Abstract
Influenza surveillance is critical to monitoring the situation during epidemic seasons and predictive mathematic models may aid the early detection of epidemic patterns. The objective of this study was to design a real-time spatial predictive model of ILI (Influenza Like Illness) incidence rate in Catalonia using one- and two-week forecasts. The available data sources used to select explanatory variables to include in the model were the statutory reporting disease system and the sentinel surveillance system in Catalonia for influenza incidence rates, the official climate service in Catalonia for meteorological data, laboratory data and Google Flu Trend. Time series for every explanatory variable with data from the last 4 seasons (from 2010–2011 to 2013–2014) was created. A pilot test was conducted during the 2014–2015 season to select the explanatory variables to be included in the model and the type of model to be applied. During the 2015–2016 season a real-time model was applied weekly, obtaining the intensity level and predicted incidence rates with 95% confidence levels one and two weeks away for each health region. At the end of the season, the confidence interval success rate (CISR) and intensity level success rate (ILSR) were analysed. For the 2015–2016 season a CISR of 85.3% at one week and 87.1% at two weeks and an ILSR of 82.9% and 82% were observed, respectively. The model described is a useful tool although it is hard to evaluate due to uncertainty. The accuracy of prediction at one and two weeks was above 80% globally, but was lower during the peak epidemic period. In order to improve the predictive power, new explanatory variables should be included.
Highlights
Influenza is one of the biggest public health challenges worldwide
Values of mean temperature, humidity and sunlight for each region were included as explanatory variables for study in two ways: first, the last daily value of temperature, humidity and sunlight for week T was taken into account(Temp, Irr, Hum) and, secondly, a functional variable was created to represent with a single value the evolution of temperature, humidity and sunlight over the last 14 days, (Temp_F, Irr_F, Hum_F)
Before the 2015–2016 season starts, we identified five levels of intensity for this season according to the Moving Epidemic Method (MEM), [39] and based on MDO data from the last five seasons, which helped to identify the epidemic period and influenza intensity during the 2015–2016 season
Summary
Influenza is one of the biggest public health challenges worldwide. Seasonal influenza is a matter of concern due to its attributable excess morbidity and mortality.[1] Demand for health services increases exponentially during the cold season, highlighting the need for efficiently-programmed health policies to coordinate all stakeholders involved in managing the epidemic.[2]. The main mitigating health action during influenza epidemics is the organization of the influenza vaccine supply in the at-risk population.[3] Other activities needed at a policy-maker level during the cold season are the deployment of resources to mitigate the effects of influenza (e.g. antiviral drugs) and the organization of healthcare centres.[4] predictive mathematical models that provide advance information on the intensity of the epidemic are important preparative measures.[5]
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