b94ecd18b56692627fd4e9feb4fde1d012e4bead6b371e5fe30ca7b9997b7764d1dd16e5422115f61e90568a70bbd52823eb4bd0ae30607bea13d612eff14030f2c979bf36a24ea346928a8ef80642e87789ce8996721b0a0b53ce690b6e9a6ad64b8595cd8d530bcd691f9194086b27868c0d1ffd5f9b5a9afb0f433c7503092a65fa0c1076d825bdf820c10aab9f1bd10f4622d6fc28b6e08273114b4f1708c27323079a8f8cbd37821fa90a3663c03b08b4bce63cc4ca406dce1890b90232c080f204e12c0c74b1a8bfbc3566f2984acb44f7eef19efded826708f1d2a608a9d23a3830795315dfb92)
![france data from: France data [ link ], as of April 30 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/1601b8f2-c909-4705-b00a-073bd815dbb6/images/cover.jpg?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=insightmakercloud-file-access%40insightmakercloud.iam.gserviceaccount.com%2F20241123%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20241123T080435Z&X-Goog-Expires=3600&X-Goog-SignedHeaders=host&X-Goog-Signature=56b815869615a4bd1e7a8f53857cd0132f0fbddf1368e92b26504c09753903766fc7a6d2f3d595419bfca5ff8d3444d726e6ca896482e9a5ad75ba29cf9ebea1d99c917798450d7f0701f3d2322c1f18c37604766a95fe21f69f5e43d7a9863e672bf91378384894c13b2730f280e275e41e5fdcc7896f2f5b140ada3fadeb5be1c6755b3cee73b71f90b43581c0ba2a12a5319ca6b4e1266aa3bcd0ef546229622ea10288043736b4b587394ec117d9f0c5609af24a940e6bbb5eab6fe4f19586bb16edbfd3637ee7d1b01b4f2399b2e2b7c096820737ca599305c50a8e54a8d9199a712421f8c895d9c291c6d921c7ce4ce242f83ccf47b5ee80c0b5ca1e45)
