Predictive indicators for Ross River virus infection in the Darwin area of tropical northern Australia, using long‐term mosquito trapping data

Abstract

Summaryobjectives To describe the epidemiology of Ross River virus (RRV) infection in the endemic Darwin region of tropical northern Australia and to develop a predictive model for RRV infections.methods Analysis of laboratory confirmed cases of RRV infection between 01 January 1991 and 30 June 2006, together with climate, tidal and mosquito data collected weekly over the study period from 11 trap sites around Darwin. The epidemiology was described, correlations with various lag times were performed, followed by Poisson modelling to determine the best main effects model to predict RRV infection.results Ross River virus infection was reported equally in males and females in 1256 people over the 15.5 years. Average annual incidence was 113 ⁄ 100 000 people. Infections peaked in the 30-34 agegroup for both sexes. Correlations revealed strong associations between monthly RRV infections and climatic variables and also each of the four implicated mosquito species populations. Three models were created to identify the best predictors of RRV infections for the Darwin area. The climate-only model included total rainfall, average daily minimum temperature and maximum tide. This model explained 44.3% deviance. Using vector-only variables, the best fit was obtained with average monthly trap numbers of Culex annulirostris, Aedes phaecasiatus, Aedes notoscriptus and Aedes vigilax. This model explained 59.5% deviance. The best global model included rainfall, minimum temperature and three mosquito species. This model explained 63.5% deviance, and predicted disease accurately.conclusions We have produced a model that accurately predicts RRV infections throughout the year, in the Darwin region. Our model also indicates that predicted anthropogenic global climatic changes may result in an increase in RRV infections. Further research needs to target other high-risk areas elsewhere in tropical Australia to ascertain the best local climatic and vector predictive RRV infection models for each region. This methodology can also be tested for assessing utility of predictive models for other mosquito-borne diseases endemic to locations outside Australia.

Spatial analysis of risk factors for transmission of the Barmah Forest virus in Queensland, Australia

“…Our results suggest that minimum rather than maximum temperature plays a significant role in the BFV transmission and these results are consistent with other studies (Tong and Hu, 2002;Hu et al, 2004;Naish et al, 2006;Woodruff et al, 2006;Jacups et al, 2008;Wu et al, 2009). Since some species of mosquitoes are temperature-specific in their breeding (Hardy, 1988;Mackenzie et al, 1994;Russell, 1995), the dominant species of mosquitoes in the study area may be more sensitive to minimum temperature than maximum temperature because the latter changes little in Queensland (a tropical/ sub-tropical state) particularly in summer.…”

“…Adult females of these mosquitoes lay their eggs on soil, moist mud and the bases of plants around the margins of their breeding sites. Our results confirm findings from previous research about tidal influence on mosquito-borne disease transmission (Kelly-Hope et al, 2004;Naish et al, 2006;Dale and Knight, 2008;Jacups et al, 2008;Kurucz et al, 2009). Spatial interaction analysis is not feasible in this study as GeoDa does not incorporate spatial interaction.…”

Arbovirus Models to Provide Practical Management Tools for Mosquito Control and Disease Prevention in the Northern Territory, Australia

“…Daily rainfall, daily temperature (minimum, maximum, mean daily), humidity (minimum, maximum, mean daily), and tidal (sea level) data were provided by the Australian Bureau of Meteorology for each of the relevant NT townships throughout the study period. To identify the most strongly associated variables for each predictive model, meteorological data were cumulated or averaged, with lag times applied for up to 3 mo (Jacups et al 2008b). Tide was only included in models of coastal or subcoastal townships (Jabiru has tidally inßuenced river systems within saltmarsh mosquito ßight range).…”

“…Although the symptoms of RRV infection are mild, the arthritis and fatigue can be debilitating for 3Ð 6 mo, with postinfection fatigue further affecting a minority of cases (12%) (Hickie et al 2006). This disease is of economic concern because RRV infections occur most commonly in 30 Ð34 yr olds, a critical age group for workforce participation (Jacups et al 2008b). Financial costing from a 1997Ð1999 Australian cohort estimated $1,018 (Australian currency) per patient averaged across all severity levels.…”

“…By mapping weekly counts of RRV infection by Local Government Area against a threshold (algorithm not given), it provides public health ofÞcials with access to disease data for RRV and Barmah Forest virus in Queensland (Queensland Institute of Medical Research 2010). The model for Darwin requires counts of three mosquito species, plus total rainfall from the preceding month and the average minimum monthly temperature from the preceding 3 mo (Jacups et al 2008b). Moreover, the timeliness of predictive models is usually limited; as such early-warning systems notify the public to avoid mosquito attack, but rarely can models provide timely notiÞcation for mosquito control.…”