Predictive modelling of Ross River virus notifications in southeastern Australia

Cutcher, Zoe; Williamson, Elizabeth; Lynch, SE; Rowe, Stacey L; Clothier, Hazel J; Firestone, S

doi:10.1017/s0950268816002594

Cited by 20 publications

(49 citation statements)

References 27 publications

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“…In southern Tasmania, Ae. camptorhynchus (Thomson) (Diptera: Culicidae) is a recognized vector of RRV (Harley et al , Russell , Cutcher et al ). This halotolerant species is the dominant mosquito in saltmarsh habitats and notoriously bites humans (Barton et al , Williams et al ), with populations in southern Tasmania most abundant from Spring to Autumn (October ‐ April) (Werner et al ).…”

Section: Introductionmentioning

confidence: 99%

Mosquito distribution in a saltmarsh: determinants of eggs in a variable environment

Rowbottom

Carver

Barmuta

et al. 2017

Journal of Vector Ecology

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Two saltmarsh mosquitoes dominate the transmission of Ross River virus (RRV, Togoviridae: Alphavirus), one of Australia's most prominent mosquito-borne diseases. Ecologically, saltmarshes vary in their structure, including habitat types, hydrological regimes, and diversity of aquatic fauna, all of which drive mosquito oviposition behavior. Understanding the distribution of vector mosquitoes within saltmarshes can inform early warning systems, surveillance, and management of vector populations. The aim of this study was to identify the distribution of Ae. camptorhynchus, a known vector for RRV, across a saltmarsh and investigate the influence that other invertebrate assemblage might have on Ae. camptorhynchus egg dispersal. We demonstrate that vegetation is a strong indicator for Ae. camptorhynchus egg distribution, and this was not correlated with elevation or other invertebrates located at this saltmarsh. Also, habitats within this marsh are less frequently inundated, resulting in dryer conditions. We conclude that this information can be applied in vector surveillance and monitoring of temperate saltmarsh environments and also provides a baseline for future investigations into understanding mosquito vector habitat requirements. Journal of Vector Ecology 42 (1): 161-170. 2017.

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Section: Introductionmentioning

confidence: 99%

Mosquito distribution in a saltmarsh: determinants of eggs in a variable environment

Rowbottom

Carver

Barmuta

et al. 2017

Journal of Vector Ecology

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“…These time lags allow for predictions of RRV notifications to be made for the future. After the introduction of lag periods, pairwise correlations between independent variables were assessed in the independent approach, using Spearman's correlation coefficient, similar to that of other RRV prediction modelling [1,2,17]. If two independent variables were found to be highly correlated with one another (cut-off of 0.75), the variable with the largest mean absolute correlation with the other independent variables was removed.…”

Section: Discussionmentioning

confidence: 99%

“…Epidemiological analyses have typically focused on locations where attack rates of RRV are highest and areas where transmission is seasonally driven with either an annual or biannual oscillation of human disease cases [6,15]. Statistical models predicting RRV notifications include, but are not limited to: logistic and Poisson regressions, negative binomial regressions, seasonal and non-seasonal auto-regressive integrated moving average models, and generalised additive models [e.g., 1,2,6,[13][14][15][16][17][18]. The use of these models has primarily been to estimate the probability of an RRV outbreak at a given time, or to predict counts of notifications using a combination of environmental and meteorological factors, and mosquito surveillance [e.g., 13,17].…”

Section: Introductionmentioning

confidence: 99%

“…Statistical models predicting RRV notifications include, but are not limited to: logistic and Poisson regressions, negative binomial regressions, seasonal and non-seasonal auto-regressive integrated moving average models, and generalised additive models [e.g., 1,2,6,[13][14][15][16][17][18]. The use of these models has primarily been to estimate the probability of an RRV outbreak at a given time, or to predict counts of notifications using a combination of environmental and meteorological factors, and mosquito surveillance [e.g., 13,17]. The sensitivity and specificity of predicting outbreaks in previous forecasting studies vary, yet there has been, to our knowledge, no evaluation of the relative performance of the types of models used in forecasting RRV.…”

Section: Introductionmentioning

confidence: 99%

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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 Early Aberration Reporting System, which offers aberration detection methods by analysing recent surveillance data [77]. The time-lag effects of RRV activity are generated not only by climatic factors but also by mosquito abundance, host populations and some geographical elements such as river flow and flooding [9,15,52,56,63,78]. The time lags are also influenced by the species diversity and abundance of mosquitoes in the research area.…”

Section: Plos Neglected Tropical Diseasesmentioning

confidence: 99%

Epidemiological models for predicting Ross River virus in Australia: A systematic review

et al. 2020

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Ross River virus (RRV) is the most common and widespread arbovirus in Australia. Epidemiological models of RRV increase understanding of RRV transmission and help provide early warning of outbreaks to reduce incidence. However, RRV predictive models have not been systematically reviewed, analysed, and compared. The hypothesis of this systematic review was that summarising the epidemiological models applied to predict RRV disease and analysing model performance could elucidate drivers of RRV incidence and transmission patterns. We performed a systematic literature search in PubMed, EMBASE, Web of Science, Cochrane Library, and Scopus for studies of RRV using population-based data, incorporating at least one epidemiological model and analysing the association between exposures and RRV disease. Forty-three articles, all of high or medium quality, were included. Twenty-two (51.2%) used generalised linear models and 11 (25.6%) used timeseries models. Climate and weather data were used in 27 (62.8%) and mosquito abundance or related data were used in 14 (32.6%) articles as model covariates. A total of 140 models were included across the articles. Rainfall (69 models, 49.3%), temperature (66, 47.1%) and tide height (45, 32.1%) were the three most commonly used exposures. Ten (23.3%) studies published data related to model performance. This review summarises current knowledge of RRV modelling and reveals a research gap in comparing predictive methods. To improve predictive accuracy, new methods for forecasting, such as non-linear mixed models and machine learning approaches, warrant investigation.

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Predictive modelling of Ross River virus notifications in southeastern Australia

Cited by 20 publications

References 27 publications

Mosquito distribution in a saltmarsh: determinants of eggs in a variable environment

Mosquito distribution in a saltmarsh: determinants of eggs in a variable environment

Optimising predictive modelling of Ross River virus using meteorological variables

Epidemiological models for predicting Ross River virus in Australia: A systematic review

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