OBJECTIVES:
Disease burden and high financial cost of seasonal influenza emphasize the importance of studying the epidemics transmission dynamics. Our aim in this article is to extend the Susceptible Exposed Infectious Recovered (SEIR) model, a well-studied classical compartmental epidemic model, by incorporating socio-environmental factors. Particularly, the potential influence of mass media function and absolute humidity are examined on the model simultaneously.
STUDY DESIGN:
The proposed model is fitted to Center for Disease Control and Prevention (CDC) influenza data of region five of the US for four outbreak seasons. Then, a full-performance comparison between the conventional and extended model is carried out.
METHODS:
Implementing the mass media and climate factors into the classical epidemic models, e.g., Susceptible Infectious Recovered (SIR) and SEIR, is a promising and ongoing research field in the public health area. In this article, we particularly address the potential effect of mass media and absolute humidity to modify the SEIR model.
RESULTS:
Computational simulations are carried out for both standard and extended models for four influenza seasons in CDC region five of the US. Moreover, the accuracy assessment is performed based on the following criteria: i) the root mean square error (RMSE); ii) the Akaike information criterion (AIC); iii) the outbreak peak time; and iv) the number of infected individuals at the peak time. Based on these criteria, the proposed model provided a better fit than a null model with smaller RMSE and AIC values for the last three study seasons. Specifically, RMSE values declined from 20 to 11.08 and from 26.87 to 19.15 for seasons 2010/11 and 2011/12, respectively; also, lower AIC values for these seasons indicate that the modified SEIR (referred to M-SEIR) model is a better-fitting model.
CONCLUSIONS:
Parameter estimation techniques are important tools to determine the key parameters of the epidemic models. Based on our results, introducing the mass media and climate factors into the classic models will improve the model precision.