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Multi-criteria simulation model for determining dengue outbreaks

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this study, we develop a multi-criteria model to identify dengue outbreak periods. To validate the model, we performed a simulation using dengue transmission-related data in Sri Lanka’s Western Province. Our results indicated that the developed model can be used to predict a dengue outbreak situation in a given region up to one month in advance.
Słowa kluczowe
Wydawca
Czasopismo
Rocznik
Tom
Strony
353–370
Opis fizyczny
Bibliogr. 30 poz., rys., tab.
Twórcy
  • Military University of Technology, Faculty of Cybernetics, 00-908 Warsaw, ul. Gen. Kaliskiego 2, Poland
  • University of Colombo, Research & Development Center for Mathematical Modeling, Department of Mathematics, Colombo 03, Sri Lanka
  • University of Colombo, Research & Development Center for Mathematical Modeling, Department of Mathematics, Colombo 03, Sri Lanka
  • University of Colombo, Research & Development Center for Mathematical Modeling, Department of Mathematics, Colombo 03, Sri Lanka
  • Military University of Technology, Faculty of Cybernetics, 00-908 Warsaw, ul. Gen. Kaliskiego 2, Poland
Bibliografia
  • [1] Ameljańczyk A.: Properties of the Algorithm for Determining an Initial Medical Diagnosis Based on a Two-Criteria Similarity Model, Biuletyn Instytutu Systemów Informatycznych, vol. 8, pp. 9–16, 2011.
  • [2] Ameljańczyk A.: Pareto filter in the process of multi-label classifier synthesis in medical diagnostics support algorithms, Computer Science and Mathematical Modelling, vol. 1, pp. 5–10, 2015.
  • [3] Ameljańczyk A., Długosz P., Strawa M.: Komputerowa implementacja algorytmu wyznaczania wstępnej diagnozy medycznej. In: VII Konferencja Naukowa Modelowanie Cybernetyczne Systemów Biologicznych, MCSB2010, Kraków, 2010.
  • [4] Andersson J., Wallace D.: Pareto optimization using the struggle genetic crowding algorithm, Engineering Optimization, vol. 34(6), pp. 623–643, 2002.
  • [5] Berkhout F., Bouwer L.M., Bayer J., Bouzid M., Cabeza M., Hanger S., Hof A., Hunter P., Meller L., Patt A.: Deep Emissions Reductions and Mainstreaming of Mitigation and Adaptation: Key Findings, 2013.
  • [6] Berkhout F., Bouwer L.M., Bayer J., Bouzid M., Cabeza M., Hanger S., Hof A., Hunter P., Meller L., Patt A., et al.: European policy responses to climate change: progress on mainstreaming emissions reduction and adaptation, Regional Environmental Change, vol. 15, pp. 949–959, 2015.
  • [7] Christophers S.R.: Aedes aegypti: the yellow fever mosquito, CUP Archive, 1960.
  • [8] De Weck O.L.: Multiobjective optimization: History and promise. In: Invited Keynote Paper, GL2-2, The Third China–Japan–Korea Joint Symposium on Optimization of Structural and Mechanical Systems, Kanazawa, Japan, vol. 2, p. 34, 2004.
  • [9] Descloux E., Mangeas M., Menkes C.E., Lengaigne M., Leroy A., Tehei T., Guillaumot L., Teurlai M., Gourinat A.C., Benzler J., et al.: Climate-Based Models for Understanding and Forecasting Dengue Epidemics, PLoS Neglected Tropical Diseases, vol. 6(2), 2012.
  • [10] Ehrgott M.: Vilfredo Pareto and Multi-Objective Optimization, Documenta Mathematica, pp. 447–453, 2012.
  • [11] Enduri M.K., Jolad S.: Dynamics of dengue disease with human and vector mobility, Spatial and Spatio-temporal Epidemiology, vol. 25, pp. 57–66, 2018.
  • [12] Erandi K.K.W.H., Perera S.S.N., Mahasinghe A.C.: Dengue Outbreaks Prediction Model for Urban Colombo using Meteorological Data, International Journal of Dynamical Systems and Differential Equations (in press).
  • [13] He Z., Yen G.G., Zhang J.: Fuzzy-Based Pareto Optimality for Many-Objective Evolutionary Algorithms, IEEE Transactions on Evolutionary Computation, vol. 18(2), pp. 269–285, 2013.
  • [14] Hii Y.L.: Climate and dengue fever: early warning based on temperature and rainfall. Ph.D. thesis, Ume˚a University, 2013.
  • [15] Gubler D.J.: Epidemic Dengue/Dengue Haemorrhagic Fever: a global public health problem in the 21st century, Dengue Bulletin, vol. 12, pp. 1–19, 1997.
  • [16] Johansson M.A., Dominici F., Glass G.E.: Local and Global Effects of Climate on Dengue Transmission in Puerto Rico, PLoS Neglected Tropical Diseases, vol. 3(2), 2009.
  • [17] Karim M.N., Munshi S.U., Anwar N., Alam M.S.: Climatic factors influencing dengue cases in Dhaka city: a model for dengue prediction, The Indian Journal of Medical Research, vol. 136(1), p. 32–39, 2012.
  • [18] 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 Outbreak in China, Scientific Reports, vol. 6, p. 38040, 2016.
  • [19] Massad E., Ortega N.R.S., de Barros L.C., Struchiner C.J.: Fuzzy Logic in Action: Applications in Epidemiology and Beyond, Springer Science & Business Media, 2009.
  • [20] McCullagh P., Nelder J.A.: Generalized Linear Models, 2nd Edition, Chapman and Hall, London, UK, 1989.
  • [21] Morin C.W., Comrie A.C., Ernst K.: Climate and dengue transmission: evidence and implications, Environmental health perspectives, vol. 121(11–12), pp. 1264–1272, 2013.
  • [22] Ramadona A.L., Lazuardi L., Hii Y.L., Holmner A., Kusnanto H., Rocklov J.: Prediction of Dengue Outbreaks Based on Disease Surveillance and Meteorological Data, PloS one, vol. 11(3), p. e0152688, 2016.
  • [23] Schumpeter J.A.: Vilfredo Pareto (1848–1923), The Quarterly Journal of Economics, pp. 147–173, 1949.
  • [24] Seidahmed O.M.E., Eltahir E.A.B.: A Sequence of Flushing and Drying of Breeding Habitats of Aedes Aegypti (L.) Prior to the Low Dengue Season in Singapore, PLoS Neglected Tropical Diseases, vol. 10(7), 2016.
  • [25] Wesolowski A., Qureshi T., Boni M.F., Sundsøy P.R., Johansson M.A., Rasheed S.B., Engø-Monsen K., Buckee C.O.: Impact of human mobility on the emergence of dengue epidemics in Pakistan, Proceedings of the National Academy of Sciences, vol. 112(38), pp. 11887–11892, 2015.
  • [26] Wickramaarachchi W.P.T.M., Perera S.S.N.: Developing a two dimensional climate risk model for dengue disease transmission in Urban Colombo, Journal of Basic and Applied Research International, vol. 20(3), pp. 168–177, 2017.
  • [27] Wickramaarachchi W.P.T.M., Perera S.S.N.: The nonlinear dynamics of the dengue mosquito reproduction with respect to climate in urban Colombo: a discrete time density dependent fuzzy model, International Journal of Mathematical Modelling and Numerical Optimisation, vol. 8(2), pp. 145–161, 2017.
  • [28] Wilder-Smith A.: Dengue vaccine development: status and future, Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz, vol. 63(1), pp. 40–44, 2020.
  • [29] Wu P.C., Lay J.G., Guo H.R., Lin C.Y., Lung S.C., Su H.J.: Higher temperature and urbanization affect the spatial patterns of dengue fever transmission in subtropical Taiwan, Science of the Total Environment, vol. 407(7), pp. 2224–2233, 2009.
  • [30] Zadeh L.A.: Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic, Fuzzy Sets and Systems, vol. 90(2), pp. 111–127, 1997.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-16225790-3339-4759-9289-1c3922d10360
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