---
# System prepended metadata

title: ILI Forecast

---

To forecast influenza-like illness (ILI) using machine learning, it is essential to consider the impurity of ILI data as a measure of actual influenza transmission. Mechanistic models may be at a disadvantage compared to more structurally flexible statistical approaches when modeling and forecasting ILI (Reich et al., 2019). demonstrated the effectiveness of aggregating forecasts of multiple respiratory pathogens to support more accurate forecasting of ILI, which included separate predictions for contributing pathogens and a method to forecast ILI by aggregating these predictions (Pei & Shaman, 2020). Additionally, leveraging Google search data with deep learning, machine learning, and time series modeling has been shown to produce forecasts close in accuracy to those fitted to actual ILI data (Olukanmi et al., 2021).

Furthermore, a stacked ensemble method has been proposed for forecasting ILI visit volumes at emergency departments, leading to multiple candidate models suitable for forecasting ILI activity within a country or region (Amorim et al., 2021). It is also important to note that even simple statistical models may perform as well as or better than more complex machine learning models for forecasting ILI during the COVID-19 pandemic (Turner et al., 2022). Additionally, adaptively stacking ensembles for influenza forecasting has been explored, with probabilistic forecasts for component models submitted for the 2018/2019 season (McAndrew & Reich, 2021).

Moreover, the integration of Google search data and artificial intelligence methods has been leveraged for provincial-level influenza forecasting, demonstrating the potential of time series, machine learning, and deep learning methods for forecasting ILI rates (Olukanmi et al., 2022). Evaluation of mechanistic and statistical methods in forecasting ILI has shown the representation of previous outbreaks by normal distributions, with maximum-likelihood estimation used to obtain candidate trajectory weights that best represent observed ILI during the training period (Kandula et al., 2018).

In conclusion, the synthesis of these references highlights the importance of considering the impurity of ILI data, the effectiveness of aggregating forecasts of multiple respiratory pathogens, the potential of leveraging Google search data with deep learning and machine learning, and the adaptability of statistical and machine learning models for forecasting ILI. These findings provide valuable insights for developing accurate and effective machine learning-based ILI forecasting models.

References:
Amorim, A., Deardon, R., & Saini, V. (2021). A stacked ensemble method for forecasting influenza-like illness visit volumes at emergency departments. Plos One, 16(3), e0241725. https://doi.org/10.1371/journal.pone.0241725
Kandula, S., Yamana, T., Pei, S., Yang, W., Morita, H., & Shaman, J. (2018). Evaluation of mechanistic and statistical methods in forecasting influenza-like illness. Journal of the Royal Society Interface, 15(144), 20180174. https://doi.org/10.1098/rsif.2018.0174
McAndrew, T. and Reich, N. (2021). Adaptively stacking ensembles for influenza forecasting. Statistics in Medicine, 40(30), 6931-6952. https://doi.org/10.1002/sim.9219
Olukanmi, S., Nelwamondo, F., & Nwulu, N. (2021). Utilizing google search data with deep learning, machine learning and time series modeling to forecast influenza-like illnesses in south africa. Ieee Access, 9, 126822-126836. https://doi.org/10.1109/access.2021.3110972
Olukanmi, S., Nelwamondo, F., & Nwulu, N. (2022). Leveraging google search data and artificial intelligence methods for provincial-level influenza forecasting: a south african case study. International Journal of Online and Biomedical Engineering (Ijoe), 18(11), 95-126. https://doi.org/10.3991/ijoe.v18i11.29899
Pei, S. and Shaman, J. (2020). Aggregating forecasts of multiple respiratory pathogens supports more accurate forecasting of influenza-like illness. Plos Computational Biology, 16(10), e1008301. https://doi.org/10.1371/journal.pcbi.1008301
Reich, N., Brooks, L., Fox, S., Kandula, S., McGowan, C., Moore, E., … & Shaman, J. (2019). A collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the united states. Proceedings of the National Academy of Sciences, 116(8), 3146-3154. https://doi.org/10.1073/pnas.1812594116
Turner, S., Hulme-Lowe, C., & Nagraj, V. (2022). Forecasting influenza-like illness (ili) during the covid-19 pandemic.. https://doi.org/10.1101/2022.10.27.22281617