Ross River virus (RRV) is the most common mosquito-borne infection in Australia. RRV disease is characterised by joint pain and lethargy, placing a substantial burden on individual patients, the healthcare system and economy. This burden is compounded by a lack of effective treatment or vaccine for the disease. The complex RRV disease ecology cycle includes a number of reservoirs and vectors that inhabit a range of environments and climates across Australia. Climate is known to influence humans, animals and the environment and has previously been shown to be useful to RRV prediction models. We developed a negative binomial regression model to predict monthly RRV case numbers and outbreaks in the Darling Downs region of Queensland, Australia. Human RRV notifications and climate data for the period July 2001 – June 2014 were used for model training. Model predictions were tested using data for July 2014 – June 2019. The final model was moderately effective at predicting RRV case numbers (Pearson's r = 0.427) and RRV outbreaks (accuracy = 65%, sensitivity = 59%, specificity = 73%). Our findings show that readily available climate data can provide timely prediction of RRV outbreaks.

Corporate political strategies have been used extensively by firms attempting to shape their political environments. In this context, access to targeted policymakers is essential to allow their deployment. Thus, we propose to study the determinants of access to the European Commission representatives. This research builds on the resource-based view (RBV) of firms to argue that political knowledge is a valuable resource to increase firms' degree of access to the European Commission. To test our hypotheses, we built a novel dataset merging firms characteristics with lobbying meetings information, and analyze it through negative binomial regression. The results suggest the importance of political knowledge, emphasizing that it may represent a source of sustainable competitive advantage. This study highlights interesting information that broadens the understanding of corporate political strategies in the European Union.

Climate change, by its influence on the ecology of vectors might affect the occurrence of vector-borne diseases. This study examines the effects of meteorological factors in Japan on the occurrence of scrub typhus, a mite-borne zoonosis caused by Orientia tsutsugamushi. Using negative binomial regression, we analysed the relationships between meteorological factors (including temperature, rainfall, snowfall) and spring–early summer cases of scrub typhus in Yamagata Prefecture, Japan, during 1984–2014. The average temperature in July and August of the previous year, cumulative rainfall in September of the previous year, snowfall throughout the winter, and maximum depth of snow cover in January and February were positively correlated with the number of scrub typhus cases. By contrast, cumulative rainfall in July of the previous year showed a negative relationship to the number of cases. These associations can be explained by the life-cycle of Leptotrombidium pallidum, a predominant vector of spring–early summer cases of scrub typhus in northern Japan. Our findings show that several meteorological factors are useful to estimate the number of scrub typhus cases before the endemic period. They are applicable to establish an early warning system for scrub typhus in northern Japan.

This study examines the adoption of best-management practices (BMPs) in terms of the total number of practices implemented up to a certain period, using count data analysis. Poisson and negative binomial regressions were used to examine the likely determinants of producers' decisions to adopt greater numbers of technologies, and the specific case of dairy producers' adoption of BMPs was explored. Our results emphasize the significant effect of producers' awareness of the efforts to control non-point source pollution, information about BMPs, farm size, producer's educational attainment, and risk aversion on the number of BMPs adopted.