Early Warning Methods Based on a Real Time Series Dataset: A Comparative Study

The implementation of early warning systems is an indispensable strategy in the containment of infectious diseases. Prior research has extensively employed the Long Short-Term Memory (LSTM) or Transformer network, equipped with a prediction or reconstruction error. Despite these advancements, the majority of existing early warning systems continue to rely on traditional models devoid of artificial neural networks. A comparative analysis of various models is conducted, ranging from traditional methods to advanced machine learning networks. The findings revealed that in certain instances, the performance of complex models did not significantly surpass that of simpler methods. This insight is particularly beneficial for the development of cost-effective early warning systems.