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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 overdispersion 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 nonlinearity 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. [ABSTRACT FROM AUTHOR]

Nontyphoidal Salmonella is a major contributor to the global burden of foodborne disease, with invasive infections contributing substantially to illnesses and deaths. We analyzed notifiable disease surveillance data for invasive nontyphoidal Salmonella disease (iNTS) in Queensland, Australia. We used Poisson regression to estimate incidence rate ratios by gender, age group, and geographical area over 2007–2016. There were 995 iNTS cases, with 945 (92%) confirmed by blood culture. Salmonella Virchow accounted for 254 (25%) of 1,001 unique iNTS isolates. Invasive NTS disease notification rates peaked among infants, during the summer months, and in outback Queensland where the notification rate (95% CI) was 17.3 (14.5–20.1) cases per 100,000 population. Overall, there was a 6,5% annual increase (p<0.001) in iNTS disease incidence. In conclusion, high iNTS rates among males, infants, and the elderly require investigation of household level risk factors for NTS infection. Controlling Salmonella Virchow infections is a public health priority. [ABSTRACT FROM AUTHOR]

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., Competing Interests: The authors declare there are no competing interests., (©2022 Qian et al.)

Salmonella enterica is a common contaminant of macadamia nut kernels in the subtropical state of Queensland (QLD), Australia. We hypothesized that nonhuman sources in the plantation environment contaminate macadamia nuts. We applied a modified Hald source attribution model to attribute Salmonella serovars and phage types detected on macadamia nuts from 1998 to 2017 to specific animal and environmental sources. Potential sources were represented by Salmonella types isolated from avian, companion animal, biosolids-soil-compost, equine, porcine, poultry, reptile, ruminant, and wildlife samples by the QLD Health reference laboratory. Two attribution models were applied: model 1 merged data across 1998-2017, whereas model 2 pooled data into 5-year time intervals. Model 1 attributed 47% (credible interval, CrI: 33.6-60.8) of all Salmonella detections on macadamia nuts to biosolids-soil-compost. Wildlife and companion animals were found to be the second and third most important contamination sources, respectively. Results from model 2 showed that the importance of the different sources varied between the different time periods; for example, Salmonella contamination from biosolids-soil-compost varied from 4.4% (CrI: 0.2-11.7) in 1998-2002 to 19.3% (CrI: 4.6-39.4) in 2003-2007, and the proportion attributed to poultry varied from 4.8% (CrI: 1-11) in 2008-2012 to 24% (CrI: 11.3-40.7) in 2013-2017. Findings suggest that macadamia nuts were contaminated by direct transmission from animals with access to the plantations (e.g., wildlife and companion animals) or from indirect transmission from animal reservoirs through biosolids-soil-compost. The findings from this study can be used to guide environmental and wildlife sampling and analysis to further investigate routes of Salmonella contamination of macadamia nuts and propose control options to reduce potential risk of human salmonellosis.