Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19

Forecasting influenza in a timely manner aids health organizations and policymakers in adequate preparation and decision making. However, effective influenza forecasting still remains a challenge despite increasing research interest. It is even more challenging amidst the COVID pandemic, when the influenza-like illness (ILI) counts is affected by various factors such as symptomatic similarities with COVID-19 and shift in healthcare seeking patterns of the general population. We term the ILI values observed when it is potentially affected as COVID-ILI. Under the current pandemic, historical influenza models carry valuable expertise about the disease dynamics but face difficulties adapting. Therefore, we propose CALI-NET, a neural transfer learning architecture which allows us to 'steer' a historical disease forecasting model to new scenarios where flu and COVID co-exist. Our framework enables this adaptation by automatically learning when it is should emphasize learning from COVID-related signals and when from the historical model. In such way, we exploit representations learned from historical ILI data as well as the limited COVID-related signals. Our experiments demonstrate that our approach is successful in adapting a historical forecasting model to the current pandemic. In addition, we show that success in our primary goal, adaptation, does not sacrifice overall performance as compared with state-of-the-art influenza forecasting approaches.

[1]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[2]  Yasin Almalioglu,et al.  Distilling Knowledge From a Deep Pose Regressor Network , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[3]  Dave Osthus,et al.  A Collaborative Multi-Model Ensemble for Real-Time Influenza Season Forecasting in the U.S , 2019, bioRxiv.

[4]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[5]  Rich Caruana,et al.  Do Deep Nets Really Need to be Deep? , 2013, NIPS.

[6]  Ronald Rosenfeld,et al.  Nonmechanistic forecasts of seasonal influenza with iterative one-week-ahead distributions , 2018, PLoS Comput. Biol..

[7]  R. Rosenfeld,et al.  A collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the United States , 2019, Proceedings of the National Academy of Sciences.

[8]  E. Nsoesie,et al.  Monitoring Influenza Epidemics in China with Search Query from Baidu , 2013, PloS one.

[9]  A. Rodriguez,et al.  DeepCOVID: An Operational Deep Learning-driven Framework for Explainable Real-time COVID-19 Forecasting , 2020, medRxiv.

[10]  Alex C. Kot,et al.  Heterogeneous Transfer Learning via Deep Matrix Completion with Adversarial Kernel Embedding , 2019, AAAI.

[11]  Mark Dredze,et al.  HealthTweets.org: A Platform for Public Health Surveillance Using Twitter , 2014, AAAI 2014.

[12]  Zachary Chase Lipton,et al.  The Covid-Tracking Project , 2020 .

[13]  Alok N. Choudhary,et al.  Real-time disease surveillance using Twitter data: demonstration on flu and cancer , 2013, KDD.

[14]  Geoffrey E. Hinton,et al.  Distilling the Knowledge in a Neural Network , 2015, ArXiv.

[15]  Dave Osthus,et al.  Accuracy of real-time multi-model ensemble forecasts for seasonal influenza in the U.S. , 2019, PLoS Comput. Biol..

[16]  J. Shaman,et al.  Forecasting seasonal outbreaks of influenza , 2012, Proceedings of the National Academy of Sciences.

[17]  Vijay V. Raghavan,et al.  A novel data-driven model for real-time influenza forecasting , 2017, bioRxiv.

[18]  Nikhil Muralidhar,et al.  DyAt Nets: Dynamic Attention Networks for State Forecasting in Cyber-Physical Systems , 2019, IJCAI.

[19]  Naren Ramakrishnan,et al.  Syndromic surveillance of Flu on Twitter using weakly supervised temporal topic models , 2016, Data Mining and Knowledge Discovery.

[20]  Ronald Rosenfeld,et al.  Flexible Modeling of Epidemics with an Empirical Bayes Framework , 2014, PLoS Comput. Biol..

[21]  V. Colizza,et al.  Excess cases of influenza-like illnesses synchronous with coronavirus disease (COVID-19) epidemic, France, March 2020 , 2020, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

[22]  Teck-Hua Ho,et al.  Unmasking the Actual COVID-19 Case Count , 2020, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[23]  Les E. Atlas,et al.  Recurrent neural networks and robust time series prediction , 1994, IEEE Trans. Neural Networks.

[24]  Daniela Perrotta,et al.  Forecasting Seasonal Influenza Fusing Digital Indicators and a Mechanistic Disease Model , 2017, WWW.

[25]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[26]  Jaime G. Carbonell,et al.  Completely Heterogeneous Transfer Learning with Attention - What And What Not To Transfer , 2017, IJCAI.

[27]  Li Li,et al.  Using LSTM and GRU neural network methods for traffic flow prediction , 2016, 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC).

[28]  Wen Li,et al.  Semi-Supervised Optimal Transport for Heterogeneous Domain Adaptation , 2018, IJCAI.

[29]  Mikhail Belkin,et al.  Regularization and Semi-supervised Learning on Large Graphs , 2004, COLT.

[30]  Arindam Banerjee,et al.  Climate Multi-model Regression Using Spatial Smoothing , 2013, SDM.

[31]  Reid Priedhorsky,et al.  Dynamic Bayesian Influenza Forecasting in the United States with Hierarchical Discrepancy (with Discussion) , 2017, Bayesian Analysis.

[32]  Aaron C. Miller,et al.  A Smartphone-Driven Thermometer Application for Real-time Population- and Individual-Level Influenza Surveillance , 2018, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[33]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[34]  Ravi Sankar,et al.  Time Series Prediction Using Support Vector Machines: A Survey , 2009, IEEE Computational Intelligence Magazine.

[35]  Madhav V. Marathe,et al.  Forecasting a Moving Target: Ensemble Models for ILI Case Count Predictions , 2014, SDM.

[36]  Paola Velardi,et al.  Results from the centers for disease control and prevention’s predict the 2013–2014 Influenza Season Challenge , 2016, BMC Infectious Diseases.

[37]  Jiangzhuo Chen,et al.  DEFSI: Deep Learning Based Epidemic Forecasting with Synthetic Information , 2019, AAAI.

[38]  Naren Ramakrishnan,et al.  EpiDeep: Exploiting Embeddings for Epidemic Forecasting , 2019, KDD.

[39]  Marc Lipsitch,et al.  Absolute humidity and pandemic versus epidemic influenza. , 2011, American journal of epidemiology.

[40]  Jeremy Ginsberg,et al.  Detecting influenza epidemics using search engine query data , 2009, Nature.

[41]  Hitoshi Imaoka,et al.  An Efficient Method of Training Small Models for Regression Problems with Knowledge Distillation , 2020, 2020 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR).

[42]  Alessandro Vespignani,et al.  Real-time numerical forecast of global epidemic spreading: case study of 2009 A/H1N1pdm , 2012, BMC Medicine.

[43]  Evan L. Ray,et al.  Infectious disease prediction with kernel conditional density estimation , 2017, Statistics in medicine.

[44]  Ellyn Ayton,et al.  Forecasting influenza-like illness dynamics for military populations using neural networks and social media , 2017, PloS one.