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Top1. Introduction
Infectious diseases are worrying, increasingly, authorities and public health officials. Despite continuous and effective monitoring of infectious diseases, it should be noted that their etiology remains unrecognized amplification.
To date, few information systems have been developed to help public health officials in the analysis of multidisciplinary data (Younsi et al., 2017). The epidemiological surveillance systems developed aim various objectives, such as: monitoring, surveillance, prevention, alert, etc. Indeed, some researchers consider these systems as decision support tools in the field of epidemiological disease prevention and control (Toma et al., 1991). However, in our opinion, these systems suffer from deficiencies related to the analytical aspects as well as their limits to deal with several actions at once, such as: monitoring, prevention and alert. In this context, some problems related to the spread of infectious diseases are complex in nature and involve many factors obtained from several fields, qualitative and/or quantitative in nature. These factors are considered as the main causes of the spread of many diseases among the human population. Indeed, the problems related to the spread of infectious diseases are multiple and dependent on many factors that need to be analyzed in a global dimension. Among these factors characteristics and dynamics of the disease (mode of transmission, duration of infections, etc.), the social structure of individuals such as the concentration of the population in urban areas and individual exchanges and encounters, socio-economic factors, etc.
Tracking dynamics of infectious diseases and detecting changes in a disease process are impossible without development and implementation of mathematical and social methodology dedicated to spatiotemporal disease surveillance. Mathematical and simulation models are, increasingly, used to study the transmission of infectious diseases in order to predict how they might spread in a human population during pandemic event, to evaluate different intervention strategies and to help public health decision makers with disease outbreak control and management (Younsi et al., 2019a). Various mathematical models existing in literature can be used to represent the different state of patients (compartment) in the course of the illness (S: susceptible, I: infective, R: removed, etc.). These deterministic models assume that populations are completely mixed and ignore spatial effects of spread epidemics, also interaction between individuals is neglected since they model populations as continuous entities (Perez and Dragicevic, 2009). In this study, we used the Susceptible, Infected, Removed (SIR) model within Small World network (SW) to explore and understand the dynamics of flu epidemic.
Epidemic transmission occurs through person-to-person contact, and contact between susceptible and/or infected individuals takes the form of a network. Moreover, the complex nature of human life (its relations contacts forming a complex human network, its commuting, its socio-economic life, etc.) makes it difficult to understand how changes will affect the incidence. Unlike to compartment models, the agent based model is particularly appropriate in our study. Processes are defined at the individual level, allowing a definition of the patient's history and the complexity of within-host phenomena. In addition, we discuss the effect of the vaccination to show the effectiveness of vaccine at preventing disease and infection.
Geographic Information System (GIS) and Database Management System (DBMS) are, widely, used in the scientific world and are recognized as tools for decision support exploited in various fields who are interested in the study of the territory and its people (Mesgari and Masoomi, 2008). Indeed, the health officials are in the need for a geo-decision-making tool for monitoring and surveillance of epidemic in this network. Herein, we highlight the role of GIS in monitoring epidemic spread by visualizing risk seasonal influenza map.