1,001 research outputs found
Decisions and disease: a mechanism for the evolution of cooperation
In numerous contexts, individuals may decide whether they take actions to
mitigate the spread of disease, or not. Mitigating the spread of disease
requires an individual to change their routine behaviours to benefit others,
resulting in a 'disease dilemma' similar to the seminal prisoner's dilemma. In
the classical prisoner's dilemma, evolutionary game dynamics predict that all
individuals evolve to 'defect.' We have discovered that when the rate of
cooperation within a population is directly linked to the rate of spread of the
disease, cooperation evolves under certain conditions. For diseases which do
not confer immunity to recovered individuals, if the time scale at which
individuals receive information is sufficiently rapid compared to the time
scale at which the disease spreads, then cooperation emerges. Moreover, in the
limit as mitigation measures become increasingly effective, the disease can be
controlled, and the rate of infections tends to zero. Our model is based on
theoretical mathematics and therefore unconstrained to any single context. For
example, the disease spreading model considered here could also be used to
describe social and group dynamics. In this sense, we may have discovered a
fundamental and novel mechanism for the evolution of cooperation in a broad
sense
Equation-Free Multiscale Computational Analysis of Individual-Based Epidemic Dynamics on Networks
The surveillance, analysis and ultimately the efficient long-term prediction
and control of epidemic dynamics appear to be one of the major challenges
nowadays. Detailed atomistic mathematical models play an important role towards
this aim. In this work it is shown how one can exploit the Equation Free
approach and optimization methods such as Simulated Annealing to bridge
detailed individual-based epidemic simulation with coarse-grained,
systems-level, analysis. The methodology provides a systematic approach for
analyzing the parametric behavior of complex/ multi-scale epidemic simulators
much more efficiently than simply simulating forward in time. It is shown how
steady state and (if required) time-dependent computations, stability
computations, as well as continuation and numerical bifurcation analysis can be
performed in a straightforward manner. The approach is illustrated through a
simple individual-based epidemic model deploying on a random regular connected
graph. Using the individual-based microscopic simulator as a black box
coarse-grained timestepper and with the aid of Simulated Annealing I compute
the coarse-grained equilibrium bifurcation diagram and analyze the stability of
the stationary states sidestepping the necessity of obtaining explicit closures
at the macroscopic level under a pairwise representation perspective
Evolutionary-Game-Theory-Based Epidemiological Model for Prediction of Infections with Application to Demand Forecasting in Pharmaceutical Inventory Management Problems
Pharmaceuticals play a critical role in the eradication of infectious diseases. Effective pharmaceutical inventory management is important for controlling epidemics since medical resources such as pharmaceuticals, medical staff, and hospitals are limited. In this study, a novel epidemiological model is proposed to evaluate the resource requirements for pharmaceuticals and is applied to analyze different pharmaceutical inventory management strategies. We formulate the relationship between the number of infected individuals and the risk of infection to account for virus mutation. Evolutionary game theory is integrated into an epidemiological model to represent human behavioral choices. The proposed model can be developed to forecast the demand for pharmaceuticals and analyze how human behavior affects the demand of pharmaceuticals. This study found that making people aware of the risk of disease has a positive impact on both reducing the number of infections and managing the pharmaceutical inventory. The main contribution of this study is to enhance areas of research in pharmaceutical inventory management. This study revealed that the correct recognition of the risk of disease leads to appropriate pharmaceutical management. There are a few studies on the application of infectious disease models to inventory control problems. This study provides clues toward proper pharmaceutical management
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