In the clouds
©Sourendu Gupta

Indian data resources

  1. Ministry of Health and Family Welfare
  2. TIFR-IISc COVID-19 website
  3. ISRC: a multi-institutional collaboration of Indian Scientists
  4. Wikipedia timeline for India
  5. Non-governmental India COVID-19 tracker

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A very short introduction to epidemiology

I wrote an explainer about a model of epidemics: one that has worked for the Black Death in Europe and Influenza in New York. The aim is to explain the meaning of terms like the basic reproduction rate (R0), the case fatality ratio, etc, and to give the underlying mathematical reasoning why social distancing causes flattening of the curve. This only requires mathematics at the level of calculus.

[PDF] Authors: Sourendu Gupta (TIFR)

Estimating the number of COVID-19 infections

In India the COVID-19 infected population has not yet been accurately established. As always in the early stages of any epidemic, the need to test serious cases first has meant that the population with asymptomatic or mild sub-clinical symptoms has not yet been analyzed. Using counts of fatalities, and previously estimated parameters for the progress of the disease, we give statistical estimates of the infected population. We suggest a Bayesian method for estimating epidemiological parameters for COVID-19 in different locations within a few days, so adding to the information required for gauging the success of public health interventions.

[PDF] Authors: Sourendu Gupta (TIFR) and R. Shankar (IMSc)

Copyright: Sourendu Gupta ; Last modified on 26 Feb, 2024.
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