A hybrid forecasting technique for infection and death from the mpox virus

Abstract

Objectives The rising of new cases and death counts from the mpox virus (MPV) is alarming. In order to mitigate the impact of the MPV it is essential to have information of the virus's future position using more precise time series and stochastic models. In this present study, a hybrid forecasting system has been developed for new cases and death counts for MPV infection using the world daily cumulative confirmed and death series. Methods The original cumulative series was decomposed into new two subseries, such as a trend component and a stochastic series using the Hodrick–Prescott filter. To assess the efficacy of the proposed models, a comparative analysis with several widely recognized benchmark models, including auto-regressive (AR) model, auto-regressive moving average (ARMA) model, non-parametric auto-regressive (NPAR) model and artificial neural network (ANN), was performed. Results The introduction of two novel hybrid models, [Formula: see text] and [Formula: see text], which demonstrated superior performance compared to all other models, as evidenced by their remarkable results in key performance indicators such as root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE), is a significant advancement in disease prediction. Conclusion The new models developed can be implemented in forecasting other diseases in the future. To address the current situation effectively, governments and stakeholders must implement significant changes to ensure strict adherence to standard operating procedures (SOPs) by the public. Given the anticipated continuation of increasing trends in the coming days, these measures are essential for mitigating the impact of the outbreak.

Marburg Virus Outbreak and a New Conspiracy Theory: Findings from a Comprehensive Analysis and Forecasting of Web Behavior

“…The work of Iftikhar et al [127] focused on forecasting new cases and death counts related to the MPox virus using a hybrid forecasting system that combined time-series and stochastic models. Long et al [128] worked on addressing the global health concern during the MPox outbreak, particularly in the United States, using short-term forecasting, and, somewhat similar to the comparative studies discussed in [129,130], the authors compared the working and performance of multiple machine learning models.…”

Evaluating the forecasting performance of ensemble sub-epidemic frameworks and other time series models for the 2022–2023 mpox epidemic

“…Several methodologies have been employed to forecast the trajectory of the 2022-2023 mpox epidemic in various geographical regions, including, but not limited to, models focused on human judgement [20], deep learning and artificial intelligence models [21,22], machine learning models [21][22][23][24][25], statistical models such as auto-regressive integrated moving average (ARIMA) models [21,[23][24][25][26][27], compartmental models [28] and semi-mechanistic sub-epidemic models [7]. Performance metrics employed have mainly included variations of mean absolute error (MAE) [7,21,[23][24][25]27], mean squared error (m.s.e.) [7,[21][22][23][24][25][26] and mean absolute percentage error [21,[23][24][25].…”

“…Only two studies used probabilistic measures of performance, such as the weighted interval score (WIS) [7,29]. Of the studies included here, four focused on the ascending phase of the epidemic (January-mid-August 2022) [20,[24][25][26], three included forecasts of the epidemic's ascension and peak (February-September 2022) [21,22,29] and four focused on the ascending, peak and declining phases of the epidemic (January 2022-present) [7,23,27,28].…”