Abstract: The COVID-19 epidemics has now lasted for 2 years. A vaccine has been found, but other complementary measures are still required, in particular testing, tracing contacts, isolating infected individuals, and respecting sanitary measures (physical distancing, masks). However these measures are not always well accepted and many fake news circulate about the virus or the vaccine. We believe that explaining the mechanisms behind the epidemics and the reasons for the sanitary measures is key to protect the general population from disinformation. To this end, we have developed a simple agent-based epidemic simulator that includes various screening strategies. We show that it can be used to compare the efficiency of various targeting strategies, starting date, and number of daily tests. We also ran an optimisation algorithm that proves that the best strategies consist in testing widely and early. Our simulator is already available to play online, to raise awareness in the general population.

Abstract: The relevance of modeling epidemics? spread goes beyond the academic. The mathematical understanding of infectious diseases has become an important tool in policy making. Our research interest is modeling of dynamics in crisis situations. This paper explores the extant body of literature of mathematical models in epidemiology, with particular emphasis on theories and methodologies used beyond them. Our goal is to identify core building blocks of models and research patterns to model the dynamics of crisis situations such as epidemics. The wide range of applications of epidemic models to many other disciplines that show biological analogies, makes this paper helpful for many modelers and mathematicians within the broader field of Crisis Management.

Abstract: Crisis management efforts in the United States public health sector aim to prepare and protect the life of an individual, family or group against a health-related event. These efforts span governmental, nongovernmental and private sectors. The need for coordination between these organizations has never been more apparent. A solution will depend heavily on standardized communication protocols using information and communication technology (ICT). Numerous initiatives are currently addressing the needs of our nation with respect to homeland security and public health, yet remain in the early stages for the nongovernmental sector. The emphasis of our research is at the local level where the governmental sector extends to the nongovernmental sector (NGO), particularly community outreach. Our analysis of the local community suggests focusing on the management of communication during public health crises to better understand the complexities and variations presented in these communities. Leveraging experiences from media-technology literature findings and emergency-response efforts, we seek to identify a framework and tools to enable effective communication for those public health practitioners who serve as front-line responders to public health crises. The major contributions of this research will be to extend the use of information systems and mobile technology to the local United States public health communities to increase effective communication between organizations, while providing a state of readiness for homeland security related events.

Abstract: Epidemic spreading simulation in social networks denotes a set of techniques that allow to assess the temporal evolution and the consequences of a pandemic. They were largely used by governments and International health organizations during the COVID-19 world crisis to decide the appropriate countermeasures to limit the diffusion of the disease. Among them, the existing simulation techniques based on a network model aimed at studying the infectious disease dynamics have a prominent role and are widely adopted. However, even if they leverage the topological structure of a social network, they disregard the intrinsic and individual features of its members. A semantic social network is defined as a structure consisting of interlinking layers, which include a social network layer, to represent people and their relationships and a concept network layer, to represent concepts, their ontological relationships and implicit similarities. Here, we propose a novel epidemic simulation framework that allows to describe a community of people as a semantic social network, to adopt the most commonly used compartmental models for describing epidemic spreading, such as Susceptible-Infected-Susceptible (SIS) or Susceptible-Infected-Removed (SIR), and to enable semantic reasoning to increase the accuracy of the simulation. Finally, we show how to use the framework to simulate the impact of a pandemic in a community where the job of each member is known in advance.