## 4 Contributions of the Systematic Literature Review
The C ++ programming language was used to develop the tool. The spread model is used to create hypothetical large-scale health events to be used by officials to plan events. To create a disease spread and facility location simulation, the researcher can run the disease spread and clinical facility location models simultaneously and seamlessly. * ISIS is a web browser based on a modeling and decision environment for public health epidemiologists [81]. Its components are a web user interface, databases, and models that connect to the user through middleware, the rules and models to generate the simulations, and a structured and semi-structured database management system. One aspect to highlight is the Synthetic Information Library--SIL, which contains all the information necessary to create, execute, and analyze experiments. In this case, they use machine learning methods to manage complex unstructured and semi-structured data. * EpiSimdemics is one of the first algorithms to simulate epidemics in large, real social networks [82]. This article defines computational epidemiology as the development and use of computer models to understand the spatio-temporal spread of the disease through populations. The main design objective of EpiSimdemics is to explore the effects of complex pharmaceutical and non-pharmaceutical interventions on the spread of infectious diseases through realistic populations. In this case, they use a synthetic population that is generated from the United States census. * Epinome is a tool that allows users to reproduce simulation scenarios, investigate a deployment outbreak using a variety of visualization tools, and direct the simulation by implementing different public health policies at predefined decision points [114]. Epinome records user actions--for example, tool selection, interactions with each tool, and policy changes--and stores them in a database for later analysis. A psychological team can use that information to study strategies that users use to search for information on diseases and possible outbreaks. * MiTAP was developed based on natural language for the monitoring of infectious diseases [83]. It captures information from different sources such as emails, search engines, news, etc. During the processing phase, information is normalized using machine learning rules. It is a tool that helps reduce the information overload that results from staying informed from different sources. * PopHT is a semantic web application that automates the processes of integration and extraction of massive amounts of data from different distributed sources to support the measurement and monitoring of the health system [31]. The main objective of this project is the integration of heterogeneous information: public health policies, information in health centers, and information on diseases. * Spatiotemporal Epidemiological Modeler (STEM) is a platform developed to create spatial and temporal models of infectious diseases in humans. It uses geographic, population, demographic data, transportation information, and basic disease models. The platform is based on systems of differential equations [84]. In STEM, the researcher starts by composing a scenario and reporting information on the infected population, the city where the population is located, and the target city for evaluation. The models generated facilitate study of the spread of infectious diseases. However, there is no evidence that they consider the handling of contextual variables, and there is no user classification for the management of the application. These works present a graphical interface to capture information and present the results of the queries. The results are presented in the form of tables and graphs. They are applications for users who are experts in the field and who have knowledge in the analysis of epidemiological data. They are oriented toward infectious diseases in humans, and the data used are from censuses that have been carried out in different years.