SUMMARYA recent outbreak of Q fever was linked to an intensive goat and sheep dairy farm in Victoria, Australia, 2012-2014. Seventeen employees and one family member were confirmed with Q fever over a 28-month period, including two culture-positive cases. The outbreak investigation and management involved a One Health approach with representation from human, animal, environmental and public health. Seroprevalence in non-pregnant milking goats was 15% [95% confidence interval (CI) 7–27]; active infection was confirmed by positive quantitative PCR on several animal specimens. Genotyping of Coxiella burnetii DNA obtained from goat and human specimens was identical by two typing methods. A number of farming practices probably contributed to the outbreak, with similar precipitating factors to the Netherlands outbreak, 2007-2012. Compared to workers in a high-efficiency particulate arrestance (HEPA) filtered factory, administrative staff in an unfiltered adjoining office and those regularly handling goats and kids had 5·49 (95% CI 1·29–23·4) and 5·65 (95% CI 1·09–29·3) times the risk of infection, respectively; suggesting factory workers were protected from windborne spread of organisms. Reduction in the incidence of human cases was achieved through an intensive human vaccination programme plus environmental and biosecurity interventions. Subsequent non-occupational acquisition of Q fever in the spouse of an employee, indicates that infection remains endemic in the goat herd, and remains a challenge to manage without source control.

Gendered norms are embedded in social structures, operating to restrict the rights, opportunities, and capabilities, of women and girls, causing significant burdens, discrimination, subordination, and exploitation. This review, developed for the Women and Gender Equity Knowledge Network of the WHO Commission on the Social Determinants of Health, sought to identify the best available research evidence about programmatic interventions, at the level of household and community, that have been effective for changing gender norms to increase the status of women. The focus was on developing countries. A wide range of single and multiple databases were searched, utilizing database specific keywords such as: women and girls; men and boys; household and community; intervention; and gender norms. Key themes were identified: education of women and girls; economic empowerment of women; violence against women, including female genital mutilation/cutting; and men and boys. Types of interventions, levels of action, populations of interest, and key outcomes from evaluations are identified. Evaluations are limited, with little evidence or measurement of changes in gender equity and women's empowerment. A key finding is, that targeting women and girls is a sound investment, but outcomes are dependent on integrated approaches and the protective umbrella of policy and legislative actions.

Objective: Recent studies have used Bayesian methods to predict timing of influenza epidemics many weeks in advance, but there is no documented evaluation of how such forecasts might support the day-to-day operations of public health staff. Methods:During the 2015 influenza season in Melbourne, Australia, weekly forecasts were presented at Health Department surveillance unit meetings, where they were evaluated and updated in light of expert opinion to improve their accuracy and usefulness.Results: Predictive capacity of the model was substantially limited by delays in reporting and processing arising from an unprecedented number of notifications, disproportionate to seasonal intensity. Adjustment of the predictive algorithm to account for these delays and increased reporting propensity improved both current situational awareness and forecasting accuracy. Conclusions:Collaborative engagement with public health practitioners in model development improved understanding of the context and limitations of emerging surveillance data. Incorporation of these insights in a quantitative model resulted in more robust estimates of disease activity for public health use. Implications for public health:In addition to predicting future disease trends, forecasting methods can quantify the impact of delays in data availability and variable reporting practice on the accuracy of current epidemic assessment. Such evidence supports investment in systems capacity.

Please cite this paper as: Kelly et al. (2012) The significance of increased influenza notifications during spring and summer of 2010–11 in Australia. Influenza and Other Respiratory Viruses. DOI: 10.1111/irv.12057. Background & objective  During the temperate out‐of‐season months in Australia in late 2010 and early 2011, an unprecedented high number of influenza notifications were recorded. We aimed to assess the significance of these notifications. Methods  For Australia, we used laboratory‐confirmed cases notified to the WHO FluNet surveillance tool; the percentage of these that were positive; notifications by state and influenza type and subtype; and surveillance data from Google FluTrends. For the state of Victoria, we used laboratory‐confirmed notified cases and influenza‐like illness (ILI) proportions. We compared virus characterisation using haemagglutination‐inhibition assays and phylogenetic analysis of the haemagglutinin gene for seasonal and out‐of‐season notifications. Results  The increase in notifications was most marked in tropical and subtropical Australia, but the number of out‐of‐season notifications in temperate Victoria was more than five times higher than the average of the previous three seasons. However, ILI proportions in spring‐summer were not different to previous years. All out‐of‐season viruses tested were antigenically and genetically similar to those tested during either the 2010 or 2011 influenza seasons. An increase in the number of laboratories testing for influenza has led to an increase in the number of tests performed and cases notified. Conclusion  An increase in influenza infections in spring‐summer of 2010–11 in tropical and temperate Australia was not associated with any differences in virus characterisation compared with viruses that circulated in the preceding and following winters. This increase probably reflected a natural variation in out‐of‐season virus circulation, which was amplified by increased laboratory testing.