Tracking uncertainty in a spatially explicit susceptible-infected epidemic model

In this paper we conceive an interval-valued continuous cellular automaton for describing the spatio-temporal dynamics of an epidemic, in which the magnitude of the initial outbreak and/or the epidemic properties are only imprecisely known. In contrast to well-established approaches that rely on probability distributions for keeping track of the uncertainty in spatio-temporal models, we resort to an interval representation of uncertainty. Such an approach lowers the amount of computing power that is needed to run model simulations, and reduces the need for data that are indispensable for constructing the probability distributions upon which other paradigms are based

Towards generalized measures grasping CA dynamics

In this paper we conceive Lyapunov exponents, measuring the rate of separation between two initially close configurations, and Jacobians, expressing the sensitivity of a CA's transition function to its inputs, for cellular automata (CA) based upon irregular tessellations of the n-dimensional Euclidean space. Further, we establish a relationship between both that enables us to derive a mean-field approximation of the upper bound of an irregular CA's maximum Lyapunov exponent. The soundness and usability of these measures is illustrated for a family of 2-state irregular totalistic CA

Continuous cellular automata on irregular tessellations : mimicking steady-state heat flow

Leaving a few exceptions aside, cellular automata (CA) and the intimately related coupled-map lattices (CML), commonly known as continuous cellular automata (CCA), as well as models that are based upon one of these paradigms, employ a regular tessellation of an Euclidean space in spite of the various drawbacks this kind of tessellation entails such as its inability to cover surfaces with an intricate geometry, or the anisotropy it causes in the simulation results. Recently, a CCA-based model describing steady-state heat flow has been proposed as an alternative to Laplace's equation that is, among other things, commonly used to describe this process, yet, also this model suffers from the aforementioned drawbacks since it is based on the classical CCA paradigm. To overcome these problems, we first conceive CCA on irregular tessellations of an Euclidean space after which we show how the presented approach allows a straightforward simulation of steady-state heat flow on surfaces with an intricate geometry, and, as such, constitutes an full-fledged alternative for the commonly used and easy-to-implement finite difference method, and the more intricate finite element method

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

Agent Based Modeling on Dynamic Spreading Dengue Fever Epidemic

Agent based model (ABM) is a computational model for simulation, behavioral representation and interaction of autonomous agents. The main problem definition related to how to make a dynamic model of dengue fever with consideration of their behavioral and interaction agent. This paper aims to develop interactive behavioral agents and related simulation models for such dynamic spreading dengue fever epidemic. This model construction consists of two agents, namely a human agent as a host and mosquito as a vector, where temperature and humidity are the environmental parameters. These environmental parameters deployed data and information from National Meteorology Climatology and Geophysics agency and supported by recent community health data of Bogor region. The verification stage evaluated model performance of two periods between January to June and between July to December 2015 showed the fitness of the model. During simulation stage where 100 humans agent and 10 mosquitoes agent were observed, indicating the decreasing of mosquito by 26.3% and the number of infected human decrease to 16% from the period of January until June to July until December 2015 respectively. These evaluation results showed that the agent based model results succeeded to follow a similar trend of decreasing pattern as actual data

Using Cellular Automata to study the effect of competition for epidemic diseases

AbstractCellular Automata (CA) has shown to be a valuable approach in ecological modeling, in particular when dealing with local interactions between species and their environment. A stochastic cellular automata model, which included two competitors (the inferior species which is immune to a disease and the superior one which is sensitive to the disease), is constructed. Through time series analysis and spatial pattern analysis, the influence of competition effect upon the behavior of epidemic diseases has been investigated to know whether the competition effect is in favor of epidemics control. Then, some strategies for introducing competitors to the infectious system are explored. The result shows that introducing some right competitors into the infection region may be a considerable policy. The population with high colonization rate, low extinction rate and long colonization radius is introduced preferably. The result may give us some suggestions for epidemic control in conservation of wild populations

A cellular automata to model epidemics

Compartmental models are very popular in epidemiology, they provide excellent results when the populations satisfy certain hypotheses as large population size or population homogeneity, the complexity of this models is low making their analysis intuitive. In the other hand, they ignore important factors inherent to the problem, such as the nature of contacts between individuals and population heterogeneity. Cellular automata models are adequate to describe natural systems consisting of a massive collection of simple objects. They represent the global system behavior as a colection of simpler objects or cells. In this paper we propouse a cellular automata model to study the time evolution of a heterogeneous population through the various stages of disease resulting from the individuals interactions (epidemic). We validate the model with real data of flu that hit Geneva (Switzerland) in 1918 and then we will test the model under different assumptions discussing the result that each has on the disease dynamics.Sociedad Argentina de Informática e Investigación Operativ

A cellular automata to model epidemics

Compartmental models are very popular in epidemiology, they provide excellent results when the populations satisfy certain hypotheses as large population size or population homogeneity, the complexity of this models is low making their analysis intuitive. In the other hand, they ignore important factors inherent to the problem, such as the nature of contacts between individuals and population heterogeneity. Cellular automata models are adequate to describe natural systems consisting of a massive collection of simple objects. They represent the global system behavior as a colection of simpler objects or cells. In this paper we propouse a cellular automata model to study the time evolution of a heterogeneous population through the various stages of disease resulting from the individuals interactions (epidemic). We validate the model with real data of flu that hit Geneva (Switzerland) in 1918 and then we will test the model under different assumptions discussing the result that each has on the disease dynamics.Sociedad Argentina de Informática e Investigación Operativ