The forecasting of dynamical Ross River virus outbreaks: Victoria, Australia

Koolhof, Iain S.; Gibney, Katherine B; Bettiol, SS; Charleston, Michael A.; Wiethoelter, Anke; Arnold, Anna-Lena; Campbell, Patricia T.; Neville, Peter J.; Aung, Phyo Thu Zar; Shiga, Tsubasa; Carver, Scott; Firestone, Simon M.

doi:10.1016/j.epidem.2019.100377

Cited by 29 publications

(59 citation statements)

References 42 publications

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“…Due to the complex nature of the COVID-19 outbreak and its irregularity in different countries, the standard epidemiological models, i.e., susceptible-infected-resistant (SIR)-based models, had been challenged for delivering higher performance in individual nations. Furthermore, as the COVID-19 outbreak showed significant differences with other recent outbreaks, e.g., Ebola, Cholera, swine fever, H1N1 influenza, dengue fever, and Zika, advanced epidemiological models have been emerged to provide higher accuracy [7]. Nevertheless, due to several unknown variables involved in the spread, the complexity of population-wide behavior in various countries, and differences in containment strategies model uncertainty has been reported inevitable [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach

Pintér

Felde

Mosavi

et al. 2020

Preprint

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Several epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to a high level of uncertainty or even lack of essential data, the standard epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptibleinfected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19 and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are used to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for nine days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. Based on the results reported here, and due to the complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.

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Section: Introductionmentioning

confidence: 99%

COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach

Pintér

Felde

Mosavi

et al. 2020

Preprint

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“…Meteorological factors influence the transmission ecology of pathogen, host and vector species populations, and human behaviour, which can act directly or indirectly to drive mosquitoborne disease dynamics [1,2]. Climate events (such as rainfall or tidal events) impact upon mosquito population dynamics and the presentation of disease in host and human populations preceding these events.…”

Section: Introductionmentioning

confidence: 99%

“…It is the most common mosquito-borne virus affecting humans in Australia, with an annual average incidence rate of 40 cases per 100,000 population [11]. Over the past two decades, epidemiological studies on environmental and meteorological factors have been conducted across multiple regions of Australia, providing insight into the factors and complexity of RRV transmission across different locations [1,2,6,[12][13][14][15]. The variations reported include site-specific meteorological, environmental, and geographic factors, mosquito vector species, and host species [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Optimising predictive modelling of Ross River virus using meteorological variables

et al. 2021

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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 models’ 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. However, this approach did not result in as many best fit models than when not using this approach. Conclusions/Significance We demonstrate that models which are developed and used for predicting disease notifications may not be suitable for predicting disease outbreaks, or visa 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.

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“…The recent global COVID-19 pandemic has exhibited a nonlinear and complex nature [2]. In addition, the outbreak has differences with other recent outbreaks, which brings into question the ability of standard models to deliver accurate results [3]. Besides the numerous known and unknown variables involved in the spread, the complexity of population-wide behavior in various geopolitical areas and differences in containment strategies had dramatically increased model uncertainty [4].…”

Section: Introductionmentioning

confidence: 99%