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CDS releases first version of COVID-19 IISC model

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New Delhi, May 31: The CDS IISC Bangalore have released the first version of the COVID-19 IISC model which is based in a high dimensional population balance equation.

CDS releases first version of COVID-19 IISC model

The aim of this project is to develop and apply a predictive computational model for the COVID-19 epidemic based on a high-dimensional population balance modelling.

The team comprises Prof. Sashikumaar Ganesan, Prof. Deepak Subramani and Chris Francis.

Key Features:

  • A six-dimensional population balance predictive computational model for an epidemic.
  • Unlike the existing (Compartment or Network) models, proposed model predicts the distribution of infected population across the region, the age of the infected people, the day since infection, and the severity of infection, over a period of time.
  • Incorporates the immunity, pre-medical history, effective treatment, point-to-point movement of infected population (e.g., by air, train etc), interactivity (community spread), hygiene and the social distancing of the population.
  • Finite element operator-splitting scheme for the high-dimensional PDE.
  • Proposed model can be used to predict region-wise and age-wise COVID-19 spread accurately, and consequently it can be used to frame policies on periodic lockdown, staggered opening of educational institutions and public facilities.

Computational Model

  • Data* between 23 Mar and 3 May, 2020 is used partially to tune the parameters of the data-driven model. An update is planned to tune the parameters based on the data between 23 Mar and 26 May, 2020.
  • State-wise results are computed with the parameters of national trend to compare the performance of the respective state with the national trend. See Discussion for interpretation.
  • Quarantine of Active Cases so as to prevent new infections is the key to contain the pandemic. An adaptive quarantine function in our model ensures that infected population is quarantined based on their infection level (showing symptoms) and based on latest published literature on how the infection spreads from infected population.
  • Total Recovered includes both recovered and dead populations.
  • The severity of the infection is taken into consideration while modeling the infectious death rate function (see the rate functions in the model).

Observations:

  • Current Trend: Hits a peak of 6.55 lakh 'Active Cases' in the first week of October 2020. Further, there will be around 35 thousand 'Active Cases' and 34.9 lakh 'Total' infected cases at the end of March 2021. Note that the Current Trend is based on the data between 23 Mar and 3 May, 2020. An update is planned to get the "Current Trend" based on the data between 23 Mar and 26 May, 2020.
  • Better Scenario: Hits a peak of 4.68 lakh Active Cases in the second week of September 2020. Further, there will be around 8 thousand 'Active Cases' and 22.4 lakh 'Total' infected cases at the end of March 2021.
  • Worse Scenario: Hits a peak of 35 lakh 'Active Cases' in the last week of Jan 2021. Further, there will around 28.5 lakh 'Active Cases' and 2.16 crore 'Total' infected cases at the end of March 2021.
  • Current Trend with Sunday Lockdown:Hits a peak of 3.05 lakh 'Active Cases' in the second week of September 2020. Further, there will be around 12 thousand 'Active Cases' and 17.1 lakh 'Total' infected cases at the end of March 2021.
  • Current Trend with Sun & Wed Lockdown: Hits a peak of 1.42 lakh Active Cases in the second week of August 2020. Further, there will around 3.5 thousand 'Active' Cases and 8.3 lakh 'Total' infected cases at the end of Mar 2021.

Discussion:

  • In order to achieve and follow the Current Trend prediction for the next one year, people should maintain the same or even better level of social distancing as maintained during 23 Mar - 3rd May 2020. It is assumed that awareness increases with time and there is more compliance of social distancing and other norms. Also, we anticipate that more members of the susceptible population improve their hygiene practice and immunity levels.
  • Until the development of vaccines, social distancing and other practices to reduce interaction among people (such as avoiding mass gathering etc.) are the key tools to contain the spread of COVID-19. As such, public awareness of these practices through several modes (advertisements through TV, radios, new papers, social media, etc.) is crucial.
  • The Better Scenario and Worse Scenario model the infection spread when there is better and worse compliance of social distancing and other norms among susceptible population.
  • Short, periodic (e.g., one or two days per week) lockdown with complete compliance to stay at home and avoid interaction helps reduce infection. This must be combined with increased social distancing and quarantining of suspected population during non-lockdown phases. The 'Sunday Lockdown' and 'Sun & Wed Lockdown' scenarios model these strategies.
  • Since this is an active situation with regular ongoing interventions and policy changes from State and Central governments, we do not predict each state individually and the state numbers have to be interpreted as follows.
  • In each scenario, the state numbers are computed with the national parameters. This is done to compare the actual data of the state with the national trend.
  • For example, Kerala, Karnataka, Uttar Pradesh (and others) have done better than the national trend. Whereas, Maharashtra, Tamil Nadu, Madhya Pradesh (and others) have done worse than the national trend. Some states such as Rajasthan, Andhra Pradesh (and others) have done similar to the national trend.
  • The range of all predicted scenarios (some shown here, others not shown) can be viewed by enabling the uncertainty region in the time series plots.

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