Modeling seasonal tropical cyclone activity in the Fiji region as a binary classification problem
- Authors: Chand, Savin , Walsh, Kevin
- Date: 2012
- Type: Text , Journal article
- Relation: Journal of Climate Vol. 25, no. 14 (2012), p. 5057-5071
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- Description: This study presents a binary classification model for the prediction of tropical cyclone (TC) activity in the Fiji, Samoa, and Tonga regions (the FST region) using the accumulated cyclone energy (ACE) as a proxy of TC activity. A probit regression model, which is a suitable probabilitymodel for describing binary response data, is developed to determine at least a fewmonths in advance (by July in this case) the probability that an upcoming TC season may have for high or low TC activity. Years of "high TC activity" are defined as those years when ACE values exceeded the sample climatology (i.e., the 1985-2008 mean value). Model parameters are determined using the Bayesian method. Various combinations of the El Nin{ogonek} o-Southern Oscillation (ENSO) indices and large-scale environmental conditions that are known to affect TCs in the FST region are examined as potential predictors. It was found that a set of predictors comprising low-level relative vorticity, upper-level divergence, and midtropspheric relative humidity provided the best skill in terms of minimum hindcast error. Results based on hindcast verification clearly suggest that the model predicts TC activity in the FST region with substantial skill up to the May-July preseason for all years considered in the analysis, in particular for ENSO-neutral years when TC activity is known to show large variations. © 2012 American Meteorological Society.
Forecasting tropical cyclone formation in the Fiji region: A probit regression approach using bayesian fitting
- Authors: Chand, Savin , Walsh, Kevin
- Date: 2011
- Type: Text , Journal article
- Relation: Weather and Forecasting Vol. 26, no. 2 (2011), p. 150-165
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- Description: An objective methodology for forecasting the probability of tropical cyclone (TC) formation in the Fiji, Samoa, and Tonga regions (collectively the FST region) using antecedent large-scale environmental conditions is investigated. Three separate probabilistic forecast schemes are developed using a probit regression approach where model parameters are determined via Bayesian fitting. These schemes provide forecasts of TC formation from an existing system (i) within the next 24 h (W24h), (ii) within the next 48 h (W48h), and (iii) within the next 72 h (W72h). To assess the performance of the three forecast schemes in practice, verification methods such as the posterior expected error, Brier skill scores, and relative operating characteristic skill scores are applied. Results suggest that the W24h scheme, which is formulated using large-scale environmental parameters, on average, performs better than that formulated using climatology and persistence (CLIPER) variables. In contrast, the W48h (W72h) scheme formulated using large-scale environmental parameters performs similar to (poorer than) that formulated using CLIPER variables. Therefore, large-scale environmental parameters (CLIPER variables) are preferred as predictors when forecasting TC formation in the FST region within 24 h (at least 48 h) using models formulated in the present investigation. © 2011 American Meteorological Society.
A bayesian regression approach to seasonal prediction of tropical cyclones affecting the Fiji region
- Authors: Chand, Savin , Walsh, Kevin , Chan, Johnny
- Date: 2010
- Type: Text , Journal article
- Relation: Journal of Climate Vol. 23, no. 13 (2010), p. 3425-3445
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- Description: This study presents seasonal prediction schemes for tropical cyclones (TCs) affecting the Fiji, Samoa, and Tonga (FST) region. Two separate Bayesian regression models are developed: (i) for cyclones forming within the FST region (FORM) and (ii) for cyclones entering the FST region (ENT). Predictors examined include various El Niño-Southern Oscillation (ENSO) indices and large-scale environmental parameters. Only those predictors that showed significant correlations with FORM and ENT are retained. Significant preseason correlations are found as early as May-July (approximately three months in advance). Therefore, May-July predictors are used to make initial predictions, and updated predictions are issued later using October-December early-cyclone-season predictors. A number of predictor combinations are evaluated through a cross-validation technique. Results suggest that a model based on relative vorticity and the Niño-4 index is optimal to predict the annual number of TCs associated with FORM, as it has the smallest RMSE associated with its hindcasts (RMSE = 1.63). Similarly, the all-parameter-combined model, which includes the Niño-4 index and some large-scale environmental fields over the East China Sea, appears appropriate to predict the annual number of TCs associated with ENT (RMSE = 0.98). While the all-parameter-combined ENT model appears to have good skill over all years, the May-July prediction of the annual number of TCs associated with FORM has two limitations. First, it underestimates (overestimates) the formation for years where the onset of El Niño (La Niña) events is after the May-July preseason or where a previous La Niña (El Niño) event continued through May-July during its decay phase. Second, its performance in neutral conditions is quite variable. Overall, no significant skill can be achieved for neutral conditions even after an October-December update. This is contrary to the performance during El Niño or La Niña events, where model performance is improved substantially after an October-December early-cyclone-season update. © 2010 American Meteorological Society.
Bayesian analysis of random finite element method slip surfaces for slope stability
- Authors: Dyson, Ashley , Tolooiyan, Ali
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 5th ISRM Young Scholars' Symposium on Rock Mechanics and International Symposium on Rock Engineering for Innovative Future, YSRM 2019 p. 118-123
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- Description: The Random Finite Element Method (RFEM) is a powerful technique for incorporating spatially variable shear strength parameters with slope stability numerical simulations. In this research, two-dimensional probabilistic analyses of a large open-cut brown coal mine are presented with particular consideration given to slope Factors of Safety (FoS), when faced with highly anisotropic cohesion and friction angle shear strength parameters. Bayesian methods are implemented to determine updated shear strength parameters based on Factors of Safety and Representative Slip Surfaces (RSS) categorizations. By this method, the impact of observed slip surface depths and safety factors is further investigated. Monte Carlo simulation is implemented in the Finite Element environment Abaqus, with an optimised Strength Reduction Method to determine Factors of Safety. Comparisons of conditional shear strength distributions are made for associated slope safety factors and shallow slip surfaces from a cross-section of the Yallourn open-cut brown coal mine, in Victoria, Australia. The updated shear strength distributions provide a greater understanding of the necessary conditions of particular slope failure mechanisms, contributing further understanding of the stability of Victorian brown coal mines. ©2019 Japanese Society for Rock Mechanics
The relationship between team dynamics with healthcare coordination and clinical work satisfaction among Commune Health Workers: A Bayesian model averaging study
- Authors: Ngo, Tuan , Nguyen, Huy , Pham, Thanh , Nguyen, Tien , Vu, Kien , Pham, Minh , Phung, Dung , Thi Tran, Anh , Nguyen, Phuong , Le, Phuong , Thi Dao, An , Ngo, Hiep , Hoang, Minh
- Date: 2022
- Type: Text , Journal article
- Relation: International Journal of Health Planning and Management Vol. 37, no. 5 (2022), p. 2684-2696
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- Description: Objective To determine the relationship between team dynamics with healthcare coordination and clinical job satisfaction of the community health workers (CHWs). Methods A cross‐sectional study was conducted among 133 health workers (including doctors, nurses, or midwives) at 21 Commune Health Cent in Quoc Oai District, Vietnam, from July 2015 to May 2017. A self‐administered questionnaire consisting of 5‐Likert items regarding team dynamics and healthcare coordination clinical work satisfaction was utilised. Descriptive statistics and correlation matrix were applied for seven factors of team dynamic, clinical work satisfaction, and patient care coordination queried by primary care providers. Bayesian model averaging (BMA) was used to identify the predictors of the level of team dynamics and healthcare coordination. Results The mean score of overall team dynamics among the study participants was 4.08. Clinical work satisfaction and patient care coordination scores among resident physicians were higher than those of attending clinicians however, the differences were not statistically significant. The results of BMA analysis indicated that team dynamics significantly associated with clinical work satisfaction, and it explains 9% of the total variance in clinical work satisfaction. Team dynamics level was also positively associated with patient care coordination. Patient care coordination was not a significant mediator between team dynamics and clinical work satisfaction. Conclusion Team dynamics is a potential contributor to improving patient care coordination and clinical job satisfaction of CHWs. As no significant correlation between patient care coordination and clinical job satisfaction was observed, to improve team performance, providing conditions that facilitate team building and teamwork should be conducted for CHWs in CHCs. Highlights Team dynamics, healthcare coordination and clinical work satistsfaction among health professionals are critical to quality of healthcare. In lower‐middle income countries like Vietnam, little has been known about these contributors at community‐based healthcare system. Identifying that team dynamics significantly associated with both patient care coordination and clinical work satisfaction among community health workers, but this association being not mediated by patient care coordination informs healthcare planning at community level. As team dynamics is a contributor to both patient care coordination and clinical job satisfaction, improving team dynamics should be considered as one of priorities for better community healthcare in low or middle resource setting.
Global age-sex-specific fertility, mortality, healthy life expectancy (HALE), and population estimates in 204 countries and territories, 1950–2019 : a comprehensive demographic analysis for the Global Burden of Disease Study 2019
- Authors: Abd-Allah, Foad , Adebayo, Oladimeji , Agrawal, Anurag , Alam, Tahiya , Rahman, Muhammad Aziz
- Date: 2020
- Type: Text , Journal article
- Relation: Lancet Vol. 396, no. 10258 (2020), p. 1160-1203
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- Description: Accurate and up-to-date assessment of demographic metrics is crucial for understanding a wide range of social, economic, and public health issues that affect populations worldwide. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 produced updated and comprehensive demographic assessments of the key indicators of fertility, mortality, migration, and population for 204 countries and territories and selected subnational locations from 1950 to 2019. 8078 country-years of vital registration and sample registration data, 938 surveys, 349 censuses, and 238 other sources were identified and used to estimate age-specific fertility. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate age-specific fertility rates for 5-year age groups between ages 15 and 49 years. With extensions to age groups 10–14 and 50–54 years, the total fertility rate (TFR) was then aggregated using the estimated age-specific fertility between ages 10 and 54 years. 7417 sources were used for under-5 mortality estimation and 7355 for adult mortality. ST-GPR was used to synthesise data sources after correction for known biases. Adult mortality was measured as the probability of death between ages 15 and 60 years based on vital registration, sample registration, and sibling histories, and was also estimated using ST-GPR. HIV-free life tables were then estimated using estimates of under-5 and adult mortality rates using a relational model life table system created for GBD, which closely tracks observed age-specific mortality rates from complete vital registration when available. Independent estimates of HIV-specific mortality generated by an epidemiological analysis of HIV prevalence surveys and antenatal clinic serosurveillance and other sources were incorporated into the estimates in countries with large epidemics. Annual and single-year age estimates of net migration and population for each country and territory were generated using a Bayesian hierarchical cohort component model that analysed estimated age-specific fertility and mortality rates along with 1250 censuses and 747 population registry years. We classified location-years into seven categories on the basis of the natural rate of increase in population (calculated by subtracting the crude death rate from the crude birth rate) and the net migration rate. We computed healthy life expectancy (HALE) using years lived with disability (YLDs) per capita, life tables, and standard demographic methods. Uncertainty was propagated throughout the demographic estimation process, including fertility, mortality, and population, with 1000 draw-level estimates produced for each metric. The global TFR decreased from 2·72 (95% uncertainty interval [UI] 2·66–2·79) in 2000 to 2·31 (2·17–2·46) in 2019. Global annual livebirths increased from 134·5 million (131·5–137·8) in 2000 to a peak of 139·6 million (133·0–146·9) in 2016. Global livebirths then declined to 135·3 million (127·2–144·1) in 2019. Of the 204 countries and territories included in this study, in 2019, 102 had a TFR lower than 2·1, which is considered a good approximation of replacement-level fertility. All countries in sub-Saharan Africa had TFRs above replacement level in 2019 and accounted for 27·1% (95% UI 26·4–27·8) of global livebirths. Global life expectancy at birth increased from 67·2 years (95% UI 66·8–67·6) in 2000 to 73·5 years (72·8–74·3) in 2019. The total number of deaths increased from 50·7 million (49·5–51·9) in 2000 to 56·5 million (53·7–59·2) in 2019. Under-5 deaths declined from 9·6 million (9·1–10·3) in 2000 to 5·0 million (4·3–6·0) in 2019. Global population increased by 25·7%, from 6·2 billion (6·0–6·3) in 2000 to 7·7 billion (7·5–8·0) in 2019. In 2019, 34 countries had negative natural rates of increase in 17 of these, the population declined because immigration was not sufficient to counteract the negative rate of decline. Globally, HALE increased from 58·6 years (56·1–60·8) in 2000 to 63·5 years (60·8–66·1) in 2019. HALE increased in 202 of 204 countries and territories between 2000 and 2019. Over the past 20 years, fertility rates have been dropping steadily and life expectancy has been increasing, with few exceptions. Much of this change follows historical patterns linking social and economic determinants, such as those captured by the GBD Socio-demographic Index, with demographic outcomes. More recently, several countries have experienced a combination of low fertility and stagnating improvement in mortality rates, pushing more populations into the late stages of the demographic transition. Tracking demographic change and the emergence of new patterns will be essential for global health monitoring. Bill & Melinda Gates Foundation. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Muhammad Aziz Rahman” is provided in this record**