Twenty ways to estimate the Log Gaussian Cox Process model with point and aggregated case data: the rts2 package for R
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| Format: | Preprint |
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2024
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| _version_ | 1866916159745425408 |
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| author | Watson, Samuel I |
| author_facet | Watson, Samuel I |
| contents | The R package rts2 provides data manipulation and model fitting tools for Log Gaussian Cox Process (LGCP) models. LGCP models are a key method for disease and other types of surveillance, and provide a means of predicting risk across an area of interest based on spatially-referenced and time-stamped case data. However, these models can be difficult to specify and computationally demanding to estimate. For many surveillance scenarios we require results in near real-time using routinely available data to guide and direct policy responses, or due to limited availability of computational resources. There are limited software implementations available for this real-time context with reliable predictions and quantification of uncertainty. The rts2 package provides a range of modern Gaussian process approximations and model fitting methods to fit the LGCP, including estimation of covariance parameters, using both Bayesian and stochastic Maximum Likelihood methods. The package provides a suite of data manipulation tools. We also provide a novel implementation to estimate the LGCP when case data are aggregated to an irregular grid such as census tract areas. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_09448 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Twenty ways to estimate the Log Gaussian Cox Process model with point and aggregated case data: the rts2 package for R Watson, Samuel I Computation The R package rts2 provides data manipulation and model fitting tools for Log Gaussian Cox Process (LGCP) models. LGCP models are a key method for disease and other types of surveillance, and provide a means of predicting risk across an area of interest based on spatially-referenced and time-stamped case data. However, these models can be difficult to specify and computationally demanding to estimate. For many surveillance scenarios we require results in near real-time using routinely available data to guide and direct policy responses, or due to limited availability of computational resources. There are limited software implementations available for this real-time context with reliable predictions and quantification of uncertainty. The rts2 package provides a range of modern Gaussian process approximations and model fitting methods to fit the LGCP, including estimation of covariance parameters, using both Bayesian and stochastic Maximum Likelihood methods. The package provides a suite of data manipulation tools. We also provide a novel implementation to estimate the LGCP when case data are aggregated to an irregular grid such as census tract areas. |
| title | Twenty ways to estimate the Log Gaussian Cox Process model with point and aggregated case data: the rts2 package for R |
| topic | Computation |
| url | https://arxiv.org/abs/2403.09448 |