Epidemic Simulator

For the past couple of months, I came across several articles and videos depicting elegant simulations of how an epidemic spreads. I want to follow up with something I have been working on: a stochastic epidemic simulation of a disease outbreak. 

Link here: https://knaticat.github.io/Epidemic-Simulator/

It is an educational attempt to visualize how certain precautions like better social distancing, better hygiene, proper quarantine centers, travel restrictions can account for how drastically an epidemic unfolds. I better warn you first that it would never be close to anything how an actual disease spreads like the current pandemic COVID-19 (an epidemiologist can help). What I want is to provide you with a better intuition of how little precaution and care can help lower the risk of infection while letting you play around with the simulation. In my project, I also aimed to bring up how such epidemics are dangerous to senior citizens (60 yrs or above)/ immuno-compromised patients. The simulation handles this risk by keeping a higher mortality rate for such category of people.

The Model

The simulation is based upon the SIRD (Susceptible Infected Recovered Dead) model of disease spread. Considering a population of individuals, we divide the community such that there are people who are susceptible to getting the disease, the infected ones, the people who have recovered and sadly who die. Each susceptible human has a certain probability of getting infected from the virus when he lies close to a certain radius around an infected individual. In the simulation, you can fiddle around by manipulating this probability and the boundary beyond which a susceptible person contracts the infection (Infection Radius). Larger infection radius can be thought of as poor hygiene/ closer proximity and less social distancing. After a specific interval of time, the infected person either recovers (and is unable to spread the disease further) or dies due to the disease. The chance that an individual dies is governed by which age group he/she lies in and users can change the structure of the community to accommodate varying percent of the age group.

The Simple Case

In the simplest of cases, everybody in the community simply roams around the city with some infected individuals acting as a source of infection. The graph shows the spread of infection, notice you can always scroll around the chart to display more data points.

Social Distancing

Introducing “Social Distancing” a strategy many of you might be familiar with from Social Media, televisions or the newspapers. In the simulation, I have added this as a repulsion force experienced if an individual seems to approach closer to the infection radius of any fellow individual. Tweak around!

Market Mode

But you may argue people usually don’t roam cluelessly around the city as depicted in the Simple case. People tend to gather around a central location, a common destination. The market mode takes care of this. Run the simulation and find out how such crowding helps in the drastic spread of the infection.

Quarantine Mode

Now the most effective approach: Identify and isolate infected humans. The Quarantine mode when pressed isolates infected individuals and puts them in quarantine. I have tried to be realistic and given a probability that measures whether an infected person gets quarantined. This accounts for cases in which a person may be infected but is not isolated as he/she does not show symptoms of the disease.

Community Mode

People tend to form communities and with the present age of globalization it perfectly fits to simulate conditions of travel among different communities. The community mode organizes people into four communities and after some interval of days individuals travel across other communities. 

Lastly, I hope you get the message to take proper care of the older people in your family in this present time. Articles for further readings are attached below.

Further Readings:

  1. https://www.washingtonpost.com/graphics/2020/world/corona-simulator/
  2. https://meltingasphalt.com/interactive/outbreak/
  3. https://youtu.be/gxAaO2rsdIs
  4. https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology


Shoubhik C. Banerjee

Department of Biological Science

IISER Bhopal

About the author: Shoubhik is currently studying in IISER Bhopal.He is a 3rd year BS-MS student.He is fond of computational biology. Shoubhik is one of the chess champions in Nagpur,Maharastra. He loves to read novels and write science blogs.He is also a writer of an interesting series published in The Qrius Rhino page. Currently he is developing different web pages on Biological Sciences out of his own interest.

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