  India has been hit by a huge second wave of Covid-19 that started in
mid-February 2021. Mumbai was amongst the first cities to see the increase. In
this report, we use our agent based simulator to computationally study the
second wave in Mumbai. We build upon our earlier analysis, where projections
were made from November 2020 onwards. We use our simulator to conduct an
extensive scenario analysis - we play out many plausible scenarios through
varying economic activity, reinfection levels, population compliance,
infectiveness, prevalence and lethality of the possible variant strains, and
infection spread via local trains to arrive at those that may better explain
the second wave fatality numbers. We observe and highlight that timings of peak
and valley of the fatalities in the second wave are robust to many plausible
scenarios, suggesting that they are likely to be accurate projections for
Mumbai. During the second wave, the observed fatalities were low in February
and mid-March and saw a phase change or a steep increase in the growth rate
after around late March. We conduct extensive experiments to replicate this
observed sharp convexity. This is not an easy phenomena to replicate, and we
find that explanations such as increased laxity in the population, increased
reinfections, increased intensity of infections in Mumbai transportation,
increased lethality in the virus, or a combination amongst them, generally do a
poor job of matching this pattern. We find that the most likely explanation is
presence of small amount of extremely infective variant on February 1 that
grows rapidly thereafter and becomes a dominant strain by Mid-March. From a
prescriptive view, this points to an urgent need for extensive and continuous
genome sequencing to establish existence and prevalence of different virus
strains in Mumbai and in India, as they evolve over time.
