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Buruli ulcer, a chronic subcutaneous infection caused by Mycobacterium ulcerans, is increasing in prevalence in southeastern Australia. Possums are a local wildlife reservoir for M. ulcerans and, although mosquitoes have been implicated in transmission, it remains unclear how humans acquire infection. We conducted extensive field survey analyses of M. ulcerans prevalence among mosquitoes in the Mornington Peninsula region of southeastern Australia. PCR screening of trapped mosquitoes revealed a significant association between M. ulcerans and Aedes notoscriptus. Spatial scanning statistics revealed overlap between clusters of M. ulcerans-positive Ae. notoscriptus, M. ulcerans-positive possum excreta and Buruli ulcer cases, and metabarcoding analyses showed individual mosquitoes had fed on humans and possums. Bacterial genomic analysis confirmed shared single-nucleotide-polymorphism profiles for M. ulcerans detected in mosquitoes, possum excreta and humans. These findings indicate Ae. notoscriptus probably transmit M. ulcerans in southeastern Australia and highlight mosquito control as a Buruli ulcer prevention measure., (© 2024. The Author(s).)

Background: Buruli ulcer (BU) is a neglected tropical disease caused by infection of subcutaneous tissue with Mycobacterium ulcerans . BU is commonly reported across rural regions of Central and West Africa but has been increasing dramatically in temperate southeast Australia around the major metropolitan city of Melbourne, with most disease transmission occurring in the summer months. Previous research has shown that Australian native possums are reservoirs of M. ulcerans and that they shed the bacteria in their fecal material (excreta). Field surveys show that locales where possums harbor M. ulcerans overlap with human cases of BU, raising the possibility of using possum excreta surveys to predict the risk of disease occurrence in humans., Methods: We thus established a highly structured 12 month possum excreta surveillance program across an area of 350 km 2 in the Mornington Peninsula area 70 km south of Melbourne, Australia. The primary objective of our study was to assess using statistical modeling if M. ulcerans surveillance of possum excreta provided useful information for predicting future human BU case locations., Results: Over two sampling campaigns in summer and winter, we collected 2,282 possum excreta specimens of which 11% were PCR positive for M. ulcerans -specific DNA. Using the spatial scanning statistical tool SaTScan , we observed non-random, co-correlated clustering of both M. ulcerans positive possum excreta and human BU cases. We next trained a statistical model with the Mornington Peninsula excreta survey data to predict the future likelihood of human BU cases occurring in the region. By observing where human BU cases subsequently occurred, we show that the excreta model performance was superior to a null model trained using the previous year's human BU case incidence data (AUC 0.66 vs 0.55). We then used data unseen by the excreta-informed model from a new survey of 661 possum excreta specimens in Geelong, a geographically separate BU endemic area to the southwest of Melbourne, to prospectively predict the location of human BU cases in that region. As for the Mornington Peninsula, the excreta-based BU prediction model outperformed the null model (AUC 0.75 vs 0.50) and pinpointed specific locations in Geelong where interventions could be deployed to interrupt disease spread., Conclusions: This study highlights the One Health nature of BU by confirming a quantitative relationship between possum excreta shedding of M. ulcerans and humans developing BU. The excreta survey-informed modeling we have described will be a powerful tool for the efficient targeting of public health responses to stop BU., Funding: This research was supported by the National Health and Medical Research Council of Australia and the Victorian Government Department of Health (GNT1152807 and GNT1196396)., Competing Interests: KV, AB, JP, AV, JW, KB, MD, VB, JF, ET, PJ, SW, NG, TS No competing interests declared, (© 2023, Vandelannoote, Buultjens et al.)

Objective: To describe the burden of and trends in paediatric tuberculosis (TB) in Australia between 2003 and 2012., Design: A retrospective analysis of TB data from the National Notifiable Diseases Surveillance System (NNDSS) on TB in children (under 15 years of age) during the 10-year period, 2003-2012., Results: TB notifications in Australia during the study period included 538 children (range, 37-66 cases per year), representing 4.6% of the total TB case load during the period (range, 3.8%-5.8% each year). Place of birth was recorded for 524 patients (97.4%); of these, 230 (43.9%) were born in Australia, 294 (56.1%) overseas. The average annual notification rate was 1.31 (95% CI, 1.20-1.43) cases per 100 000 child population. The rate was higher for overseas-born than for Australian-born children (9.57 [95% CI, 8.51-10.73] v 0.61 [95% CI, 0.53-0.69] cases per 100 000 children. The overall rate was highest among those aged 0-4 years. The annual notification rate was three times higher for Indigenous children than for non-Indigenous Australian-born children. Of 427 patients (79.4% of total) for whom the method of case detection was recorded, 37.0% were detected by contact screening, 8.7% by post-arrival immigration screening, and 54.3% by passive case detection. Pulmonary TB was the most common diagnostic classification (64.7% of patients). The most common risk factors were close contact with a TB case and recent residence in a country with a high incidence of TB. Treatment outcomes were satisfactory; 89.4% of children had completed treatment or were cured., Conclusions: The burden of paediatric TB in Australia is low but has not changed over the past decade. The highest rates are among children born overseas, emphasising the important role of immigration screening as Australia aspires to eliminate TB.

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., (© 2012 John Wiley & Sons Ltd.)

Introduction: Health authorities find thresholds useful to gauge the start and severity of influenza seasons. We explored a method for deriving thresholds proposed in an influenza surveillance manual published by the World Health Organization (WHO)., Methods: For 2002-2011, we analysed two routine influenza-like-illness (ILI) datasets, general practice sentinel surveillance and a locum medical service sentinel surveillance, plus laboratory data and hospital admissions for influenza. For each sentinel dataset, we created two composite variables from the product of weekly ILI data and the relevant laboratory data, indicating the proportion of tested specimens that were positive. For all datasets, including the composite datasets, we aligned data on the median week of peak influenza or ILI activity and assigned three threshold levels: seasonal threshold, determined by inspection; and two intensity thresholds termed average and alert thresholds, determined by calculations of means, medians, confidence intervals (CI) and percentiles. From the thresholds, we compared the seasonal onset, end and intensity across all datasets from 2002-2011. Correlation between datasets was assessed using the mean correlation coefficient., Results: The median week of peak activity was week 34 for all datasets, except hospital data (week 35). Means and medians were comparable and the 90% upper CIs were similar to the 95(th) percentiles. Comparison of thresholds revealed variations in defining the start of a season but good agreement in describing the end and intensity of influenza seasons, except in hospital admissions data after the pandemic year of 2009. The composite variables improved the agreements between the ILI and other datasets. Datasets were well correlated, with mean correlation coefficients of >0.75 for a range of combinations., Conclusions: Thresholds for influenza surveillance are easily derived from historical surveillance and laboratory data using the approach proposed by WHO. Use of composite variables is helpful for describing influenza season characteristics.