Can mathematical modeling help us in keeping the individual
spread – or basic reproduction number - of COVID 19 to less than 1?
By: Ringo Bones
Since COVID 19 transmission started in late January 2020,
the use of mathematical modeling has been at the forefront of shaping the
decisions around different non-pharmaceutical interventions to confine the
spread of the virus. Mathematical modeling can be used to understand how a
virus spreads within a population. The essence of mathematical modeling lies in
writing down a set of mathematical equations that mimic reality. These are then
solved for certain values of the parameters within the equations.
The solutions of the
mathematical model can be refined when we use information that we already know
about the virus spread, for example, available data on reported number of
infections, the reported number of hospitalizations or the confirmed number of
deaths due to the infection. This process of model refinement – or calibration –
can be done a number of times until the solutions of the mathematical equations
agree with what we already know about the virus spread. The calibrated model can
then be used to tell us more about the future behavior of the virus spread.
One outcome of mathematical models is the predicted epidemic
curve representing the number of infections caused by the virus over time.
Using different parameters in the model, which may illustrate different
interventions, or calibrating the model against different data, can change the
predicted epidemic curve.
Mathematical modeling is a powerful tool for understanding transmission
of COVID 19 and exploring different scenarios. But, instead of focusing on
which model is correct, we should accept that “one model can’t answer it all”
and that we need more models that answer complementary separate questions that
can piece together the jigsaw and halt the COVID 19 spread.
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