  COVID-19 pandemic has reshaped our world in a timescale much shorter than
what we can understand. Particularities of SARS-CoV-2, such as its persistence
in surfaces and the lack of a curative treatment or vaccine against COVID-19,
have pushed authorities to apply restrictive policies to control its spreading.
As data drove most of the decisions made in this global contingency, their
quality is a critical variable for decision-making actors, and therefore should
be carefully curated. In this work, we analyze the sources of error in
typically reported epidemiological variables and usual tests used for
diagnosis, and their impact on our understanding of COVID-19 spreading
dynamics. We address the existence of different delays in the report of new
cases, induced by the incubation time of the virus and testing-diagnosis time
gaps, and other error sources related to the sensitivity/specificity of the
tests used to diagnose COVID-19. Using a statistically-based algorithm, we
perform a temporal reclassification of cases to avoid delay-induced errors,
building up new epidemiologic curves centered in the day where the contagion
effectively occurred. We also statistically enhance the robustness behind the
discharge/recovery clinical criteria in the absence of a direct test, which is
typically the case of non-first world countries, where the limited testing
capabilities are fully dedicated to the evaluation of new cases. Finally, we
applied our methodology to assess the evolution of the pandemic in Chile
through the Effective Reproduction Number $R_t$, identifying different moments
in which data was misleading governmental actions. In doing so, we aim to raise
public awareness of the need for proper data reporting and processing protocols
for epidemiological modelling and predictions.
