Dataset Information

Estimated Covid-19 burden in Spain: ARCH underreported non-stationary time series.

ABSTRACT:

Background

The problem of dealing with misreported data is very common in a wide range of contexts for different reasons. The current situation caused by the Covid-19 worldwide pandemic is a clear example, where the data provided by official sources were not always reliable due to data collection issues and to the high proportion of asymptomatic cases. In this work, a flexible framework is proposed, with the objective of quantifying the severity of misreporting in a time series and reconstructing the most likely evolution of the process.

Methods

The performance of Bayesian Synthetic Likelihood to estimate the parameters of a model based on AutoRegressive Conditional Heteroskedastic time series capable of dealing with misreported information and to reconstruct the most likely evolution of the phenomenon is assessed through a comprehensive simulation study and illustrated by reconstructing the weekly Covid-19 incidence in each Spanish Autonomous Community.

Results

Only around 51% of the Covid-19 cases in the period 2020/02/23-2022/02/27 were reported in Spain, showing relevant differences in the severity of underreporting across the regions.

Conclusions

The proposed methodology provides public health decision-makers with a valuable tool in order to improve the assessment of a disease evolution under different scenarios.

SUBMITTER: Morina D

PROVIDER: S-EPMC10043853 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Estimated Covid-19 burden in Spain: ARCH underreported non-stationary time series.

Moriña David D Fernández-Fontelo Amanda A Cabaña Alejandra A Arratia Argimiro A Puig Pedro P

BMC medical research methodology 20230328 1

<h4>Background</h4>The problem of dealing with misreported data is very common in a wide range of contexts for different reasons. The current situation caused by the Covid-19 worldwide pandemic is a clear example, where the data provided by official sources were not always reliable due to data collection issues and to the high proportion of asymptomatic cases. In this work, a flexible framework is proposed, with the objective of quantifying the severity of misreporting in a time series and recon ...[more]

PMID: 36977977

Similar Datasets

Project description:Of febrile illnesses in Europe, dengue is second only to malaria as a cause of travellers being hospitalized. Local transmission has been reported in several European countries, including Spain. This study assesses the evolution of dengue-related admissions in Spain in terms of time, geographical distribution and individuals' common characteristics; it also creates a predictive model to evaluate the risk of local transmission. This is a retrospective study using the Hospital Discharge Records Database from 1997 to 2016. We calculated hospitalization rates and described clinical characteristics. Spatial distribution and temporal behaviour were also assessed, and a predictive time series model was created to estimate expected cases in the near future. Figures for resident foreign population, Spanish residents' trips to endemic regions and the expansion of Aedes albopictus were also evaluated. A total of 588 dengue-related admissions were recorded: 49.6% were women, and the mean age was 34.3 years. One person died (0.2%), 82% presented with mild-to-moderate dengue and 7-8% with severe dengue. We observed a trend of steady and consistent increase in incidence (P < 0.05), in parallel with the increase in trips to dengue-endemic regions. Most admissions occurred during the summer, showing significant seasonality with 3-year peaks. We also found important regional differences. According to the predictive time series analysis, a continuing increase in imported dengue incidence can be expected in the near future, which, in the worst case scenario (upper 95% confidence interval), would mean an increase of 65% by 2025. We present a nationwide study based on hospital, immigration, travel and entomological data. The constant increase in dengue-related hospitalizations, in combination with wider vector distribution, could imply a higher risk of autochthonous dengue transmission in the years to come. Strengthening the human and vector surveillance systems is a necessity, as are improvements in control measures, in the education of the general public and in fostering their collaboration in order to reduce the impact of imported dengue and to prevent the occurrence of autochthonous cases.

Dataset Information

Estimated Covid-19 burden in Spain: ARCH underreported non-stationary time series.

Background

Methods

Results

Conclusions

Publications

Estimated Covid-19 burden in Spain: ARCH underreported non-stationary time series.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets