COVID-19: JNCASR scientists develop forecasting model to estimate medical inventory requirements
The scientists of the Jawaharlal Nehru Centre for Advanced Scientific Research (JNCASR), an institution under the Department of Science and Technology (DST), tested the model by predicting the number of infections and deaths in Italy and New York state, based on an adaptive algorithm which uses early available data.PTI | New Delhi | Updated: 02-08-2020 18:01 IST | Created: 02-08-2020 18:01 IST
Scientists at the Jawaharlal Nehru Centre for Advanced Scientific Research, Bengaluru have developed a forecasting model to estimate the key aspects of medical inventory requirements during the coronavirus pandemic, a statement said on Sunday. The scientists of the Jawaharlal Nehru Centre for Advanced Scientific Research (JNCASR), an institution under the Department of Science and Technology (DST), tested the model by predicting the number of infections and deaths in Italy and New York state, based on an adaptive algorithm which uses early available data. The predictions closely matched the actual outcomes, it said. They have also carried out a similar exercise for India, where in addition to projecting the number of infections and deaths, they have projected the expected range of critical resource requirements for hospitalisation in a location.
The calculation is required to scale up both the testing capabilities and critical care facilities, which are essential to reduce mortality. The team demonstrated that with this approach, there is a universality to the evolution of the disease across countries, which can then be used to make reliable predictions.
This approach allows for planning of requirements for critical resources such as ICUs and PPEs during the pandemic. The approach is designed for simplicity of interpretation and adaptability over time, the statement said. The model is based on the recent work of a team accepted for publication in the journal 'Physical Review E' in which they showed that uncertainties in parameters and reported infections can be compensated for by using (phase-space) representations.
"This reduces errors, exploiting any universality across geographies which shows similar behaviour, and by a regular weekly update of the predictions made for a month," it said. "It would be extremely relevant for COVID-19, as the disease character and behavioral patterns of people change and affect the efficacy of disease spread and management, in a second-wave, requiring constant alertness on the part of the forecasters," the statement added.
DST secretary Ashutosh Sharma said mathematical modelling and simulations are some of the key tools for understanding, planning and decision making in the time of COVID-19. "This example further brings to the fore the power of collaborations rather than competition among the best of research groups,” he said.
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