use eprint field

_data/biblio.yml

@@ -79,7 +79,7 @@
   year: "2021"
   title: "Statistical inference for spatiotemporal partially observed Markov processes via the R package spatPomp"
   journal: "arXiv"
-  eid: "2101.01157"
+  eprint: "2101.01157"
   doi: "https://doi.org/10.48550/arXiv.2101.01157"
 
 - key: AugerMethe2021
@@ -1460,7 +1460,7 @@
   year: "2023"
   title: "Informing policy via dynamic models: cholera in Haiti"
   journal: "arXiv"
-  eid: "2301.08979"
+  eprint: "2301.08979"
   doi: "https://doi.org/10.48550/arXiv.2301.08979"
 
 - key: Whittles2017

biblio.html

@@ -13,7 +13,7 @@ <h1>Bibliography</h1>
   <dd>
     {{ rec.author }} ({{ rec.year }}).
     {{ rec.title }}.
-    {% if rec.journal %}<i>{{ rec.journal }}</i>{% if rec.volume %} <b>{{ rec.volume }}</b>:{% endif %}{% if rec.pages %}&nbsp;{{ rec.pages }}{% elsif rec.eid %}&nbsp;{{ rec.eid }}{% endif %}.
+    {% if rec.journal %}<i>{{ rec.journal }}</i>{% if rec.volume %} <b>{{ rec.volume }}</b>:{% endif %}{% if rec.pages %}&nbsp;{{ rec.pages }}{% elsif rec.eprint %}&nbsp;{{ rec.eprint }}{% endif %}.
     {% elsif rec.school %} Thesis: {{ rec.school }}.
     {% elsif rec.booktitle %} In: <i>{{ rec.booktitle }}</i>{% if rec.editor %} (edited by {{ rec.editor }}){% endif %}{% if rec.publisher %} {{ rec.publisher }}{% if rec.address %}, {{ rec.address }}{% endif %}{% endif %}.
     {% elsif rec.publisher %} {{ rec.publisher }}{% if rec.address %}, {{ rec.address }}{% endif %}.{% endif %}

vignettes/pomp.bib

@@ -708,6 +708,18 @@ @InCollection{McCleery1991
   modificationdate = {2024-01-11T08:51:23},
 }
 
+@Article{Mietchen2024,
+  author           = {Mietchen, Matthew S. and Clancey, Erin and McMichael, Corrin and Lofgren, Eric T.},
+  journal          = {medRxiv},
+  title            = {Estimating {SARS-CoV-2} transmission parameters between coinciding outbreaks in a university population and the surrounding community},
+  year             = {2024},
+  creationdate     = {2024-01-16T07:24:06},
+  doi              = {10.1101/2024.01.10.24301116},
+  eprint           = {2024.01.10.24301116},
+  modificationdate = {2024-01-16T07:29:06},
+  owner            = {kingaa},
+}
+
 @InCollection{Murphy2001,
   author           = {Murphy, Kevin and Russell, Stuart},
   booktitle        = {{Sequential Monte Carlo Methods in Practice}},
@@ -1722,16 +1734,17 @@ @Article{Hayman2015
 }
 
 @MastersThesis{Barrows2016,
-  author       = {Barrows, Dexter},
-  school       = {McMaster University},
-  title        = {A comparative study of techniques for estimation and inference of nonlinear stochastic time series},
-  year         = {2016},
-  note         = {Advisor: Benjamin Bolker},
-  abstract     = {Forecasting tools play an important role in public response to epidemics. Despite this, limited work has been done in comparing best-in-class techniques across the broad spectrum of time series forecasting methodologies. Forecasting frameworks were developed that utilised three methods designed to work with nonlinear dynamics: Iterated Filtering (IF) 2, Hamiltonian MCMC (HMC), and S-mapping. These were compared in several forecasting scenarios including a seasonal epidemic and a spatiotemporal epidemic. IF2 combined with parametric bootstrapping produced superior predictions in all scenarios. S-mapping combined with Dewdrop Regression produced forecasts slightly less-accurate than IF2 and HMC, but demonstrated vastly reduced running times. Hence, S-mapping with or without Dewdrop Regression should be used to glean initial insight into future epidemic behaviour, while IF2 and parametric bootstrapping should be used to refine forecast estimates in time.},
-  creationdate = {2021-04-29},
-  groups       = {pomp},
-  keywords     = {Forecasting, Time series, Estimation, Fitting},
-  url          = {http://hdl.handle.net/11375/19103},
+  author           = {Barrows, Dexter},
+  school           = {McMaster University},
+  title            = {A comparative study of techniques for estimation and inference of nonlinear stochastic time series},
+  year             = {2016},
+  note             = {Advisor: Benjamin Bolker},
+  abstract         = {Forecasting tools play an important role in public response to epidemics. Despite this, limited work has been done in comparing best-in-class techniques across the broad spectrum of time series forecasting methodologies. Forecasting frameworks were developed that utilised three methods designed to work with nonlinear dynamics: Iterated Filtering (IF) 2, Hamiltonian MCMC (HMC), and S-mapping. These were compared in several forecasting scenarios including a seasonal epidemic and a spatiotemporal epidemic. IF2 combined with parametric bootstrapping produced superior predictions in all scenarios. S-mapping combined with Dewdrop Regression produced forecasts slightly less-accurate than IF2 and HMC, but demonstrated vastly reduced running times. Hence, S-mapping with or without Dewdrop Regression should be used to glean initial insight into future epidemic behaviour, while IF2 and parametric bootstrapping should be used to refine forecast estimates in time.},
+  creationdate     = {2021-04-29},
+  groups           = {pomp},
+  keywords         = {Forecasting, Time series, Estimation, Fitting},
+  modificationdate = {2024-01-16T07:35:55},
+  url              = {http://hdl.handle.net/11375/19103},
 }
 
 @Article{Fasiolo2016,
@@ -2473,10 +2486,10 @@ @Article{Wheeler2023
   abstract         = {Public health decisions must be made about when and how to implement interventions to control an infectious disease epidemic. These decisions should be informed by data on the epidemic as well as current understanding about the transmission dynamics. Such decisions can be posed as statistical questions about scientifically motivated dynamic models. Thus, we encounter the methodological task of building credible, data-informed decisions based on stochastic, partially observed, nonlinear dynamic models. This necessitates addressing the tradeoff between biological fidelity and model simplicity, and the reality of misspecification for models at all levels of complexity. As a case study, we consider the 2010-2019 cholera epidemic in Haiti. We study three dynamic models developed by expert teams to advise on vaccination policies. We assess previous methods used for fitting and evaluating these models, and we develop data analysis strategies leading to improved statistical fit. Specifically, we present approaches to diagnosis of model misspecification, development of alternative models, and computational improvements in optimization, in the context of likelihood-based inference on nonlinear dynamic systems. Our workflow is reproducible and extendable, facilitating future investigations of this disease system.},
   creationdate     = {2023-04-16T18:40:56},
   doi              = {10.48550/arXiv.2301.08979},
-  eid              = {2301.08979},
+  eprint           = {2301.08979},
   file             = {:Wheeler2023.pdf:PDF},
   groups           = {pomp},
-  modificationdate = {2023-08-01T14:35:13},
+  modificationdate = {2024-01-16T07:29:22},
   owner            = {kingaa},
 }
 
@@ -3311,11 +3324,11 @@ @Article{Asfaw2021
   abstract         = {We address inference for a partially observed nonlinear non-Gaussian latent stochastic system comprised of interacting units. Each unit has a state, which may be discrete or continuous, scalar or vector valued. In biological applications, the state may represent a structured population or the abundances of a collection of species at a single location. Units can have spatial locations, allowing the description of spatially distributed interacting populations arising in ecology, epidemiology and elsewhere. We consider models where the collection of states is a latent Markov process, and a time series of noisy or incomplete measurements is made on each unit. A model of this form is called a spatiotemporal partially observed Markov process (SpatPOMP). The R package spatPomp provides an environment for implementing SpatPOMP models, analyzing data, and developing new inference approaches. We describe the spatPomp implementations of some methods with scaling properties suited to SpatPOMP models. We demonstrate the package on a simple Gaussian system and on a nontrivial epidemiological model for measles transmission within and between cities. We show how to construct user-specified SpatPOMP models within spatPomp.},
   creationdate     = {2022-09-28T08:27:03},
   doi              = {10.48550/arXiv.2101.01157},
-  eid              = {2101.01157},
+  eprint           = {2101.01157},
   file             = {:Asfaw2021.pdf:PDF},
   groups           = {lab, king, pomp},
   keywords         = {Methodology (stat.ME), Computation (stat.CO), FOS: Computer and information sciences},
-  modificationdate = {2023-08-01T14:35:07},
+  modificationdate = {2024-01-16T07:29:30},
   owner            = {kingaa},
 }