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Uncertainties in above ground tree biomass estimation

Journal of Forestry Research 2021年第5期32卷 1989-2000页

作者： Lihou Qin Shengwang Meng Guang Zhou Qijing Liu Zhenzhao Xu College of Forestry Beijing Forestry UniversityBeijing 100083People’s Republic of China Qianyanzhou Ecological Research Station Key Laboratory of Ecosystem Network Observation and ModelingInstitute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101People’s Republic of China Jiangxi Academy of Forestry Nanchang 330022People’s Republic of China

Models of above-ground tree biomass have been widely used to estimate forest biomass using national forest inventory ***,many sources of uncertainty affect above-ground biomass estimation and are challenging to *** this study,the uncertainties associated with the measurement error in independent variables(diameter at breast height,tree height),residual variability,variances of the parameter estimates,and the sampling variability of national inventory data are estimated for five above-ground biomass *** results show sampling variability is the most significant source of *** measurement error and residual variability have negligible effects on forests above-ground biomass ***,a reduction in the uncertainty of the sampling variability has the greatest potential to decrease the overall *** power model containing only the diameter at breast height has the smallest *** findings of this study provide suggestions to achieve a trade-off between accuracy and cost for above-ground biomass estimation using field work.

关键词： Above-ground biomass Measurement error Residual variability parameter estimates Sampling variability

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Mathematical Modelling of the COVID-19 Epidemic in Northern Ireland in 2020

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Open Journal of Modelling and Simulation 2021年第2期9卷 91-110页

作者： Peter A. Hall Gabor Kiss Tilman Kuhn Salissou Moutari Ellen Patterson Emily Smith Mathematical Science Research Centre School of Mathematics & Physics Queen’s University Belfast Belfast Northern Ireland United Kingdom School of Biological Sciences Queen’s University Belfast Belfast Northern Ireland United Kingdom Heidelberg Institute of Global Health (HIGH) Heidelberg University Hospital Heidelberg Germany

In this study, we investigate the dynamics of the COVID-19 epidemic in Northern Ireland from 1st March 2020 up to 25th December 2020, using several copies of a Susceptible-Exposed-Infectious-Recovered (SEIR) compartmental model, and compare it to a detailed publicly available dataset. We split the data into 10 time intervals and fit the models on the consecutive intervals to the cumulative number of confirmed positive cases on each interval. Using the fitted parameter estimates, we also provide estimates of the reproduction number. We also discuss the limitations and possible extensions of the employed model.

关键词： Pandemic Epidemic SARS-CoV-2 COVID-19 Compartmental Model SEIR Model Basic Reproduction Number Effective Reproduction Number parameter estimates Fitted Model

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Estimating the Level of Asymptomatic COVID-19 Infections in Northern Ireland in 2020

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Open Journal of Modelling and Simulation 2022年第2期10卷 190-218页

作者： Peter A. Hall Gabor Kiss Tilman Kuhn Salissou Moutari Ellen Patterson Emily Smith Mathematical Sciences Research Centre School of Mathematics & Physics Queen’s University Belfast Belfast UK School of Biological Sciences Queen’s University Belfast Belfast UK Heidelberg Institute of Global Health (HIGH) Heidelberg University Hospital Heidelberg Germany

The identification and understanding of COVID-19 potential routes of transmission are fundamental to informing policies and strategies to successfully control the outbreak. Various studies highlighted asymptomatic infections as one of the silent drivers of the epidemic. An accurate estimation of the asymptomatic cases and the understanding of their contribution to the spread of the disease could enhance the effectiveness of current control strategies, mainly based on the symptom onset, to curb transmission. We investigate the dynamics of the COVID-19 epidemic in Northern Ireland during the period 1st March 25th to December 2020 to estimate the proportion of the asymptomatic infections in the country. We extended our previous model to include the stage of the asymptomatic infection, and we implement the corresponding deterministic model using a publicly available dataset. We partition the data into 11 sets over the period of study and fit the model parameters on the consecutive intervals using the cumulative number of confirmed positive cases for each interval. Moreover, we assess numerically the impacts of uncertainty in testing and we provide estimates of the reproduction numbers using the fitted parameters. We found that the proportion of asymptomatically infectious subpopulations, in Northern Ireland during the period of study, ranged between 5% and 25% of exposed individuals. Also, the estimate of the basic reproduction number, R0, is 3.3089. The lower and upper estimates for herd immunity are (0.6181, 0.7243) suggesting that around 70% of the population of Northern Ireland should acquire immunity via infection or vaccination, which is in line with estimates reported in other studies.

关键词： Pandemic Epidemic SARS-CoV-2 COVID-19 Compartmental Model SEIAR Model Basic Reproduction Number Effective Reproduction Number parameter estimates Fitted Model Testing Uncertainty Asymptomatic Infection Northern Ireland

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