Hierarchical log Gaussian Cox process for regeneration in uneven-aged forests

Kuronen, Mikko; Särkkä, Aila; Vihola, Matti; Myllymäki, Mari

doi:10.1007/s10651-021-00514-3

Cited by 2 publications

(2 citation statements)

References 48 publications

(64 reference statements)

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“…The proposed models which are Log Gaussian Cox Processes (or models) are already existing spatiotemporal models. These are commonly used in ecology [7], geography [8], and climate related research [9]. The uniqueness of our approach is to apply all of the model parameters to the epidemiological setting.…”

Section: Literature Reviewmentioning

confidence: 99%

Extending the R number by applying hyperparameters of Log Gaussian Cox process models in an epidemiological context to provide insights into COVID-19 positivity in the City of Edinburgh and in students residing at Edinburgh University

Laxton,

Nightingale,

Lindgren

et al. 2023

PLoS ONE

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The impact of the COVID-19 pandemic on University students has been a topic of fiery debate and of public health research. This study demonstrates the use of a combination of spatiotemporal epidemiological models to describe the trends in COVID-19 positive cases on spatial, temporal and spatiotemporal scales. In addition, this study proposes new epidemiological metrics to describe the connectivity between observed positivity; an analogous metric to the R number in conventional epidemiology. The proposed indices, Rspatial, Rspatiotemporal and Rscaling will aim to improve the characterisation of the spread of infectious disease beyond that of the COVID-19 framework and as a result inform relevant public health policy. Apart from demonstrating the application of the novel epidemiological indices, the key findings in this study are: firstly, there were some Intermediate Zones in Edinburgh with noticeably high levels of COVID-19 positivity, and that the first outbreak during the study period was observed in Dalry and Fountainbridge. Secondly, the estimation of the distance over which the COVID-19 counts at the halls of residence are spatially correlated (or related to each other) was found to be 0.19km (0.13km to 0.27km) and is denoted by the index, Rspatial. This estimate is useful for public health policy in this setting, especially with contact tracing. Thirdly, the study indicates that the association between the surrounding community level of COVID-19 positivity (Intermediate Zones in Edinburgh) and that of the University of Edinburgh’s halls of residence was not statistically significant. Fourthly, this study reveals that relatively high levels of COVID-19 positivity were observed for halls for which higher COVID-19 fines were issued (Spearman’s correlation coefficient = 0.34), and separately, for halls which were non-ensuite relatively to those which were not (Spearman’s correlation coefficient = 0.16). Finally, Intermediate Zones with the highest positivity were associated with student residences that experienced relatively high COVID-19 positivity (Spearman’s correlation coefficient = 0.27).

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Section: Literature Reviewmentioning

confidence: 99%

Extending the R number by applying hyperparameters of Log Gaussian Cox process models in an epidemiological context to provide insights into COVID-19 positivity in the City of Edinburgh and in students residing at Edinburgh University

Laxton,

Nightingale,

Lindgren

et al. 2023

PLoS ONE

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“…El escalado adecuado hace que el análisis sea invariante a la resolución de la cuadrícula. Kuronen et al (2022) muestran un LGCP jerárquico, en el que un conjunto de puntos afecta a otro, pero no a la inversa. Hessellund et al (2020) exponen un método de validación cruzada para la selección de modelos y un algoritmo para la inferencia regularizada.…”

Section: Introductionunclassified

Estructuras estocásticas notables en procesos puntuales espacio-temporales y medidas de riesgo bivariantes

Villa¹

2023

Preprint

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La modelización estadística constituye un conjunto de formulaciones matemáticas que proporcionan un mejor entendimiento de los fenómenos, especialmente en los que subyacen relaciones entre variables que a su vez poseen influencia aleatoria. En este trabajo de tesis se realiza la modelización estadística del fenómeno delictivo, mediante el desarrollo de estructuras estocásticas en procesos puntuales espacio-temporales, con el propósito de reproducir eventos de la forma más exacta posible a la realidad en función de su evolución en el espacio, tiempo y espacio-tiempo.

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Hierarchical log Gaussian Cox process for regeneration in uneven-aged forests

Cited by 2 publications

References 48 publications

Extending the R number by applying hyperparameters of Log Gaussian Cox process models in an epidemiological context to provide insights into COVID-19 positivity in the City of Edinburgh and in students residing at Edinburgh University

Extending the R number by applying hyperparameters of Log Gaussian Cox process models in an epidemiological context to provide insights into COVID-19 positivity in the City of Edinburgh and in students residing at Edinburgh University

Estructuras estocásticas notables en procesos puntuales espacio-temporales y medidas de riesgo bivariantes

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