A Type-2 Fuzzy Logic Based System for Malaria Epidemic Prediction in Ethiopia

Hani Hagras Hagras, Belay Enyew Chekol


Malaria is the most prevalent mosquito-borne disease throughout tropical and subtropical regions of the world with severe medical, economic, and social impact. Malaria is a serious public health problem in Ethiopia since 1959, even if, its morbidity and mortality have been reduced starting from 2001. Various studies were conducted to predict the malaria epidemic using mathematical and statistical approaches, nevertheless, they had no learning capabilities. In this paper, we present a Type-2 Fuzzy Logic Based System for Malaria epidemic prediction in Ethiopia which was trained using real data collected throughout Ethiopia from 2013 to 2017. Fuzzy Logic Based Systems provide a transparent model which employs IF-Then rules for the prediction that could be easily analyzed and interpreted by decision-makers. This is quite important to fight the sources of Malaria and take the needed preventive measures where the generated rules from our system were able to explain the situations and intensity of input factors which contributed to Malaria epidemic incidence up to three months ahead. The presented Type-2 Fuzzy Logic System (T2FLS) learns its rules and fuzzy set parameters from data and was able to outperform its counterparts T1FLS in 2% and ANFIS in 0.33% in the accuracy of prediction of Malaria epidemic in Ethiopia. In addition, the proposed system did shed light on the main causes behind such outbreaks in Ethiopia because of its high level of interpretability


Type-2 fuzzy logic system, Fuzzy C-means, malaria prediction, machine learning

Full Text:



Abebe A, Gemeda A, Wondewossen T, Lemu G. (2011). Climatic variables and malaria transmission

dynamics in Jimma town, South West Ethiopia. Parasites and Vectors 4.

World Health Organisation. (2018).World malaria report. Geneva

Taffese H., Hemming-Schroeder E, Koepfli C, Tesfaye G, Lee M., Kazura J, Yan G., Zhou G. (2018). Malaria epidemiology and interventions in Ethiopia from 2001 to 2016. Infectious Diseases of Poverty 7:1–9.

Ethiopia FMOH. (2007). Entomological Profile of Malaria in Etiopia.

Thomson M, Connor S. (2001). The development of Malaria Early Warning Systems for Africa. TRENDS in Parasitology 17:438–445.

National Malaria Control Team, Ethiopian Public Health Institute, World Health Organisation, Addis Ababa University, INFORM Project. (2014). An Epidemiological Profile of Malaria in Ethiopia Report. Nairobi, Kenya.

Zhou G., Minakawa N., Githeko A.andYan G. (2004). Association between climate variability and malaria epidemics in the East African highlands. Proceedings of the National Academy of Sciences of the United States of America 101:75–80.

Teklehaimanot H., Lipsitch M., Teklehaimanot A. and Schwartz J. (2004). Weather-based prediction of Plasmodium falciparum malaria in epidemic- prone regions of Ethiopia I . Patterns of lagged weather effects reflect biological mechanisms. Malaria Journal 11:1–11.

Zhao X., Chen F., Feng Z., Li X. and Zhou X. (2014).The temporal lagged association between meteorological factors and malaria in 30 counties in south-west China: A multilevel distributed lag non-linear analysis. Malaria Journal 13:1–12.

Eskinder .L, and Bern L. (2010). Model variations in predicting incidence of Plasmodium falciparum malaria using 1998-2007 morbidity and meteorological data from south Ethiopia. Malaria journal 9.

Kifle M., Teklemariam T., Teweldeberhan A., Tesfamariam E., Andegiorgish A. and Azaria K. (2019). Malaria Risk Stratification and Modeling the Effect of Rainfall on Malaria Incidence in Eritrea. Journal of Environmental and Public Health 2019:1–11.

Midekisa A, Senay G, Henebry G, Semuniguse P, Wimberly M. (2012). Remote sensing-based time series models for malaria early warning in the highlands of Ethiopia. Malaria Journal 11:1–10.

Abeku T., Van Oortmarssen G., Borsboom G., De Vlas S.and Habbema J. (2003). Spatial and temporal variations of malaria epidemic risk in Ethiopia: Factors involved and implications. Acta Tropica 87:331–340.

Chekol B., Hagras H. (2019). Employing Machine Learning Techniques for the Malaria Epidemic Prediction in Ethiopia. In: Proceedings of 10th Computer Science and Electronic Engineering (CEEC).IEEE: 89–94.

Rismala R., Liang H. and Ardiyanti A. (2013). Prediction of Malaria Incidence in Banggai Regency Using Evolving Neural Network. In: International Conference on Technology, Informatics, Management, and Engineering & Environment (TIME-E 2013) Bandung, Indonesia.

Santosh T. and Ramesh D. (2019). Artificial neural network based prediction of malaria abundances using big data: A knowledge capturing approach. Clinical Epidemiology and Global Health 7:121– 126.

Sudheer Ch., Sohani S., Deepak K., Anushree M., Chahar B., Nema A, Panigrahi B. and Dhiman R. (2014). A Support Vector Machine-Firefly Algorithm based forecasting model to determine malaria transmission. Neurocomputing 129:279– 288.

Hagras H. and Wagner C. (2009). Introduction to Interval Type-2 Fuzzy Logic Controllers - Towards Better Uncertainty Handling in Real World Applications. The IEEE Systems and Cybernetics eNewsletter 27.

Sakalli A., Kumbasar T., Yesil Y. and Hagras H. (2014). Analysis of the performances of type-1, self-tuning type-1 and interval type-2 fuzzy PID controllers on the Magnetic Levitation system. In: Proceedings of the 2014 IEEE International Conference on Fuzzy Systems, Beijing, China.

Doctor F, Hagras H. and Callaghan V. (2005). A type-2 fuzzy embedded agent to realise ambient intelligence in ubiquitous computing environments. Information Sciences 171:309–334.

Olatunji S., Selamat A. and Raheem A. (2011). Expert Systems with Applications Predicting correlations properties of crude oil systems using type-2 fuzzy logic systems. Expert Systems with Applications 38:10911–10922.

Wang C. and Dong M. (2012). Engineering Applications of Artificial Intelligence Modeling data uncertainty on electric load forecasting based on Type-2 fuzzy logic set theory. Engineering Applications of Artificial Intelligence 25:1567– 1576.

Bernardo D., Hagras H. andTsang E. (2013). A genetic type-2 fuzzy logic based system for the generation of summarised linguistic predictive models for financial applications. Soft Computing 17:2185–2201.

ZADEH L. (1965). Fuzzy Sets. Information and Control 8:338–353.

Wagner C. and Hagras H. (2010). Toward general type-2 fuzzy logic systems based on zSlices. IEEE Transactions on Fuzzy Systems18:637–660

Mendel J., Hagras H., Tan W. and Melek W, Ying

H. (2017). Introduction to type-2 fuzzy logic control. IEEE Press Canada

Mendel J. (2017). Uncertain Rule-Based Fuzzy Systems Second Edition. Springer, Los Angeles.

Zadeh L. (1975). The Concept of a Linguistic Variable and its Application to Approxirmate Reasoning-III*. Information Sciences 9:43–80.

Karnik N, Mendel J. (1999). Applications of type- 2 fuzzy logic systems to forecasting of time-series. Information Sciences 120:89–111.

Hagras H., Wagner C. (2009). Introduction to Interval Type • 2 Fuzzy Logic Controllers • Towards Better Uncertainty Handling in Real World Applications. The IEEE Systems, Man and Cybernetics eNewsletter June 2009:1–9.

Liang Q. and Mendel J. (2000). Interval type-2 fuzzy logic systems: Theory and design. IEEE Transactions on Fuzzy Systems 8:535–550.

Dunn J. (1974). A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters. Journal of Cybernetics 3:32–57.

James B., Robert E., William F. (1984). FCM : The Fuzzy C-Means Clustering Algorithm. Computers & Geosciences 10:191–203.

Valente de O. and Pedrycz W. (2007). Advances in Fuzzy Clustering and its Applications. John Wiley & Sons, Chichester England.

Ogasawara E., Martinez L., De Oliveira D., Zimbrão G, Pappa G. and Mattoso M. (2010) Adaptive Normalization: A novel data normalization approach for non-stationary time series. Proceedings of the International Joint Conference on Neural Networks. IEEE.

Doctor F., Hagras H. and Callaghan V. (2006). An Incremental Adaptive Life Long Learning

Approach for Type-2 Fuzzy Embedded Agents in Ambient Intelligent Environments. IEEE International Conference on Fuzzy Systems 15:915–922.

Wang L. (2003). The WM Method Completed: A Flexible Fuzzy System Approach to Data Mining.

IEEE Transactions on Fuzzy Systems 11:768–782

Ferreyra E, Hagras H, Mohamed A, Owusu G, Telecom B, Park A. (2017). A Type-2 Fuzzy Logic System for Engineers Estimation in the Workforce Allocation Domain. IEEE: 0–5.

Garcia-Valverde T., Garcia-Sola A., Gomez- Skarmeta A., Botia J., Hagras H, Dooley J. and Callaghan V. (2012). An adaptive learning fuzzy logic system for indoor localisation using Wi-Fi in Ambient Intelligent Environments. IEEE International Conference on Fuzzy Systems/FUZZ- IEEE.

Chimatapu R., Hagras H., Starkey A.and Owusu G. (2018). A big-bang big-crunch type-2 fuzzy logic system for generating interpretable models in workforce optimization. IEEE International Conference on Fuzzy Systems 2:1–8.

Hyndman R. andKoehler A. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting 22:679–688.

Bo Y., Hagras H., Daniyal A.and Mohammed A. (2016). A Big Bang-Big Crunch Type-2 Fuzzy Logic System for Machine Vision-Based Event Detection and Summarization in Real-world Ambient Assisted Living. IEEE.

Starkey A., Hagras H., Shakya S.and Owusu G. (2016). A multi-objective genetic type-2 fuzzy logic based system for mobile field workforce area optimization. Information Sciences 329:390–411.

Michela A., Dario B., Hani H. (2016). Multi- Objective Evolutionary Optimization of Type-2 Fuzzy Rule-based Systems for Financial Data Classification. IEEE Transactions on Fuzzy Systems