Data-Driven AI-Based Parameters Tuning Using Grid Partition Algorithm for Predicting Climatic Effect on Epidemic Diseases

Sunusi Bala Abdullahi, Kanikar Muangchoo*, Auwal Bala Abubakar, Abdulkarim Hassan Ibrahim, Kazeem Olalekan Aremu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


Adaptive Neuro-fuzzy Inference System (ANFIS) remains one of the promising AI techniques to handle data over-fitting and as well, improves generalization. Presently, many ANFIS optimization techniques have been synergized and found effective at some points through trial and error procedures.In this work, we tune ANFIS using Grid partition algorithm to handle unseen data effectively with fast convergence. This model is initialized using a careful selection of effective parameters that discriminate climate conditions; minimum temperature, maximum temperature, average temperature, windspeed and relative humidity. These parameters are used as inputs for ANFIS, whereas confirmed casesof COVID-19 is chosen as dependent values for two consecutive months and first ten days of Decemberfor new COVID-19 confirmed cases according to the Department of disease control (DDC) Thailand. Theproposed ANFIS model provides outstanding achievement to predict confirmed cases of COVID-19 with $R{2} of 0.99. Furthermore, data set trend analysis is done to compare fluctuations of daily climatic parameters, to satisfy our proposition, and illustrates the serious effect of these parameters onCOVID-19 epidemic virus spread.

Original languageEnglish
Article number9400168
Pages (from-to)55388-55412
Number of pages25
JournalIEEE Access
Publication statusPublished - 2021
Externally publishedYes


  • Adaptive neuro fuzzy inference system
  • COVID-19
  • artificial Intelligence
  • climatic impacts
  • epidemic diseases
  • grid partition algorithm
  • parameters tuning


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