Real-time predictive seasonal influenza model in Catalonia, Spain

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.

Fig 1. Flow diagram of pilot study (A) and real-time model (B).

Fig 2. Global observed epidemic curve for week 8 (T) and predicted rates with 95% CI for week 9 (T+1) and 10 (T+2).

The blue curve represent the observed incidence rates from week 40 of 2015 to week 8 of 2016.The red curve represent the predicted incidence rates for week 9 and 10. The pink area represent the 95%CI for predictions. Horizontal lines represent the epidemic intensity levels of influenza for 2015–2016 calculated with MEM.

Fig 3. Observed epidemic intensity levels for week 8 (A) for each region and predicted intensity for week 9 (B) and 10 (C).

(A) Represent Catalonia map in which the observed intensity level during week 8 of 2016 (T) for each region are illustrated: for example, Alt Pirineu presented in week 8 a medium epidemic intensity level of influenza. (B) Show in the same map the predicted intensity level for each region for week 9 (T+1): for example, a high intensity level of influenza was predicted for Barcelona for week 9. (C) Show in the same map the predicted intensity level for each region for week 10 (T+2).

Fig 4. Observed curve vs. Predicted curve one and two week previously (T+1 and T+2, respectively), with 95% CI. Catalonia 2015–2016.

(A) Observed curve is compared to predicted curve and corresponding 95% CI one week before. Horizontal lines represents the epidemic intensity levels for 2015–2016 season. (B) Observed curve is compared to predicted curve and corresponding 95% CI two weeks before. Horizontal lines represents the epidemic intensity levels of influenza for 2015–2016 calculated with MEM.