Short-Term Forecasting of Monkeypox Cases Using a Novel Filtering and Combining Technique

Abstract

In the modern world, new technologies such as artificial intelligence, machine learning, and big data are essential to support healthcare surveillance systems, especially for monitoring confirmed cases of monkeypox. The statistics of infected and uninfected people worldwide contribute to the growing number of publicly available datasets that can be used to predict early-stage confirmed cases of monkeypox through machine-learning models. Thus, this paper proposes a novel filtering and combination technique for accurate short-term forecasts of infected monkeypox cases. To this end, we first filter the original time series of the cumulative confirmed cases into two new subseries: the long-term trend series and residual series, using the two proposed and one benchmark filter. Then, we predict the filtered subseries using five standard machine learning models and all their possible combination models. Hence, we combine individual forecasting models directly to obtain a final forecast for newly infected cases one day ahead. Four mean errors and a statistical test are performed to verify the proposed methodology's performance. The experimental results show the efficiency and accuracy of the proposed forecasting methodology. To prove the superiority of the proposed approach, four different time series and five different machine learning models were included as benchmarks. The results of this comparison confirmed the dominance of the proposed method. Finally, based on the best combination model, we achieved a forecast of fourteen days (two weeks). This can help to understand the spread and lead to an understanding of the risk, which can be utilized to prevent further spread and enable timely and effective treatment.

Keywords: filtering and combining technique; machine learning models; monkeypox virus; short-term forecasting; time series models.

Figure 1

World Monkeypox Virus Data: the daily confirmed cases of the monkeypox virus are filtered by the two proposed filters: (top) RSF, (middle) SSF, and the benchmark filter HPF (bottom). Within each subfigure, the top panel shows the long-term trend (blue curve-${\mathrm{L}}_{\mathrm{t}}$), and the bottom panel shows the residual part (red curve-${\mathrm{R}}_{\mathrm{t}}$).

Figure 1

World Monkeypox Virus Data: the daily confirmed cases of the monkeypox virus are filtered by the two proposed filters: (top) RSF, (middle) SSF, and the benchmark filter HPF (bottom). Within each subfigure, the top panel shows the long-term trend (blue curve-${\mathrm{L}}_{\mathrm{t}}$), and the bottom panel shows the residual part (red curve-${\mathrm{R}}_{\mathrm{t}}$).

Figure 2

A flowchart of the proposed filtering and combination technique.

Figure 3

World Monkeypox Virus Data: the cumulative confirmed cases of monkeypox virus (left) and the daily confirmed cases of monkeypox virus (right) from 7 May 2022 to 10 February 2023.

Figure 4

World Monkeypox Virus Data: accuracy measurement plots: MAPE (1st), MAE (2nd), RMAPE (3rd), and RMSE (4th), for all combination models using two proposed filters and a benchmark filter.

Figure 5

World Monkeypox Virus Data: accuracy measurement plots for the best three models, (left) (MAPE-red circle and RMSPE-purple triangle) and (right) (MAE-blue star and RMSE-green square) for the best three final models using the two proposed filtering methods and a benchmark filter.