An agent-based model for assessment of aedes aegypti pupal productivity
Abstract
Dengue is a febrile disease whose main vector transmitter is the Aedes Aegypti mosquito. This disease has an annual register of 50 million infections worldwide. Simulations are an important tool in helping to combat and prevent the epidemic and, consequently, save lives and resources. Therefore, in this paper, we propose an Agent-Based Model for assessment of the pupal productivity of the Aedes Aegypti mosquito. In this model, the reproduction of the mosquito takes into account the productivity of each type of container. The preliminary results show the effects of considering the pupal productivity for the control and prevention of dengue. As a result, we observed that the prevention methods must consider pupal productivity and that the distance between containers might leverage productivity and increase transmission risk. We verify the completeness and functionality of the model through experimentation using Netlogo.
References
[1]
Brasil 2013. Larval Index Rapid Assay of Aedes aegypti (LIRAa) for Entomological Surveillance of Aedes aegypti in Brazil: methodology for assessment of Breteau and Building's indexes and type of containers. 1 ed. Brasília: Ministrio da Saúde. Secretaria de Vigilância em Saúde. Departamento de Vigilância das Doenças Transmissíveis.
[2]
Brito-Arduino, M. 2014. "Assessment of Aedes aegypti Pupal Productivity during the Dengue Vector Control Program in a Costal Urban Centre of São Paulo State, Brazil". Journal of Insects 2014:9.
[3]
Chung, C. A. 2003. Simulation modeling handbook: a practical approach. CRC press.
[4]
Focks, D. A. 2003. A review of entomological sampling methods and indicators for dengue vectors. Document TDR/IDE/Den/03, World Health Organization, Geneva, Switzerland.
[5]
Focks, D. A., and N. Alexander. 2006. Multi-country study of Aedes aegypti pupal productivity survey methodology: findings and recommendations. Document TDR/IRM/DEN/06, World Health Organization, Geneva, Switzerland.
[6]
Focks, D. A., R. J. Brenner, J. Hayes, and E. Daniels. 2000. "Transmission thresholds for dengue in terms of Aedes aegypti pupae per person with discussion of their utility in source reduction efforts". The American Journal of Tropical Medicine and Hygiene 62 (1): 11--18.
[7]
Focks, D. A., and D. D. Chadee. 1997. "Pupal Survey: An Epidemiologically Significant Surveillance Method for Aedes aegypti: An Example Using Data from Trinidad". The American Journal of Tropical Medicine and Hygiene 56 (2): 159--167.
[8]
Isidoro, C., N. Fachada, F. Barata, and A. Rosa. 2009. "Agent-Based Model of Aedes aegypti Population Dynamics". In Progress in Artificial Intelligence, edited by L. Lopes, N. Lau, P. Mariano, and L. Rocha, Volume 5816 of Lecture Notes in Computer Science, 53--64: Springer Berlin Heidelberg.
[9]
Jacintho, L. F. O., A. F. M. Batista, T. L. Ruas, M. G. B. Marietto, and F. A. Silva. 2010. "An Agent-based Model for the Spread of the Dengue Fever: A Swarm Platform Simulation Approach". In Proceedings of the 2010 Spring Simulation Multiconference, SpringSim '10, 2:1--2:8. San Diego, CA, USA: Society for Computer Simulation International.
[10]
Lima, T., T. Carneiro, L. Silva, R. Lana, C. Codeco, I. Reis, R. Maretto, L. Santos, A. Monteiro, L. Medeiros, and F. Coelho. 2014. "A framework for modeling and simulating Aedes aegypti and dengue fever dynamics". In Proceedings of the 2014 Winter Simulation Conference, edited by A. Tolk, S. Y. Diallo, I. O. Ryzhov, L. Yilmaz, S. Buckley, and J. A. Miller, 1481--1492. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers, Inc.
[11]
Louis, V., R. Phalkey, O. Horstick, P. Ratanawong, A. Wilder-Smith, Y. Tozan, and P. Dambach. 2014. "Modeling tools for dengue risk mapping - a systematic review". International Journal of Health Geographics 13 (1): 50.
[12]
Muller, G., P. Grébaut, and J.-P. Gouteux. 2004. "An agent-based model of sleeping sickness: simulation trials of a forest focus in southern Cameroon". Comptes Rendus Biologies 327 (1): 1--11.
[13]
Rao, D. M. 2014. "Accelerating Parallel Agent-based Epidemiological Simulations". In Proceedings of the 2Nd ACM SIGSIM/PADS Conference on Principles of Advanced Discrete Simulation, SIGSIM-PADS '14, 127--138. New York, NY, USA: ACM.
[14]
Rao, D. M., and A. Chernyakhovsky. 2008. "Parallel simulation of the global epidemiology of Avian influenza". In Proceedings of the 2008 Winter Simulation Conference, edited by S. J. Mason, R. R. Hill, L. Mönch, O. Rose, T. Jefferson, and J. W. Fowler, 1583--1591. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers, Inc.
[15]
Roche, B., J. Drake, and P. Rohani. 2011. "An Agent-Based Model to study the epidemiological and evolutionary dynamics of Influenza viruses". BMC Bioinformatics 12 (1): 87.
[16]
Roche, B., J.-F. Guégan, and F. Bousquet. 2008. "Multi-agent systems in epidemiology: a first step for computational biology in the study of vector-borne disease transmission". BMC Bioinformatics 9 (1): 435.
[17]
Segovia-Juarez, J. L., S. Ganguli, and D. Kirschner. 2004. "Identifying control mechanisms of granuloma formation during M. tuberculosis infection using an agent-based model". Journal of Theoretical Biology 231 (3): 357--376.
[18]
Tun-Lin, W., B. H. Kay, and A. Barnes. 1995. "Understanding Productivity, A Key to Aedes aegypti Surveillance". The American Journal of Tropical Medicine and Hygiene 53 (6): 595--601.
[19]
Tun-Lin, W., A. Lenhart, V. S. Nam, E. Rebollar-Tllez, A. C. Morrison, P. Barbazan, M. Cote, J. Midega, F. Sanchez, P. Manrique-Saide, A. Kroeger, M. B. Nathan, F. Meheus, and M. Petzold. 2009. "Reducing costs and operational constraints of dengue vector control by targeting productive breeding places: a multi-country non-inferiority cluster randomized trial". Tropical Medicine & International Health 14 (9): 1143--1153.
[20]
Wilensky, U. 1999. "NetLogo". Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. Accessed Apr. 02, 2015. http://ccl.northwestern.edu/netlogo/.
[21]
World Health Organization 2009. Dengue: Guidelines for Diagnosis, Treatment, Prevention and Control. Switzerland: World Health Organization Press. Accessed Mar. 07, 2015. http://www.who.int/tdr/publications/documents/dengue-diagnosis.pdf.
[22]
World Health Organization 2015. "Dengue Control". Accessed Mar. 25, 2015. http://www.who.int/denguecontrol/en/.
Recommendations
A framework for modeling and simulating aedes aegypti and dengue fever dynamics
WSC '14: Proceedings of the 2014 Winter Simulation ConferenceDengue fever represents a great challenge for many countries, and methodologies to prevent and/or control its transmission have been largely discussed by the research community. Modeling is a powerful tool to understand epidemic dynamics and to evaluate ...
Agent-based model of dengue disease transmission by aedes aegypti populations
ECAL'09: Proceedings of the 10th European conference on Advances in artificial life: Darwin meets von Neumann - Volume Part IThis paper presents an agent based model of the Aedes aegypti mosquito showing not only population dynamics but also the Dengue disease propagation in both the vector and host populations (mosquitoes and humans, respectively); this study will focus on ...
Dengue Infection Model with Temperature and the biting of Aedes Aegypti and Ades Albopictus in Thailand
IPMV '20: Proceedings of the 2020 2nd International Conference on Image Processing and Machine VisionDengue is the transmission disease occurred by biting of infected Aedes aegypti and infected Aedes albopictus. The temperature of each area influences to the transmission of this disease. In this paper, we describe the transmission of this disease by ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
December 2015
4051 pages
ISBN:9781467397414
Sponsors
Publisher
IEEE Press
Publication History
Published: 06 December 2015
Check for updates
Qualifiers
- Research-article
Conference
WSC '15
Sponsor:
Acceptance Rates
WSC '15 Paper Acceptance Rate 202 of 296 submissions, 68%;
Overall Acceptance Rate 3,413 of 5,075 submissions, 67%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 143Total Downloads
- Downloads (Last 12 months)1
- Downloads (Last 6 weeks)0
Reflects downloads up to 14 Jan 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in