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Dynamic Poisson Autoregression for Influenza-Like-Illness Case Count Prediction

Zheng Wang, Prithwish Chakraborty, Sumiko R. Mekaru, John S. Brownstein, Jieping Ye, Naren Ramakrishnan
2015 Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '15  
In this paper, we focus on short-term ILI case count prediction and develop a dynamic Poisson autoregressive model with exogenous inputs variables (DPARX) for flu forecasting.  ...  Influenza-like-illness (ILI) is among of the most common diseases worldwide, and reliable forecasting of the same can have significant public health benefits.  ...  The US Government is authorized to reproduce and distribute reprints of this work for Governmental purposes notwithstanding any copyright annotation thereon.  ... 
doi:10.1145/2783258.2783291 dblp:conf/kdd/WangCMBYR15 fatcat:bceuhqzkcjfhvdtloultwxvfoy

Influenza Forecasting with Google Flu Trends

Andrea Freyer Dugas, Mehdi Jalalpour, Yulia Gel, Scott Levin, Fred Torcaso, Takeru Igusa, Richard E. Rothman, Cécile Viboud
2013 PLoS ONE  
The model, on the average, predicts weekly influenza cases during 7 out-of-sample outbreaks within 7 cases for 83% of estimates.  ...  number of influenza cases, thus allowing for sufficient time to implement interventions.  ...  External variables include any data used for prediction that is independent of the outcome predicted (i.e. weekly counts of influenza cases).  ... 
doi:10.1371/journal.pone.0056176 pmid:23457520 pmcid:PMC3572967 fatcat:3y3d63jcrzddvjzvlrrlrksbv4

Influenza epidemic surveillance and prediction based on electronic health record data from an out-of-hours general practitioner cooperative: model development and validation on 2003–2015 data

Barbara Michiels, Van Kinh Nguyen, Samuel Coenen, Philippe Ryckebosch, Nathalie Bossuyt, Niel Hens
2017 BMC Infectious Diseases  
Adding a secular trend (5 year cycle) and using a first-order autoregressive modelling for the epidemic component together with the use of Poisson likelihood produced the best prediction results.  ...  The Scientific Institute of Public Health in Belgium (WIV-ISP) monitors the incidence of influenza and influenza-like illnesses (ILIs) and reports on a weekly basis.  ...  Acknowledgements The authors would like to thank the GPs of the OOH GPC Deurne-Borgerhout for providing the clinical ILI data as well as the sentinel GPs reporting ILI cases to the WIV-ISP.  ... 
doi:10.1186/s12879-016-2175-x pmid:28100186 pmcid:PMC5241973 fatcat:5t5ycjtjcreenid6ejaftgdqvm

The accuracy and timeliness of neuraminidase inhibitor dispensing data for predicting laboratory-confirmed influenza

2015 Epidemiology and Infection  
Our secondary objective was to use the same metrics to compare NI dispensing to visits for influenza-like illness (ILI) in emergency departments (EDs).  ...  Provincial weekly counts of positive influenza laboratory tests were used as a reference measure for the level of influenza circulation. We applied ARIMA models to account for serial correlation.  ...  Douville-Fradet, MD, MHSc for kindly providing RQSUCH data; IMS Brogan for generously providing the antiviral dispensing data.  ... 
doi:10.1017/s095026881500299x pmid:26611607 fatcat:polwlooy5jad3niaz5rkchp54y

Probabilistic Daily ILI Syndromic Surveillance with a Spatio-Temporal Bayesian Hierarchical Model

Ta-Chien Chan, Chwan-Chuen King, Muh-Yong Yen, Po-Huang Chiang, Chao-Sheng Huang, Chuhsing K. Hsiao, Fabio Rapallo
2010 PLoS ONE  
Methods and Findings: Based on the Bayesian posterior probability of influenza-like illness (ILI) visits, the intensity of outbreak can be directly assessed.  ...  For daily syndromic surveillance to be effective, an efficient and sensible algorithm would be expected to detect aberrations in influenza illness, and alert public health workers prior to any impending  ...  Acknowledgments We would like to thank Dr. Chung-Ming Liu at Department of Atmospheric Sciences, College of Science, National Taiwan University, for his comments and expertise in meteorology.  ... 
doi:10.1371/journal.pone.0011626 pmid:20661275 pmcid:PMC2905374 fatcat:nw23h3gyhvcwzp6dgu5dwclkte

Estimating age-stratified influenza-associated invasive pneumococcal disease in England: A time-series model based on population surveillance data

Chiara Chiavenna, Anne M. Presanis, Andre Charlett, Simon de Lusignan, Shamez Ladhani, Richard G. Pebody, Daniela De Angelis, Aziz Sheikh
2019 PLoS Medicine  
Measures of the contribution of influenza to Streptococcus pneumoniae infections, both in the seasonal and pandemic setting, are needed to predict the burden of secondary bacterial infections in future  ...  We proposed a novel multivariate model for age-stratified disease incidence, incorporating contact patterns and estimating disease transmission within and across groups.  ...  They also acknowledge the contribution of Zahin Amin, Ana Correa, and Praveen SebastianPillai for providing the data, and they would like to thank patients and practices in the RCGP RSC network for allowing  ... 
doi:10.1371/journal.pmed.1002829 pmid:31246954 pmcid:PMC6597037 fatcat:eudfupdbwvcs7hmkevc6acysv4

On Parameter Estimation Approaches for Predicting Disease Transmission Through Optimization, Deep Learning and Statistical Inference Methods

Mazair Raissi, Niloofar Ramezani, Padmanabhan Seshaiyer
2019 Letters in Biomathematics  
Our results indicate the efficiency and importance of statistical inference methods for researchers to understand and analyse time-series data to make confident predictions. ARTICLE HISTORY  ...  The paper also presents the need for researchers to understand different types of dependencies exhibited in a typical data set and discovering the most appropriate approaches for statistical inference.  ...  Figure 3 . 3 Sample influenza dataset epidemic in an English boarding school Figure 4 . 4 Dynamics of SIR model predictions and comparison to data for α = 0.5 and β = 0.0026.  ... 
doi:10.30707/lib6.2raissi fatcat:5o6xw66mancijfr2yovp5vn7fa

Graph Message Passing with Cross-location Attentions for Long-term ILI Prediction [article]

Songgaojun Deng, Shusen Wang, Huzefa Rangwala, Lijing Wang, Yue Ning
2019 arXiv   pre-print
Forecasting influenza-like illness (ILI) is of prime importance to epidemiologists and health-care providers.  ...  The proposed method shows strong predictive performance and leads to interpretable results for long-term epidemic predictions.  ...  Related Work Influenza Prediction In many studies, forecasting influenza or influenza-like illnesses (ILI) case counts is formulated as time series regression problems, where autoregressive models are  ... 
arXiv:1912.10202v2 fatcat:i26dbx3c75dl7etf3ywezbfiqe

Effect of climate on incidence of respiratory syncytial virus infections in a refugee camp in Kenya: A non-Gaussian time-series analysis

Raymond Nyoka, Jimmy Omony, Samuel M. Mwalili, Thomas N. O. Achia, Anthony Gichangi, Henry Mwambi, Oliver Schildgen
2017 PLoS ONE  
We also thank Nina Marano and Rachael Joseph for their assistance with the study design and data collection.  ...  Acknowledgments The authors wish to acknowledge the CDC Kenya Refugee Health Program for their tireless work in the surveillance in the refugee camp and their assistance with the data collection and management  ...  Paediatric and adult patients who presented at a camp medical unit, and met the case definition for influenza-like illness (ILI) or severe acute respiratory infection (SARI), were enrolled into the laboratory-enhanced  ... 
doi:10.1371/journal.pone.0178323 pmid:28570627 pmcid:PMC5453485 fatcat:dbbr67qjpbbadnrsymhvfoyx3m

Predictability limit of partially observed systems [article]

Andrés Abeliuk, Zhishen Huang, Emilio Ferrara, Kristina Lerman
2020 arXiv   pre-print
Applications from finance to epidemiology and cyber-security require accurate forecasts of dynamic phenomena, which are often only partially observed.  ...  On a variety of prediction tasks---forecasting new infections, the popularity of topics in online discussions, or interest in cryptocurrency projects---predictability irrecoverably decays as a function  ...  Acknowledgments The authors thank Linhong Zhu for collecting the Twitter data and America Mazuela for the illustration.  ... 
arXiv:2001.06547v1 fatcat:usfdm2lysfhc5fznr2uq74r5oa

Using Extreme Value Theory Approaches to Forecast the Probability of Outbreak of Highly Pathogenic Influenza in Zhejiang, China

Jiangpeng Chen, Xun Lei, Li Zhang, Bin Peng, Lijun Rong
2015 PLoS ONE  
Acknowledgments We would like to thank the anonymous reviewers and the academic editor for their valuable comments and suggestions. Author Contributions  ...  Goldstein E et al [2] proposed a generalized linear model demonstrating how routine virologic and influenza-like illness (ILI) surveillance data can be used to quantify the dynamics of co-circulating  ...  We fit a Poisson point process to the highly pathogenic influenza incidence data with threshold parameter 9. Counts of all values over the threshold were described by the Poisson point process.  ... 
doi:10.1371/journal.pone.0118521 pmid:25710503 pmcid:PMC4339379 fatcat:btnwjle2rjfo5oayrr4etrxyom

Time series regression model for infectious disease and weather

Chisato Imai, Ben Armstrong, Zaid Chalabi, Punam Mangtani, Masahiro Hashizume
2015 Environmental Research  
Modifications proposed to standard time series regression practice include using sums of past cases as proxies for the immune population, and using the logarithm of lagged disease counts to control autocorrelation  ...  The potential approaches are illustrated with datasets of cholera cases and rainfall from Bangladesh and influenza and temperature in Tokyo.  ...  Acknowledgments We thank Yoonhee Kim for her support with demonstration analysis, ASG Faruque for sharing cholera data with us, and Katia Koelle for explaining her research to us that provided important  ... 
doi:10.1016/j.envres.2015.06.040 pmid:26188633 fatcat:xiqkgmdkizdx3d5lbotjp5lgfm

Avian Influenza Risk Surveillance in North America with Online Media

Colin Robertson, Lauren Yee, Dena L. Schanzer
2016 PLoS ONE  
In this paper we investigated the use of one social media outlet, Twitter, for surveillance of avian influenza risk in North America.  ...  Topic models found themes related to specific AI events for the dynamic threshold method, while many for the static method were ambiguous.  ...  For example, the avian influenza A(H5N1) outbreaks in poultry during 2004-2009 caused an estimated $30 billion in damages [4] Zoonoses like avian influenza are particularly challenging to plan for and  ... 
doi:10.1371/journal.pone.0165688 pmid:27880777 pmcid:PMC5120807 fatcat:vps4znxlezbbna32xdptaih2na

Statistical methods for the prospective detection of infectious disease outbreaks: a review

Steffen Unkel, C. Paddy Farrington, Paul H. Garthwaite, Chris Robertson, Nick Andrews
2011 Journal of the Royal Statistical Society: Series A (Statistics in Society)  
Unusual clusters of disease must be detected rapidly for effective public health interventions to be introduced.  ...  Over the past decade there has been a surge in interest in statistical methods for the early detection of infectious disease outbreaks.  ...  Acknowledgements This study was supported by grants from the National Institute for Health Research and Medical Research Council.  ... 
doi:10.1111/j.1467-985x.2011.00714.x fatcat:r4hrksexmzh5xolf4lst44ys2a

Nowcasting by Bayesian Smoothing: A flexible, generalizable model for real-time epidemic tracking [article]

Sarah F. McGough, Michael A. Johansson, Marc Lipsitch, Nicolas A. Menzies
2019 bioRxiv   pre-print
We test NobBS on dengue in Puerto Rico and influenza-like illness (ILI) in the United States to examine performance and robustness across settings exhibiting a range of common reporting delay characteristics  ...  "Nowcast" approaches attempt to estimate the complete case counts for a given reporting date, using a time series of case reports that is known to be incomplete due to reporting delays.  ...  solely the responsibility of the authors and does not necessarily represent the official views of the National Institute Of General Medical Sciences, the National Institutes of Health, or the Centers for  ... 
doi:10.1101/663823 fatcat:tkzvs3ptlnd5ddbqwefvya54f4
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