Estimating the real burden of disease under a pandemic situation: The SARS-CoV2 case

Abstract

The present paper introduces a new model used to study and analyse the severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) epidemic-reported-data from Spain. This is a Hidden Markov Model whose hidden layer is a regeneration process with Poisson immigration, Po-INAR(1), together with a mechanism that allows the estimation of the under-reporting in non-stationary count time series. A novelty of the model is that the expectation of the unobserved process’s innovations is a time-dependent function defined in such a way that information about the spread of an epidemic, as modelled through a Susceptible-Infectious-Removed dynamical system, is incorporated into the model. In addition, the parameter controlling the intensity of the under-reporting is also made to vary with time to adjust to possible seasonality or trend in the data. Maximum likelihood methods are used to estimate the parameters of the model.

Assessing population-sampling strategies for reducing the COVID-19 incidence

“…According to the Spanish health authorities, only around 70% of the infected individuals were detected, which results in a scale factor of 1.42. Other works [ 9 ], estimate a larger scale factor. Fig.…”

New statistical model for misreported data with application to current public health challenges

“…In the Catalan case, the model reveals that more than 74% of the cases in the period 2021/05/16–2021/06/20 were registered. These examples are only used to illustrate the performance of the proposed methodology, but to properly analyze the evolution of an infectious disease with the behaviour shown by Covid-19 models that take the spreading dynamics into account are probably more appropriate (see 33 , 34 for instance).…”

“…The methods introduced in this paper could certainly be considered as a starting point to develop more general methods, able to deal with non-stationary continuous time series, adapting the ideas developed in 33 for the discrete case. From the applied point of view, it would be very interesting to use these kind of models to analyze other issues that might be potentially underreported and to analyze more thoroughly the examples used to illustrate the performance of the discussed models.…”

Hospitalization as reliable indicator of second wave COVID-19 pandemic in eight European countries

“…The lack of large-scale diagnostic tests (swabs) mainly due to economic and logistic reasons adopted by the different governmental authorities could cause a serious underestimation of the actual number of COVID-19 cases in the population (Perico et al, 2020; Riccardo et al, 2020; Whittaker et al, 2020), namely in case of high-incidence of the epidemic (Fan et al, 2020; Stefanelli et al, 2020). The problem of under-reporting the total number of actual infected patients distorts the epidemic trends (Fernández-Fontelo et al, 2020), making government measures potentially ineffective or even out of time. On the other hand, mortality rates suffer from a time delay compared to the trend of the epidemic.…”