India COVID-19 SEIR Model: A Technical Introduction

#FlattenTheCurve

When India locked down back in March, there was just one question on everyone’s mind: How can we flatten the curve? 

Chiseled for ~90 days, the India COVID-19 SEIR model, which went live mid June, 2020, is the digital public good to meet this purpose. It pulls publicly available data to predict infection trends across all of India’s districts over the next 15-30 days.

Creating the India COVID-19 SEIR Model

The India COVID-19 SEIR Model is developed on the foundations of SEIR, a mathematical model popular in the study of infectious diseases. This model labels each individual in the given population as either Susceptible, Exposed, Infectious, or Recovered. Typically the entire population starts from the Susceptible state and then moves from Exposed to Infectious to Recovered.  Each individual is assumed to be equally susceptible to the disease and equally likely to recover. The model assumes a closed population i.e. births and migrants are ignored. This implies that the number of individuals in the Susceptible bucket is highest at start of the simulation, and can only decrease thereafter.

The India COVID-19 SEIR Model is a SEIR Model with modifications that increase granularity. The crucial difference between the preexisting SEIR Model and the current one is this: the modified model categorises the population by granular districts and age. Given India’s large population of young people, this is critical. Moreover, this modification allows the model to be run on a district-level (e.g. Mumbai, Chennai, Bangalore), grouping populations with similar behavior patterns and environmental circumstances while also catering to district-specific on-ground interventions. Simulations run on the district-levels are then aggregated to provide metrics for the entire population i.e. India.

The model is optimised — a mathematical optimiser ensures that the prediction curves adapt to fresh data each day. So, the model self-corrects every night when the most recent statistics flow in.

What the data really means

Like any predictive model, the curves on the India COVID-19 SEIR Model will undergo constant flux. The absolute numbers are less important than the trend. The model’s power lies in its ability to predict early trends, identify potential hotspots in the next 15-30 days, and plan for resource surge, especially in terms of hospital beds, PPE, medical equipment, ventilators, and emergency supplies.

The model’s power lies in each one of us. Not sure how to get started? Check our blogs for Use Cases and use the India COVID-19 SEIR Model!

Pass the word around. #FlattenTheCurve.

About the contributors: The blog post is co-authored by our volunteers Nikhila Natarajan and Yashvi Jaju.

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