  The main goal of this work is to present a new model able to deal with
potentially misreported continuous time series. The proposed model is able to
handle the autocorrelation structure in continuous time series data, which
might be partially or totally underreported or overreported. Its performance is
illustrated through a comprehensive simulation study considering several
autocorrelation structures and two real data applications on human
papillomavirus incidence in Girona (Catalunya, Spain) and COVID-19 incidence in
the Chinese region of Heilongjiang.
