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However, following the methodology and prediction tool based on the article "A quick prediction tool for unfavorable outcome in COVID-19 inpatients: Development and internal validation" by Salto-Alejandre et al.,
we performed prediction of mpox based on previously reported cases.
Early in May of 2022, the first cases of mpox outside of endemic regions were recorded, and new cases are continually being detected in various endemic regions. Often these patients with a travel history visited Europe and North America, not West or Central Africa, where mpox is widespread.
A. Zumla, S.R. Valdoleiros, N. Haider, D. Asogun, F. Ntoumi, E. Petersen, et al., Monkeypox outbreaks outside endemic regions: scientific and social priorities, Lancet Infect Dis, 22(7), 2022, 929-931.
In light of the current mpox outbreak, the Director-General of the World Health Organization (WHO) has declared a Public Health Emergency of International Concern (PHEIC).
Two forecasting models were implemented to get significant outcomes. The one is a widely used time-series model, autoregressive integrated moving average (ARIMA). The second is that artificial neural networks (ANNs) have developed a potent tool for machine learning (ML) and artificial intelligence (AI). ARIMA is a generalized form of autoregressive moving average (ARMA). ARIMA is a linear model for forecasting the upcoming trend based on historical data.
ANNs are prediction techniques that permit complex, nonlinear relationships between the predictor factors and the response. They are based on basic mathematical models of the brain. A neural network resembles a network of "neurons" controlled in layers. The lower layer is made up of the predictors (or inputs), and the upper layer is made up of the forecasts (or outputs). The lag values can be utilized as inputs to the neural network in the instance of time series data; this model is referred to as neural network autoregression (NNAR). This paper considers the feed-forward neural network with one hidden layer, designated by NNAR (p, k) and consisting of p delayed inputs and k hidden nodes.
This study predicts the mpox outbreak worldwide for the next month (31st January 2023). The dataset was obtained from the worldwide website (https://ourworldindata.org/mpox), which covers the period from 1st May 2022 to 29th November 2022 and is considered for the prediction. In addition to offering insight into the transmission patterns of the outbreaks, the purpose of this study is to furnish accurate predictions of the outbreak to the authorities and severity by applying fundamentally significant models. These tools can assist in predicting future medical requirements and timely planning to curb the disease.
The comparison between the two models indicates that the ARIMA (5,2,3) for mpox cases and ARIMA (0,2,1) for mpox deaths are more effective in explaining the estimates of mpox trends. The predicted number of daily infected cases and deaths for the next two months upto (31st January 2023) estimates might reach 87,276 (CI 95%: 66,224- 108,328) for cases, and the estimate might reach 94 (CI 95%: 69–118) for deaths. Figs. 1 and 2 depict both models' predicted performance, showing that the lines increase for the confirmed cases and deaths. We checked the models' accuracy through mean absolute error (MAE), root mean square error (RMSE), and akaike information criterion (AIC). ARIMA (5,2,3) daily confirmed cases model with AIC: 2955, MAE: 306.12 and RMSE: 391.39 was significantly predicted. Significant predictions were made using the ARIMA (0,2,1) daily deaths model, which had an AIC of 477, MAE of 0.35, and RMSE:0.73.
Fig. 1ARIMA (5,2,3) more effectively explains the estimation of mpox cases trend with an AIC: 2955, MAE: 306.12, and RMSE: 391.39. The predicted line shows a significant increase for the upcoming month (up to January 31, 2023).
Fig. 2ARIMA (0,2,1) more effectively explains the estimation of mpox deaths trend with an AIC of 477, MAE of 0.35, and RMSE:0.73. The predicted line shows a significant increase for the upcoming month (up to January 31, 2023).
Mpox can be transmitted from animals to people by direct contact with diseased body parts or fluids, clawing or scratching, eating infectious meat, and handling contaminated objects.
However, the person-to-person transmission of mpox is reported via intimate contact with an infected person's respiratory secretions, skin sores or genitals, face-to-face contact, bedding, and clothes.
In addition, cases have primarily, though not solely, involved males who have intercourse with other men, and the vast majority have been detected to be infected with the mpox virus.
The rise in mpox cases is most likely attributable to natural and man-made factors. On the other side, human–wildlife interactions have increased owing to, among many other things, climate change, forest fires, and the Ukrainian–Russian conflict.
Following the designation of mpox as a public health emergency, global health communities should strengthen their awareness campaigns, animal screening camps, immunization programs, quarantine facilities, and diagnostic capabilities for the mpox outbreak in order to stop the virus's spread. Public health workers, in particular, need to be better educated on mpox and its clinical care and more adept at infection prevention and control. At the same time, efforts should be made to address stigma and prejudice within the MSM community adequately, and fair access to treatment and immunizations should be assured; otherwise, mpox free world would be a dream.
Ethics committee approval
Not applicable.
Role of funding source
There is no role of any funding source for this manuscript.
CRediT authorship contribution statement
Muhammad Imran Khan: Conceptualization, Visualization, Formal analysis, Data curation, Writing – original draft, Writing – review & editing. Humera Qureshi: Conceptualization, Visualization, Formal analysis, Data curation, Writing – review & editing. Suk Joo Bae: Supervision, Writing – review & editing. Usman Ayub Awan: Conceptualization, Visualization, Formal analysis, Data curation, Writing – original draft, Supervision, Writing – review & editing. Zaheera Saadia: Writing – original draft, Writing – review & editing. Aamer Ali Khattak: Writing – original draft, Writing – review & editing.
Declaration of Competing Interest
We declare no competing interest.
Acknowledgement
This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20213030030190, 2021202090056C).
A. Zumla, S.R. Valdoleiros, N. Haider, D. Asogun, F. Ntoumi, E. Petersen, et al., Monkeypox outbreaks outside endemic regions: scientific and social priorities, Lancet Infect Dis, 22(7), 2022, 929-931.
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