## 1 Introduction  
The control of outbreaks of viral infectious diseases in Brazil presents a pertinent challenge, given the size of the population, population density, social habits, and constrained testing strategies with limited test availability [1]. This challenge is further amplified when simultaneous outbreaks of diseases occur, such as COVID-19 and influenza [2]. Therefore, studies are needed to assist in mitigating issues associated with concurrent outbreaks of such diseases. Due to their constrained testing resources, test prioritization is a pertinent public health strategy for low- and middle-income countries. Machine Learning (ML) models can be a foundation for developing eHealth and mHealth systems [3; 4; 5]. These systems can support healthcare professionals and policymakers in test prioritization. To facilitate real-world clinical application and integration into the current clinical workflow, classification models can be made accessible, and attribute relevance information can be leveraged through web services for consumption by a healthcare system. These eHealth and mHealth systems should provide classification results to healthcare professionals through clear and concise graphical user interfaces. Thus, the direct interpretability of the ML models is crucial to enhance the confidence of healthcare professionals in the classification results [6]. For instance, healthcare systems of Brazilian public healthcare units can reuse models deployed by web services to prioritize scarce testing resources. Amidst the COVID-19 pandemic, the challenge of testing resource scarcity became evident in numerous countries, such as Brazil [7]. Brazil's first COVID-19 case occurredin March 2020, and over an extended period, the Ministry of Health reported a consistent increase in confirmed cases and fatalities. The Brazilian government reported over 30 million COVID-19 cases and over 664,000 fatalities. Considering Brazil's most recent data from the Ministry of Health, from January to September 2023, the unfortunate toll of COVID-19 resulted in over 12,000 individuals ([[https://covid.saude.gov.br/](https://covid.saude.gov.br/)]([https://covid.saude.gov.br/](https://covid.saude.gov.br/)), accessed on 10 October 2023 ). The limited number of tests becomes even more critical when concurrent outbreaks of viral infectious diseases (concomitant outbreaks) occur. During the most challenging pandemic phases, the Brazilian population faced at least two coexisting outbreaks of viral infectious diseases: COVID-19 and influenza. Therefore, it is pertinent to assist policymakers in formulating solutions to address concurrent outbreaks of viral infectious diseases (present and future). This article extends our previous research [8] by presenting ML models to assist in test prioritization based on symptoms during concurrent outbreaks of COVID-19 and influenza in Brazil. To our knowledge, no prior studies are experimenting with Brazilian datasets for COVID-19 and influenza classification in this context. In the clinical scenario we envision, symptomatic patients present themselves at the hospital's testing site during a coexisting outbreak of COVID-19 and influenza. Before conducting tests for COVID-19 or influenza, which can be limited resources in certain countries, healthcare professionals can gather patient information as input data for an ML model. This approach can empower healthcare providers to make more informed decisions about which test to administer to specific patients, optimizing resource allocation and patient care. We implemented ML models that rely on supervised learning, employing the following algorithms: Decision Tree (DT), Multilayer Perceptron (MLP), Gradient Boosting Machine (GBM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Logistic Regression (LR).