  Researchers have shown that even simple empirical models stemming from
biological growth modeling have the potential to provide useful information on
the development and severity of ongoing epidemics since they can be employed as
tools for carrying out projections on the size of the affected population,
timing of turning points, as well as best- and worst-case scenarios.
Nevertheless, they commonly exhibit considerable sensitivity to some input
parameters' variance which results in large fluctuations in the generated
projections, thus rendering predictions difficult and even risky. In this work
we examine a novel meta-projections-based approach which allows us to evaluate
the model's current trends and assess whether generated projections are at a
transient or stable state. Meta-projections can be extracted from graphs of
successive estimations of model's parameters and resulting projections, over a
sequence of days being gradually added to the employed model. In other words,
projections are carried out on truncated time series of cumulative numbers of
confirmed cases with increased lengths at each successive evaluation. This
allows us to trace the values of model parameters over a certain period of time
and examine their trends which may converge to specific values for
settled-growth cases or exhibit a changing or even an erratic behavior for
cases that undergo epidemiological transitions and/or are inappropriately
described by the current model instance(s). We have computed meta-projections
and compared our findings for countries at different stages of the epidemic
with stable or unstable behaviors and increasing or decreasing numbers of
confirmed cases. Our results indicate that meta-projections can aid researchers
in assessing the appropriateness of their relevant models and in effect
decrease the uncertainty in their estimations of an epidemic's severity and
development.
