ORCID Profile
0000-0002-4870-8353
Current Organisation
Telethon Kids Institute
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Publisher: eLife Sciences Publications, Ltd
Date: 14-04-2023
DOI: 10.7554/ELIFE.84983
Abstract: 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. 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. Over two s ling c aigns 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. 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. This research was supported by the National Health and Medical Research Council of Australia and the Victorian Government Department of Health (GNT1152807 and GNT1196396).
Publisher: Ubiquity Press, Ltd.
Date: 10-2021
DOI: 10.5334/JORS.249
Publisher: Cold Spring Harbor Laboratory
Date: 17-11-2022
DOI: 10.1101/2022.11.16.516821
Abstract: 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. 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. 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 if M. ulcerans surveillance of possum excreta provided useful information for predicting future human BU case locations. Over two s ling c aigns in summer and winter, we collected 2282 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. 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 efficient targeting of public health responses to stop BU.
Publisher: Wiley
Date: 24-06-2018
DOI: 10.1111/GCB.14319
Abstract: Nontidal wetlands are estimated to contribute significantly to the soil carbon pool across the globe. However, our understanding of the occurrence and variability of carbon storage between wetland types and across regions represents a major impediment to the ability of nations to include wetlands in greenhouse gas inventories and carbon offset initiatives. We performed a large-scale survey of nontidal wetland soil carbon stocks and accretion rates from the state of Victoria in south-eastern Australia-a region spanning 237,000 km
Publisher: Wiley
Date: 10-2023
DOI: 10.1111/JEB.14230
Publisher: Wiley
Date: 20-04-2021
Publisher: eLife Sciences Publications, Ltd
Date: 20-03-2023
Publisher: Wiley
Date: 29-06-2021
Publisher: Wiley
Date: 18-08-2023
DOI: 10.1111/ECOG.06048
Abstract: Ecological models used to forecast range change (range change models RCM) have recently ersified to account for a greater number of ecological and observational processes in pursuit of more accurate and realistic predictions. Theory suggests that process‐explicit RCMs should generate more robust forecasts, particularly under novel environmental conditions. RCMs accounting for processes are generally more complex and data hungry, and so, require extra effort to build. Thus, it is necessary to understand when the effort of building a more realistic model is likely to generate more reliable forecasts. Here, we review the literature to explore whether process‐explicit models have been tested through benchmarking their temporal predictive performance (i.e. their predictive performance when transferred in time) and model transferability (i.e. their ability to keep their predictive performance when transferred to generate predictions into a different time) against simpler models, and highlight the gaps between the rapid development of process‐explicit RCMs and the testing of their potential improvements. We found that, out of five ecological processes (dispersal, demography, physiology, evolution, species interactions) and two observational processes (s ling bias, imperfect detection) that may influence reliability of forecasts, only the effects of dispersal, demography and imperfect detection have been benchmarked using temporally‐independent datasets. Only nine out of twenty‐nine process‐explicit model types have been tested to assess whether accounting for processes improves temporal predictive performance. We found no benchmarks assessing model transferability. We discuss potential reasons for the lack of empirical validation of process‐explicit models. Considering these findings, we propose an expanded research agenda to properly test the performance of process‐explicit RCMs, and highlight some opportunities to fill the gaps by suggesting models to be benchmarked using existing historical datasets.
Publisher: Springer Science and Business Media LLC
Date: 03-04-2023
Start Date: 2022
End Date: 2025
Funder: Marsden Fund
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