A model to assess dengue using type 2 fuzzy inference system

Abstract Recently the vector-borne disease dengue has become one of those infectious diseases with the potential of becoming endemic. Though dengue fever may occur at any time of the year, it depends very much on a suitable environment. Taking this into account, in this work we have developed a mathematical model using type-2 fuzzy inference system to predict suitable conditions for dengue outbreak so that control measures can be implemented as soon as possible. Here temperature, rainfall, humidity is taken as input parameters and the chance of dengue fever is taken as the output parameter. To understand the system easily we have used MATLAB software to generate various simulation works.

[1]  Mohamed Esmail Karar,et al.  Optimal adaptive intuitionistic fuzzy logic control of anti-cancer drug delivery systems , 2020, Biomed. Signal Process. Control..

[2]  Jerry M. Mendel,et al.  Interval type-2 fuzzy logic systems , 2000, Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063).

[3]  Natasha Markuzon,et al.  Statistical modeling of the effect of rainfall flushing on dengue transmission in Singapore , 2018, PLoS neglected tropical diseases.

[4]  B. Alto,et al.  Temperature and dengue virus infection in mosquitoes: independent effects on the immature and adult stages. , 2013, The American journal of tropical medicine and hygiene.

[5]  B. A. Harrison,et al.  Effect of temperature on the vector efficiency of Aedes aegypti for dengue 2 virus. , 1987, The American journal of tropical medicine and hygiene.

[6]  Chaur-Dong Chen,et al.  Re-assess Vector Indices Threshold as an Early Warning Tool for Predicting Dengue Epidemic in a Dengue Non-endemic Country , 2015, PLoS neglected tropical diseases.

[7]  Dean F. Sittig,et al.  The emerging science of very early detection of disease outbreaks. , 2001, Journal of public health management and practice : JPHMP.

[8]  Rama Komaragiri,et al.  Design of efficient fractional operator for ECG signal detection in implantable cardiac pacemaker systems , 2019, Int. J. Circuit Theory Appl..

[9]  M.C.S. Ribeiro,et al.  An integrated recycling approach for GFRP pultrusion wastes: recycling and reuse assessment into new composite materials using Fuzzy Boolean Nets , 2014 .

[10]  A. K. Hati.  Dengue serosurveillance in Kolkata, facing an epidemic in West Bengal, India. , 2009, Journal of vector borne diseases.

[11]  Francesca Dominici,et al.  Local and Global Effects of Climate on Dengue Transmission in Puerto Rico , 2009, PLoS neglected tropical diseases.

[12]  Roozbeh Razavi-Far,et al.  A Dual Approach for Positive T-S Fuzzy Controller Design and Its Application to Cancer Treatment Under Immunotherapy and Chemotherapy , 2019, Biomed. Signal Process. Control..

[13]  Soovoojeet Jana,et al.  A study on stegomyia indices in dengue control: a fuzzy approach , 2020, Soft Comput..

[14]  Rama Komaragiri,et al.  Design of wavelet transform based electrocardiogram monitoring system. , 2018, ISA transactions.

[15]  Hong-Chao Zhang,et al.  A fuzzy logic based aggregation method for life cycle impact assessment , 2014 .

[16]  Rama Komaragiri,et al.  Heart rate monitoring and therapeutic devices: A wavelet transform based approach for the modeling and classification of congestive heart failure. , 2018, ISA transactions.

[17]  R. Christian,et al.  A Unique Variable Selection Approach in Fuzzy Modeling to Predict Biogas Production in Upflow Anaerobic Sludge Blanket Reactor (UASBR) Treating Distillery Wastewater , 2020 .

[18]  Mahmoud Omid,et al.  Prediction of potato yield based on energy inputs using multi-layer adaptive neuro-fuzzy inference system , 2014 .

[19]  Pei-Chih Wu,et al.  Weather as an effective predictor for occurrence of dengue fever in Taiwan. , 2007, Acta tropica.

[20]  Lotfi A. Zadeh,et al.  Fuzzy logic , 1988, Computer.

[21]  Rama Komaragiri,et al.  From Pacemaker to Wearable: Techniques for ECG Detection Systems , 2018, Journal of Medical Systems.

[22]  Mohammed Al-Smadi,et al.  Fuzzy conformable fractional differential equations: novel extended approach and new numerical solutions , 2020, Soft Comput..

[23]  Soovoojeet Jana,et al.  A fuzzy rule-based model to assess the effects of global warming, pollution and harvesting on the production of Hilsa fishes , 2020, Ecol. Informatics.

[24]  Michael A. Johansson,et al.  The Incubation Periods of Dengue Viruses , 2012, PloS one.

[25]  N. Alavi,et al.  Quality determination of Mozafati dates using Mamdani fuzzy inference system , 2013 .

[26]  L. Harrington,et al.  Flushing Effect of Rain on Container-Inhabiting Mosquitoes Aedes aegypti and Culex pipiens (Diptera: Culicidae) , 2008, Journal of medical entomology.

[27]  Dipak Kumar Jana,et al.  Prediction of maximum oil-yield from almond seed in a chemical industry: A novel type-2 fuzzy logic approach , 2019, South African Journal of Chemical Engineering.

[28]  Simon I. Hay,et al.  Dengue disease outbreak definitions are implicitly variable , 2015, Epidemics.

[29]  Liang Liang,et al.  Measuring the performance of thermal power firms in China via fuzzy Enhanced Russell measure model with undesirable outputs , 2015 .