Data analytics ensures transparency and consistency in the choice of the adopted parameters. In contrast, novelty and inertia enable a comprehensible approach to estimate under-report.
COVID-19 causes a rupture in the SARI series inertial behavior, changing the statistical properties of the time series. Event detection techniques identify this rupture. Assuming that the change occurred is due to COVID-19, the computed novelty then corresponds to estimates of the values of cases and deaths from the disease. From this, under-reporting rates were computed for both cases and deaths.
The rates of under-reporting of cases were estimated for all states except for Mato Grosso do Sul. The values vary between 0.124 (Espírito Santo) and 1.811 (Minas Gerais), thus reaching almost two under-reported cases for each notified case. The novelty observed by our SARI analysis in the states is lower, in their majority, compared to the cases reported by the Ministry of Health. It is expected since many diagnosed cases of COVID-19 are asymptomatic.
Under-reporting rates for deaths were estimated for 25 of the 27 states in Brazil. For the states of Acre and Mato Grosso do Sul, the under-report was not verified and, therefore, death rates were not calculated for these states. Rates vary between 0.072 (Espírito Santo) and 0.983 (the Rio Grande do Sul), thus indicating that there may be more than twice as many deaths as reported. The novelties for deaths cases using SARI analysis in the states are commonly higher when compared to the deaths notified by the Ministry of Health. It helps to corroborate the justification that the death rates are better estimated since SARI covers most of the individuals who die.
No pattern of behavior was observed for the events detected or for the evolution and values of under-reporting rates between states in the same Brazilian region. Therefore, it is observed that the states behave in different and independent ways concerning the occurrence/notification of COVID-19.
The methodology developed in this paper can be adapted to support the under-report rate for other diseases as long as it exists a proxy variable that presents an inertial behavior. Besides, the methodology is also able to support the detection of outbreaks, as it uses both the combination of event detection and inertia concepts.