The Basic Reproduction Number for Chagas Disease Transmission Using Cellular Automata

Abstract

Abstract. This paper presents mathematical and numerical results for a cellular automaton model describing the transmission dynamics of Chagas disease in both homogeneous and heterogeneous environments. The basic reproduction number R0 which integrates factors that determine whether the pathogen can establish or not will be computed using the next-generation matrix approach. The simulation results show the effect of landscape heterogeneity in the vector transmission.

A Virtual Network Embedding Algorithm Based on Cellular Automata Genetic Mechanism

“…Each simulation experiment runs about 50,000 time units and contains 2,500 virtual network requests, where the requests in 100 time units follow the Poisson distribution of mean 5 and the life cycle obeys an exponential distribution. The virtual nodes in each virtual network request connect on probability 0.5, and the number of nodes and resource requirements satisfy the uniform distribution of [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] and [0-50] respectively. Initial population size sets to 16, the maximum number of iterations sets to 10, the crossover probability sets to 0.5, and the mutation probability sets to 0.01.…”

“…Most of the above studies are solved and optimized from the perspective of mathematical models, and less consideration is given to the impact of the changes of nodes and links in the virtual network embedding (VNE) on the final results. Cellular automata (CA) rely on simple local rules to study the overall complexity and are widely used in fields such as information transfer [10], image encryption [11], complex function optimization [12] and biological simulation [13]. Especially for many singleobjective optimization problems, cellular genetic algorithm has achieved good results as a mature and efficient solution [14,15].…”

The spatial reproduction number in a cellular automaton model for vector-borne diseases applied to the transmission of Chagas disease

“…In the non-spatial case developed by Cissé et al 43 and for the homogeneous case where θ I = θ S → 1 , equation (18) gives…”

“…The proposed set of rules are derived from a compartmental model of Susceptible-Infectious-Recovered for hosts and Susceptible-Infectious for vectors, denoted by SIR-SI, in which the host and vector populations are partitioned into subclasses. 43 The host population is formed of healthy individuals who are susceptible to infection (S), infected individuals (I) that can transmit the disease through contact with the susceptible vectors, and recovered individuals (R) that are either unable or much less able to transmit the disease than infected individuals. We assumed also that the hosts can be competent (i.e.…”