Predicting Ross River virus epidemics from regional weather data

Towards development of functional climate-driven early warning systems for climate-sensitive infectious diseases: Statistical models and recommendations

Climate, weather and environmental change have significantly influenced patterns of infectious disease transmission, necessitating the development of early warning systems to anticipate potential impacts and respond in a timely and effective way. Statistical modelling plays a pivotal role in understanding the intricate relationships between climatic factors and infectious disease transmission. For example, time series regression modelling and spatial cluster analysis have been employed to identify risk factors and predict spatial and temporal patterns of infectious diseases. Recently advanced spatio-temporal models and machine learning offer an increasingly robust framework for modelling uncertainty, which is essential in climate-driven disease surveillance due to the dynamic and multifaceted nature of the data. Moreover, Artificial Intelligence (AI) techniques, including deep learning and neural networks, excel in capturing intricate patterns and hidden relationships within climate and environmental data sets. Web-based data has emerged as a powerful complement to other datasets encompassing climate variables and disease occurrences. However, given the complexity and non-linearity of climate-disease interactions, advanced techniques are required to integrate and analyse these diverse data to obtain more accurate predictions of impending outbreaks, epidemics or pandemics. This article presents an overview of an approach to creating climate-driven early warning systems with a focus on statistical model suitability and selection, along with recommendations for utilizing spatio-temporal and machine learning techniques. By addressing the limitations and embracing the recommendations for future research, we could enhance preparedness and response strategies, ultimately contributing to the safeguarding of public health in the face of evolving climate challenges.

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.

Health impacts of daily weather fluctuations: Empirical evidence from COVID-19 in U.S. counties

The emergence of the novel coronavirus has necessitated immense research efforts to understand how several non-environmental and environmental factors affect transmission. With the United States leading the path in terms of case incidence, it is important to investigate how weather variables influence the spread of the disease in the country. This paper assembles a detailed and comprehensive dataset comprising COVID-19 cases and climatological variables for all counties in the continental U.S. and uses a developed econometric approach to estimate the causal effect of certain weather factors on the growth rate of infection. The results indicate a non-linear and significant negative relationship between the individual weather measures and the growth rate of COVID-19 in the U.S. Specifically, the paper finds that a 1 °C rise in daily temperature will reduce daily covid growth rate in the U.S. by approximately 6 percent in the following week, while a marginal increase in relative humidity reduces the same outcome by 1 percent over a similar period. In comparison, a 1 m/s increase in daily wind speed will bring about an 8 percent drop in daily growth rate of COVID-19 in the country. These results differ by location and are robust to several sensitivity checks, so large deviations are unexpected.

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.

The environmental and ecological determinants of elevated Ross River Virus exposure in koalas residing in urban coastal landscapes

Koala populations in many areas of Australia have declined sharply in response to habitat loss, disease and the effects of climate change. Koalas may face further morbidity from endemic mosquito-borne viruses, but the impact of such viruses is currently unknown. Few seroprevalence studies in the wild exist and little is known of the determinants of exposure. Here, we exploited a large, spatially and temporally explicit koala survey to define the intensity of Ross River Virus (RRV) exposure in koalas residing in urban coastal environments in southeast Queensland, Australia. We demonstrate that RRV exposure in koalas is much higher (> 80%) than reported in other sero-surveys and that exposure is uniform across the urban coastal landscape. Uniformity in exposure is related to the presence of the major RRV mosquito vector, Culex annulirostris, and similarities in animal movement, tree use, and age-dependent increases in exposure risk. Elevated exposure ultimately appears to result from the confinement of remaining coastal koala habitat to the edges of permanent wetlands unsuitable for urban development and which produce large numbers of competent mosquito vectors. The results further illustrate that koalas and other RRV-susceptible vertebrates may serve as useful sentinels of human urban exposure in endemic areas.

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.

Spatial and temporal patterns of Ross River virus in south east Queensland, Australia: identification of hot spots at the rural-urban interface

Background

Ross River virus (RRV) is responsible for the most common vector-borne disease of humans reported in Australia. The virus circulates in enzootic cycles between multiple species of mosquitoes, wildlife reservoir hosts and humans. Public health concern about RRV is increasing due to rising incidence rates in Australian urban centres, along with increased circulation in Pacific Island countries. Australia experienced its largest recorded outbreak of 9544 cases in 2015, with the majority reported from south east Queensland (SEQ). This study examined potential links between disease patterns and transmission pathways of RRV.

Methods

The spatial and temporal distribution of notified RRV cases, and associated epidemiological features in SEQ, were analysed for the period 2001–2016. This included fine-scale analysis of disease patterns across the suburbs of the capital city of Brisbane, and those of 8 adjacent Local Government Areas, and host spot analyses to identify locations with significantly high incidence.

Results

The mean annual incidence rate for the region was 41/100,000 with a consistent seasonal peak in cases between February and May. The highest RRV incidence was in adults aged from 30 to 64 years (mean incidence rate: 59/100,000), and females had higher incidence rates than males (mean incidence rates: 44/100,000 and 34/100,000, respectively). Spatial patterns of disease were heterogeneous between years, and there was a wide distribution of disease across both urban and rural areas of SEQ. Overall, the highest incidence rates were reported from predominantly rural suburbs to the north of Brisbane City, with significant hot spots located in peri-urban suburbs where residential, agricultural and conserved natural land use types intersect.

Conclusions

Although RRV is endemic across all of SEQ, transmission is most concentrated in areas where urban and peri-urban environments intersect. The drivers of RRV transmission across rural-urban landscapes should be prioritised for further investigation, including identification of specific vectors and hosts that mediate human spillover.

Space-Time Conditional Autoregressive Modeling to Estimate Neighborhood-Level Risks for Dengue Fever in Cali, Colombia

Vector-borne diseases affect more than 1 billion people a year worldwide, causing more than 1 million deaths, and cost hundreds of billions of dollars in societal costs. Mosquitoes are the most common vectors responsible for transmitting a variety of arboviruses. Dengue fever (DENF) has been responsible for nearly 400 million infections annually. Dengue fever is primarily transmitted by female Aedes aegypti and Aedes albopictus mosquitoes. Because both Aedes species are peri-domestic and container-breeding mosquitoes, dengue surveillance should begin at the local level—where a variety of local factors may increase the risk of transmission. Dengue has been endemic in Colombia for decades and is notably hyperendemic in the city of Cali. For this study, we use weekly cases of DENF in Cali, Colombia, from 2015 to 2016 and develop space–time conditional autoregressive models to quantify how DENF risk is influenced by socioeconomic, environmental, and accessibility risk factors, and lagged weather variables. Our models identify high-risk neighborhoods for DENF throughout Cali. Statistical inference is drawn under Bayesian paradigm using Markov chain Monte Carlo techniques. The results provide detailed insight about the spatial heterogeneity of DENF risk and the associated risk factors (such as weather, proximity to Aedes habitats, and socioeconomic classification) at a fine level, informing public health officials to motivate at-risk neighborhoods to take an active role in vector surveillance and control, and improving educational and surveillance resources throughout the city of Cali.

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.

Climate change induced vulnerability and adaption for dengue incidence in Colombo and Kandy districts: the detailed investigation in Sri Lanka

Background

Assessing the vulnerability of an infectious disease such as dengue among endemic population is an important requirement to design proactive programmes in order to improve resilience capacity of vulnerable communities. The current study aimed to evaluate the climate change induced socio-economic vulnerability of local communities to dengue in Colombo and Kandy districts of Sri Lanka.

Methods

A total of 42 variables (entomological, epidemiological, meteorological parameters, land-use practices and socio-demographic data) of all the 38 Medical Officer of Health (MOH) areas in the districts of Colombo and Kandy were considered as candidate variables for a composite index based vulnerability assessment. The Principal Component Analysis (PCA) was used in selecting and setting the weight for each indicator. Exposure, Sensitivity, Adaptive Capacity and Vulnerability of all MOH areas for dengue were calculated using the composite index approach recommended by the Intergovernmental Panel on Climate Change.

Results

Out of 42 candidate variables, only 23 parameters (Exposure Index: six variables; Sensitivity Index: 11 variables; Adaptive Capacity Index: six variables) were selected as indicators to assess climate change vulnerability to dengue. Colombo Municipal Council (CMC) MOH area denoted the highest values for exposure (0.89: exceptionally high exposure), sensitivity (0.86: exceptionally high sensitivity) in Colombo, while Kandy Municipal Council (KMC) area reported the highest exposure (0.79: high exposure) and sensitivity (0.77: high sensitivity) in Kandy. Piliyandala MOH area denoted the highest level of adaptive capacity (0.66) in Colombo followed by Menikhinna (0.68) in Kandy. The highest vulnerability (0.45: moderate vulnerability) to dengue was indicated from CMC and the lowest indicated from Galaha MOH (0.15; very low vulnerability) in Kandy. Interestingly the KMC MOH area had a notable vulnerability of 0.41 (moderate vulnerability), which was the highest within Kandy.

Conclusions

In general, vulnerability for dengue was relatively higher within the MOH areas of Colombo, than in Kandy, suggesting a higher degree of potential susceptibility to dengue within and among local communities of Colombo. Vector Controlling Entities are recommended to consider the spatial variations in vulnerability of local communities to dengue for decision making, especially in allocation of limited financial, human and mechanical resources for dengue epidemic management.

Epidemiologic, Entomologic, and Virologic Factors of the 2014-15 Ross River Virus Outbreak, Queensland, Australia

Australia experienced its largest recorded outbreak of Ross River virus (RRV) during the 2014-15 reporting year, comprising >10,000 reported cases. We investigated epidemiologic, entomologic, and virologic factors that potentially contributed to the scale of the outbreak in Queensland, the state with the highest number of notifications (6,371). Spatial analysis of human cases showed that notifications were geographically widespread. In Brisbane, human case notifications and virus detections in mosquitoes occurred across inland and coastal locations. Viral sequence data demonstrated 2 RRV lineages (northeastern genotypes I and II) were circulating, and a new strain containing 3 unique amino acid changes in the envelope 2 protein was identified. Longitudinal mosquito collections demonstrated unusually high relative abundance of Culex annulirostris and Aedes procax mosquitoes, attributable to extensive freshwater larval habitats caused by early and persistent rainfall during the reporting year. Increased prevalence of these mosquitoes probably contributed to the scale of this outbreak.

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.

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.

Fine-temporal forecasting of outbreak probability and severity: Ross River virus in Western Australia

Health warnings of mosquito-borne disease risk require forecasts that are accurate at fine-temporal resolutions (weekly scales); however, most forecasting is coarse (monthly). We use environmental and Ross River virus (RRV) surveillance to predict weekly outbreak probabilities and incidence spanning tropical, semi-arid, and Mediterranean regions of Western Australia (1991–2014). Hurdle and linear models were used to predict outbreak probabilities and incidence respectively, using time-lagged environmental variables. Forecast accuracy was assessed by model fit and cross-validation. Residual RRV notification data were also examined against mitigation expenditure for one site, Mandurah 2007–2014. Models were predictive of RRV activity, except at one site (Capel). Minimum temperature was an important predictor of RRV outbreaks and incidence at all predicted sites. Precipitation was more likely to cause outbreaks and greater incidence among tropical and semi-arid sites. While variable, mitigation expenditure coincided positively with increased RRV incidence (r2 = 0·21). Our research demonstrates capacity to accurately predict mosquito-borne disease outbreaks and incidence at fine-temporal resolutions. We apply our findings, developing a user-friendly tool enabling managers to easily adopt this research to forecast region-specific RRV outbreaks and incidence. Approaches here may be of value to fine-scale forecasting of RRV in other areas of Australia, and other mosquito-borne diseases.

Climate change and sugarcane expansion increase Hantavirus infection risk

Hantavirus Cardiopulmonary Syndrome (HCPS) is a disease caused by Hantavirus, which is highly virulent for humans. High temperatures and conversion of native vegetation to agriculture, particularly sugarcane cultivation can alter abundance of rodent generalist species that serve as the principal reservoir host for HCPS, but our understanding of the compound effects of land use and climate on HCPS incidence remains limited, particularly in tropical regions. Here we rely on a Bayesian model to fill this research gap and to predict the effects of sugarcane expansion and expected changes in temperature on Hantavirus infection risk in the state of São Paulo, Brazil. The sugarcane expansion scenario was based on historical data between 2000 and 2010 combined with an agro-environment zoning guideline for the sugar and ethanol industry. Future evolution of temperature anomalies was derived using 32 general circulation models from scenarios RCP4.5 and RCP8.5 (Representative greenhouse gases Concentration Pathways adopted by IPCC). Currently, the state of São Paulo has an average Hantavirus risk of 1.3%, with 6% of the 645 municipalities of the state being classified as high risk (HCPS risk ≥ 5%). Our results indicate that sugarcane expansion alone will increase average HCPS risk to 1.5%, placing 20% more people at HCPS risk. Temperature anomalies alone increase HCPS risk even more (1.6% for RCP4.5 and 1.7%, for RCP8.5), and place 31% and 34% more people at risk. Combined sugarcane and temperature increases led to the same predictions as scenarios that only included temperature. Our results demonstrate that climate change effects are likely to be more severe than those from sugarcane expansion. Forecasting disease is critical for the timely and efficient planning of operational control programs that can address the expected effects of sugarcane expansion and climate change on HCPS infection risk. The predicted spatial location of HCPS infection risks obtained here can be used to prioritize management actions and develop educational campaigns.

Modelling the association of dengue fever cases with temperature and relative humidity in Jeddah, Saudi Arabia-A generalised linear model with break-point analysis

The aim of this study was to examine the role of environmental factors in the temporal distribution of dengue fever in Jeddah, Saudi Arabia. The relationship between dengue fever cases and climatic factors such as relative humidity and temperature was investigated during 2006-2009 to determine whether there is any relationship between dengue fever cases and climatic parameters in Jeddah City, Saudi Arabia. A generalised linear model (GLM) with a break-point was used to determine how different levels of temperature and relative humidity affected the distribution of the number of cases of dengue fever. Break-point analysis was performed to modelled the effect before and after a break-point (change point) in the explanatory parameters under various scenarios. Akaike information criterion (AIC) and cross validation (CV) were used to assess the performance of the models. The results showed that maximum temperature and mean relative humidity are most probably the better predictors of the number of dengue fever cases in Jeddah. In this study three scenarios were modelled: no time lag, 1-week lag and 2-weeks lag. Among these scenarios, the 1-week lag model using mean relative humidity as an explanatory variable showed better performance. This study showed a clear relationship between the meteorological variables and the number of dengue fever cases in Jeddah. The results also demonstrated that meteorological variables can be successfully used to estimate the number of dengue fever cases for a given period of time. Break-point analysis provides further insight into the association between meteorological parameters and dengue fever cases by dividing the meteorological parameters into certain break-points.

Predictive modelling of Ross River virus notifications in southeastern Australia

Ross River virus (RRV) is a mosquito-borne virus endemic to Australia. The disease, marked by arthritis, myalgia and rash, has a complex epidemiology involving several mosquito species and wildlife reservoirs. Outbreak years coincide with climatic conditions conducive to mosquito population growth. We developed regression models for human RRV notifications in the Mildura Local Government Area, Victoria, Australia with the objective of increasing understanding of the relationships in this complex system, providing trigger points for intervention and developing a forecast model. Surveillance, climatic, environmental and entomological data for the period July 2000-June 2011 were used for model training then forecasts were validated for July 2011-June 2015. Rainfall and vapour pressure were the key factors for forecasting RRV notifications. Validation of models showed they predicted RRV counts with an accuracy of 81%. Two major RRV mosquito vectors (Culex annulirostris and Aedes camptorhynchus) were important in the final estimation model at proximal lags. The findings of this analysis advance understanding of the drivers of RRV in temperate climatic zones and the models will inform public health agencies of periods of increased risk.

Developing a Time Series Predictive Model for Dengue in Zhongshan, China Based on Weather and Guangzhou Dengue Surveillance Data

Background

Dengue is a re-emerging infectious disease of humans, rapidly growing from endemic areas to dengue-free regions due to favorable conditions. In recent decades, Guangzhou has again suffered from several big outbreaks of dengue; as have its neighboring cities. This study aims to examine the impact of dengue epidemics in Guangzhou, China, and to develop a predictive model for Zhongshan based on local weather conditions and Guangzhou dengue surveillance information.

Methods

We obtained weekly dengue case data from 1^st January, 2005 to 31^st December, 2014 for Guangzhou and Zhongshan city from the Chinese National Disease Surveillance Reporting System. Meteorological data was collected from the Zhongshan Weather Bureau and demographic data was collected from the Zhongshan Statistical Bureau. A negative binomial regression model with a log link function was used to analyze the relationship between weekly dengue cases in Guangzhou and Zhongshan, controlling for meteorological factors. Cross-correlation functions were applied to identify the time lags of the effect of each weather factor on weekly dengue cases. Models were validated using receiver operating characteristic (ROC) curves and k-fold cross-validation.

Results

Our results showed that weekly dengue cases in Zhongshan were significantly associated with dengue cases in Guangzhou after the treatment of a 5 weeks prior moving average (Relative Risk (RR) = 2.016, 95% Confidence Interval (CI): 1.845–2.203), controlling for weather factors including minimum temperature, relative humidity, and rainfall. ROC curve analysis indicated our forecasting model performed well at different prediction thresholds, with 0.969 area under the receiver operating characteristic curve (AUC) for a threshold of 3 cases per week, 0.957 AUC for a threshold of 2 cases per week, and 0.938 AUC for a threshold of 1 case per week. Models established during k-fold cross-validation also had considerable AUC (average 0.938–0.967). The sensitivity and specificity obtained from k-fold cross-validation was 78.83% and 92.48% respectively, with a forecasting threshold of 3 cases per week; 91.17% and 91.39%, with a threshold of 2 cases; and 85.16% and 87.25% with a threshold of 1 case. The out-of-sample prediction for the epidemics in 2014 also showed satisfactory performance.

Conclusion

Our study findings suggest that the occurrence of dengue outbreaks in Guangzhou could impact dengue outbreaks in Zhongshan under suitable weather conditions. Future studies should focus on developing integrated early warning systems for dengue transmission including local weather and human movement.

Emerging and re-emerging infectious diseases in an urban city could expand due to increased urbanization, population density, and travel. Dengue, as a mosquito-borne viral disease, has rapidly spread from endemic areas to dengue-free regions, with social, demographic, entomological, and environmental factors affecting its transmission. In recent decades, Guangzhou has again suffered from several big outbreaks of dengue; as have its neighboring cities. In this study, we demonstrated that the dengue outbreaks in Guangzhou could impact outbreaks in Zhongshan, one of its neighboring cities, if suitable climate conditions are present. Such associations between dengue epidemics in two cities may also suggest the important role human movement has played in the transmission of the disease. Based on the association between dengue epidemics in Guangzhou and Zhongshan, and the association between dengue epidemics and weather conditions, we developed a reliable and robust model that predicts the occurrence of epidemics at diffrent thresholds in Zhongshan. These results could be used by local health departments in developing strategies towards dengue prevention and control, and push the public to pay more attention to social factors like human movement in disease transmission.

Avian influenza infection dynamics under variable climatic conditions, viral prevalence is rainfall driven in waterfowl from temperate, south-east Australia

Understanding Avian Influenza Virus (AIV) infection dynamics in wildlife is crucial because of possible virus spill over to livestock and humans. Studies from the northern hemisphere have suggested several ecological and environmental drivers of AIV prevalence in wild birds. To determine if the same drivers apply in the southern hemisphere, where more irregular environmental conditions prevail, we investigated AIV prevalence in ducks in relation to biotic and abiotic factors in south-eastern Australia. We sampled duck faeces for AIV and tested for an effect of bird numbers, rainfall anomaly, temperature anomaly and long-term ENSO (El-Niño Southern Oscillation) patterns on AIV prevalence. We demonstrate a positive long term effect of ENSO-related rainfall on AIV prevalence. We also found a more immediate response to rainfall where AIV prevalence was positively related to rainfall in the preceding 3-7 months. Additionally, for one duck species we found a positive relationship between their numbers and AIV prevalence, while prevalence was negatively or not affected by duck numbers in the remaining four species studied. In Australia largely non-seasonal rainfall patterns determine breeding opportunities and thereby influence bird numbers. Based on our findings we suggest that rainfall influences age structures within populations, producing an influx of immunologically naïve juveniles within the population, which may subsequently affect AIV infection dynamics. Our study suggests that drivers of AIV dynamics in the northern hemisphere do not have the same influence at our south-east Australian field site in the southern hemisphere due to more erratic climatological conditions.

The role of environmental factors in the spatial distribution of Japanese encephalitis in mainland China

Japanese encephalitis (JE) is the most common cause of viral encephalitis and an important public health concern in the Asia-Pacific region, particularly in China where 50% of global cases are notified. To explore the association between environmental factors and human JE cases and identify the high risk areas for JE transmission in China, we used annual notified data on JE cases at the center of administrative township and environmental variables with a pixel resolution of 1 km×1 km from 2005 to 2011 to construct models using ecological niche modeling (ENM) approaches based on maximum entropy. These models were then validated by overlaying reported human JE case localities from 2006 to 2012 onto each prediction map. ENMs had good discriminatory ability with the area under the curve (AUC) of the receiver operating curve (ROC) of 0.82-0.91, and low extrinsic omission rate of 5.44-7.42%. Resulting maps showed JE being presented extensively throughout southwestern and central China, with local spatial variations in probability influenced by minimum temperatures, human population density, mean temperatures, and elevation, with contribution of 17.94%-38.37%, 15.47%-21.82%, 3.86%-21.22%, and 12.05%-16.02%, respectively. Approximately 60% of JE cases occurred in predicted high risk areas, which covered less than 6% of areas in mainland China. Our findings will help inform optimal geographical allocation of the limited resources available for JE prevention and control in China, find hidden high-risk areas, and increase the effectiveness of public health interventions against JE transmission.

Global decline in suitable habitat for Angiostrongylus ( = Parastrongylus) cantonensis: the role of climate change

Climate change is implicated in the alteration of the ranges of species worldwide. Such shifts in species distributions may introduce parasites/pathogens, hosts, and vectors associated with disease to new areas. The parasite Angiostrongylus ( = Parastrongylus) cantonensis is an invasive species that causes eosinophilic meningitis in humans and neurological abnormalities in domestic/wild animals. Although native to southeastern Asia, A. cantonensis has now been reported from more than 30 countries worldwide. Given the health risks, it is important to describe areas with potentially favorable climate for the establishment of A. cantonensis, as well as areas where this pathogen might become established in the future. We used the program Maxent to develop an ecological niche model for A. cantonensis based on 86 localities obtained from published literature. We then modeled areas of potential A. cantonensis distribution as well as areas projected to have suitable climatic conditions under four Representative Concentration Pathways (RCP) scenarios by the 2050s and the 2070s. The best model contained three bioclimatic variables: mean diurnal temperature range, minimum temperature of coldest month and precipitation of warmest quarter. Potentially suitable habitat for A. cantonensis was located worldwide in tropical and subtropical regions. Under all climate change RCP scenarios, the center of the projected distribution shifted away from the equator at a rate of 68–152 km per decade. However, the extent of areas with highly suitable habitat (>50%) declined by 10.66–15.66% by the 2050s and 13.11–16.11% by the 2070s. These results conflict with previous studies, which have generally found that the prevalence of tropical pathogens will increase during the 21st century. Moreover, it is likely that A. cantonensis will continue to expand its current range in the near future due to introductions and host expansion, whereas climate change will reduce the total geographic area of most suitable climatic conditions during the coming decades.

Analysis and prediction of Ross River virus transmission in New South Wales, Australia

BACKGROUND

Ross River virus (RRV) disease is the most widespread mosquito-borne disease in Australia. The disease is maintained in enzootic cycles between mosquitoes and reservoir hosts. During outbreaks and in endemic regions, RRV transmission can be sustained between vectors and reservoir hosts in zoonotic cycles with spillover to humans. Symptoms include arthritis, rash, fever and fatigue and can persist for several months. The prevalence and associated morbidity make this disease a medically and economically important mosquito-borne disease in Australia.

METHODS

Climate, environment, and RRV vector and reservoir host information were used to develop predictive models in four regions in NSW over a 13-year period (1991-2004). Polynomial distributed lag (PDL) models were used to explore long-term influences of up to 2 years ago that could be related to RRV activity.

RESULTS

Each regional model consisted of a unique combination of predictors for RRV disease highlighting the differences in the disease ecology and epidemiology in New South Wales (NSW). Events up to 2 years before were found to influence RRV activity. The shorter-term associations may reflect conditions that promote virus amplification in RRV vectors whereas long-term associations may reflect RRV reservoir host breeding and herd immunity. The models indicate an association between host populations and RRV disease, lagged by 24 months, suggesting two or more generations of susceptible juveniles may be necessary for an outbreak. Model sensitivities ranged from 60.4% to 73.1%, and model specificities ranged from 57.9% to 90.7%. This was the first study to include reservoir host data into statistical RRV models; the inclusion of host parameters was found to improve model fit significantly.

CONCLUSION

The research presents the novel use of a combination of climate, environment, and RRV vector and reservoir host information in statistical predictive models. The models have potential for public health decision-making.

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.

Time series analysis of dengue incidence in Guadeloupe, French West Indies: forecasting models using climate variables as predictors

Background

During the last decades, dengue viruses have spread throughout the Americas region, with an increase in the number of severe forms of dengue. The surveillance system in Guadeloupe (French West Indies) is currently operational for the detection of early outbreaks of dengue. The goal of the study was to improve this surveillance system by assessing a modelling tool to predict the occurrence of dengue epidemics few months ahead and thus to help an efficient dengue control.

Methods

The Box-Jenkins approach allowed us to fit a Seasonal Autoregressive Integrated Moving Average (SARIMA) model of dengue incidence from 2000 to 2006 using clinical suspected cases. Then, this model was used for calculating dengue incidence for the year 2007 compared with observed data, using three different approaches: 1 year-ahead, 3 months-ahead and 1 month-ahead. Finally, we assessed the impact of meteorological variables (rainfall, temperature and relative humidity) on the prediction of dengue incidence and outbreaks, incorporating them in the model fitting the best.

Results

The 3 months-ahead approach was the most appropriate for an effective and operational public health response, and the most accurate (Root Mean Square Error, RMSE = 0.85). Relative humidity at lag-7 weeks, minimum temperature at lag-5 weeks and average temperature at lag-11 weeks were variables the most positively correlated to dengue incidence in Guadeloupe, meanwhile rainfall was not. The predictive power of SARIMA models was enhanced by the inclusion of climatic variables as external regressors to forecast the year 2007. Temperature significantly affected the model for better dengue incidence forecasting (p-value = 0.03 for minimum temperature lag-5, p-value = 0.02 for average temperature lag-11) but not humidity. Minimum temperature at lag-5 weeks was the best climatic variable for predicting dengue outbreaks (RMSE = 0.72).

Conclusion

Temperature improves dengue outbreaks forecasts better than humidity and rainfall. SARIMA models using climatic data as independent variables could be easily incorporated into an early (3 months-ahead) and reliably monitoring system of dengue outbreaks. This approach which is practicable for a surveillance system has public health implications in helping the prediction of dengue epidemic and therefore the timely appropriate and efficient implementation of prevention activities.

Climate change and infectious diseases in Australia: future prospects, adaptation options, and research priorities

Climate change will have significant and diverse impacts on human health. These impacts will include changes in infectious disease incidence. In this article, the authors review the current situation and potential future climate change impacts for respiratory, diarrheal, and vector-borne diseases in Australia. Based on this review, the authors suggest adaptive strategies within the health sector and also recommend future research priorities.

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.

Arbovirus models to provide practical management tools for mosquito control and disease prevention in the Northern Territory, Australia

Ross River virus (RRV) causes the most common human arbovirus disease in Australia. Although the disease is nonfatal, the associated arthritis and postinfection fatigue can be debilitating for many months, impacting on workforce participation. We sought to create an early-warning system to notify of approaching RRV disease outbreak conditions for major townships in the Northern Territory. By applying a logistic regression model to meteorologic factors, including rainfall, a postestimation analysis of sensitivity and specificity can create rainfall cut-points. These rainfall cut-points indicate the rainfall level above which previous epidemic conditions have occurred. Furthermore, rainfall cut-points indirectly adjust for vertebrate host data from the agile wallaby (Macropus agilis) as the life cycle of the agile wallaby is intricately meshed with the wet season. Once generated, cut-points can thus be used prospectively to allow timely implementation of larval survey and control measures and public health warnings to preemptively reduce RRV disease incidence. Cut-points are location specific and have the capacity to replace previously used models, which require data management and input, and rarely provide timely notification for vector control requirements and public health warnings. These methods can be adapted for use elsewhere.

Does mosquito control have an effect on mosquito-borne disease? The case of Ross River virus disease and mosquito management in Queensland, Australia

We examined the relationship between types of mosquito control programs and the mosquito-borne Ross River virus (RRV) disease in Queensland, Australia. Mosquito control information was collected through a survey of the responsible agencies (local governments), and RRV disease notification data were provided by the Queensland state health authority. The study developed a typology of mosquito control programs, based on the approaches used. Based on the analysis of data on RRV disease rates between mosquito control types within 4 climatic regions, each region had different combinations of mosquito control strategies in their programs; there were also general similarities in the relationship between program types and RRV rates between the regions. The long-term RRV disease rates were lower in areas where the mosquito control program included pre-emptive (rather than reactive) surveillance based on an extensive (rather than incomplete) knowledge of mosquito habitats, and where treatment of both saltwater and freshwater habitats (compared to only saltwater habitats, in coastal areas) occurred. The data indicate that mosquito control is an effective public health intervention to reduce mosquito-borne disease; hence, climate change adaptation strategies should ensure that adequate resources are available for effective vector control so as to manage the risk of mosquito-borne diseases.

Bayesian spatiotemporal analysis of socio-ecologic drivers of Ross River virus transmission in Queensland, Australia

This study aims to examine the impact of socio-ecologic factors on the transmission of Ross River virus (RRV) infection and to identify areas prone to social and ecologic-driven epidemics in Queensland, Australia. We used a Bayesian spatiotemporal conditional autoregressive model to quantify the relationship between monthly variation of RRV incidence and socio-ecologic factors and to determine spatiotemporal patterns. Our results show that the average increase in monthly RRV incidence was 2.4% (95% credible interval (CrI): 0.1-4.5%) and 2.0% (95% CrI: 1.6-2.3%) for a 1 degrees C increase in monthly average maximum temperature and a 10 mm increase in monthly average rainfall, respectively. A significant spatiotemporal variation and interactive effect between temperature and rainfall on RRV incidence were found. No association between Socio-economic Index for Areas (SEIFA) and RRV was observed. The transmission of RRV in Queensland, Australia appeared to be primarily driven by ecologic variables rather than social factors.

Precipitation and temperature as predictors of the local abundance of Ixodes scapularis (Acari: Ixodidae) nymphs

Populations of Ixodes scapularis Say nymphs were surveyed at a Lyme disease- endemic area for 8 consecutive yr (1998-2005) to characterize annual changes in abundance. Precipitation and temperature were also monitored over the period 1998-2004 to determine their potential value as predictors of tick abundance. Although both parameters showed annual variation, no statistical differences in the annual abundance of I. scapularis nymphs were observed over the 8-yr period. Our results suggest that precipitation and temperature were not predictive of the abundance of I. scapularis nymphs.

Climate change and the transmission of vector-borne diseases: a review

This article reviews studies examining the relationship between climate variability and the transmission of vector- and rodent-borne diseases, including malaria, dengue fever, Ross River virus infection, and hemorrhagic fever with renal syndrome. The review has evaluated their study designs, statistical analysis methods, usage of meteorological variables, and results of those studies. The authors found that the limitations of analytical methods exist in most of the articles. Besides climatic variables, few of them have included other factors that can affect the transmission of vector-borne disease (eg, socioeconomic status). In addition, the quantitative relationship between climate and vector-borne diseases is inconsistent. Further research should be conducted among different populations with various climatic/ecological regions by using appropriate statistical models.

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

Ross River virus (RRV) infection is the most common notifiable vector-borne disease in Australia, with around 6000 cases annually. This study aimed to examine the relationship between climate variability and notified RRV infections in the Riverland region of South Australia in order to set up an early warning system for the disease in temperate-climate regions. Notified data of RRV infections were collected by the South Australian Department of Health. Climatic variables and monthly river flow were provided by the Australian Bureau of Meteorology and South Australian Department of Water, Land and Biodiversity Conservation over the period 1992-2004. Spearman correlation and time-series-adjusted Poisson regression analysis were performed. The results indicate that increases in monthly mean minimum and maximum temperatures, monthly total rainfall, monthly mean Southern Oscillation Index and monthly flow in the Murray River increase the likelihood, but an increase in monthly mean relative humidity decreases the likelihood, of disease transmission in the region, with different time-lag effects. This study demonstrates that a useful early warning system can be developed for local regions based on the statistical analysis of readily available climate data. These early warning systems can be utilized by local public health authorities to develop disease prevention and control activities.

Prediction of a Rift Valley fever outbreak

El Niño/Southern Oscillation related climate anomalies were analyzed by using a combination of satellite measurements of elevated sea-surface temperatures and subsequent elevated rainfall and satellite-derived normalized difference vegetation index data. A Rift Valley fever (RVF) risk mapping model using these climate data predicted areas where outbreaks of RVF in humans and animals were expected and occurred in the Horn of Africa from December 2006 to May 2007. The predictions were subsequently confirmed by entomological and epidemiological field investigations of virus activity in the areas identified as at risk. Accurate spatial and temporal predictions of disease activity, as it occurred first in southern Somalia and then through much of Kenya before affecting northern Tanzania, provided a 2 to 6 week period of warning for the Horn of Africa that facilitated disease outbreak response and mitigation activities. To our knowledge, this is the first prospective prediction of a RVF outbreak.

Climate variability, social and environmental factors, and ross river virus transmission: research development and future research needs

BACKGROUND

Arbovirus diseases have emerged as a global public health concern. However, the impact of climatic, social, and environmental variability on the transmission of arbovirus diseases remains to be determined.

OBJECTIVE

Our goal for this study was to provide an overview of research development and future research directions about the interrelationship between climate variability, social and environmental factors, and the transmission of Ross River virus (RRV), the most common and widespread arbovirus disease in Australia.

METHODS

We conducted a systematic literature search on climatic, social, and environmental factors and RRV disease. Potentially relevant studies were identified from a series of electronic searches.

RESULTS

The body of evidence revealed that the transmission cycles of RRV disease appear to be sensitive to climate and tidal variability. Rainfall, temperature, and high tides were among major determinants of the transmission of RRV disease at the macro level. However, the nature and magnitude of the interrelationship between climate variability, mosquito density, and the transmission of RRV disease varied with geographic area and socioenvironmental condition. Projected anthropogenic global climatic change may result in an increase in RRV infections, and the key determinants of RRV transmission we have identified here may be useful in the development of an early warning system.

CONCLUSIONS

The analysis indicates that there is a complex relationship between climate variability, social and environmental factors, and RRV transmission. Different strategies may be needed for the control and prevention of RRV disease at different levels. These research findings could be used as an additional tool to support decision making in disease control/surveillance and risk management.

Ross River virus and Barmah Forest virus infections: a review of history, ecology, and predictive models, with implications for tropical northern Australia

The purpose of the present article is to present a review of the Ross River virus (RRV) and Barmah Forest virus (BFV) literature in relation to potential implications for future disease in tropical northern Australia. Ross River virus infection is the most common and most widespread arboviral disease in Australia, with an average of 4,800 national notifications annually. Of recent concern is the sudden rise in BFV infections; the 2005-2006 summer marked the largest BFV epidemic on record in Australia, with 1,895 notifications. Although not life-threatening, infection with either virus can cause arthritis, myalgia, and fatigue for 6 months or longer, resulting in substantial morbidity and economic impact. The geographic distribution of mosquito species and their seasonal activity is determined in large part by temperature and rainfall. Predictive models can be useful tools in providing early warning systems for epidemics of RRV and BFV infection. Various models have been developed to predict RRV outbreaks, but these appear to be mostly only regionally valid, being dependent on local ecological factors. Difficulties have arisen in developing useful models for the tropical northern parts of Australia, and to date no models have been developed for the Northern Territory. Only one model has been developed for predicting BFV infections using climate and tide variables. It is predicted that the exacerbation of current greenhouse conditions will result in longer periods of high mosquito activity in the tropical regions where RRV and BFV are already common. In addition, the endemic locations may expand further within temperate regions, and epidemics may become more frequent in those areas. Further development of predictive models should benefit public health planning by providing early warning systems of RRV and BFV infection outbreaks in different geographical locations.

Time series analysis performed on nephropathia epidemica in humans of northern Sweden in relation to bank vole population dynamic and the NAO index

Time series analysis was performed on two data series of human nephropathia epidemica (NE) infections in northern Sweden between the years 1959-1975 and 1985-2006. The analysis confirms that the bank vole (Myodes glareolus), the main reservoir species, shows regular peaks in population density approximately every fourth year. The periodicity in NE cases of the more recent time period (1985-2006) is 2.8-3.3 years and the older period shows a periodicity ranging 3.4-4.2 years, but this is not significantly different from that expected by vole dynamics. A comparison of North Atlantic Oscillation (NAO) index between the two periods reveals higher mean winter NAO index in the period 1985-2006 than in 1959-1975. No difference was found in frequency of the NAO index between the older period (2.8-3.4 years) compared with the recent period (2.4-2.8 years). Cross-correlation revealed a delayed effect by NAO index on vole abundance but a multivariate model showed that NAO index did not explain the variation in NE cases. Vole index was the only factor that contributed significantly to the variation in numbers of NE cases and that no climate effect could be detected. The climate signal from NAO index does not appear to significantly affect the human NE cases and this suggests that the transmission of disease to man is not particularly sensitive to variations in weather factors. The results favour the hypothesis that higher NAO index will not increase the likelihood of virus transmission from voles to man in northern Sweden under present climatic conditions.