Author: Shreepada Shivananda, Data scientist
How to model SIR?
As SIR needs 3 important indicators to build the model we can define the indicators as:
1. Stock of susceptible population(S)
In case of the CoVID-19 this can be as big as the global population of 7.7 billion. But with the self-awareness of individuals and effort of governments the susceptible will be lower. Hence considering the awareness of people I would consider the modest number of 100 million
Author: Shreepada Shivananda, Data scientist
According to WHO CoVID-19 is already a global pandemic,
Germany announces that close to 60-70% people can be exposed to the virus. With
more than 600 thousand cases the epicentre of CoVID-19 moved from Chain to
Europe and then to USA in a span of mere 3 weeks getting into the new heights
every week. The alarming impacts what we have seen so far are just tip of the
ice berg but the second wave of impact are coming from all directions including
like job loss, declined per capita, global slowdown, renewed fight for global dominance
(China and USA) and more.
This article talks about spread of CoVID-19, how can the
epidemic model be used to estimate the global cases and life cycle of corona
virus. Also, the article talks about why an epidemic model better suits than a
traditional time series model for these estimation. The data source used for
the analysis is from Johns Hopkins University Center for Systems Science and
Engineering (JHU CSSE).
Where do we stand now?
It took 67 days for CovID-19 to infect 100 thousand people
globally but for subsequent 100 thousand just took 11 days, 4 days and the
latest one took less than 2 days. This means the Infection rate is exponential,
reason being susceptible population is large with current infection rate at
about 13% (for last one week). Because of which coronavirus is increasing
drastically and managing to find new host every day. Below charts represents
the increase of active cases globally.
Above chart gives clear indication that Infected cases has
dynamic pattern and epidemiological models like SIR or SIER can help estimating
such growth.
What is SIR model?
An SIR model is an epidemiological model that computes the
number of people infected with a contagious illness in a closed population over
time. The name of this class of models derives from the fact that they involve
coupled equations relating the number of susceptible people S(t), number of
people infected I(t), and number of people who have recovered R(t)
The dynamics of an epidemic, for example the flu, are often
much faster than the dynamics of birth and death, therefore, birth and death
are often omitted in simple compartmental models. The SIR system without
so-called vital dynamics (birth and death) described above can be expressed by
the following set of ordinary differential equations:
where S is the stock of susceptible population, I is the stock of infected, R is the stock of recovered population(It’s also called removed
population because part of the population end up with death), and N is the sum of these three.
How to model SIR?
As SIR needs 3 important indicators to build the model we can define the indicators as:
1. Stock of susceptible population(S)
In case of the CoVID-19 this can be as big as the global population of 7.7 billion. But with the self-awareness of individuals and effort of governments the susceptible will be lower. Hence considering the awareness of people I would consider the modest number of 100 million
2. Infection rate (It)
Infected rate is the rate at which the coronavirus is spreading. Currently it’s at an average rate of 7% in last 30 days and 12.5% in last one week. This means the rate is going to increase but on the other hand governments is trying to contain the spread by imposing the quarantine. Gradually by these efforts will yield results but I am considering infected rate(I) of 12.5% for next 1 week as any new case which would be reported from today are already infected and quarantine cannot help.
3. Recovery rate (Rt)
Infected rate is the rate at which the coronavirus is spreading. Currently it’s at an average rate of 7% in last 30 days and 12.5% in last one week. This means the rate is going to increase but on the other hand governments is trying to contain the spread by imposing the quarantine. Gradually by these efforts will yield results but I am considering infected rate(I) of 12.5% for next 1 week as any new case which would be reported from today are already infected and quarantine cannot help.
3. Recovery rate (Rt)
Recovery rate is the ratio of number of patients
are getting recovered and Number of active cases. Currently its same for a week
and month, which is at 2%
Let’s see how the trend
looks when we build the model with above indicators. Chart is for infected
cases in next week and it will increase exponentially.
Supporting data for next one
week:
The trend continues for
30 days globally, more and more measure would dampen the spread of infection
but this would be the completely responsibility of the government and people to
contain it. The chart below suggests with current set of measures below will be
the trend which would take the total infection reported close to 6 million.
What is the
lifespan of Corona?
Considering above
indicators of SIR model we have estimated the spread as below:
- We would see the impact for an year in various parts of the world (In a best case scenario)
- The increase in new cases would continue globally
for next 45 days at least, with all the measures
- The estimated number of people who would get
infected will be a max of 60 million people at a given point of time
The lifespan of CoVID-19 has
to be estimated considering various factors like Government policies, Response
of people, breakthrough in medicine, self-developed immunity, healthcare
facility, seasonality, and more. Please note the model do not considers the
impact of all these factors which will have a greater effect on the Infection
rate in future.
The reported cases are
already sent shockwaves from last one week and the measures are still not
containing the spread. Model like SIR will help to understand our position in
near future, also grab the attention of decision makers to act fast. When compared
with time series models, SIR is effective because of transparency in the
assumption and gives the clear picture of complete life span of the epidemic.
Disclaimer:
This article does not
talk about authenticity of the data because of various reasons like minor
infections are not getting reported, lack of testing kits, governments trying
to shield reality, etc.
Author: Shreepada Shivananda, Data scientist