Spatio-Temporal Multi-step Prediction of Influenza Outbreaks
Flu circulates all over the world. The worldwide infection places a substantial burden on people's health every year. Regardless of the characteristic of the worldwide circulation of flu, most previous studies focused on regional prediction of flu outbreaks. The methodology of considering the spatio-temporal correlation could help forecast flu outbreaks more precisely. Furthermore, forecasting a long-term flu outbreak, and understanding flu infection trends more accurately could help hospitals, clinics, and pharmaceutical companies to better prepare for annual flu outbreaks. Predicting a sequence of values in the future, namely, the multi-step prediction of flu outbreaks should cause concern. Therefore, we highlight the importance of developing spatio-temporal methodologies to perform multi-step prediction of worldwide flu outbreaks. We compared the MAPEs of SVM, RF, LSTM models of predicting flu data of the 1-4 weeks ahead with and without other countries' flu data. We found the LSTM models achieved the lowest MAPEs in most cases. As for countries in the Southern hemisphere, the MAPEs of predicting flu data with other countries are higher than those of predicting without other countries. For countries in the Northern hemisphere, the MAPEs of predicting flu data of the 2-4 weeks ahead with other countries are lower than those of predicting without other countries; and the MAPEs of predicting flu data of the 1-weeks ahead with other countries are higher than those of predicting without other countries, except for the UK. In this study, we performed the spatio-temporal multi-step prediction of influenza outbreaks. The methodology considering the spatio-temporal features improves the multi-step prediction of flu outbreaks.
Code (0)
등록된 구현이 없습니다.
Tasks
PredictionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Comprehensive learning particle swarm optimization enabled modeling framework for multi-step-ahead influenza prediction
Epidemics of influenza are major public health concerns. Since influenza prediction always relies on the weekly clinical or laboratory surveillance data, typically the weekly Influenza-like illness (ILI) rate series, acc…
PredictionGenerative diffusion models for spatiotemporal influenza forecasting
Forecasting infectious disease incidence can provide important information to guide public health planning, yet is difficult because epidemic dynamics are complex. Current mechanistic and statistical approaches often str…
Early Stage Influenza Detection from Twitter
Influenza is an acute respiratory illness that occurs virtually every year and results in substantial disease, death and expense. Detection of Influenza in its earliest stage would facilitate timely action that could red…
Beyond Time Series: Spatial Reasoning for Epidemic Forecasting via Multimodal Learning
Epidemic forecasting models typically rely on surveillance data reported over administrative regions, treating them as atomic units, thereby obscuring sub-regional spatial structure that shapes disease dynamics. We intro…
Spatial ReasoningPrior Knowledge-enhanced Spatio-temporal Epidemic Forecasting
Spatio-temporal epidemic forecasting is critical for public health management, yet existing methods often struggle with insensitivity to weak epidemic signals, over-simplified spatial relations, and unstable parameter es…