# Approaches for Dealing with Seasonality in Clinical Prediction Models for Infections
## 1 Introduction  
Seasonal variations in the prevalence of infectious diseases, which are commonly known as seasonality, have been widely documented and studied [1; 2; 3; 4]. Although these variations are usually attributed to seasonal changes in humidity, temperature, or even different human behaviors throughout the year, the detailed causes of seasonality remain a recurrent research topic [1; 2; 5; 6].  
Seasonality is usually considered in epidemiological time series studies, in which the objectives are to estimate the number of future cases or assess the factors correlated with the spread of infections [2; 7]. However, seasonal variations are rarely considered in clinical prediction models, which have objectives related to prognostic and health service research [8].  
In this paper, we explore the main techniques used to address the challenge of seasonality in prediction models for infectious diseases. We focus on classification problems, and use the approach of simply ignoring season in the models as a gold standard. Two common approaches used to deal with seasonality [2; 7; 8; 9; 10; 11; 12; 13], namely, adding the season as an additional feature and generating different models for different season, are considered in this work. Furthermore, we propose two algorithms based on common methods from the field of datastream mining research, namely, sliding windows and ensembles of models trained on different time periods. The effects of these approaches are studied with regard to both synthetic datasets and data related to infectious diseases extracted from the MIMICIII database [14]. This work greatly extends our preliminary proposal presented in [15]. The main contributions of this paper are:  
* New approaches for dealing with seasonality based on sliding windows and ensembles;
* An extensive study of the effects of seasonality on clinical prediction models in the presence of high dimensionality and imbalanced data;
* Experimental settings based on freely available interpretable techniques and open data. With the aim of ensuring the reproducibility and usability of these results in future research, we have made the code developed for this work freely available at: [[https://github.com/berncase/seasonality-rProject](https://github.com/berncase/seasonality-rProject)]([https://github.com/berncase/seasonality-rProject](https://github.com/berncase/seasonality-rProject)) (accessed on 25 May 2023).  
The remainder of this paper is structured as follows. Section 2 presents a comprehensive analysis of the issue of seasonality in clinical data and discusses relevant research in the field. In Section 3, we explain the approaches employed to deal with seasonality that are considered in this work, including the two new proposed algorithms. In addition, we provide a detailed description of the two synthetic datasets with seasonal variations considered in this work along with the two clinical datasets extracted from the MIMIC-III database. Section 4 provides insights into the conducted experiments and their results, which are further discussed in Section 5. In Section 6, we outline the limitations of this work and highlight future research directions. Finally, Section 7 presents the conclusions drawn from this study.