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Objectives. Bayesian mapping is an effective spatiotemporal approach to identify high-risk geographic areas for diseases and has not been used to identify oral cancer hotspots in Australia previously. This retrospective disease mapping study was undertaken to identify the oral cancer trends and patterns within the Queensland state in Australia. Methods. This study included data obtained from Queensland state Cancer Registry from 1982 to 2018. Domains mapped included the oral cancer incidence and mortality in Queensland (QLD). Local government areas (LGAs) and suburbs were utilized as geographical units for the estimation using Bayesian mapping approach. Results. Of the 78 LGAs, 21 showed high-oral cancer incidence as measured using higher median smoothed incidence risk (SIR), above the state average. Specifically, nine LGAs within predominantly rural areas had SIR above 100% of the state average. Of these, only one LGA (Mount Isa City) had a median smoothed SIR and 95% CI of 2.61 (2.14–3.15) which was constantly above 100% of the state average. Furthermore, mortality risk estimated using smoothed mortality risk (SMR), were significantly higher than the state average in 31 LGAs. Seventeen LGAs had a median SMR above 100% of the state average while three LGAs had the highest overall, 3- and 5-year mortality risks. Considering the 95% credible interval which is indicative of the uncertainty around the estimates, three LGAs had the highest overall mortality risks—Yarrabah Aboriginal Shire (3.80 (2.16–6.39)), Cook Shire (3.37 (2.21–5.06)), and Mount Isa City (3.04 (2.40–3.80)). Conclusion. Bayesian disease mapping approach identified multiple incidence and mortality hotspots within regional areas of the Queensland. Findings from our study can aid in designing targeted public health screening and interventions for primary prevention of oral cancer in regional and remote communities. [ABSTRACT FROM AUTHOR]

Water quality control helps in the estimation of water bodies and detects the span of pollutants and their effect on the neighboring environment. This is why the water quality of the northern part of Lake Manzala has been studied here from January to March, 2016. This study aims to model and create a program for linear and nonlinear regression of the water elements in Lake Manzala to assess and predict the water quality. Water samples have been extracted from various depths, and physio-chemical properties and heavy metal concentrations have been evaluated. This study has proposed a new algorithm for predicting water quality called "Algorithm of Estimation Regression Model" (AERM). On the contrary, in renewable energy applications, statistical modeling and forecasting the solar radiation remains a significant issue with detect to reinforce power management. A new proposed method for forecasting the average Monthly Global Solar Energy (MGSE) in Queensland, Australia, is called, Converting Data Set into Markov Model (CDMM). It was used to obtain Markov transition probability matrices for three and six states of the solar energy. The proposed forecasting method yielded accurate results with minimal error. [ABSTRACT FROM AUTHOR]