IISc team develops six-dimensional COVID-19 pandemic model; predicts 66 lakh total cases by 30 Sept in 'worst case' scenario
The best-case scenario predicts a more conservative estimate of 26 lakh total cases and 4.5 lakh ongoing cases by 30 September.
A worst-case scenario prediction for coronavirus case in India developed by a team of researchers at the Indian Institute of Science in Bengaluru shows that the country could see the total number of cases rise to over 66 lakh by 30 September this year at the current trend of relaxation to lockdown rules.
The interactive prediction model also accounts for the "best-case" scenario, in which the number of active/ongoing COVID-19 cases starts to fall by mid-September. The best-case model predicts a more conservative estimate of 26 lakh total cases, and 4.5 lakh ongoing cases by 30 September.
The prediction model is a six-dimensional, data-based computational model for epidemics that has been adapted to model the COVID-19 pandemic in India by Professors Sashikumaar Ganesan and Deepak Subramani at the Department of Computational and Data Sciences at IISc Bangalore.
The trend figures, researchers said on their website, are "business as usual" predictions, representing the pandemic evolving in different directions, and considering different degrees of relaxation to lockdown rules. It considers weekly lockdowns, one on Sundays only, and another on Sundays and Wednesdays. The Sundays-only complete lockdown scenario shows 2,40,000 active cases on top of the figures for the Sundays and Wednesdays lockdown as of 30 September.
Apart from the predictive model, researchers have also made observations about how specific measures have affected the COVID-19 infection curve in India, in national and state-specific scenarios. For instance, the recovery rate has increased since 3 May 2020 hand-in-hand with a drop in new infections. This coincided with access to better medical care and the effects of timely quarantine for infected COVID-19 patients.
The researchers claim that the model predicts region-wise and age-wise COVID-19 spread accurately. It also includes predictions of infected people by region, age of infected individuals, number of days since the start of the infection and severity, over time.
The data fed into the model, the researchers said, includes "immunity, pre-medical history, effective treatment, point-to-point movement of infected people (by air, train, etc), interactivity (spread in the community), hygiene and physical distancing".
The model has been pre-published in arXiv and is awaiting peer review. If it survives this test of scrutiny, the model could come in useful to frame policies for effective periodic lockdowns and staggered opening of educational institutions and public facilities. The insights, the team added, could also be useful in planning public health policies like quarantine rules, hospital beds and vaccination/treatment schedules.
A "one or two-day lockdown per week (e.g., Sunday, Sunday & Wednesday etc) with complete compliance [and] adequate social distancing during other days is effective to reduce the spread", they observed. They also outline their finding that contact tracing, quarantine and social distancing are key to contain the spread in the absence of a working vaccine.
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