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.
In this paper, we present a new mathematical model for pandemics that have asymptomatic patients many of whom remain undetected , 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. We present our predictions for countries with three quite distinct types of disease progression, namely: (i) countries where nearly all of population still remains outside the reach of the pandemic , (ii) countries where a reasonable fraction of population is both within and outside the reach , and (iii) countries where nearly all of population s within the reach of the pandemic . In all cases, the predictions closely match the actually observed outcomes.
A
many of whom remain undetected
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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.
R
three countries with
countries with three
R
India which has had a smooth rise followed by an equally smooth fall-off in the number of active cases
countries where nearly all of population still remains outside the reach of the pandemic
R
Italy, which has witnessed multiple peaks in the number of active cases, and has also witnessed multiple "phases" of the pandemic
countries where a reasonable fraction of population is both within and outside the reach
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the USA which has erratic recovery data
countries where nearly all of population s within the reach of the pandemic