The catch and effort data of Sillago sihama fishery in Pakistani waters were used to investigate the performance of two closely related stock assessment models: logistic and generalized surplus-production models. Comp...The catch and effort data of Sillago sihama fishery in Pakistani waters were used to investigate the performance of two closely related stock assessment models: logistic and generalized surplus-production models. Compared with the generalized production model, the logistic model produced more reasonable estimates for parameters such as maximum sustainable yield. The Akaike's Information Criterion values estimated at 4.265 and -51.152 respectively by the logistic and generalized models. Simulation analyses of the S. sihama fishery showed that the estimated and observed abundance indices for the logistic model were closer than those for the generalized production model. Standardized residuals were distributed closer for logistic model, but exhibited a slightly increasing trend for the generalized model. Statistical outliers were seen in 1989 and 1993 for the logistic model, and in 1981 and 1999 for the generalized model. Simulated results revealed that the logistic estimates were close to the true value for low CVs (coefficients of variation) but widely dispersed for high CVs. In contrast, the generalized model estimates were loose for all CV levels. The estimated production model curve parameter was not reasonable at all the tested levels of white noise. With the increase in white noise R2 for the catch per unit effort decreased. Therefore, we conclude that the logistic model performs more reasonably than the generalized production model.展开更多
Introduction:COVID-19 has affected almost every country in the world,which causing many negative implications in terms of education,economy and mental health.Worryingly,the trend of second or third wave of the pandemi...Introduction:COVID-19 has affected almost every country in the world,which causing many negative implications in terms of education,economy and mental health.Worryingly,the trend of second or third wave of the pandemic has been noted in multiple regions despite early success of flattening the curve,such as in the case of Malaysia,post Sabah state election in September 2020.Hence,it is imperative to predict ongoing trend of COVID-19 to assist crucial policymaking in curbing the transmission.Method:Generalized logistic growth modelling(GLM)approach was adopted to make prediction of growth of cases according to each state in Malaysia.The data was obtained from official Ministry of Health Malaysia daily report,starting from 26 September 2020 until 1 January 2021.Result:Sabah,Johor,Selangor and Kuala Lumpur are predicted to exceed 10,000 cumulative cases by 2 February 2021.Nationally,the growth factor has been shown to range between 0.25 to a peak of 3.1 throughout the current Movement Control Order(MCO).The growth factor range for Sabah ranged from 1.00 to 1.25,while Selangor,the state which has the highest case,has a mean growth factor ranging from 1.22 to 1.52.The highest growth rates reported were inWP Labuan for the time periods of 22 Nov-5 Dec 2020 with growth rates of 4.77.States with higher population densities were predicted to have higher cases of COVID-19.Conclusion:GLM is helpful to provide governments and policymakers with accurate and helpful forecasts on magnitude of epidemic and peak time.This forecast could assist government in devising short-and long-term plan to tackle the ongoing pandemic.展开更多
基金supported by the special research fund of the Ocean University of China (No.201022001)
文摘The catch and effort data of Sillago sihama fishery in Pakistani waters were used to investigate the performance of two closely related stock assessment models: logistic and generalized surplus-production models. Compared with the generalized production model, the logistic model produced more reasonable estimates for parameters such as maximum sustainable yield. The Akaike's Information Criterion values estimated at 4.265 and -51.152 respectively by the logistic and generalized models. Simulation analyses of the S. sihama fishery showed that the estimated and observed abundance indices for the logistic model were closer than those for the generalized production model. Standardized residuals were distributed closer for logistic model, but exhibited a slightly increasing trend for the generalized model. Statistical outliers were seen in 1989 and 1993 for the logistic model, and in 1981 and 1999 for the generalized model. Simulated results revealed that the logistic estimates were close to the true value for low CVs (coefficients of variation) but widely dispersed for high CVs. In contrast, the generalized model estimates were loose for all CV levels. The estimated production model curve parameter was not reasonable at all the tested levels of white noise. With the increase in white noise R2 for the catch per unit effort decreased. Therefore, we conclude that the logistic model performs more reasonably than the generalized production model.
文摘Introduction:COVID-19 has affected almost every country in the world,which causing many negative implications in terms of education,economy and mental health.Worryingly,the trend of second or third wave of the pandemic has been noted in multiple regions despite early success of flattening the curve,such as in the case of Malaysia,post Sabah state election in September 2020.Hence,it is imperative to predict ongoing trend of COVID-19 to assist crucial policymaking in curbing the transmission.Method:Generalized logistic growth modelling(GLM)approach was adopted to make prediction of growth of cases according to each state in Malaysia.The data was obtained from official Ministry of Health Malaysia daily report,starting from 26 September 2020 until 1 January 2021.Result:Sabah,Johor,Selangor and Kuala Lumpur are predicted to exceed 10,000 cumulative cases by 2 February 2021.Nationally,the growth factor has been shown to range between 0.25 to a peak of 3.1 throughout the current Movement Control Order(MCO).The growth factor range for Sabah ranged from 1.00 to 1.25,while Selangor,the state which has the highest case,has a mean growth factor ranging from 1.22 to 1.52.The highest growth rates reported were inWP Labuan for the time periods of 22 Nov-5 Dec 2020 with growth rates of 4.77.States with higher population densities were predicted to have higher cases of COVID-19.Conclusion:GLM is helpful to provide governments and policymakers with accurate and helpful forecasts on magnitude of epidemic and peak time.This forecast could assist government in devising short-and long-term plan to tackle the ongoing pandemic.
