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

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 canaccount for up to 30% of national RRV notifications. However, little is known about factors which canforecast RRV transmission in Victoria. We aimed to understand factors associated with RRVtransmission in epidemiologically important regions of Victoria and establish an early warningforecast system. We developed negative binomial regression models to forecast human RRVnotifications across 11 Local Government Areas (LGAs) using climatic, environmental, andoceanographic variables. Data were collected from July 2008 to June 2018. Data from July 2008 toJune 2012 were used as a training data set, while July 2012 to June 2018 were used as a testing dataset. Evapotranspiration and precipitation were found to be common factors for forecasting RRVnotifications across sites. Several site-specific factors were also important in forecasting RRVnotifications which varied between LGA. From the 11 LGAs examined, nine experienced an outbreakin 2011/12 of which the models for these sites were a good fit. All 11 LGAs experienced an outbreakin 2016/17, however only six LGAs could predict the outbreak using the same model. We documentsimilarities and differences in factors useful for forecasting RRV notifications across Victoria anddemonstrate 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 complexityof RRV transmission where additional epidemiological information is needed to accurately predictRRV activity. Our findings have been applied to produce a Ross River virus Outbreak SurveillanceSystem (ROSS) to aid in public health decision making in Victoria

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 Influence of Meteorology on the Spread of Influenza: Survival Analysis of an Equine Influenza (A/H3N8) Outbreak

The influences of relative humidity and ambient temperature on the transmission of influenza A viruses have recently been established under controlled laboratory conditions. The interplay of meteorological factors during an actual influenza epidemic is less clear, and research into the contribution of wind to epidemic spread is scarce. By applying geostatistics and survival analysis to data from a large outbreak of equine influenza (A/H3N8), we quantified the association between hazard of infection and air temperature, relative humidity, rainfall, and wind velocity, whilst controlling for premises-level covariates. The pattern of disease spread in space and time was described using extraction mapping and instantaneous hazard curves. Meteorological conditions at each premises location were estimated by kriging daily meteorological data and analysed as time-lagged time-varying predictors using generalised Cox regression. Meteorological covariates time-lagged by three days were strongly associated with hazard of influenza infection, corresponding closely with the incubation period of equine influenza. Hazard of equine influenza infection was higher when relative humidity was <60% and lowest on days when daily maximum air temperature was 20–25°C. Wind speeds >30 km hour−1 from the direction of nearby infected premises were associated with increased hazard of infection. Through combining detailed influenza outbreak and meteorological data, we provide empirical evidence for the underlying environmental mechanisms that influenced the local spread of an outbreak of influenza A. Our analysis supports, and extends, the findings of studies into influenza A transmission conducted under laboratory conditions. The relationships described are of direct importance for managing disease risk during influenza outbreaks in horses, and more generally, advance our understanding of the transmission of influenza A viruses under field conditions

Hot and Cold Dark Matter Search with GENIUS

GENIUS is a proposal for a large volume detector to search for rare events. An array of 40-400 'naked' HPGe detectors will be operated in a tank filled with ultra-pure liquid nitrogen. After a description of performed technical studies of detector operation in liquid nitrogen and of Monte Carlo simulations of expected background components, the potential of GENIUS for detecting WIMP dark matter, the neutrinoless double beta decay in 76-Ge and low-energy solar neutrinos is discussed

Hot and Cold Dark Matter Search with GENIUS

GENIUS is a proposal for a large volume detector to search for rare events. An array of 40-400 'naked' HPGe detectors will be operated in a tank filled with ultra-pure liquid nitrogen. After a description of performed technical studies of detector operation in liquid nitrogen and of Monte Carlo simulations of expected background components, the potential of GENIUS for detecting WIMP dark matter, the neutrinoless double beta decay in 76-Ge and low-energy solar neutrinos is discussed.Comment: 11 pages, latex, 3 eps figures, requires svmult.cls. To appear in: Proceedings of "Sources and detection of dark matter in the Universe", Marina del Rey, CA, February 23-25, 2000, Springer 2000, edited by D. Clin

Action ability modulates time‑to‑collision judgments

Time-to-collision (TTC) underestimation has been interpreted as an adaptive response that allows observers to have more time to engage in a defensive behaviour. This bias seems, therefore, strongly linked to action preparation. There is evidence that the observer’s physical fitness modulates the underestimation effect so that people who need more time to react (i.e. those with less physical fitness) show a stronger underestimation effect. Here we investigated whether this bias is influenced by the momentary action capability of the observers. In the first experiment, participants estimated the time-to-collision of threatening or non-threatening stimuli while being mildly immobilized (with a chin rest) or while standing freely. Having reduced the possibility of movement led participants to show more underestimation of the approaching stimuli. However, this effect was not stronger for threatening relative to non-threatening stimuli. The effect of the action capability found in the first experiment could be interpreted as an expansion of peripersonal space (PPS). In the second experiment, we thus investigated the generality of this effect using an established paradigm to measure the size of peripersonal space. Participants bisected lines from different distances while in the chin rest or standing freely. The results replicated the classic left-to-right gradient in lateral spatial attention with increasing viewing distance, but no effect of immobilization was found. The manipulation of the momentary action capability of the observers influenced the participants’ performance in the TTC task but not in the line bisection task. These results are discussed in relation to the different functions of PPS