Model Assumptions

  1. All data has been sourced from https://api.covid19india.org
  2. Source Code (now open-sourced on GitHub): https://github.com/seirforindia/seirdistrictmodel
  3. The base model used has been the SEIR Model.
  4. Some of the standard SEIR Model assumptions include:
    1. On any given day, an individual X is equally likely to contact any individual Y from the population sample ie expects homogeneous mixing.
    2. The disease’s incubation process is standard across all individuals in the population sample.
    3. Every individual from the population sample is equally susceptible to contracting the disease.
    4. Linearity in the Δinfectious in relation with total number of infectious individuals.
    5. Ignores births.
  5. All fatalities come from hospitals, and all fatal cases are admitted to hospitals immediately after the infectious period.
  6. Modifications to the SEIR Model include:
    1. The SEIR approach assumes a starting base transmission rate (R) and key parameters as the initial conditions.
    2. Fits the Transmission Rate (Rt) to real-time data on a daily basis (model predictions are updated daily). This allows the model to be more flexible to on-ground interventions, like the India Lockdown 1.0 (25 Mar, 2020), Lockdown 2.0, Lockdown 3.0, Lockdown 4.0, Unlock 1.0+, and the creation of containment zones.
    3. Categorises the population by age groups in the S, E, I, R compartments to increase granularity and therefore allow defining the key parameters like hospitalisation rate, mortality rate by age groups as opposed to a flat rate for the entire population.
    4. Defines model configurations on a global and nodal level. This allows node-specific parameters to override global configurations.
    5. India Model and state-level models were obtained as an aggregation of many district-level models which are connected in a network. Connected district-level models allow for the migration of population from one model system to another model system and thereby simulating the migration patterns. People from one model system can migrate from any model state (S,E,I,R) to another model system in any of its states.
  7. Key features of the India COVID-19 SEIR Model include:
    1. Runs at granular levels, e.g. district (Separate node-specific zones can also be created)
    2. Optimizes the prediction to match the actual reported infections daily
    3. State model is not an aggregation of all district models
    4. National model is an aggregation of all state models
    5. Provides 15-30 day district-wise (or zone-wise) predictions
  8. Mortality Rate and Test Positivity Rate:
    1. Mortality Rate Definition: (Cumulative deaths reported till date) / (Cumulative infected cases reported, 15 days ago)
    2. Test Positivity Rate Definition: (Total positive cases reported in last 5 days) / (Total tests conducted in last 5 days)
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