integrating pso-ga with anfis for predictive analytics of confirmed cases of covid-19 in iran

The first case of the unknown coronavirus, referred to as covid-19, was detected in wuhan, china, in late december 2019, and spread throughout china and globally. the total confirmed cases globally are rising day by day. this study proposes a novel prediction model to estimate and predict the total confirmed cases of covid-19 in the next two days, according to iran’s confirmed cases reported before. the proposed model is an improved adaptive neuro-fuzzy inference system (anfis) using a coevolutionary pso-ga algorithm. pso-ga is generally used to strike a balance between exploration and exploitation capabilities enhanced further by integrating the genetic operators, i.e., mutation and crossover in the pso algorithm. the proposed model (i.e., pso-ga-anfis) thus aims to enhance the efficiency of the anfis model by determining anfis parameters using pso-ga. the model is assessed by utilizing epidemiological data provided by john hopkins university to forecast the covid-19 epidemic prevalence trend of iran in 02.20.2020-06.10.2020-time window. a comparison was also made between the proposed model and a couple of available models. the results indicated that the proposed model outperforms the other models regarding mse, rmse, mape, and 𝑅2 .