Spatio-temporal small area surveillance of the COVID-19 pandemic

Martínez-Beneito, Miguel A.; Mateu, Jorge; Botella-Rocamora, Paloma

doi:10.1016/j.spasta.2021.100551

Cited by 5 publications

(5 citation statements)

References 13 publications

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“…Modifications (including in EpiNow2) have been made to account for weekly noise in the incidence data by explicitly including a day of the week effect. [ 18 , 25 – 27 , 31 , 34 , 35 , 47 , 52 , 54 , 56 ]…”

Section: Resultsmentioning

confidence: 99%

“…[ 32 ] Some approaches model transmission in different regions using linear predictors (which can include environmental, demographic, or intervention related factors), as in Epidemia, where parameters can be fixed or varying by location. [ 37 , 46 , 56 , 64 ]…”

Section: Resultsmentioning

confidence: 99%

“…Beyond accounting for imported cases [ 14 , 63 ] and estimating R t across different regions at the same time, [ 18 , 25 , 27 , 31 , 37 , 46 , 47 , 52 , 54 ] we found no R packages that explicitly addressed spatial interactions. This means that, aside from bespoke studies, [ 36 , 40 , 53 , 56 , 58 , 59 , 64 , 66 ] important aspects of the spatial structure in transmission may have been overlooked. For instance, to our knowledge, there is no ready-to-use tool to analyse how easing restrictions in an area may influence transmission in neighbouring locations.…”

Section: Discussionmentioning

confidence: 99%

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Real-time estimation of the epidemic reproduction number: Scoping review of the applications and challenges

2022

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The time-varying reproduction number (Rt) is an important measure of transmissibility during outbreaks. Estimating whether and how rapidly an outbreak is growing (Rt > 1) or declining (Rt < 1) can inform the design, monitoring and adjustment of control measures in real-time. We use a popular R package for Rt estimation, EpiEstim, as a case study to evaluate the contexts in which Rt estimation methods have been used and identify unmet needs which would enable broader applicability of these methods in real-time. A scoping review, complemented by a small EpiEstim user survey, highlight issues with the current approaches, including the quality of input incidence data, the inability to account for geographical factors, and other methodological issues. We summarise the methods and software developed to tackle the problems identified, but conclude that significant gaps remain which should be addressed to enable easier, more robust and applicable estimation of Rt during epidemics.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Real-time estimation of the epidemic reproduction number: Scoping review of the applications and challenges

2022

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“…In addition to its computational convenience, this approach is characterized by compact support, providing a particularly sensible fit of the incidence rates at the extremes of the study period, specifically at the end of that period. This is particularly convenient for epidemiological surveillance purposes ( 24 ). The optimal number of approximation degrees was achieved by minimizing the mean squared error (MSE).…”

Section: Methodsmentioning

confidence: 99%

COVID-19 hospitalizations and patients' age at admission: The neglected importance of data variability for containment policies

Azzolina

Comoretto

Lanera

et al. 2022

Front. Public Health

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IntroductionAn excess in the daily fluctuation of COVID-19 in hospital admissions could cause uncertainty and delays in the implementation of care interventions. This study aims to characterize a possible source of extravariability in the number of hospitalizations for COVID-19 by considering age at admission as a potential explanatory factor. Age at hospitalization provides a clear idea of the epidemiological impact of the disease, as the elderly population is more at risk of severe COVID-19 outcomes. Administrative data for the Veneto region, Northern Italy from February 1, 2020, to November 20, 2021, were considered.MethodsAn inferential approach based on quasi-likelihood estimates through the generalized estimation equation (GEE) Poisson link function was used to quantify the overdispersion. The daily variation in the number of hospitalizations in the Veneto region that lagged at 3, 7, 10, and 15 days was associated with the number of news items retrieved from Global Database of Events, Language, and Tone (GDELT) regarding containment interventions to determine whether the magnitude of the past variation in daily hospitalizations could impact the number of preventive policies.ResultsThis study demonstrated a significant increase in the pattern of hospitalizations for COVID-19 in Veneto beginning in December 2020. Age at admission affected the excess variability in the number of admissions. This effect increased as age increased. Specifically, the dispersion was significantly lower in people under 30 years of age. From an epidemiological point of view, controlling the overdispersion of hospitalizations and the variables characterizing this phenomenon is crucial. In this context, the policies should prevent the spread of the virus in particular in the elderly, as the uncontrolled diffusion in this age group would result in an extra variability in daily hospitalizations.DiscussionThis study demonstrated that the overdispersion, together with the increase in hospitalizations, results in a lagged inflation of the containment policies. However, all these interventions represent strategies designed to contain a mechanism that has already been triggered. Further efforts should be directed toward preventive policies aimed at protecting the most fragile subjects, such as the elderly. Therefore, it is essential to implement containment strategies before the occurrence of potentially out-of-control situations, resulting in congestion in hospitals and health services.

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“…The analysis of small units of space and time, where decisions often need to be made, is generally characterised by presenting high variability and noise, and traditional approaches may struggle to provide accurate estimates 3 . Using spatial and spatio-temporal disease mapping models, we can overcome many of these challenges by borrowing strength from spatial and temporal neighbours, allowing us to obtain reliable estimates for these small units and to uncover and understand the patterns of disease spread across space and time 4 .…”

Section: Introductionmentioning

confidence: 99%

Bayesian spatio-temporal analysis of the COVID-19 pandemic in Catalonia

Satorra,

Tebé

2024

Sci Rep

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In this study, we modelled the incidence of COVID-19 cases and hospitalisations by basic health areas (ABS) in Catalonia. Spatial, temporal and spatio-temporal incidence trends were described using estimation methods that allow to borrow strength from neighbouring areas and time points. Specifically, we used Bayesian hierarchical spatio-temporal models estimated with Integrated Nested Laplace Approximation (INLA). An exploratory analysis was conducted to identify potential ABS factors associated with the incidence of cases and hospitalisations. High heterogeneity in cases and hospitalisation incidence was found between ABS and along the waves of the pandemic. Urban areas were found to have a higher incidence of COVID-19 cases and hospitalisations than rural areas, while socio-economic deprivation of the area was associated with a higher incidence of hospitalisations. In addition, full vaccination coverage in each ABS showed a protective effect on the risk of COVID-19 cases and hospitalisations.

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Spatio-temporal small area surveillance of the COVID-19 pandemic

Cited by 5 publications

References 13 publications

Real-time estimation of the epidemic reproduction number: Scoping review of the applications and challenges

Real-time estimation of the epidemic reproduction number: Scoping review of the applications and challenges

COVID-19 hospitalizations and patients' age at admission: The neglected importance of data variability for containment policies

Bayesian spatio-temporal analysis of the COVID-19 pandemic in Catalonia

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