Getting good estimates for R0 is key to answering such questions with
accuracy. But R0 is notoriously tricky to nail down. It depends not
only on the biological characteristics of a virus—which are a mystery at
the beginning of an outbreak—but also on understanding how often people
come into contact with one another. Faced with uncertainty, modelers
have to make assumptions about the factors that determine human
movement, which can limit the precision of their models and the accuracy
of the predictions they generate. With
some notable exceptions, R0 forms a centerpiece in most disease
forecasting models. The metric is often misconstrued as a fixed property
of a pathogen, and it is indeed influenced by biological factors such
as mode of transmission that stay more or less constant throughout an
epidemic. But R0 also depends on how often people come into contact with
one another, and that can differ drastically between countries, cities,
or neighborhoods...For that reason,
epidemiologists typically distinguish between two forms of the
reproductive number R: the basic reproductive number R0, which describes
the initial spread of an infection in a completely susceptible
population, and the effective reproductive number, Re, which captures
transmission once a virus becomes more common and as public health
measures are initiated. Re is typically much lower than R0. In the
current pandemic, many policymakers are looking toward Re to gauge
whether their policies reduce viral transmission, .... “What you care about is, can we get the [Re] below one?”
Overall to understand the spread a complex sector by sector analysis is demanded, not a State wide one. As we have demonstrated with the limited data available the propagation is from highly identifiable clusters. No surprises there at all!
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