  In conducting preliminary analysis during an epidemic, data on reported
disease cases offer key information in guiding the direction to the in-depth
analysis. Models for growth and transmission dynamics are heavily dependent on
preliminary analysis results. When a particular disease case is reported more
than once or alternatively is never reported or detected in the population,
then in such a situation, there is a possibility of existence of multiple
reporting or under reporting in the population. In this work, a theoretical
approach for studying reporting error in epidemiology is explored. The upper
bound for the error that arises due to multiple reporting is higher than that
which arises due to under reporting. Numerical examples are provided to support
the arguments. This article mainly treats reporting error as deterministic and
one can explore a stochastic model for the same.
