SUTRA: A Novel Approach to Modelling Pandemics with Applications to COVID-19

Abstract

In this paper, we present a new mathematical model for pandemics that have asymptomatic patients, called SUTRA. The acronym stands for Susceptible, Undetected, Tested (positive), and Removed Approach. There are several novel features of our proposed model. First, whereas previous papers have divided the patient population into Asymptomatic and Infected, we have explicitly accounted for the fact that, due to contact tracing and other such protocols, some fraction of asymptomatic patients could also be detected; in addition, there would also be large numbers of undetected asymptomatic patients. Second, we have explicitly taken into account the spatial spread of a pandemic over time, through a parameter called "reach." Third, we present numerically stable methods for estimating the parameters in our model.We have applied our model to predict the progression of the COVID-19 pandemic in several countries. Where data on the number of recovered patients is available, we predict the number of active cases as a function of time. Where recovery data is not available, we predict the number of daily new cases. We present our predictions for three countries with quite distinct types of disease progression, namely: (i) India which has had a smooth rise followed by an equally smooth fall-off in the number of active cases, (ii) Italy, which has witnessed multiple peaks in the number of active cases, and has also witnessed multiple "phases" of the pandemic, and (iii) the USA which has erratic recovery data. In all cases, the predictions closely match the actually observed outcomes.

COVID-19 India Dataset: Parsing COVID-19 Data in Daily Health Bulletins from States in India

“…3. We anticipate that this data can be helpful in (A) validating or extending models developed for other countries to India (Friedman et al, 2021;Borchering et al, 2021), (B) developing pandemic models which integrate additional variables available in our dataset (Hethcote, 2000;Agrawal et al, 2021;Adiga et al, 2020;Bhaduri et al, 2020), (C) Understanding various aspects of the pandemic (Ray et al, 2020;Ghosh et al, 2020), among others.…”

Deep Learning-based Spatially Explicit Emulation of an Agent-Based Simulator for Pandemic in a City

“…They use detailed attributes pertaining to the specific demographics and social contact structure in France and implement a statistical model for city spaces and transport. [Agrawal et al(2021)] came up with the SUTRA compartmental model which includes more compartments like asymptomatic cases.…”

In search of an optimal public policy in a pandemic: The question of lives versus of livelihood

“…The parameters of relevance, such as rate of infection and its severity, death rate and other factors such as demography, health infrastructure etc. determine the evolution of the disease which has been found to differ across countries (please see Agarwal et al, 2022 for a comparison of India and other countries and Bhattacharjee et al, 2022, for states of USA). However, parameters of our model can be amended to characterize any other country with different institutional structure that includes demographic structure, population density, rural-urban divide etc.…”