Iain S Koolhof1,2, Simon M Firestone3, Silvana Bettiol1, Michael Charleston2, Katherine B Gibney4, Peter J Neville4,5, Andrew Jardine5, Scott Carver2. 1. College of Health and Medicine, School of Medicine, University of Tasmania, Hobart, Tasmania, Australia. 2. School of Natural Sciences, University of Tasmania, Hobart, Tasmania, Australia. 3. Melbourne Veterinary School, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, Victoria, Australia. 4. Victorian Department of Health and Human Services, Communicable Disease Epidemiology and Surveillance, Health Protection Branch, Melbourne, Victoria, Australia. 5. Department of Health, Western Australia, Environmental Health Directorate, Public and Aboriginal Health Division, Perth, Western Australia, Australia.
Abstract
BACKGROUND: Statistical models are regularly used in the forecasting and surveillance of infectious diseases to guide public health. Variable selection assists in determining factors associated with disease transmission, however, often overlooked in this process is the evaluation and suitability of the statistical model used in forecasting disease transmission and outbreaks. Here we aim to evaluate several modelling methods to optimise predictive modelling of Ross River virus (RRV) disease notifications and outbreaks in epidemiological important regions of Victoria and Western Australia. METHODOLOGY/PRINCIPAL FINDINGS: We developed several statistical methods using meteorological and RRV surveillance data from July 2000 until June 2018 in Victoria and from July 1991 until June 2018 in Western Australia. Models were developed for 11 Local Government Areas (LGAs) in Victoria and seven LGAs in Western Australia. We found generalised additive models and generalised boosted regression models, and generalised additive models and negative binomial models to be the best fit models when predicting RRV outbreaks and notifications, respectively. No association was found with a model's ability to predict RRV notifications in LGAs with greater RRV activity, or for outbreak predictions to have a higher accuracy in LGAs with greater RRV notifications. Moreover, we assessed the use of factor analysis to generate independent variables used in predictive modelling. In the majority of LGAs, this method did not result in better model predictive performance. CONCLUSIONS/SIGNIFICANCE: We demonstrate that models which are developed and used for predicting disease notifications may not be suitable for predicting disease outbreaks, or vice versa. Furthermore, poor predictive performance in modelling disease transmissions may be the result of inappropriate model selection methods. Our findings provide approaches and methods to facilitate the selection of the best fit statistical model for predicting mosquito-borne disease notifications and outbreaks used for disease surveillance.
BACKGROUND: Statistical models are regularly used in the forecasting and surveillance of infectious diseases to guide public health. Variable selection assists in determining factors associated with disease transmission, however, often overlooked in this process is the evaluation and suitability of the statistical model used in forecasting disease transmission and outbreaks. Here we aim to evaluate several modelling methods to optimise predictive modelling of Ross River virus (RRV) disease notifications and outbreaks in epidemiological important regions of Victoria and Western Australia. METHODOLOGY/PRINCIPAL FINDINGS: We developed several statistical methods using meteorological and RRV surveillance data from July 2000 until June 2018 in Victoria and from July 1991 until June 2018 in Western Australia. Models were developed for 11 Local Government Areas (LGAs) in Victoria and seven LGAs in Western Australia. We found generalised additive models and generalised boosted regression models, and generalised additive models and negative binomial models to be the best fit models when predicting RRV outbreaks and notifications, respectively. No association was found with a model's ability to predict RRV notifications in LGAs with greater RRV activity, or for outbreak predictions to have a higher accuracy in LGAs with greater RRV notifications. Moreover, we assessed the use of factor analysis to generate independent variables used in predictive modelling. In the majority of LGAs, this method did not result in better model predictive performance. CONCLUSIONS/SIGNIFICANCE: We demonstrate that models which are developed and used for predicting disease notifications may not be suitable for predicting disease outbreaks, or vice versa. Furthermore, poor predictive performance in modelling disease transmissions may be the result of inappropriate model selection methods. Our findings provide approaches and methods to facilitate the selection of the best fit statistical model for predicting mosquito-borne disease notifications and outbreaks used for disease surveillance.
Authors: Lachlan McIver; Jianguo Xiao; Michael D A Lindsay; Trenna Rowe; Grace Yun Journal: Aust N Z J Public Health Date: 2010-02 Impact factor: 2.939
Authors: Linda A Selvey; Jenny A Donnelly; Michael D Lindsay; Sudha PottumarthyBoddu; Victoria C D'Abrera; David W Smith Journal: Commun Dis Intell Q Rep Date: 2014-06-30
Authors: Michael A Johansson; Karyn M Apfeldorf; Scott Dobson; Jason Devita; Anna L Buczak; Benjamin Baugher; Linda J Moniz; Thomas Bagley; Steven M Babin; Erhan Guven; Teresa K Yamana; Jeffrey Shaman; Terry Moschou; Nick Lothian; Aaron Lane; Grant Osborne; Gao Jiang; Logan C Brooks; David C Farrow; Sangwon Hyun; Ryan J Tibshirani; Roni Rosenfeld; Justin Lessler; Nicholas G Reich; Derek A T Cummings; Stephen A Lauer; Sean M Moore; Hannah E Clapham; Rachel Lowe; Trevor C Bailey; Markel García-Díez; Marilia Sá Carvalho; Xavier Rodó; Tridip Sardar; Richard Paul; Evan L Ray; Krzysztof Sakrejda; Alexandria C Brown; Xi Meng; Osonde Osoba; Raffaele Vardavas; David Manheim; Melinda Moore; Dhananjai M Rao; Travis C Porco; Sarah Ackley; Fengchen Liu; Lee Worden; Matteo Convertino; Yang Liu; Abraham Reddy; Eloy Ortiz; Jorge Rivero; Humberto Brito; Alicia Juarrero; Leah R Johnson; Robert B Gramacy; Jeremy M Cohen; Erin A Mordecai; Courtney C Murdock; Jason R Rohr; Sadie J Ryan; Anna M Stewart-Ibarra; Daniel P Weikel; Antarpreet Jutla; Rakibul Khan; Marissa Poultney; Rita R Colwell; Brenda Rivera-García; Christopher M Barker; Jesse E Bell; Matthew Biggerstaff; David Swerdlow; Luis Mier-Y-Teran-Romero; Brett M Forshey; Juli Trtanj; Jason Asher; Matt Clay; Harold S Margolis; Andrew M Hebbeler; Dylan George; Jean-Paul Chretien Journal: Proc Natl Acad Sci U S A Date: 2019-11-11 Impact factor: 11.205