  Modeling the spread of COVID-19 is crucial for informing public health
policy. All models for COVID-19 epidemiology rely on parameters describing the
dynamics of the infection process. The meanings of epidemiological parameters
like R_0, R_t, the "serial interval" and "generation interval" can be
challenging to understand, especially as these and other parameters are
conceptually overlapping and sometimes confusingly named. Moreover, the
procedures used to estimate these parameters make various assumptions and use
different mathematical approaches that should be understood and accounted for
when relying on parameter values and reporting them to the public. Here, we
offer several insights regarding the derivation of commonly-reported
epidemiological parameters, and describe how mitigation measures like lockdown
are expected to affect their values. We aim to present these quantitative
relationships in a manner that is accessible to the widest audience possible.
We hope that better communicating the intricacies of epidemiological models
will improve our collective understanding of their strengths and weaknesses,
and will help avoid possible pitfalls when using them.
