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

Abstract

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.

Optimising predictive modelling of Ross River virus using meteorological variables

“…There are multiple environmental, meteorological, biological, socioeconomic, geographical, host, and vector components which contribute to the transmission dynamics of RRV [ 10 , 36 , 44 ]. Factors included in the predictive models developed here use meteorological data which are readily accessible without the need for extensive data requests or data gathering processes, allowing for the approach used here to be easily replicated and integrated into predictive disease surveillance systems.…”

“…However, a caveat to this approach is the omission of variables that have previously been found to be important in the transmission of RRV among regions studied here. For example, variation in tide heights, river flow and height, and climatic conditions (e.g., Southern Oscillation Index) which are known to be associated with increases in mosquito breeding and potential host movement which can lead to greater RRV transmission [ 1 , 2 , 10 , 17 , 18 , 43 ]. Moreover, mosquito and host species vary between the LGAs examined here.…”

“…Ross River virus (RRV, family Togaviridae, genus Alphavirus) is an important arbovirus that is endemic in Australia having a complex epidemiology with a multi-vector and multihost transmission system being dependent on ecological context [7][8][9][10]. 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].…”

“…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. Of studies that have focused on predicting RRV transmission, few present the models’ predictive performance [ 10 ].…”

Ross River Virus Infection: A Cross-Disciplinary Review with a Veterinary Perspective

“…This is further evident by the fact that studies showed varying results [ 47 , 53 , 54 , 108 , 109 ]. Integration of statistical, mechanistic, and non-linear mixed models, for example characterization of non-linear thermal responses to identify transmission temperature optima and limits [ 107 ], would improve model prediction (recently reviewed in [ 110 , 111 ]).…”