Evaluation of mechanistic and statistical methods in forecasting influenza-like illness

Kandula, Sasikiran; Yamana, Teresa K.; Pei, Sen; Yang, Wan; Morita, Hiromichi; Shaman, Jeffrey

doi:10.1098/rsif.2018.0174

Cited by 53 publications

(55 citation statements)

References 48 publications

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“…Accurate forecasting of ILI can support better planning of both pharmaceutical (e.g., vaccines, antivirals and prophylaxis) and non-pharmaceutical (e.g., school closure, social distancing and travel restrictions) interventions against respiratory infections, as well as early preparation for patient surges in healthcare facilities during peak periods of incidence. In recent years, a number of forecasting systems for ILI have been developed [3][4][5][6][7][8][9][10][11][12][13][14], many of which have been applied operationally to forecast ILI in the United States [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Aggregating forecasts of multiple respiratory pathogens supports more accurate forecasting of influenza-like illness

Pei

Shaman

2020

PLoS Comput Biol

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Influenza-like illness (ILI) is a commonly measured syndromic signal representative of a range of acute respiratory infections. Reliable forecasts of ILI can support better preparation for patient surges in healthcare systems. Although ILI is an amalgamation of multiple pathogens with variable seasonal phasing and attack rates, most existing process-based forecasting systems treat ILI as a single infectious agent. Here, using ILI records and virologic surveillance data, we show that ILI signal can be disaggregated into distinct viral components. We generate separate predictions for six contributing pathogens (influenza A/H1, A/ H3, B, respiratory syncytial virus, and human parainfluenza virus types 1-2 and 3), and develop a method to forecast ILI by aggregating these predictions. The relative contribution of each pathogen to the total ILI signal is estimated using a Markov Chain Monte Carlo (MCMC) method upon forecast aggregation. We find highly variable overall contributions from influenza type A viruses across seasons, but relatively stable contributions for the other pathogens. Using historical data from 1997 to 2014 at US national and regional levels, the proposed forecasting system generates improved predictions of both seasonal and near-term targets relative to a baseline method that simulates ILI as a single pathogen. The hierarchical forecasting system can generate predictions for each viral component, as well as infer and predict their contributions to ILI, which may additionally help physicians determine the etiological causes of ILI in clinical settings.

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Section: Introductionmentioning

confidence: 99%

Aggregating forecasts of multiple respiratory pathogens supports more accurate forecasting of influenza-like illness

Pei

Shaman

2020

PLoS Comput Biol

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“…Lu et al tested autoregressive nowcasts using several internet data sources and found that they improved 1-week-ahead forecasts [23]. Kandula et al measured the benefit of nowcasting to their flu forecasting model at 8-35% [21]. Finally, our own work shows that a Bayesian seasonal flu forecasting model using ordinary differential equations benefits from filling a one-week reporting delay with internet-based nowcasts [25].…”

Section: Introductionmentioning

confidence: 91%

Estimating influenza incidence using search query deceptiveness and generalized ridge regression

et al. 2019

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Seasonal influenza is a sometimes surprisingly impactful disease, causing thousands of deaths per year along with much additional morbidity. Timely knowledge of the outbreak state is valuable for managing an effective response. The current state of the art is to gather this knowledge using in-person patient contact. While accurate, this is time-consuming and expensive. This has motivated inquiry into new approaches using internet activity traces, based on the theory that lay observations of health status lead to informative features in internet data. These approaches risk being deceived by activity traces having a coincidental, rather than informative, relationship to disease incidence; to our knowledge, this risk has not yet been quantitatively explored. We evaluated both simulated and real activity traces of varying deceptiveness for influenza incidence estimation using linear regression. We found that deceptiveness knowledge does reduce error in such estimates, that it may help automatically-selected features perform as well or better than features that require human curation, and that a semantic distance measure derived from the Wikipedia article category tree serves as a useful proxy for deceptiveness. This suggests that disease incidence estimation models should incorporate not only data about how internet features map to incidence but also additional data to estimate feature deceptiveness. By doing so, we may gain one more step along the path to accurate, reliable disease incidence estimation using internet data. This capability would improve public health by decreasing the cost and increasing the timeliness of such estimates.

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“…The Existing researches on modeling influenza epidemic falls into two categories: Mechanistic and Statistical models. They are summarized in literature reviews [3][4][5][6] and in the CDC comparisons [7,8]. Researches under statistical category vary according to the different Features and methods used.…”

Section: Introductionmentioning

confidence: 99%

A comparative study on predicting influenza outbreaks using different feature spaces: application of influenza-like illness data from Early Warning Alert and Response System in Syria

2020

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Objective: An accurate forecasting of outbreaks of influenza-like illness (ILI) could support public health officials to suggest public health actions earlier. We investigated the performance of three different feature spaces in different models to forecast the weekly ILI rate in Syria using EWARS data from World Health Organization (WHO). Time series feature space was first used and we applied the seven models which are Naïve, Average, Seasonal naïve, drift, dynamic harmonic regression (Dhr), seasonal and trend decomposition using loess (STL) and TBATS. The Second feature space is like some state-of-the-art, which we named 53 − weeks − before_52 − first − order − difference feature space. The third one, we proposed and named n − years − before_m − weeks − around (YnWm) feature space. Machine learning (ML) and deep learning (DL) model were applied to the second and third feature spaces (generalized linear model (GLM), support vector regression (SVR), gradient boosting (GB), random forest (RF) and long short term memory (LSTM)). Results:It was indicated that the LSTM model of four layers with 1 − year − before_4 − weeks − around feature space gave more accurate results than other models and reached the lowest MAPE of 3.52% and the lowest RMSE of 0.01662. I hope that this modelling methodology can be applied in other countries and therefore help prevent and control influenza worldwide.

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Evaluation of mechanistic and statistical methods in forecasting influenza-like illness

Cited by 53 publications

References 48 publications

Aggregating forecasts of multiple respiratory pathogens supports more accurate forecasting of influenza-like illness

Aggregating forecasts of multiple respiratory pathogens supports more accurate forecasting of influenza-like illness

Estimating influenza incidence using search query deceptiveness and generalized ridge regression

A comparative study on predicting influenza outbreaks using different feature spaces: application of influenza-like illness data from Early Warning Alert and Response System in Syria

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