A research paper authored by three HU doctoral students has been published by forecasting scholarly journal.
The paper, written by Data Sciences Ph.D. students Bowen Long, Fangya Tan, and Mark Newman, details the students’ development of an efficient forecasting tool that allows health experts to implement effective prevention policies for monkeypox and shed light on case development of diseases that share similar characteristics.
According to the publication, “Forecasting the Monkeypox Outbreak Using ARIMA, Prophet, NeuralProphet, and LSTM Models in the United States,” the students incorporated five machine learning models: ARIMA, LSTM, Prophet, NeuralProphet, and a stacking model, on the monkeypox datasets taken from the Center for Disease Control (CDC) website to forecast the next 7-day trend of monkeypox cases in the United States. The result showed that NeuralProphet generated the best results with a RMSE of 49.27 and R2 of 0.76.
The following is the paper abstract:
“Since May 2022, over 64,000 Monkeypox cases have been confirmed globally up until September 2022. The United States leads the world in cases, with over 25,000 cases nationally. This recent escalation of the Monkeypox outbreak has become a severe and urgent worldwide public health concern. We aimed to develop an efficient forecasting tool that allows health experts to implement effective prevention policies for Monkeypox and shed light on the case development of diseases that share similar characteristics to Monkeypox. This research utilized five machine learning models, namely, ARIMA, LSTM, Prophet, NeuralProphet, and a stacking model, on the Monkeypox datasets from the CDC official website to forecast the next 7-day trend of Monkeypox cases in the United States. The result showed that NeuralProphet achieved the most optimal performance with a RMSE of 49.27 and R2 of 0.76. Further, the final trained NeuralProphet was employed to forecast seven days of out-of-sample cases. On the basis of cases, our model demonstrated 95% accuracy.”
To view the paper, please visit this link.
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