mcmc_epidemic.bib

@article{lekoneStatistical2006,
  title = {Statistical {{Inference}} in a {{Stochastic Epidemic Seir Model}} with {{Control Intervention}}: {{Ebola}} as a {{Case Study}}},
  shorttitle = {Statistical {{Inference}} in a {{Stochastic Epidemic Seir Model}} with {{Control Intervention}}},
  author = {Lekone, Phenyo E. and Finkenstadt, Barbel F.},
  year = {2006},
  month = dec,
  journal = {Biometrics},
  volume = {62},
  number = {4},
  pages = {1170--1177},
  doi = {10.1111/j.1541-0420.2006.00609.x},
  abstract = {Summary. A stochastic discrete-time susceptible-exposed-infectious-recovered (SEIR) model for infectious diseases is developed with the aim of estimating parameters from daily incidence and mortality time series for an outbreak of Ebola in the Democratic Republic of Congo in 1995. The incidence time series exhibit many low integers as well as zero counts requiring an intrinsically stochastic modeling approach. In order to capture the stochastic nature of the transitions between the compartmental populations in such a model we specify appropriate conditional binomial distributions. In addition, a relatively simple temporally varying transmission rate function is introduced that allows for the effect of control interventions. We develop Markov chain Monte Carlo methods for inference that are used to explore the posterior distribution of the parameters. The algorithm is further extended to integrate numerically over state variables of the model, which are unobserved. This provides a realistic stochastic model that can be used by epidemiologists to study the dynamics of the disease and the effect of control interventions.},
  file = {/home/bolker/Zotero/storage/79MXEAZQ/Lekone and Finkenstadt - 2006 - Statistical Inference in a Stochastic Epidemic Sei.pdf;/home/bolker/Zotero/storage/SHGFF5WM/j1541-0420200600609.html}
}

@article{streftarisBayesian2004,
  title = {Bayesian Inference for Stochastic Epidemics in Closed Populations},
  author = {Streftaris, George and Gibson, Gavin J.},
  year = {2004},
  month = jan,
  journal = {Statistical Modelling},
  volume = {4},
  number = {1},
  pages = {63--75},
  issn = {1471-082X, 1477-0342},
  doi = {10.1191/1471082X04st065oa},
  abstract = {We consider continuous-time stochastic compartmental models that can be applied in veterinary epidemiology to model the within-herd dynamics of infectious diseases. We focus on an extension of Markovian epidemic models, allowing the infectious period of an individual to follow a Weibull distribution, resulting in a more flexible model for many diseases. Following a Bayesian approach we show how approximation methods can be applied to design efficient MCMC algorithms with favourable mixing properties for fitting non-Markovian models to partial observations of epidemic processes. The methodology is used to analyse real data concerning a smallpox outbreak in a human population, and a simulation study is conducted to assess the effects of the frequency and accuracy of diagnostic tests on the information yielded on the epidemic process.},
  langid = {english},
  keywords = {Bayesian inference,diagnostic tests,Markov Chain Monte Carlo,Metropolis-Hastings acceptance rate,non-Markovian model,stochastic epidemic modelling},
  file = {/home/bolker/Zotero/storage/Q3IT7D5K/Streftaris and Gibson - 2004 - Bayesian inference for stochastic epidemics in clo.pdf;/home/bolker/Zotero/storage/BWV388XH/63.html}
}

@article{streftarisBayesian2004b,
  title = {Bayesian Analysis of Experimental Epidemic of Foot-and-Mouth Disease},
  author = {Streftaris, George and Gibson, Gavin},
  year = {2004},
  journal = {Proceedings of the Royal Society of London B},
  volume = {271},
  pages = {1111--1117},
  doi = {10.1098/rspb.2004.2715}
}

@article{oneillAnalyses2000,
  title = {Analyses of Infectious Disease Data from Household Outbreaks by {{Markov}} Chain {{Monte Carlo}} Methods},
  author = {O'Neill, Philip D. and Balding, David J. and Becker, Niels G. and Eerola, Mervi and Mollison, Denis},
  year = {2000},
  journal = {Journal of the Royal Statistical Society: Series C (Applied Statistics)},
  volume = {49},
  number = {4},
  pages = {517--542},
  file = {/home/bolker/Zotero/storage/IS27UU25/2680786.pdf}
}

@article{oneillIntroduction2010,
  title = {Introduction and Snapshot Review: {{Relating}} Infectious Disease Transmission Models to Data},
  shorttitle = {Introduction and Snapshot Review},
  author = {O'Neill, Philip D.},
  year = {2010},
  month = sep,
  journal = {Statistics in Medicine},
  volume = {29},
  number = {20},
  pages = {2069--2077},
  issn = {02776715},
  doi = {10.1002/sim.3968},
  langid = {english},
  file = {/home/bolker/Zotero/storage/XV3QX4PU/3968_ftp.pdf}
}

@article{funkChoices2020,
  title = {Choices and Trade-Offs in Inference with Infectious Disease Models},
  author = {Funk, Sebastian and King, Aaron A.},
  year = {2020},
  month = mar,
  journal = {Epidemics},
  volume = {30},
  pages = {100383},
  issn = {1755-4365},
  doi = {10.1016/j.epidem.2019.100383},
  abstract = {Inference using mathematical models of infectious disease dynamics can be an invaluable tool for the interpretation and analysis of epidemiological data. However, researchers wishing to use this tool are faced with a choice of models and model types, simulation methods, inference methods and software packages. Given the multitude of options, it can be challenging to decide on the best approach. Here, we delineate the choices and trade-offs involved in deciding on an approach for inference, and discuss aspects that might inform this decision. We provide examples of inference with a dataset of influenza cases using the R packages pomp and rbi.},
  langid = {english},
  keywords = {Bayesian,Frequentist,Infectious disease model,Inference,Model fitting},
  file = {/home/bolker/Zotero/storage/5KU827FA/Funk and King - 2020 - Choices and trade-offs in inference with infectiou.pdf;/home/bolker/Zotero/storage/NIXV2DFP/S1755436519300441.html}
}

@article{bhadraMalaria2011,
  title = {Malaria in {{Northwest India}}: {{Data Analysis}} via {{Partially Observed Stochastic Differential Equation Models Driven}} by {{L\'evy Noise}}},
  shorttitle = {Malaria in {{Northwest India}}},
  author = {Bhadra, Anindya and Ionides, Edward L. and Laneri, Karina and Pascual, Mercedes and Bouma, Menno and Dhiman, Ramesh C.},
  year = {2011},
  month = jun,
  journal = {Journal of the American Statistical Association},
  volume = {106},
  number = {494},
  pages = {440--451},
  publisher = {{Taylor \& Francis}},
  issn = {0162-1459},
  doi = {10.1198/jasa.2011.ap10323},
  abstract = {Many biological systems are appropriately described by partially observed Markov process (POMP) models, also known as state space models. Such models also arise throughout the physical and social sciences, in engineering, and in finance. Statistical challenges arise in carrying out inference on nonlinear, nonstationary, vector-valued POMP models. Methodologies that depend on the Markov process model only through numerical solution of sample paths are said to have the plug-and-play property. This property enables consideration of models for which the evaluation of transition densities is problematic. Our case study employs plug-and-play methodology to investigate malaria transmission in Northwest India. We address the scientific question of the respective roles of environmental factors, immunity, and nonlinear disease transmission dynamics in epidemic malaria. Previous debates on this question have been hindered by the lack of a statistical investigation that gives simultaneous consideration to the roles of human immunity and the fluctations in mosquito abundance associated with environmental or ecological covariates. We present the first time series analysis integrating these various components into a single vector-valued dynamic model. We are led to investigate a POMP involving a system of stochastic differential equations driven by L\'evy noise. We find a clear role for rainfall and evidence to support models featuring the possibility of clinical immunity.  An online supplement presents details of the methodology implemented and two additional figures.},
  file = {/home/bolker/Zotero/storage/5CE4PL5R/Bhadra et al. - 2011 - Malaria in Northwest India Data Analysis via Part.pdf;/home/bolker/Zotero/storage/RPMBYRIG/jasa.2011.html}
}

@article{bretoTime2009,
  title = {Time {{Series Analysis}} via {{Mechanistic Models}}},
  author = {Bret{\'o}, Carles and He, Daihai and Ionides, Edward L. and King, Aaron A.},
  year = {2009},
  journal = {The Annals of Applied Statistics},
  volume = {3},
  number = {1},
  pages = {319--348},
  publisher = {{Institute of Mathematical Statistics}},
  issn = {1932-6157},
  abstract = {The purpose of time series analysis via mechanistic models is to reconcile the known or hypothesized structure of a dynamical system with observations collected over time. We develop a framework for constructing nonlinear mechanistic models and carrying out inference. Our framework permits the consideration of implicit dynamic models, meaning statistical models for stochastic dynamical systems which are specified by a simulation algorithm to generate sample paths. Inference procedures that operate on implicit models are said to have the plug-and-play property. Our work builds on recently developed plug-and-play inference methodology for partially observed Markov models. We introduce a class of implicitly specified Markov chains with stochastic transition rates, and we demonstrate its applicability to open problems in statistical inference for biological systems. As one example, these models are shown to give a fresh perspective on measles transmission dynamics. As a second example, we present a mechanistic analysis of cholera incidence data, involving interaction between two competing strains of the pathogen Vibrio cholerae.},
  file = {/home/bolker/Zotero/storage/RTX3MKVT/Bretó et al. - 2009 - Time Series Analysis via Mechanistic Models.pdf}
}

@article{hePlugandplay2010,
  title = {Plug-and-Play Inference for Disease Dynamics: Measles in Large and Small Populations as a Case Study},
  shorttitle = {Plug-and-Play Inference for Disease Dynamics},
  author = {He, Daihai and Ionides, Edward L. and King, Aaron A.},
  year = {2010},
  month = feb,
  journal = {Journal of The Royal Society Interface},
  volume = {7},
  number = {43},
  pages = {271--283},
  publisher = {{Royal Society}},
  doi = {10.1098/rsif.2009.0151},
  abstract = {Statistical inference for mechanistic models of partially observed dynamic systems is an active area of research. Most existing inference methods place substantial restrictions upon the form of models that can be fitted and hence upon the nature of the scientific hypotheses that can be entertained and the data that can be used to evaluate them. In contrast, the so-called plug-and-play methods require only simulations from a model and are thus free of such restrictions. We show the utility of the plug-and-play approach in the context of an investigation of measles transmission dynamics. Our novel methodology enables us to ask and answer questions that previous analyses have been unable to address. Specifically, we demonstrate that plug-and-play methods permit the development of a modelling and inference framework applicable to data from both large and small populations. We thereby obtain novel insights into the nature of heterogeneity in mixing and comment on the importance of including extra-demographic stochasticity as a means of dealing with environmental stochasticity and model misspecification. Our approach is readily applicable to many other epidemiological and ecological systems.},
  keywords = {iterated filtering,measles,mechanistic model,sequential Monte Carlo,state-space model},
  file = {/home/bolker/Zotero/storage/NDDT2538/He et al. - 2010 - Plug-and-play inference for disease dynamics meas.pdf}
}

@article{ionidesInference2006a,
  title = {Inference for Nonlinear Dynamical Systems},
  author = {Ionides, E. L. and Bret{\'o}, C. and King, A. A.},
  year = {2006},
  journal = {Proceedings of the National Academy of Sciences},
  volume = {103},
  number = {49},
  pages = {18438--18443},
  file = {/home/bolker/Zotero/storage/8S27KT49/18438.full.pdf}
}

@article{ionidesInference2015,
  title = {Inference for Dynamic and Latent Variable Models via Iterated, Perturbed {{Bayes}} Maps},
  author = {Ionides, Edward L. and Nguyen, Dao and Atchad{\'e}, Yves and Stoev, Stilian and King, Aaron A.},
  year = {2015},
  month = jan,
  journal = {Proceedings of the National Academy of Sciences},
  volume = {112},
  number = {3},
  pages = {719--724},
  issn = {0027-8424, 1091-6490},
  doi = {10.1073/pnas.1410597112},
  langid = {english},
  file = {/home/bolker/Zotero/storage/2WX8ZPGF/719.full.pdf}
}

@article{kingInapparent2008,
  title = {Inapparent Infections and Cholera Dynamics},
  author = {King, Aaron A. and Ionides, Edward L. and Pascual, Mercedes and Bouma, Menno J.},
  year = {2008},
  month = aug,
  journal = {Nature},
  volume = {454},
  number = {7206},
  pages = {877--880},
  issn = {0028-0836, 1476-4687},
  doi = {10.1038/nature07084},
  file = {/home/bolker/Zotero/storage/H4JG36IU/nature07084.pdf}
}