A multi--criterion simulation model to determine dengue outbreaks
DOI:
https://doi.org/10.7494/csci.2020.21.3.3819Keywords:
dengue, climate data, mobility, Fuzzy set theory, Pareto optimizationAbstract
In this study we develop a multi-criteria model to identify dengue outbreak periods. To validate the model, we perform simulation using dengue transmission related data in the Western Province, Sri Lanka. Our results indicate that the developed model can be used to predict the dengue outbreak situation in a given region upto one month.
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Ameljanczyk A., Długosz P., Strawa M.: Komputerowa implementacja algorytmu wyznaczania wstępnej diagnozy medycznej. In: VII Konferencja Naukowa Modelowanie Cybernetyczne Systemów Biologicznych, MCSB2010, Kraków, 2010.
Andersson J., Wallace D.: Pareto optimization using the struggle genetic crowding algorithm. In:Engineering Optimization, vol. 34(6), pp. 623–643, 2002.
Berkhout F., Bouwer L., Bayer J., Bouzid M., Cabeza M., Hanger S., Hof A.,Hunter P., Meller L., Patt A.: Deep Emissions Reductions and Mainstreaming ofMitigation and Adaptation: Key Findings. In: , 2013.
Berkhout F., Bouwer L.M., Bayer J., Bouzid M., Cabeza M., Hanger S., HofA., Hunter P., Meller L., Patt A., et al.: European policy responses to climatechange: progress on mainstreaming emissions reduction and adaptation, 2015.
Christophers S.R.:Aedes aegypti: the yellow fever mosquito. CUP Archive, 1960.
De Weck O.L.: Multiobjective optimization: History and promise. In:InvitedKeynote Paper, GL2-2, The Third China-Japan-Korea Joint Symposium on Op-timization of Structural and Mechanical Systems, Kanazawa, Japan, vol. 2, p. 34.2004.
Descloux E., Mangeas M., Menkes C.E., Lengaigne M., Leroy A., Tehei T., Guil-laumot L., Teurlai M., Gourinat A.C., Benzler J., et al.: Climate-based modelsfor understanding and forecasting dengue epidemics. In:PLoS neglected tropicaldiseases, vol. 6(2), 2012.
Ehrgott M.: Vilfredo Pareto and multi-objective optimization. In:Doc. math, pp. 447–453, 2012.
Enduri M.K., Jolad S.: Dynamics of dengue disease with human and vectormobility. In:Spatial and spatio-temporal epidemiology, vol. 25, pp. 57–66, 2018.
He Z., Yen G.G., Zhang J.: Fuzzy-based Pareto optimality for many-objectiveevolutionary algorithms. In: IEEE Transactions on Evolutionary Computation,vol. 18(2), pp. 269–285, 2013.
Hii Y.L.:Climate and dengue fever: early warning based on temperature andrainfall. Ph.D. thesis, Ume ̊a University, 2013.
J Gubler D.: Epidemic dengue/dengue haemorrhagic fever: a global public healthproblem in the 21st century. In: , 1997.
Johansson M.A., Dominici F., Glass G.E.: Local and global effects of climateon dengue transmission in Puerto Rico. In:PLoS neglected tropical diseases,vol. 3(2), 2009.
Karim M.N., Munshi S.U., Anwar N., Alam M.S.: Climatic factors influencingdengue cases in Dhaka city: a model for dengue prediction. In:The Indianjournal of medical research, vol. 136(1), p. 32, 2012.
Liu K., Wang T., Yang Z., Huang X., Milinovich G.J., Lu Y., Jing Q., Xia Y.,Zhao Z., Yang Y., et al.: Using Baidu search index to predict Dengue outbreakin China. In:Scientific reports, vol. 6, p. 38040, 2016.
Massad E., Ortega N.R.S., de Barros L.C., Struchiner C.J.:Fuzzy logic in action:applications in epidemiology and beyond, vol. 232. Springer Science & BusinessMedia, 2009.
Morin C.W., Comrie A.C., Ernst K.: Climate and dengue transmission: evidence and implications. In:Environmental health perspectives, vol. 121(11-12), pp.1264–1272, 2013.
Schumpeter J.A.: Vilfredo Pareto (1848-1923). In:The Quarterly Journal ofEconomics, pp. 147–173, 1949.
Seidahmed O.M., Eltahir E.A.: A sequence of flushing and drying of breedinghabitats of Aedes aegypti (L.) prior to the low dengue season in Singapore. In:PLoS neglected tropical diseases, vol. 10(7), 2016.
Wesolowski A., Qureshi T., Boni M.F., Sundsøy P.R., Johansson M.A., RasheedS.B., Engø-Monsen K., Buckee C.O.: Impact of human mobility on the emergenceof dengue epidemics in Pakistan. In:Proceedings of the National Academy ofSciences, vol. 112(38), pp. 11887–11892, 2015.
Wickramaarachchi W., Perera S.: Developing a two dimensional climate riskmodel for dengue disease transmission in Urban Colombo. In:Journal of Basicand Applied Research International, vol. 20(3), pp. 168–177, 2017.
Wickramaarachchi W., Perera S.: The nonlinear dynamics of the denguemosquito reproduction with respect to climate in urban Colombo: a discretetime density dependent fuzzy model. In:International Journal of Mathematical Modelling and Numerical Optimisation, vol. 8(2), pp. 145–161, 2017.
Wilder-Smith A.:Dengue vaccine development:status and future. In: Bundesgesundheitsblatt-Gesundheitsforschung-Gesundheitsschutz, vol. 63(1), pp.40–44, 2020.
Wu P.C., Lay J.G., Guo H.R., Lin C.Y., Lung S.C., Su H.J.: Higher temperatureand urbanization affect the spatial patterns of dengue fever transmission in sub-tropical Taiwan. In:Science of the total Environment, vol. 407(7), pp. 2224–2233,2009.
Zadeh L.A.: Toward a theory of fuzzy information granulation and its centralityin human reasoning and fuzzy logic. In:Fuzzy sets and systems, vol. 90(2), pp.111–127, 1997.
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