Researchers at the Indian Institute of Technology in Delhi have developed a web-based dashboard for predicting the spread of COVID-19 in India.
The institute on Friday said that the mobile-friendly dashboard, PRACRITI (PRediction and Assessment of CoRona Infections and Transmission in India) gives detailed State-wise and district-wise predictions of COVID-19 cases in India for a three-week period, which is updated on a weekly basis.
The institute said that researchers believe that such a platform will be highly useful for healthcare organisations as well as local and central authorities to efficiently plan for different future scenarios and resource allocation.
Researchers said that a key parameter of interest on COVID-19 is the basic reproduction number (R0), pronounced ‘R naught’ and its countrywide variability. “R0 refers to the number of people to whom the disease spreads from a single infected person. For instance, if an active COVID-19 patient infects two uninfected persons, the R0 value is two. Reduction of R0 is key in controlling and mitigating COVID-19 in India,” the researchers added.
PRACRITI provides the R0 values of each district and State based on data available from the Ministry of Health and Family Welfare, National Disaster Management Authority, and the World Health Organization.
N.M. Anoop Krishnan, Civil Engineering Department, IIT Delhi, said: “Getting the district-wise R0 value is crucial as this will enable authorities to know the exact rate of spread in India locally.” His colleague Hariprasad Kodamana, Chemical Engineering Department, said that the three-week prediction provided by the dashboard can be of immense help to policymakers for planning strategic interventions for controlling COVID-19 spread.
The model also accounts for the effect of different lockdown scenarios such as the effect of locking down district boundaries, and implementing different levels of lockdown within a district. The distinguishing feature of the model, the institute said, is the inclusion of the effect of movement of population across district/State boarders in the spread of COVID-19.
