In December 2019, a novel coronavirus, called COVID-19, was discovered in Wuhan, China, and has spread to different cities in China as well as to 24 other countries. The number of confirmed cases is increasing daily and reached 34,598 on 8 February 2020. In the current study, we present a new forecasting model to estimate and forecast the number of confirmed cases of COVID-19 in the upcoming ten days based on the previously confirmed cases recorded in China. The proposed model is an improved adaptive neuro-fuzzy inference system (ANFIS) using an enhanced flower pollination algorithm (FPA) by using the salp swarm algorithm (SSA). In general, SSA is employed to improve FPA to avoid its drawbacks (i.e., getting trapped at the local optima). The main idea of the proposed model, called FPASSA-ANFIS, is to improve the performance of ANFIS by determining the parameters of ANFIS using FPASSA. The FPASSA-ANFIS model is evaluated using the World Health Organization (WHO) official data of the outbreak of the COVID-19 to forecast the confirmed cases of the upcoming ten days. More so, the FPASSA-ANFIS model is compared to several existing models, and it showed better performance in terms of Mean Absolute Percentage Error (MAPE), Root Mean Squared Relative Error (RMSRE), Root Mean Squared Relative Error (RMSRE), coefficient of determination (R 2 ), and computing time. Furthermore, we tested the proposed model using two different datasets of weekly influenza confirmed cases in two countries, namely the USA and China. The outcomes also showed good performances.

BackgroundHemorrhagic fever with renal syndrome (HFRS) is caused by different hantaviruses within the Bunyaviridae family. HFRS is a fulminant, infectious disease that occurs worldwide and is endemic in all 31 provinces of China. Since the first HFRS case in Hubei Province was reported in 1957, the disease has spread across the province and Hubei has become one of the seriously affected areas in China with the greatest number of reported HFRS cases in the 1980's. However, the epidemic characteristics of HFRS in Hubei are still not entirely clear and long-term, systematic investigations of this epidemic area have been very limited.MethodsThe spatiotemporal distribution of HFRS was investigated using data spanning the years 1980 to 2009. The annual HFRS incidence, fatality rate and seasonal incidence between 1980 and 2009 were calculated and plotted. GIS-based spatial analyses were conducted to detect the spatial distribution and seasonal pattern of HFRS. A spatial statistical analysis, using Kulldorff's spatial scan statistic, was performed to identify clustering of HFRS.ResultsA total of 104,467 HFRS cases were reported in Hubei Province between 1980 and 2009. Incidence of and mortality due to HFRS declined after the outbreak in 1980s and HFRS cases have been sporadic in recent years. The locations and scale of disease clusters have changed during the three decades. The seasonal epidemic pattern of HFRS was characterized by the shift from the unimodal type (autumn/winter peak) to the bimodal type.ConclusionsSocioeconomic development has great influence on the transmission of hantaviruses to humans and new epidemic characteristics have emerged in Hubei Province. It is necessary to reinforce preventative measures against HFRS according to the newly-presented seasonal variation and to intensify these efforts especially in the urban areas of Hubei Province.

The current pandemic of the new coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), or COVID-19, has received wide attention by scholars and researchers. The vast increase in infected people is a significant challenge for each country and the international community in general. The prediction and forecasting of the number of infected people (so-called confirmed cases) is a critical issue that helps in understanding the fast spread of COVID-19. Therefore, in this article, we present an improved version of the ANFIS (adaptive neuro-fuzzy inference system) model to forecast the number of infected people in four countries, Italy, Iran, Korea, and the USA. The improved version of ANFIS is based on a new nature-inspired optimizer, called the marine predators algorithm (MPA). The MPA is utilized to optimize the ANFIS parameters, enhancing its forecasting performance. Official datasets of the four countries are used to evaluate the proposed MPA-ANFIS. Moreover, we compare MPA-ANFIS to several previous methods to evaluate its forecasting performance. Overall, the outcomes show that MPA-ANFIS outperforms all compared methods in almost all performance measures, such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Relative Error (RMSRE), and Coefficient of Determination( R 2 ). For instance, according to the results of the testing set, the R 2 of the proposed model is 96.48%, 98.59%, 98.74%, and 95.95% for Korea, Italy, Iran, and the USA, respectively. More so, the MAE is 60.31, 3951.94, 217.27, and 12,979, for Korea, Italy, Iran, and the USA, respectively.