Climate variability and Ross River virus infections in Riverland, South Australia, 1992-2004

Testing the intrinsic mechanisms driving the dynamics of Ross River Virus across Australia

The mechanisms driving dynamics of many epidemiologically important mosquito-borne pathogens are complex, involving combinations of vector and host factors (e.g., species composition and life-history traits), and factors associated with transmission and reporting. Understanding which intrinsic mechanisms contribute most to observed disease dynamics is important, yet often poorly understood. Ross River virus (RRV) is Australia's most important mosquito-borne disease, with variable transmission dynamics across geographic regions. We used deterministic ordinary differential equation models to test mechanisms driving RRV dynamics across major epidemic centers in Brisbane, Darwin, Mandurah, Mildura, Gippsland, Renmark, Murray Bridge, and Coorong. We considered models with up to two vector species (Aedes vigilax, Culex annulirostris, Aedes camptorhynchus, Culex globocoxitus), two reservoir hosts (macropods, possums), seasonal transmission effects, and transmission parameters. We fit models against long-term RRV surveillance data (1991-2017) and used Akaike Information Criterion to select important mechanisms. The combination of two vector species, two reservoir hosts, and seasonal transmission effects explained RRV dynamics best across sites. Estimated vector-human transmission rate (average β = 8.04x10-4per vector per day) was similar despite different dynamics. Models estimate 43% underreporting of RRV infections. Findings enhance understanding of RRV transmission mechanisms, provide disease parameter estimates which can be used to guide future research into public health improvements and offer a basis to evaluate mitigation practices.

Climate change, environmental extremes, and human health in Australia: challenges, adaptation strategies, and policy gaps

Climate change presents a major public health concern in Australia, marked by unprecedented wildfires, heatwaves, floods, droughts, and the spread of climate-sensitive infectious diseases. Despite these challenges, Australia's response to the climate crisis has been inadequate and subject to change by politics, public sentiment, and global developments. This study illustrates the spatiotemporal patterns of selected climate-related environmental extremes (heatwaves, wildfires, floods, and droughts) across Australia during the past two decades, and summarizes climate adaptation measures and actions that have been taken by the national, state/territory, and local governments. Our findings reveal significant impacts of climate-related environmental extremes on the health and well-being of Australians. While governments have implemented various adaptation strategies, these plans must be further developed to yield concrete actions. Moreover, Indigenous Australians should not be left out in these adaptation efforts. A collaborative, comprehensive approach involving all levels of government is urgently needed to prevent, mitigate, and adapt to the health impacts of climate change.

Prediction of Ross River virus incidence in Queensland, Australia: building and comparing models

Transmission of Ross River virus (RRV) is influenced by climatic, environmental, and socio-economic factors. Accurate and robust predictions based on these factors are necessary for disease prevention and control. However, the complicated transmission cycle and the characteristics of RRV notification data present challenges. Studies to compare model performance are lacking. In this study, we used RRV notification data and exposure data from 2001 to 2020 in Queensland, Australia, and compared ten models (including generalised linear models, zero-inflated models, and generalised additive models) to predict RRV incidence in different regions of Queensland. We aimed to compare model performance and to evaluate the effect of statistical over-dispersion and zero-inflation of RRV surveillance data, and non-linearity of predictors on model fit. A variable selection strategy for screening important predictors was developed and was found to be efficient and able to generate consistent and reasonable numbers of predictors across regions and in all training sets. Negative binomial models generally exhibited better model fit than Poisson models, suggesting that over-dispersion in the data is the primary factor driving model fit compared to non-linearity of predictors and excess zeros. All models predicted the peak periods well but were unable to fit and predict the magnitude of peaks, especially when there were high numbers of cases. Adding new variables including historical RRV cases and mosquito abundance may improve model performance. The standard negative binomial generalised linear model is stable, simple, and effective in prediction, and is thus considered the best choice among all models.

Associations between temperature and Ross river virus infection: A systematic review and meta-analysis of epidemiological evidence

Ross River virus (RRV) infection is one of the emerging and prevalent arboviral diseases in Australia and the Pacific Islands. Although many studies have been conducted to establish the relationship between temperature and RRV infection, there has been no comprehensive review of the association so far. In this study, we performed a systematic review and meta-analysis to assess the effect of temperature on RRV transmission. We searched PubMed, Scopus, Embase, and Web of Science with additional lateral searches from references. The quality and strength of evidence from the included studies were evaluated following the Navigation Guide framework. We have qualitatively synthesized the evidence and conducted a meta-analysis to pool the relative risks (RRs) of RRV infection per 1 °C increase in temperature. Subgroup analyses were performed by climate zones, temperature metrics, and lag periods. A total of 17 studies met the inclusion criteria, of which six were included in the meta-analysis The meta-analysis revealed that the overall RR for the association between temperature and the risk of RRV infection was 1.09 (95% confidence interval (CI): 1.02, 1.17). Subgroup analyses by climate zones showed an increase in RRV infection per 1 °C increase in temperature in humid subtropical and cold semi-arid climate zones. The overall quality of evidence was "moderate" and we rated the strength of evidence to be "limited", warranting additional evidence to reduce uncertainty. The results showed that the risk of RRV infection is positively associated with temperature. However, the risk varies across different climate zones, temperature metrics and lag periods. These findings indicate that future studies on the association between temperature and RRV infection should consider local and regional climate, socio-demographic, and environmental factors to explore vulnerability at local and regional levels.

Spatial and Temporal Patterns of Ross River Virus in Queensland, 2001-2020

Ross River virus (RRV), the most common human arbovirus infection in Australia, causes significant morbidity and substantial medical costs. About half of Australian cases occur in Queensland. We describe the spatial and temporal patterns of RRV disease in Queensland over the past two decades. RRV notifications, human population data, and weather data from 2001 to 2020 were analysed by the Statistical Area Level 2 (SA2) area. Spatial interpolation or linear extrapolation were used for missing weather values and the estimated population in 2020, respectively. Notifications and incidence rates were analysed through space and time. During the study period, there were 43,699 notifications in Queensland. The highest annual number of notifications was recorded in 2015 (6182), followed by 2020 (3160). The average annual incidence rate was 5 per 10,000 people and the peak period for RRV notifications was March to May. Generally, SA2 areas in northern Queensland had higher numbers of notifications and higher incidence rates than SA2 areas in southern Queensland. The SA2 areas with high incidence rates were in east coastal areas and western Queensland. The timely prediction may aid disease prevention and routine vector control programs, and RRV management plans are important for these areas.

Optimising predictive modelling of Ross River virus using meteorological variables

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.

Using ecological variables to predict Ross River virus disease incidence in South Australia

BACKGROUND

Ross River virus (RRV) disease is Australia's most widespread vector-borne disease causing significant public health concern. The aim of this study was to identify the ecological covariates of RRV risk and to develop epidemic forecasting models in a disease hotspot region of South Australia.

METHODS

Seasonal autoregressive integrated moving average models were used to predict the incidence of RRV disease in the Riverland region of South Australia, an area known to have a high incidence of the disease. The model was developed using data from January 2000 to December 2012 then validated using disease notification data on reported cases for the following year.

RESULTS

Monthly numbers of the mosquito Culex annulirostris (β=0.033, p<0.001) and total rainfall (β=0.263, p=0.002) were significant predictors of RRV transmission in the study region. The forecasted RRV incidence in the predictive model was generally consistent with the actual number of cases in the study area.

CONCLUSIONS

A predictive model has been shown to be useful in forecasting the occurrence of RRV disease, with increased vector populations and rainfall being important factors associated with transmission. This approach may be useful in a public health context by providing early warning of vector-borne diseases in other settings.

Integrating statistical and mechanistic approaches with biotic and environmental variables improves model predictions of the impact of climate and land-use changes on future mosquito-vector abundance, diversity and distributions in Australia

Changes to Australia's climate and land-use patterns could result in expanded spatial and temporal distributions of endemic mosquito vectors including Aedes and Culex species that transmit medically important arboviruses. Climate and land-use changes greatly influence the suitability of habitats for mosquitoes and their behaviors such as mating, feeding and oviposition. Changes in these behaviors in turn determine future species-specific mosquito diversity, distribution and abundance. In this review, we discuss climate and land-use change factors that influence shifts in mosquito distribution ranges. We also discuss the predictive and epidemiological merits of incorporating these factors into a novel integrated statistical (SSDM) and mechanistic species distribution modelling (MSDM) framework. One potentially significant merit of integrated modelling is an improvement in the future surveillance and control of medically relevant endemic mosquito vectors such as Aedes vigilax and Culex annulirostris, implicated in the transmission of many arboviruses such as Ross River virus and Barmah Forest virus, and exotic mosquito vectors such as Aedes aegypti and Aedes albopictus. We conducted a focused literature search to explore the merits of integrating SSDMs and MSDMs with biotic and environmental variables to better predict the future range of endemic mosquito vectors. We show that an integrated framework utilising both SSDMs and MSDMs can improve future mosquito-vector species distribution projections in Australia. We recommend consideration of climate and environmental change projections in the process of developing land-use plans as this directly impacts mosquito-vector distribution and larvae abundance. We also urge laboratory, field-based researchers and modellers to combine these modelling approaches. Having many different variations of integrated (SDM) modelling frameworks could help to enhance the management of endemic mosquitoes in Australia. Enhanced mosquito management measures could in turn lead to lower arbovirus spread and disease notification rates.

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

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 time-series 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.

Rapid identification of shallow inundation for mosquito disease mitigation using drone-derived multispectral imagery

Mosquito breeding habitat identification often relies on slow, labour-intensive and expensive ground surveys. With advances in remote sensing and autonomous flight technologies, we endeavoured to accelerate this detection by assessing the effectiveness of a drone multispectral imaging system to determine areas of shallow inundation in an intertidal saltmarsh in South Australia. Through laboratory experiments, we characterised Near-Infrared (NIR) reflectance responses to water depth and vegetation cover, and established a reflectance threshold for mapping water sufficiently deep for potential mosquito breeding. We then applied this threshold to field-acquired drone imagery and used simultaneous in-situ observations to assess its mapping accuracy. A NIR reflectance threshold of 0.2 combined with a vegetation mask derived from Normalised Difference Vegetation Index (NDVI) resulted in a mapping accuracy of 80.3% with a Cohen's Kappa of 0.5, with confusion between vegetation and shallow water depths (< 10 cm) appearing to be major causes of error. This high degree of mapping accuracy was achieved with affordable drone equipment, and commercially available sensors and Geographic Information Systems (GIS) software, demonstrating the efficiency of such an approach to identify shallow inundation likely to be suitable for mosquito breeding.

The geography of Ross River virus infection in South Australia, 2000-2013

Introduction

Ross River virus (RRV) disease is Australia's most common arthropod-borne disease which has an important impact on population health and productivity. The aim of this study was to identify the spatial and temporal distribution of RRV notifications during 2000-2013 in South Australia (SA).

Methods

The epidemiologic patterns of RRV notifications in SA from January 2000 to December 2013 were examined at a statistical local area (SLA) level. Spatial-temporal analyses were conducted using patient-reported place of exposure to characterise the recurrence of RRV infection stratified by age and sex.

Results

During the study period, a total of 3,687 RRV disease notifications were recorded in the state with state-wide mean annual rates of 16.8 cases per 100,000 persons and a 1:1.32 male:female ratio. The SLAs reporting cases of RRV disease exhibited spatial and temporal variation. Notified cases of RRV disease occurred more frequently in summer and autumn. A geographic expansion was observed of the area within which RRV cases occur. The comparison of age- and sex-standardised incidence rates, calculated by place of residence and patient-reported place of exposure, highlights the importance of using the latter to accurately display geospatial disease trends over time. Areas with the largest proportion of visitor cases and having the highest risk were mostly along the River Murray, which provides many vector mosquito habitats.

Conclusion

Although public health interventions should be considered in all SLAs where RRV occurs, we suggest that priority should be given to the Riverland areas identified as highest risk.

Flooding and Arboviral Disease: Predicting Ross River Virus Disease Outbreaks Across Inland Regions of South-Eastern Australia

Flood frequency is expected to increase across the globe with climate change. Understanding the relationship between flooding and arboviral disease can reduce disease risk and associated costs. South-eastern Australia is dominated by the flood-prone Murray-Darling River system where the incidence of Australia's most common arboviral disease, Ross River virus (RRV), is high. This study aimed to determine the relationship between riverine flooding and RRV disease outbreaks in inland south-eastern Australia, specifically New South Wales (NSW). Each study month from 1991 to 2013, for each of 37 local government areas (LGAs) was assigned 'outbreak/non-outbreak' status based on long-term trimmed-average age-standardized RRV notification rates and 'flood/non-flood' status based on riverine overflow. LGAs were grouped into eight climate zones with the relationship between flood and RRV outbreak modeled using generalized estimating equations. Modeling adjusted for rainfall in the previous 1-3 mo. Spring-summer flooding increased the odds of summer RRV outbreaks in three climate zones before and after adjusting for rainfall 1, 2, and 3 mo prior to the outbreak. Flooding at any time of the year was not predictive of RRV outbreaks in the remaining five climate zones. Predicting RRV disease outbreaks with flood events can assist with more targeted mosquito spraying programs, thereby reducing disease transmission and mosquito resistance.

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

Ross River virus (RRV) is Australia's most epidemiologically important mosquito-borne disease. During RRV epidemics in the State of Victoria (such as 2010/11 and 2016/17) notifications can account for up to 30% of national RRV notifications. However, little is known about factors which can forecast RRV transmission in Victoria. We aimed to understand factors associated with RRV transmission in epidemiologically important regions of Victoria and establish an early warning forecast system. We developed negative binomial regression models to forecast human RRV notifications across 11 Local Government Areas (LGAs) using climatic, environmental, and oceanographic variables. Data were collected from July 2008 to June 2018. Data from July 2008 to June 2012 were used as a training data set, while July 2012 to June 2018 were used as a testing data set. Evapotranspiration and precipitation were found to be common factors for forecasting RRV notifications across sites. Several site-specific factors were also important in forecasting RRV notifications which varied between LGA. From the 11 LGAs examined, nine experienced an outbreak in 2011/12 of which the models for these sites were a good fit. All 11 LGAs experienced an outbreak in 2016/17, however only six LGAs could predict the outbreak using the same model. We document similarities and differences in factors useful for forecasting RRV notifications across Victoria and demonstrate that readily available and inexpensive climate and environmental data can be used to predict epidemic periods in some areas. Furthermore, we highlight in certain regions the complexity of RRV transmission where additional epidemiological information is needed to accurately predict RRV activity. Our findings have been applied to produce a Ross River virus Outbreak Surveillance System (ROSS) to aid in public health decision making in Victoria.

The Role of Temperature in Transmission of Zoonotic Arboviruses

We reviewed the literature on the role of temperature in transmission of zoonotic arboviruses. Vector competence is affected by both direct and indirect effects of temperature, and generally increases with increasing temperature, but results may vary by vector species, population, and viral strain. Temperature additionally has a significant influence on life history traits of vectors at both immature and adult life stages, and for important behaviors such as blood-feeding and mating. Similar to vector competence, temperature effects on life history traits can vary by species and population. Vector, host, and viral distributions are all affected by temperature, and are generally expected to change with increased temperatures predicted under climate change. Arboviruses are generally expected to shift poleward and to higher elevations under climate change, yet significant variability on fine geographic scales is likely. Temperature effects are generally unimodal, with increases in abundance up to an optimum, and then decreases at high temperatures. Improved vector distribution information could facilitate future distribution modeling. A wide variety of approaches have been used to model viral distributions, although most research has focused on the West Nile virus. Direct temperature effects are frequently observed, as are indirect effects, such as through droughts, where temperature interacts with rainfall. Thermal biology approaches hold much promise for syntheses across viruses, vectors, and hosts, yet future studies must consider the specificity of interactions and the dynamic nature of evolving biological systems.

Thermal biology of mosquito-borne disease

Mosquito-borne diseases cause a major burden of disease worldwide. The vital rates of these ectothermic vectors and parasites respond strongly and nonlinearly to temperature and therefore to climate change. Here, we review how trait-based approaches can synthesise and mechanistically predict the temperature dependence of transmission across vectors, pathogens, and environments. We present 11 pathogens transmitted by 15 different mosquito species - including globally important diseases like malaria, dengue, and Zika - synthesised from previously published studies. Transmission varied strongly and unimodally with temperature, peaking at 23-29ºC and declining to zero below 9-23ºC and above 32-38ºC. Different traits restricted transmission at low versus high temperatures, and temperature effects on transmission varied by both mosquito and parasite species. Temperate pathogens exhibit broader thermal ranges and cooler thermal minima and optima than tropical pathogens. Among tropical pathogens, malaria and Ross River virus had lower thermal optima (25-26ºC) while dengue and Zika viruses had the highest (29ºC) thermal optima. We expect warming to increase transmission below thermal optima but decrease transmission above optima. Key directions for future work include linking mechanistic models to field transmission, combining temperature effects with control measures, incorporating trait variation and temperature variation, and investigating climate adaptation and migration.

Climate drives spatial variation in Zika epidemics in Latin America

Between 2015 and 2017, Zika virus spread rapidly through populations in the Americas with no prior exposure to the disease. Although climate is a known determinant of many Aedes-transmitted diseases, it is currently unclear whether climate was a major driver of the Zika epidemic and how climate might have differentially impacted outbreak intensity across locations within Latin America. Here, we estimated force of infection for Zika over time and across provinces in Latin America using a time-varying susceptible–infectious–recovered model. Climate factors explained less than 5% of the variation in weekly transmission intensity in a spatio-temporal model of force of infection by province over time, suggesting that week to week transmission within provinces may be too stochastic to predict. By contrast, climate and population factors were highly predictive of spatial variation in the presence and intensity of Zika transmission among provinces, with pseudo-R² values between 0.33 and 0.60. Temperature, temperature range, rainfall and population size were the most important predictors of where Zika transmission occurred, while rainfall, relative humidity and a nonlinear effect of temperature were the best predictors of Zika intensity and burden. Surprisingly, force of infection was greatest in locations with temperatures near 24°C, much lower than previous estimates from mechanistic models, potentially suggesting that existing vector control programmes and/or prior exposure to other mosquito-borne diseases may have limited transmission in locations most suitable for Aedes aegypti, the main vector of Zika, dengue and chikungunya viruses in Latin America.

The MJA-Lancet Countdown on health and climate change: Australian policy inaction threatens lives

Climate plays an important role in human health and it is well established that climate change can have very significant impacts in this regard. In partnership with The Lancet and the MJA, we present the inaugural Australian Countdown assessment of progress on climate change and health. This comprehensive assessment examines 41 indicators across five broad sections: climate change impacts, exposures and vulnerability; adaptation, planning and resilience for health; mitigation actions and health co-benefits; economics and finance; and public and political engagement. These indicators and the methods used for each are largely consistent with those of the Lancet Countdown global assessment published in October 2017, but with an Australian focus. Significant developments include the addition of a new indicator on mental health. Overall, we find that Australia is vulnerable to the impacts of climate change on health, and that policy inaction in this regard threatens Australian lives. In a number of respects, Australia has gone backwards and now lags behind other high income countries such as Germany and the United Kingdom. Examples include the persistence of a very high carbon-intensive energy system in Australia, and its slow transition to renewables and low carbon electricity generation. However, we also find some examples of good progress, such as heatwave response planning. Given the overall poor state of progress on climate change and health in Australia, this country now has an enormous opportunity to take action and protect human health and lives. Australia has the technical knowhow and intellect to do this, and our annual updates of this assessment will track Australia's engagement with and progress on this vitally important issue.

Temperature explains broad patterns of Ross River virus transmission

Thermal biology predicts that vector-borne disease transmission peaks at intermediate temperatures and declines at high and low temperatures. However, thermal optima and limits remain unknown for most vector-borne pathogens. We built a mechanistic model for the thermal response of Ross River virus, an important mosquito-borne pathogen in Australia, Pacific Islands, and potentially at risk of emerging worldwide. Transmission peaks at moderate temperatures (26.4°C) and declines to zero at thermal limits (17.0 and 31.5°C). The model accurately predicts that transmission is year-round endemic in the tropics but seasonal in temperate areas, resulting in the nationwide seasonal peak in human cases. Climate warming will likely increase transmission in temperate areas (where most Australians live) but decrease transmission in tropical areas where mean temperatures are already near the thermal optimum. These results illustrate the importance of nonlinear models for inferring the role of temperature in disease dynamics and predicting responses to climate change.

Mosquitoes cannot control their body temperature, so their survival and performance depend on the temperature where they live. As a result, outside temperatures can also affect the spread of diseases transmitted by mosquitoes. This has left scientists wondering how climate change may affect the spread of mosquito-borne diseases. Predicting the effects of climate change on such diseases is tricky, because many interacting factors, including temperatures and rainfall, affect mosquito populations. Also, rising temperatures do not always have a positive effect on mosquitoes – they may help mosquitoes initially, but it can get too warm even for these animals.

Climate change could affect the Ross River virus, the most common mosquito-borne disease in Australia. The virus infects 2,000 to 9,000 people each year and can cause long-term joint pain and disability. Currently, the virus spreads year-round in tropical, northern Australia and seasonally in temperate, southern Australia. Large outbreaks have occurred outside of Australia, and scientists are worried it could spread worldwide.

Now, Shocket et al. have built a model that predicts how the spread of Ross River virus changes with temperature. Shocket et al. used data from laboratory experiments that measured mosquito and virus performance across a broad range of temperatures. The experiments showed that ~26°C (80°F) is the optimal temperature for mosquitoes to spread the Ross River virus. Temperatures below 17°C (63°F) and above 32°C (89°F) hamper the spread of the virus. These temperature ranges match the current disease patterns in Australia where human cases peak in March. This is two months after the country’s average temperature reaches the optimal level and about how long it takes mosquito populations to grow, infect people, and for symptoms to develop.

Because northern Australia is already near the optimal temperature for mosquitos to spread the Ross River virus, any climate warming should decrease transmission there. But warming temperatures could increase the disease’s transmission in the southern part of the country, where most people live. The model Shocket et al. created may help the Australian government and mosquito control agencies better plan for the future.

Hydrological features and the ecological niches of mammalian hosts delineate elevated risk for Ross River virus epidemics in anthropogenic landscapes in Australia

Background

The current understanding of the landscape epidemiology of Ross River virus (RRV), Australia’s most common arthropod-borne pathogen, is fragmented due to gaps in surveillance programs and the relatively narrow focus of the research conducted to date. This leaves public health agencies with an incomplete understanding of the spectrum of infection risk across the diverse geography of the Australian continent. The current investigation sought to assess the risk of RRV epidemics based on abiotic and biotic landscape features in anthropogenic landscapes, with a particular focus on the influence of water and wildlife hosts.

Methods

Abiotic features, including hydrology, land cover and altitude, and biotic features, including the distribution of wild mammalian hosts, were interrogated using a Maxent model to discern the landscape suitability to RRV epidemics in anthropogenically impacted environments across Australia.

Results

Water-soil balance, proximity to controlled water reservoirs, and the ecological niches of four species (Perameles nasuta, Wallabia bicolor, Pseudomys novaehollandiae and Trichosurus vulpecula) were important features identifying high risk landscapes suitable for the occurrence of RRV epidemics.

Conclusions

These results help to delineate human infection risk and thus provide an important perspective for geographically targeted vector, wildlife, and syndromic surveillance within and across the boundaries of local health authorities. Importantly, our analysis highlights the importance of the hydrology, and the potential role of mammalian host species in shaping RRV epidemic risk in peri-urban space. This study offers novel insight into wildlife hosts and RRV infection ecology and identifies those species that may be beneficial to future targeted field surveillance particularly in ecosystems undergoing rapid change.

Electronic supplementary material

The online version of this article (10.1186/s13071-018-2776-x) contains supplementary material, which is available to authorized users.

Improving public health intervention for mosquito-borne disease: the value of geovisualization using source of infection and LandScan data

Epidemiological studies use georeferenced health data to identify disease clusters but the accuracy of this georeferencing is obfuscated by incorrectly assigning the source of infection and by aggregating case data to larger geographical areas. Often, place of residence (residence) is used as a proxy for the source of infection (source) which may not be accurate. Using a 21-year dataset from South Australia of human infections with the mosquito-borne Ross River virus, we found that 37% of cases were believed to have been acquired away from home. We constructed two risk maps using age-standardized morbidity ratios (SMRs) calculated using residence and patient-reported source. Both maps confirm significant inter-suburb variation in SMRs. Areas frequently named as the source (but not residence) and the highest-risk suburbs both tend to be tourist locations with vector mosquito habitat, and camping or outdoor recreational opportunities. We suggest the highest-risk suburbs as places to focus on for disease control measures. We also use a novel application of ambient population data (LandScan) to improve the interpretation of these risk maps and propose how this approach can aid in implementing disease abatement measures on a smaller scale than for which disease data are available.

Modeling Occurrence of Urban Mosquitos Based on Land Use Types and Meteorological Factors in Korea

Mosquitoes are a public health concern because they are vectors of pathogen, which cause human-related diseases. It is well known that the occurrence of mosquitoes is highly influenced by meteorological conditions (e.g., temperature and precipitation) and land use, but there are insufficient studies quantifying their impacts. Therefore, three analytical methods were applied to determine the relationships between urban mosquito occurrence, land use type, and meteorological factors: cluster analysis based on land use types; principal component analysis (PCA) based on mosquito occurrence; and three prediction models, support vector machine (SVM), classification and regression tree (CART), and random forest (RF). We used mosquito data collected at 12 sites from 2011 to 2012. Mosquito abundance was highest from August to September in both years. The monitoring sites were differentiated into three clusters based on differences in land use type such as culture and sport areas, inland water, artificial grasslands, and traffic areas. These clusters were well reflected in PCA ordinations, indicating that mosquito occurrence was highly influenced by land use types. Lastly, the RF represented the highest predictive power for mosquito occurrence and temperature-related factors were the most influential. Our study will contribute to effective control and management of mosquito occurrences.

Role of pentraxin 3 in shaping arthritogenic alphaviral disease: from enhanced viral replication to immunomodulation

The rising prevalence of arthritogenic alphavirus infections, including chikungunya virus (CHIKV) and Ross River virus (RRV), and the lack of antiviral treatments highlight the potential threat of a global alphavirus pandemic. The immune responses underlying alphavirus virulence remain enigmatic. We found that pentraxin 3 (PTX3) was highly expressed in CHIKV and RRV patients during acute disease. Overt expression of PTX3 in CHIKV patients was associated with increased viral load and disease severity. PTX3-deficient (PTX3(-/-)) mice acutely infected with RRV exhibited delayed disease progression and rapid recovery through diminished inflammatory responses and viral replication. Furthermore, binding of the N-terminal domain of PTX3 to RRV facilitated viral entry and replication. Thus, our study demonstrates the pivotal role of PTX3 in shaping alphavirus-triggered immunity and disease and provides new insights into alphavirus pathogenesis.

The importance of climatic factors and outliers in predicting regional monthly campylobacteriosis risk in Georgia, USA

Incidence of Campylobacter infection exhibits a strong seasonal component and regional variations in temperate climate zones. Forecasting the risk of infection regionally may provide clues to identify sources of transmission affected by temperature and precipitation. The objectives of this study were to (1) assess temporal patterns and differences in campylobacteriosis risk among nine climatic divisions of Georgia, USA, (2) compare univariate forecasting models that analyze campylobacteriosis risk over time with those that incorporate temperature and/or precipitation, and (3) investigate alternatives to supposedly random walk series and non-random occurrences that could be outliers. Temporal patterns of campylobacteriosis risk in Georgia were visually and statistically assessed. Univariate and multivariable forecasting models were used to predict the risk of campylobacteriosis and the coefficient of determination (R(2)) was used for evaluating training (1999-2007) and holdout (2008) samples. Statistical control charting and rolling holdout periods were investigated to better understand the effect of outliers and improve forecasts. State and division level campylobacteriosis risk exhibited seasonal patterns with peaks occurring between June and August, and there were significant associations between campylobacteriosis risk, precipitation, and temperature. State and combined division forecasts were better than divisions alone, and models that included climate variables were comparable to univariate models. While rolling holdout techniques did not improve predictive ability, control charting identified high-risk time periods that require further investigation. These findings are important in (1) determining how climatic factors affect environmental sources and reservoirs of Campylobacter spp. and (2) identifying regional spikes in the risk of human Campylobacter infection and their underlying causes.

Exploring the potential for Ross River virus emergence in New Zealand

Ross River virus (RRV) is an exotic vector-borne disease considered highly likely to emerge as a future human health issue in New Zealand, with its range expansion from Australia being driven by exotic mosquito introduction and improving conditions for mosquito breeding. We investigated our ability to assess the potential for such emergence using deterministic modeling and making preliminary predictions based on currently available evidence. Although data on actual mosquito densities (as opposed to indices) were identified as a need for predictions to be made with greater confidence, this approach generated a contrasting prediction to current opinion. Only limited potential for RRV emergence in New Zealand was predicted, with outbreaks in the human population more likely of concern in urban areas (mainly should major exotic vectors of the virus establish). The mechanistic nature of the model also allowed the understanding that if such outbreaks do occur, they will most likely be driven by virus amplification in dense human populations (as opposed to the spillover infection from wildlife common in Australia). With implications for biosecurity and health care resource allocation, modeling approaches such as that employed here have much to offer both for disease emergence prediction and surveillance strategy design.

Projecting the impact of climate change on the transmission of Ross River virus: methodological challenges and research needs

Ross River virus (RRV) is the most common vector-borne disease in Australia. It is vitally important to make appropriate projections on the future spread of RRV under various climate change scenarios because such information is essential for policy-makers to identify vulnerable communities and to better manage RRV epidemics. However, there are many methodological challenges in projecting the impact of climate change on the transmission of RRV disease. This study critically examined the methodological issues and proposed possible solutions. A literature search was conducted between January and October 2012, using the electronic databases Medline, Web of Science and PubMed. Nineteen relevant papers were identified. These studies demonstrate that key challenges for projecting future climate change on RRV disease include: (1) a complex ecology (e.g. many mosquito vectors, immunity, heterogeneous in both time and space); (2) unclear interactions between social and environmental factors; and (3) uncertainty in climate change modelling and socioeconomic development scenarios. Future risk assessments of climate change will ultimately need to better understand the ecology of RRV disease and to integrate climate change scenarios with local socioeconomic and environmental factors, in order to develop effective adaptation strategies to prevent or reduce RRV transmission.

Environmental drivers of Ross River virus in southeastern Tasmania, Australia: towards strengthening public health interventions

In Australia, Ross River virus (RRV) is predominantly identified and managed through passive health surveillance. Here, the proactive use of environmental datasets to improve community-scale public health interventions in southeastern Tasmania is explored. Known environmental drivers (temperature, rainfall, tide) of the RRV vector Aedes camptorhynchus are analysed against cumulative case records for five adjacent local government areas (LGAs) from 1993 to 2009. Allowing for a 0- to 3-month lag period, temperature was the most significant driver of RRV cases at 1-month lag, contributing to a 23·2% increase in cases above the long-term case average. The potential for RRV to become an emerging public health issue in Tasmania due to projected climate changes is discussed. Moreover, practical outputs from this research are proposed including the development of an early warning system for local councils to implement preventative measures, such as public outreach and mosquito spray programmes.

Comparison of three time-series models for predicting campylobacteriosis risk

Three time-series models (regression, decomposition, and Box-Jenkins autoregressive integrated moving averages) were applied to national surveillance data for campylobacteriosis with the goal of disease forecasting in three US states. Datasets spanned 1998-2007 for Minnesota and Oregon, and 1999-2007 for Georgia. Year 2008 was used to validate model results. Mean absolute percent error, mean square error and coefficient of determination (R2) were the main evaluation fit statistics. Results showed that decomposition best captured the temporal patterns in disease risk. Training dataset R2 values were 72.2%, 76.3% and 89.9% and validation year R2 values were 66.2%, 52.6% and 79.9% respectively for Georgia, Oregon and Minnesota. All three techniques could be utilized to predict monthly risk of infection for Campylobacter sp. However, the decomposition model provided the fastest, most accurate, user-friendly method. Use of this model can assist public health personnel in predicting epidemics and developing disease intervention strategies.

Time series analysis of dengue fever and weather in Guangzhou, China

BACKGROUND

Monitoring and predicting dengue incidence facilitates early public health responses to minimize morbidity and mortality. Weather variables are potential predictors of dengue incidence. This study explored the impact of weather variability on the transmission of dengue fever in the subtropical city of Guangzhou, China.

METHODS

Time series Poisson regression analysis was performed using data on monthly weather variables and monthly notified cases of dengue fever in Guangzhou, China for the period of 2001-2006. Estimates of the Poisson model parameters was implemented using the Generalized Estimating Equation (GEE) approach; the quasi-likelihood based information criterion (QICu) was used to select the most parsimonious model.

RESULTS

Two best fitting models, with the smallest QICu values, are selected to characterize the relationship between monthly dengue incidence and weather variables. Minimum temperature and wind velocity are significant predictors of dengue incidence. Further inclusion of minimum humidity in the model provides a better fit.

CONCLUSION

Minimum temperature and minimum humidity, at a lag of one month, are positively associated with dengue incidence in the subtropical city of Guangzhou, China. Wind velocity is inversely associated with dengue incidence of the same month. These findings should be considered in the prediction of future patterns of dengue transmission.