Agent-based modeling to simulate the dengue spread

In this paper, we introduce a novel method ABM in simulating the unique process for the dengue spread. Dengue is an acute infectious disease with a long history of over 200 years. Unlike the diseases that can be transmitted directly from person to person, dengue spreads through a must vector of mosquitoes. There is still no any special effective medicine and vaccine for dengue up till now. The best way to prevent dengue spread is to take precautions beforehand. Thus, it is crucial to detect and study the dynamic process of dengue spread that closely relates to human-environment interactions where Agent-Based Modeling (ABM) effectively works. The model attempts to simulate the dengue spread in a more realistic way in the bottom-up way, and to overcome the limitation of ABM, namely overlooking the influence of geographic and environmental factors. Considering the influence of environment, Aedes aegypti ecology and other epidemiological characteristics of dengue spread, ABM can be regarded as a useful way to simulate the whole process so as to disclose the essence of the evolution of dengue spread.

[1]  Liu Tao,et al.  Multi-agent Systems for Simulating and Planning Land Use Development , 2006 .

[2]  Annelise Tran,et al.  On the dynamics of flying insects populations controlled by large scale information. , 2005, Theoretical population biology.

[3]  R. Tonn,et al.  Studies on the life budget of Aedes aegypti in Wat Samphaya, Bangkok, Thailand. , 1972, Bulletin of the World Health Organization.

[4]  M. Janssen,et al.  Multi-Agent Systems for the Simulation of Land-Use and LandCover Change : A Review , 2002 .

[5]  K. Abbas,et al.  MODELING INFECTIOUS DISEASES USING GLOBAL STOCHASTIC CELLULAR AUTOMATA , 2005 .

[6]  S. Fu.  Modelling Epidemic Spread using Cellular , 2002 .

[7]  Ronald L. Wasserstein,et al.  Monte Carlo: Concepts, Algorithms, and Applications , 1997 .

[8]  Paul M. Torrens,et al.  How cellular models of urban systems work (1. theory) , 2000 .

[9]  S N Dorogovtsev,et al.  Stochastic cellular automata model of neural networks. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[10]  Amy C Morrison,et al.  Characteristics of the spatial pattern of the dengue vector, Aedes aegypti, in Iquitos, Peru. , 2003, The American journal of tropical medicine and hygiene.

[11]  P. Torrens,et al.  Cellular Automata and Urban Simulation: Where Do We Go from Here? , 2001 .

[12]  Annelise Tran,et al.  On the dynamics of dengue epidemics from large-scale information. , 2006, Theoretical population biology.

[13]  Paul M. Torrens,et al.  Geosimulation: Object-based Modeling of Urban Phenomena , 2022 .

[14]  Paul M. Torrens,et al.  Cellular Automata and Multi-agent Systems as Planning Support Tools , 2003 .

[15]  A. Tran,et al.  Mapping disease incidence in suburban areas using remotely sensed data. , 2002, American journal of epidemiology.