Finding an Accurate Early Forecasting Model from Small Dataset: A Case of 2019-nCoV Novel Coronavirus Outbreak
Metadatos
Mostrar el registro completo del ítemEditorial
UNIV INT RIOJA-UNIR
Materia
Forecasting Machine learning Methods Prediction Data mining Epidemic COVID-19
Fecha
2020Referencia bibliográfica
Fong, S. J., Li, G., Dey, N., Crespo, R. G., & Herrera-Viedma, E. (2020). Finding an Accurate Early Forecasting Model from Small Dataset: A Case of 2019-nCoV Novel Coronavirus Outbreak. International Journal of Interactive Multimedia and Artificial Intelligence, Vol. 6, Nº 1, p. 132-140. [DOI: 10.9781/ijimai.2020.02.002]
Resumen
Epidemic is a rapid and wide spread of infectious disease threatening many lives and economy damages. It is
important to fore-tell the epidemic lifetime so to decide on timely and remedic actions. These measures include
closing borders, schools, suspending community services and commuters. Resuming such curfews depends on
the momentum of the outbreak and its rate of decay. Being able to accurately forecast the fate of an epidemic is
an extremely important but difficult task. Due to limited knowledge of the novel disease, the high uncertainty
involved and the complex societal-political factors that influence the widespread of the new virus, any forecast
is anything but reliable. Another factor is the insufficient amount of available data. Data samples are often
scarce when an epidemic just started. With only few training samples on hand, finding a forecasting model
which offers forecast at the best efforts is a big challenge in machine learning. In the past, three popular methods
have been proposed, they include 1) augmenting the existing little data, 2) using a panel selection to pick the
best forecasting model from several models, and 3) fine-tuning the parameters of an individual forecasting
model for the highest possible accuracy. In this paper, a methodology that embraces these three virtues of
data mining from a small dataset is proposed. An experiment that is based on the recent coronavirus outbreak
originated from Wuhan is conducted by applying this methodology. It is shown that an optimized forecasting
model that is constructed from a new algorithm, namely polynomial neural network with corrective feedback
(PNN+cf) is able to make a forecast that has relatively the lowest prediction error. The results showcase that the
newly proposed methodology and PNN+cf are useful in generating acceptable forecast upon the critical time of
disease outbreak when the samples are far from abundant.