
@article{gasparrini_distributed_2011,
	title = {Distributed {Lag} {Linear} and {Non}-{Linear} {Models} in \textit{{R}} : {The} {Package} \textbf{dlnm}},
	volume = {43},
	issn = {1548-7660},
	shorttitle = {Distributed {Lag} {Linear} and {Non}-{Linear} {Models} in \textit{{R}}},
	url = {http://www.jstatsoft.org/v43/i08/},
	doi = {10.18637/jss.v043.i08},
	abstract = {Distributed lag non-linear models (DLNMs) represent a modeling framework to ﬂexibly describe associations showing potentially non-linear and delayed eﬀects in time series data. This methodology rests on the deﬁnition of a crossbasis, a bi-dimensional functional space expressed by the combination of two sets of basis functions, which specify the relationships in the dimensions of predictor and lags, respectively. This framework is implemented in the R package dlnm, which provides functions to perform the broad range of models within the DLNM family and then to help interpret the results, with an emphasis on graphical representation. This paper oﬀers an overview of the capabilities of the package, describing the conceptual and practical steps to specify and interpret DLNMs with an example of application to real data.},
	language = {en},
	number = {8},
	urldate = {2025-05-22},
	journal = {Journal of Statistical Software},
	author = {Gasparrini, Antonio},
	year = {2011},
	file = {PDF:/home/akawieck/Documents/papers.zotero/Gasparrini - 2011 - Distributed Lag Linear and Non-Linear Models in R  The Package dlnm.pdf:application/pdf},
}

@article{rue_bayesian_2017,
	title = {Bayesian {Computing} with {INLA}: {A} {Review}},
	volume = {4},
	issn = {2326-8298, 2326-831X},
	shorttitle = {Bayesian {Computing} with {INLA}},
	url = {https://www.annualreviews.org/doi/10.1146/annurev-statistics-060116-054045},
	doi = {10.1146/annurev-statistics-060116-054045},
	abstract = {The key operation in Bayesian inference is to compute high-dimensional integrals. An old approximate technique is the Laplace method or approximation, which dates back to Pierre-Simon Laplace (1774). This simple idea approximates the integrand with a second-order Taylor expansion around the mode and computes the integral analytically. By developing a nested version of this classical idea, combined with modern numerical techniques for sparse matrices, we obtain the approach of integrated nested Laplace approximations (INLA) to do approximate Bayesian inference for latent Gaussian models (LGMs). LGMs represent an important model abstraction for Bayesian inference and include a large proportion of the statistical models used today. In this review, we discuss the reasons for the success of the INLA approach, the R-INLA package, why it is so accurate, why the approximations are very quick to compute, and why LGMs make such a useful concept for Bayesian computing.},
	language = {en},
	number = {1},
	urldate = {2025-08-01},
	journal = {Annual Review of Statistics and Its Application},
	author = {Rue, Håvard and Riebler, Andrea and Sørbye, Sigrunn H. and Illian, Janine B. and Simpson, Daniel P. and Lindgren, Finn K.},
	month = mar,
	year = {2017},
	pages = {395--421},
	file = {PDF:/home/akawieck/Documents/papers.zotero/methods/Rue et al. - 2017 - Bayesian Computing with INLA A Review.pdf:application/pdf},
}

@article{lowe_nonlinear_2018,
	title = {Nonlinear and delayed impacts of climate on dengue risk in {Barbados}: {A} modelling study},
	volume = {15},
	issn = {1549-1676},
	shorttitle = {Nonlinear and delayed impacts of climate on dengue risk in {Barbados}},
	url = {https://dx.plos.org/10.1371/journal.pmed.1002613},
	doi = {10.1371/journal.pmed.1002613},
	abstract = {Background Over the last 5 years (2013–2017), the Caribbean region has faced an unprecedented crisis of co-occurring epidemics of febrile illness due to arboviruses transmitted by the Aedes sp. mosquito (dengue, chikungunya, and Zika). Since 2013, the Caribbean island of Barbados has experienced 3 dengue outbreaks, 1 chikungunya outbreak, and 1 Zika fever outbreak. Prior studies have demonstrated that climate variability influences arbovirus transmission and vector population dynamics in the region, indicating the potential to develop public health interventions using climate information. The aim of this study is to quantify the nonlinear and delayed effects of climate indicators, such as drought and extreme rainfall, on dengue risk in Barbados from 1999 to 2016.},
	language = {en},
	number = {7},
	urldate = {2025-08-01},
	journal = {PLOS Medicine},
	author = {Lowe, Rachel and Gasparrini, Antonio and Van Meerbeeck, Cédric J. and Lippi, Catherine A. and Mahon, Roché and Trotman, Adrian R. and Rollock, Leslie and Hinds, Avery Q. J. and Ryan, Sadie J. and Stewart-Ibarra, Anna M.},
	editor = {Thomson, Madeleine},
	month = jul,
	year = {2018},
	pages = {e1002613},
	file = {PDF:/home/akawieck/Documents/papers.zotero/ghr_methodology/Lowe et al. - 2018 - Nonlinear and delayed impacts of climate on dengue risk in Barbados A modelling study.pdf:application/pdf},
}

@article{lowe_combined_2021,
	title = {Combined effects of hydrometeorological hazards and urbanisation on dengue risk in {Brazil}: a spatiotemporal modelling study},
	volume = {5},
	issn = {25425196},
	shorttitle = {Combined effects of hydrometeorological hazards and urbanisation on dengue risk in {Brazil}},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S2542519620302928},
	doi = {10.1016/S2542-5196(20)30292-8},
	abstract = {Background Temperature and rainfall patterns are known to influence seasonal patterns of dengue transmission. However, the effect of severe drought and extremely wet conditions on the timing and intensity of dengue epidemics is poorly understood. In this study, we aimed to quantify the non-linear and delayed effects of extreme hydrometeorological hazards on dengue risk by level of urbanisation in Brazil using a spatiotemporal model.},
	language = {en},
	number = {4},
	urldate = {2025-08-01},
	journal = {The Lancet Planetary Health},
	author = {Lowe, Rachel and Lee, Sophie A and O'Reilly, Kathleen M and Brady, Oliver J and Bastos, Leonardo and Carrasco-Escobar, Gabriel and De Castro Catão, Rafael and Colón-González, Felipe J and Barcellos, Christovam and Carvalho, Marilia Sá and Blangiardo, Marta and Rue, Håvard and Gasparrini, Antonio},
	month = apr,
	year = {2021},
	pages = {e209--e219},
	file = {PDF:/home/akawieck/Documents/papers.zotero/ghr_methodology/Lowe et al. - 2021 - Combined effects of hydrometeorological hazards and urbanisation on dengue risk in Brazil a spatiot.pdf:application/pdf},
}

@article{besag_bayesian_1991,
	title = {Bayesian image restoration, with two applications in spatial statistics},
	volume = {43},
	issn = {1572-9052},
	url = {https://doi.org/10.1007/BF00116466},
	doi = {10.1007/BF00116466},
	abstract = {There has been much recent interest in Bayesian image analysis, including such topics as removal of blur and noise, detection of object boundaries, classification of textures, and reconstruction of two- or three-dimensional scenes from noisy lower-dimensional views. Perhaps the most straightforward task is that of image restoration, though it is often suggested that this is an area of relatively minor practical importance. The present paper argues the contrary, since many problems in the analysis of spatial data can be interpreted as problems of image restoration. Furthermore, the amounts of data involved allow routine use of computer intensive methods, such as the Gibbs sampler, that are not yet practicable for conventional images. Two examples are given, one in archeology, the other in epidemiology. These are preceded by a partial review of pixel-based Bayesian image analysis.},
	number = {1},
	journal = {Annals of the Institute of Statistical Mathematics},
	author = {Besag, J. and York, J. and Mollié, A.},
	month = mar,
	year = {1991},
	pages = {1--20},
}

@article{simpson_penalising_2017,
	title = {Penalising {Model} {Component} {Complexity}: {A} {Principled}, {Practical} {Approach} to {Constructing} {Priors}},
	volume = {32},
	issn = {0883-4237},
	shorttitle = {Penalising {Model} {Component} {Complexity}},
	url = {https://projecteuclid.org/journals/statistical-science/volume-32/issue-1/Penalising-Model-Component-Complexity--A-Principled-Practical-Approach-to/10.1214/16-STS576.full},
	doi = {10.1214/16-STS576},
	abstract = {In this paper, we introduce a new concept for constructing prior distributions. We exploit the natural nested structure inherent to many model components, which deﬁnes the model component to be a ﬂexible extension of a base model. Proper priors are deﬁned to penalise the complexity induced by deviating from the simpler base model and are formulated after the input of a user-deﬁned scaling parameter for that model component, both in the univariate and the multivariate case. These priors are invariant to reparameterisations, have a natural connection to Jeffreys’ priors, are designed to support Occam’s razor and seem to have excellent robustness properties, all which are highly desirable and allow us to use this approach to deﬁne default prior distributions. Through examples and theoretical results, we demonstrate the appropriateness of this approach and how it can be applied in various situations.},
	language = {en},
	number = {1},
	urldate = {2025-08-05},
	journal = {Statistical Science},
	author = {Simpson, Daniel and Rue, Håvard and Riebler, Andrea and Martins, Thiago G. and Sørbye, Sigrunn H.},
	month = feb,
	year = {2017},
	file = {PDF:/home/akawieck/Documents/papers.zotero/methods/Simpson et al. - 2017 - Penalising Model Component Complexity A Principled, Practical Approach to Constructing Priors.pdf:application/pdf},
}

@book{mcelreath_statistical_2020,
	address = {New York},
	edition = {2},
	title = {Statistical {Rethinking}: {A} {Bayesian} {Course} with {Examples} in {R} and {STAN}},
	isbn = {978-0-429-02960-8},
	shorttitle = {Statistical {Rethinking}},
	abstract = {Winner of the 2024 De Groot Prize awarded by the International Society for Bayesian Analysis (ISBA)
Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds your knowledge of and confidence in making inferences from data. Reflecting the need for scripting in today's model-based statistics, the book pushes you to perform step-by-step calculations that are usually automated. This unique computational approach ensures that you understand enough of the details to make reasonable choices and interpretations in your own modeling work.
The text presents causal inference and generalized linear multilevel models from a simple Bayesian perspective that builds on information theory and maximum entropy. The core material ranges from the basics of regression to advanced multilevel models. It also presents measurement error, missing data, and Gaussian process models for spatial and phylogenetic confounding.
The second edition emphasizes the directed acyclic graph (DAG) approach to causal inference, integrating DAGs into many examples. The new edition also contains new material on the design of prior distributions, splines, ordered categorical predictors, social relations models, cross-validation, importance sampling, instrumental variables, and Hamiltonian Monte Carlo. It ends with an entirely new chapter that goes beyond generalized linear modeling, showing how domain-specific scientific models can be built into statistical analyses.
Features


Integrates working code into the main text.




Illustrates concepts through worked data analysis examples.




Emphasizes understanding assumptions and how assumptions are reflected in code.




Offers more detailed explanations of the mathematics in optional sections.




Presents examples of using the dagitty R package to analyze causal graphs.
Provides the rethinking R package on the author's website and on GitHub.},
	publisher = {Chapman and Hall/CRC},
	author = {McElreath, Richard},
	month = mar,
	year = {2020},
	doi = {10.1201/9780429029608},
}

@article{rue_approximate_2009,
	title = {Approximate {Bayesian} inference for latent {Gaussian} models by using integrated nested {Laplace} approximations},
	volume = {71},
	issn = {1467-9868},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-9868.2008.00700.x},
	doi = {10.1111/j.1467-9868.2008.00700.x},
	abstract = {Summary. Structured additive regression models are perhaps the most commonly used class of models in statistical applications. It includes, among others, (generalized) linear models, (generalized) additive models, smoothing spline models, state space models, semiparametric regression, spatial and spatiotemporal models, log-Gaussian Cox processes and geostatistical and geoadditive models. We consider approximate Bayesian inference in a popular subset of structured additive regression models, latent Gaussian models, where the latent field is Gaussian, controlled by a few hyperparameters and with non-Gaussian response variables. The posterior marginals are not available in closed form owing to the non-Gaussian response variables. For such models, Markov chain Monte Carlo methods can be implemented, but they are not without problems, in terms of both convergence and computational time. In some practical applications, the extent of these problems is such that Markov chain Monte Carlo sampling is simply not an appropriate tool for routine analysis. We show that, by using an integrated nested Laplace approximation and its simplified version, we can directly compute very accurate approximations to the posterior marginals. The main benefit of these approximations is computational: where Markov chain Monte Carlo algorithms need hours or days to run, our approximations provide more precise estimates in seconds or minutes. Another advantage with our approach is its generality, which makes it possible to perform Bayesian analysis in an automatic, streamlined way, and to compute model comparison criteria and various predictive measures so that models can be compared and the model under study can be challenged.},
	language = {en},
	number = {2},
	urldate = {2025-08-07},
	journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
	author = {Rue, Håvard and Martino, Sara and Chopin, Nicolas},
	year = {2009},
	note = {\_eprint: https://rss.onlinelibrary.wiley.com/doi/pdf/10.1111/j.1467-9868.2008.00700.x},
	keywords = {Approximate Bayesian inference, Gaussian Markov random fields, Generalized additive mixed models, Laplace approximation, Parallel computing, Sparse matrices, Structured additive regression models},
	pages = {319--392},
	file = {Full Text PDF:/home/akawieck/Zotero/storage/NNWUE58D/Rue et al. - 2009 - Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approxi.pdf:application/pdf;Snapshot:/home/akawieck/Zotero/storage/TQ853QSB/j.1467-9868.2008.00700.html:text/html},
}

@article{lindgren_explicit_2011,
	title = {An {Explicit} {Link} between {Gaussian} {Fields} and {Gaussian} {Markov} {Random} {Fields}: {The} {Stochastic} {Partial} {Differential} {Equation} {Approach}},
	volume = {73},
	copyright = {https://academic.oup.com/journals/pages/open\_access/funder\_policies/chorus/standard\_publication\_model},
	issn = {1369-7412, 1467-9868},
	shorttitle = {An {Explicit} {Link} between {Gaussian} {Fields} and {Gaussian} {Markov} {Random} {Fields}},
	url = {https://academic.oup.com/jrsssb/article/73/4/423/7034732},
	doi = {10.1111/j.1467-9868.2011.00777.x},
	abstract = {Continuously indexed Gaussian ﬁelds (GFs) are the most important ingredient in spatial statistical modelling and geostatistics. The speciﬁcation through the covariance function gives an intuitive interpretation of the ﬁeld properties. On the computational side, GFs are hampered with the big n problem, since the cost of factorizing dense matrices is cubic in the dimension. Although computational power today is at an all time high, this fact seems still to be a computational bottleneck in many applications. Along with GFs, there is the class of Gaussian Markov random ﬁelds (GMRFs) which are discretely indexed. The Markov property makes the precision matrix involved sparse, which enables the use of numerical algorithms for sparse matrices, that for ﬁelds in R2 only use the square root of the time required by general algorithms. The speciﬁcation of a GMRF is through its full conditional distributions but its marginal properties are not transparent in such a parameterization. We show that, using an approximate stochastic weak solution to (linear) stochastic partial differential equations, we can, for some GFs in the Matérn class, provide an explicit link , for any triangulation of Rd , between GFs and GMRFs, formulated as a basis function representation. The consequence is that we can take the best from the two worlds and do the modelling by using GFs but do the computations by using GMRFs. Perhaps more importantly, our approach generalizes to other covariance functions generated by SPDEs, including oscillating and non-stationary GFs, as well as GFs on manifolds. We illustrate our approach by analysing global temperature data with a non-stationary model deﬁned on a sphere.},
	language = {en},
	number = {4},
	urldate = {2025-08-07},
	journal = {Journal of the Royal Statistical Society Series B: Statistical Methodology},
	author = {Lindgren, Finn and Rue, Håvard and Lindström, Johan},
	month = sep,
	year = {2011},
	pages = {423--498},
	file = {PDF:/home/akawieck/Documents/papers.zotero/methods/Lindgren et al. - 2011 - An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields The Stochastic Partial D.pdf:application/pdf},
}

@article{van_de_schoot_gentle_2014,
	title = {A {Gentle} {Introduction} to {Bayesian} {Analysis}: {Applications} to {Developmental} {Research}},
	volume = {85},
	issn = {0009-3920, 1467-8624},
	shorttitle = {A {Gentle} {Introduction} to {Bayesian} {Analysis}},
	url = {https://srcd.onlinelibrary.wiley.com/doi/10.1111/cdev.12169},
	doi = {10.1111/cdev.12169},
	abstract = {Bayesian statistical methods are becoming ever more popular in applied and fundamental research. In this study a gentle introduction to Bayesian analysis is provided. It is shown under what circumstances it is attractive to use Bayesian estimation, and how to interpret properly the results. First, the ingredients underlying Bayesian methods are introduced using a simplified example. Thereafter, the advantages and pitfalls of the specification of prior knowledge are discussed. To illustrate Bayesian methods explained in this study, in a second example a series of studies that examine the theoretical framework of dynamic interactionism are considered. In the Discussion the advantages and disadvantages of using Bayesian statistics are reviewed, and guidelines on how to report on Bayesian statistics are provided.},
	language = {en},
	number = {3},
	urldate = {2025-08-07},
	journal = {Child Development},
	author = {Van De Schoot, Rens and Kaplan, David and Denissen, Jaap and Asendorpf, Jens B. and Neyer, Franz J. and Van Aken, Marcel A.G.},
	month = may,
	year = {2014},
	pages = {842--860},
	file = {PDF:/home/akawieck/Documents/papers.zotero/methods/Van De Schoot et al. - 2014 - A Gentle Introduction to Bayesian Analysis Applications to Developmental Research.pdf:application/pdf},
}

@article{chen_comparison_2024,
	title = {A comparison of computational algorithms for the {Bayesian} analysis of clinical trials},
	volume = {21},
	issn = {1740-7745, 1740-7753},
	url = {https://journals.sagepub.com/doi/10.1177/17407745241247334},
	doi = {10.1177/17407745241247334},
	abstract = {Methods: Data from an international Bayesian multi-platform adaptive trial that compared therapeutic-dose anticoagulation with heparin to usual care in non-critically ill patients hospitalized for COVID-19 were used to fit Bayesian hierarchical generalized mixed models. Integrated Nested Laplace Approximations was compared to two Markov Chain Monte Carlo algorithms, implemented in the software JAGS and stan, using packages available in the statistical software R. Seven outcomes were analysed: organ-support free days (an ordinal outcome), five binary outcomes related to survival and length of hospital stay, and a time-to-event outcome. The posterior distributions for the treatment and sex effects and the variances for the hierarchical effects of age, site and time period were obtained. We summarized these posteriors by calculating the mean, standard deviations and the 95\% equitailed credible intervals and presenting the results graphically. The computation time for each algorithm was recorded.
Results: The average overlap of the 95\% credible interval for the treatment and sex effects estimated using Integrated Nested Laplace Approximations was 96\% and 97.6\% compared with stan, respectively. The graphical posterior densities for these effects overlapped for all three algorithms. The posterior mean for the variance of the hierarchical effects of age, site and time estimated using Integrated Nested Laplace Approximations are within the 95\% credible interval estimated using Markov Chain Monte Carlo but the average overlap of the credible interval is lower, 77\%, 85.6\% and 91.3\%, respectively, for Integrated Nested Laplace Approximations compared to stan. Integrated Nested Laplace Approximations and stan were easily implemented in clear, well-established packages in R, while JAGS required the direct specification of the model. Integrated Nested Laplace Approximations was between 85 and 269 times faster than stan and 26 and 1852 times faster than JAGS.
Conclusion: Integrated Nested Laplace Approximations could reduce the computational complexity of Bayesian analysis in clinical trials as it is easy to implement in R, substantially faster than Markov Chain Monte Carlo methods implemented in JAGS and stan, and provides near identical approximations to the posterior distributions for the treatment effect. Integrated Nested Laplace Approximations was less accurate when estimating the posterior distribution for the variance of hierarchical effects, particularly for the proportional odds model, and future work should determine if the Integrated Nested Laplace Approximations algorithm can be adjusted to improve this estimation.},
	language = {en},
	number = {6},
	urldate = {2025-08-07},
	journal = {Clinical Trials},
	author = {Chen, Ziming and Berger, Jeffrey S and Castellucci, Lana A and Farkouh, Michael and Goligher, Ewan C and Hade, Erinn M and Hunt, Beverley J and Kornblith, Lucy Z and Lawler, Patrick R and Leifer, Eric S and Lorenzi, Elizabeth and Neal, Matthew D and Zarychanski, Ryan and Heath, Anna},
	month = dec,
	year = {2024},
	pages = {689--700},
	file = {PDF:/home/akawieck/Documents/papers.zotero/methods/Chen et al. - 2024 - A comparison of computational algorithms for the Bayesian analysis of clinical trials.pdf:application/pdf},
}

@book{gomez-rubio_bayesian_2020,
	title = {Bayesian inference with {INLA}},
	url = {https://becarioprecario.bitbucket.io/inla-gitbook/index.html},
	abstract = {This book introduces the integrated nested Laplace approximation (INLA) for Bayesian inference and its associated R package R-INLA.},
	urldate = {2025-08-07},
	publisher = {Chapman \&Hall/CRC Press},
	author = {Gómez-Rubio, Virgilio},
	month = jan,
	year = {2020},
	file = {Snapshot:/home/akawieck/Zotero/storage/QSS5WAUS/index.html:text/html},
}

@book{moraga_geospatial_2019,
	title = {Geospatial {Health} {Data}: {Modeling} and {Visualization} with {R}-{INLA} and {Shiny}},
	shorttitle = {Geospatial {Health} {Data}},
	url = {https://www.paulamoraga.com/book-geospatial/index.html},
	abstract = {A book for Geospatial Health Analysis with R.},
	language = {en},
	urldate = {2025-08-07},
	publisher = {Chapman \& Hall/CRC Biostatistics Series},
	author = {Moraga, Paula},
	month = jan,
	year = {2019},
	file = {Snapshot:/home/akawieck/Zotero/storage/6HIDIWRS/index.html:text/html},
}

@article{lowe_dengue_2014,
	title = {Dengue outlook for the {World} {Cup} in {Brazil}: an early warning model framework driven by real-time seasonal climate forecasts},
	volume = {14},
	issn = {1473-3099, 1474-4457},
	shorttitle = {Dengue outlook for the {World} {Cup} in {Brazil}},
	url = {https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(14)70781-9/abstract},
	doi = {10.1016/S1473-3099(14)70781-9},
	language = {English},
	number = {7},
	urldate = {2025-08-11},
	journal = {The Lancet Infectious Diseases},
	author = {Lowe, Rachel and Barcellos, Christovam and Coelho, Caio A. S. and Bailey, Trevor C. and Coelho, Giovanini Evelim and Graham, Richard and Jupp, Tim and Ramalho, Walter Massa and Carvalho, Marilia Sá and Stephenson, David B. and Rodó, Xavier},
	month = jul,
	year = {2014},
	pmid = {24841859},
	note = {Publisher: Elsevier},
	pages = {619--626},
	file = {Full Text:/home/akawieck/Zotero/storage/NYRAQBES/Lowe et al. - 2014 - Dengue outlook for the World Cup in Brazil an early warning model framework driven by real-time sea.pdf:application/pdf},
}

@article{gasparrini_attributable_2014,
	title = {Attributable risk from distributed lag models},
	volume = {14},
	issn = {1471-2288},
	url = {https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/1471-2288-14-55},
	doi = {10.1186/1471-2288-14-55},
	abstract = {Background: Measures of attributable risk are an integral part of epidemiological analyses, particularly when aimed at the planning and evaluation of public health interventions. However, the current definition of such measures does not consider any temporal relationships between exposure and risk. In this contribution, we propose extended definitions of attributable risk within the framework of distributed lag non-linear models, an approach recently proposed for modelling delayed associations in either linear or non-linear exposure-response associations.
Methods: We classify versions of attributable number and fraction expressed using either a forward or backward perspective. The former specifies the future burden due to a given exposure event, while the latter summarizes the current burden due to the set of exposure events experienced in the past. In addition, we illustrate how the components related to sub-ranges of the exposure can be separated.
Results: We apply these methods for estimating the mortality risk attributable to outdoor temperature in two cities, London and Rome, using time series data for the periods 1993–2006 and 1992–2010, respectively. The analysis provides estimates of the overall mortality burden attributable to temperature, and then computes the components attributable to cold and heat and then mild and extreme temperatures.
Conclusions: These extended definitions of attributable risk account for the additional temporal dimension which characterizes exposure-response associations, providing more appropriate attributable measures in the presence of dependencies characterized by potentially complex temporal patterns.},
	language = {en},
	number = {1},
	urldate = {2025-08-11},
	journal = {BMC Medical Research Methodology},
	author = {Gasparrini, Antonio and Leone, Michaela},
	month = dec,
	year = {2014},
	pages = {55},
	file = {PDF:/home/akawieck/Documents/papers.zotero/methods/Gasparrini and Leone - 2014 - Attributable risk from distributed lag models.pdf:application/pdf},
}

@article{gasparrini_distributed_2010,
	title = {Distributed lag non‐linear models},
	volume = {29},
	copyright = {http://onlinelibrary.wiley.com/termsAndConditions\#vor},
	issn = {0277-6715, 1097-0258},
	url = {https://onlinelibrary.wiley.com/doi/10.1002/sim.3940},
	doi = {10.1002/sim.3940},
	abstract = {Abstract
            
              Environmental stressors often show effects that are delayed in time, requiring the use of statistical models that are flexible enough to describe the additional time dimension of the exposure–response relationship. Here we develop the family of distributed lag non‐linear models (DLNM), a modelling framework that can simultaneously represent non‐linear exposure–response dependencies and delayed effects. This methodology is based on the definition of a ‘cross‐basis’, a bi‐dimensional space of functions that describes simultaneously the shape of the relationship along both the space of the predictor and the lag dimension of its occurrence. In this way the approach provides a unified framework for a range of models that have previously been used in this setting, and new more flexible variants. This family of models is implemented in the package
              dlnm
              within the statistical environment R. To illustrate the methodology we use examples of DLNMs to represent the relationship between temperature and mortality, using data from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) for New York during the period 1987–2000. Copyright © 2010 John Wiley \& Sons, Ltd.},
	language = {en},
	number = {21},
	urldate = {2025-08-12},
	journal = {Statistics in Medicine},
	author = {Gasparrini, A. and Armstrong, B. and Kenward, M. G.},
	month = sep,
	year = {2010},
	pages = {2224--2234},
	file = {PDF:/home/akawieck/Documents/papers.zotero/methods/Gasparrini et al. - 2010 - Distributed lag non‐linear models.pdf:application/pdf},
}

@article{fanelli_assessing_2025,
	title = {Assessing the risk of diseases with epidemic and pandemic potential in a changing world},
	language = {en},
	journal = {Science AdvAnceS},
	author = {Fanelli, Angela and Cescatti, Alessandro and Ciscar, Juan-Carlos and Dubois, Gregoire and Ibarreta, Dolores and Lowe, Rachel and Riccetti, Nicola and Robuchon, Marine and Capua, Ilaria and Szewczyk, Wojtek and Massaro, Emanuele},
	year = {2025},
	file = {PDF:/home/akawieck/Documents/papers.zotero/Fanelli et al. - 2025 - Assessing the risk of diseases with epidemic and pandemic potential in a changing world.pdf:application/pdf},
}

@article{gasparrini_modeling_2014,
	title = {Modeling exposure–lag–response associations with distributed lag non-linear models},
	volume = {33},
	copyright = {© 2013 The Authors. Statistics in Medicine published by John Wiley \& Sons, Ltd.},
	issn = {1097-0258},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.5963},
	doi = {10.1002/sim.5963},
	abstract = {In biomedical research, a health effect is frequently associated with protracted exposures of varying intensity sustained in the past. The main complexity of modeling and interpreting such phenomena lies in the additional temporal dimension needed to express the association, as the risk depends on both intensity and timing of past exposures. This type of dependency is defined here as exposure–lag–response association. In this contribution, I illustrate a general statistical framework for such associations, established through the extension of distributed lag non-linear models, originally developed in time series analysis. This modeling class is based on the definition of a cross-basis, obtained by the combination of two functions to flexibly model linear or nonlinear exposure-responses and the lag structure of the relationship, respectively. The methodology is illustrated with an example application to cohort data and validated through a simulation study. This modeling framework generalizes to various study designs and regression models, and can be applied to study the health effects of protracted exposures to environmental factors, drugs or carcinogenic agents, among others. © 2013 The Authors. Statistics in Medicine published by John Wiley \& Sons, Ltd.},
	language = {en},
	number = {5},
	urldate = {2025-08-12},
	journal = {Statistics in Medicine},
	author = {Gasparrini, Antonio},
	year = {2014},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.5963},
	keywords = {delayed effects, distributed lag models, exposure–lag–response, latency, splines},
	pages = {881--899},
	file = {Full Text PDF:/home/akawieck/Zotero/storage/7RT3JXG7/Gasparrini - 2014 - Modeling exposure–lag–response associations with distributed lag non-linear models.pdf:application/pdf;Snapshot:/home/akawieck/Zotero/storage/5FIG3VFF/sim.html:text/html},
}

@article{fletcher_compound_2025,
	title = {Compound and cascading effects of climatic extremes on dengue outbreak risk in the {Caribbean}: an impact-based modelling framework with long-lag and short-lag interactions},
	volume = {9},
	issn = {25425196},
	shorttitle = {Compound and cascading effects of climatic extremes on dengue outbreak risk in the {Caribbean}},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S2542519625001573},
	doi = {10.1016/j.lanplh.2025.06.003},
	abstract = {Background Small islands developing states in the Caribbean are exposed to increasingly frequent and intense extreme climatic events, which can exacerbate outbreaks of climate-sensitive infectious diseases. Few forecasting tools incorporate the compound and cascading effects of multiple delayed climatic indicators on disease outbreak risk. We aimed to create an impact-based modelling framework that employs interactions between climatic predictors to forecast the probability of a climate-sensitive infectious disease outbreak 3 months ahead, and to investigate the compound and cascading effects of temperature and long-lag and short-lag standardised precipitation index (SPI) on dengue outbreak risk in Barbados.},
	language = {en},
	number = {8},
	urldate = {2025-09-16},
	journal = {The Lancet Planetary Health},
	author = {Fletcher, Chloe and Moirano, Giovenale and Alcayna, Tilly and Rollock, Leslie and Van Meerbeeck, Cédric J and Mahon, Roché and Trotman, Adrian and Boodram, Laura-Lee and Browne, Tia and Best, Sabu and Lührsen, Daniela and Diaz, Avriel R and Dunbar, Willy and Lippi, Catherine A and Ryan, Sadie J and Colón-González, Felipe J and Stewart-Ibarra, Anna M and Lowe, Rachel},
	month = aug,
	year = {2025},
	pages = {101279},
	file = {PDF:/home/akawieck/Documents/papers.zotero/ghr_methodology/Fletcher et al. - 2025 - Compound and cascading effects of climatic extremes on dengue outbreak risk in the Caribbean an imp.pdf:application/pdf},
}

@misc{gelman_data_2006,
	title = {Data {Analysis} {Using} {Regression} and {Multilevel}/{Hierarchical} {Models}},
	url = {https://www.cambridge.org/highereducation/books/data-analysis-using-regression-and-multilevel-hierarchical-models/32A29531C7FD730C3A68951A17C9D983},
	abstract = {Data Analysis Using Regression and Multilevel/Hierarchical Models, first published in 2007, is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout.},
	language = {en},
	urldate = {2025-09-19},
	journal = {Cambridge Aspire website},
	author = {Gelman, Andrew and Hill, Jennifer},
	month = dec,
	year = {2006},
	doi = {10.1017/CBO9780511790942},
	note = {ISBN: 9780511790942
Publisher: Cambridge University Press},
	file = {Snapshot:/home/akawieck/Zotero/storage/354RWT2N/32A29531C7FD730C3A68951A17C9D983.html:text/html},
}
