A Forecasting Prognosis of the Monkeypox Outbreak Based on a Comprehensive Statistical and Regression Analysis

Abstract

The uncommon illness known as monkeypox is brought on by the monkeypox virus. The Orthopoxvirus genus belongs to the family Poxviridae, which also contains the monkeypox virus. The variola virus, which causes smallpox; the vaccinia virus, which is used in the smallpox vaccine; and the cowpox virus are all members of the Orthopoxvirus genus. There is no relationship between chickenpox and monkeypox. After two outbreaks of a disorder resembling pox, monkeypox was first discovered in colonies of monkeys kept for research in 1958. The illness, also known as “monkeypox”, still has no known cause. However, non-human primates and African rodents can spread the disease to humans (such as monkeys). In 1970, a human was exposed to monkeypox for the first time. Several additional nations in central and western Africa currently have documented cases of monkeypox. Before the 2022 outbreak, almost all instances of monkeypox in people outside of Africa were connected to either imported animals or foreign travel to nations where the illness frequently occurs. In this work, the most recent monkeypox dataset was evaluated and the significant instances were visualized. Additionally, nine different forecasting models were also used, and the prophet model emerged as the most reliable one when compared with all nine models with an MSE value of 41,922.55, an R2 score of 0.49, a MAPE value of 16.82, an MAE value of 146.29, and an RMSE value of 204.75, which could be considerable assistance to clinicians treating monkeypox patients and government agencies monitoring the origination and current state of the disease.

Deep Learning Architectures Framework for Emerging Outbreak Forecasting of Mpox: A Bagged Ensemble Scheme to Model Accurate Prediction Intervals

“…They are chosen based on how much performance each one can obtain for particular applications based on the available data (9). Traditional time series forecasting methods, such as exponential smoothing (ETS) and ARIMA, are commonly implemented to develop infected or death projection models over specific time frames (10)(11)(12)(13)(14). Despite their satisfactory performance and interpretability, they are not readily adaptable to non-stationary data and are usually overfit (15).…”

Retrospective evaluation of short-term forecast performance of ensemble sub-epidemic frameworks and other time-series models: The 2022-2023 mpox outbreak across multiple geographical scales, July 14th, 2022, through February 26th, 2023

“…The auto regressive integrate moving average (ARIMA) model is a tool for understanding and predicting future values in a time series. It is commonly employed in forecasting financial [54][55][56] and weather [57][58][59] trends, and have become a standard benchmark in disease forecasting [17,31,[33][34][35][36][37]60,61]. ARIMA models consist of three parts: the auto-regression (AR) part involving regressing on the most recent values of the series, the moving average (MA) of error terms occurring contemporaneously and at previous times, and the integration (I) or differencing to account for the overall trend in the data and to make the time series stable.…”

“…We selected the ARIMA model as baseline, as it has been frequently evaluated against other forecasting methodologies in the context of mpox [31,[33][34][35][36][37]. Therefore, its inclusion in skill score calculations provides a more in-depth quantitative evaluation of the forecasting abilities of the n-sub-epidemic and spatial-wave frameworks against a well-vetted methodology.…”

“…Several methodologies have been employed to forecast the case trajectory of the 2022-2023 mpox epidemic in various geographical regions, including, but not limited to, models focused on human judgment [29], deep learning, and artificial intelligence models (AI) [30][31][32], machine learning models [30][31][32][33][34][35], statistical models such as auto-regressive integrated moving average (ARIMA) models [31,[33][34][35][36][37], compartmental models [38], and semi-mechanistic sub-epidemic models [16]. Performance metrics employed have mainly included variations of mean absolute error (MAE) [16,30,31,[33][34][35]37], mean squared error (MSE) [16,[30][31][32][33][34][35][36][37], and mean absolute percentage error (MAPE) [16,31,[33][34][35], with only two studies also including probabilistic measures of performance such as the weighted interval score (WIS) [16,39]. Of the studies included here, four focused on the ascending phase of the epidemic (January 2022-Mid August 2022) [29,…”

“…Performance metrics employed have mainly included variations of mean absolute error (MAE) [16,30,31,[33][34][35]37], mean squared error (MSE) [16,[30][31][32][33][34][35][36][37], and mean absolute percentage error (MAPE) [16,31,[33][34][35], with only two studies also including probabilistic measures of performance such as the weighted interval score (WIS) [16,39]. Of the studies included here, four focused on the ascending phase of the epidemic (January 2022-Mid August 2022) [29,33,34,36], four evaluated forecasts of the ascending and peak of the epidemic (February 2022-September 2022) [30][31][32]39], and four focused on the ascending, peak and decline phases of the epidemic (January 2022-Present) [16,35,37,38].…”