Mapping influenza activity in emergency departments in France using Bayesian model-based geostatistics.

Juliette Paireau,Céline Caserio‐Schönemann,Daniel Lévy‐Bruhl,Camille Pelat,Yann Le Strat,Simon Cauchemez,Isabelle Pontais

doi:10.1111/irv.12599

Juliette Paireau, Céline Caserio‐Schönemann + Show 5 more

Open Access

https://doi.org/10.1111/irv.12599

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Abstract

BackgroundMaps of influenza activity are important tools to monitor influenza epidemics and inform policymakers. In France, the availability of a high‐quality data set from the Oscour® surveillance network, covering 92% of hospital emergency department (ED) visits, offers new opportunities for disease mapping. Traditional geostatistical mapping methods such as Kriging ignore underlying population sizes, are not suited to non‐Gaussian data and do not account for uncertainty in parameter estimates.ObjectiveOur objective was to create reliable weekly interpolated maps of influenza activity in the ED setting, to inform Santé publique France (the French national public health agency) and local healthcare authorities.MethodsWe used Oscour® data of ED visits covering the 2016‐2017 influenza season. We developed a Bayesian model‐based geostatistical approach, a class of generalized linear mixed models, with a multivariate normal random field as a spatially autocorrelated random effect. Using R‐INLA, we developed an algorithm to create maps of the proportion of influenza‐coded cases among all coded visits. We compared our results with maps obtained by Kriging.ResultsOver the study period, 45 565 (0.82%) visits were coded as influenza cases. Maps resulting from the model are presented for each week, displaying the posterior mean of the influenza proportion and its associated uncertainty. Our model performed better than Kriging.ConclusionsOur model allows producing smoothed maps where the random noise has been properly removed to reveal the spatial risk surface. The algorithm was incorporated into the national surveillance system to produce maps in real time and could be applied to other diseases.

Highlights

At least three million people are severely affected by seasonal influenza each year, leading to substantial morbidity and mortality and inducing important stress on healthcare structures.[1]
We propose to rely on an alternative statistical approach, Bayesian model-based geostatistics (MBG), a class of generalized linear mixed models, with a multivariate normal random field as a spatially autocorrelated random effect
We developed an algorithm to routinely produce weekly maps that can be used as surveillance, decision-making and communication tools, and integrated it into MASS, a web application used at Santé publique France for monitoring influenza activity.[13]

Summary

Background

Maps of influenza activity are important tools to monitor influenza epidemics and inform policymakers. In France, the availability of a high-quality data set from the Oscour® surveillance network, covering 92% of hospital emergency department (ED) visits, offers new opportunities for disease mapping. Traditional geostatistical mapping methods such as Kriging ignore underlying population sizes, are not suited to non-Gaussian data and do not account for uncertainty in parameter estimates. Objective: Our objective was to create reliable weekly interpolated maps of influenza activity in the ED setting, to inform Santé publique France (the French national public health agency) and local healthcare authorities. Using R-INLA, we developed an algorithm to create maps of the proportion of influenza-coded cases among all coded visits. Maps resulting from the model are presented for each week, displaying the posterior mean of the influenza proportion and its associated uncertainty. KEYWORDS geographic mapping, influenza, public health surveillance, spatial analysis.

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