biblio_redux.bib

@article{abou2020compartmental,
  title={Compartmental models of the {COVID-19} pandemic for physicians and physician-scientists},
  author={Abou-Ismail, Anas},
  journal={SN Comprehensive Clinical Medicine},
  volume={2},
  number={7},
  pages={852--858},
  year={2020},
  publisher={Springer}
}

@article{abu2021effectiveness,
  title={Effectiveness of the {BNT162b2} {COVID-19} Vaccine against the {B.1.1.7 }and {B.1.351} Variants},
  author={Abu-Raddad, Laith J and Chemaitelly, Hiam and Butt, Adeel A},
  journal={New England Journal of Medicine},
  volume={385},
  number={2},
  pages={187--189},
  year={2021},
  publisher={Mass Medical Soc}
}

@article{adam2020special,
  title={Special report: The simulations driving the world's response to {COVID-19}},
  author={Adam, David},
  journal={Nature},
  volume={580},
  number={7802},
  pages={316--319},
  year={2020},
  publisher={Nature Publishing Group}
}


@incollection{baez2023categorical,
	address = {Cham},
	title = {A {Categorical} {Framework} for {Modeling} with {Stock} and {Flow} {Diagrams}},
	isbn = {978-3-031-40805-2},
	url = {https://doi.org/10.1007/978-3-031-40805-2_8},
	abstract = {Stock and flow diagrams are already an important tool in epidemiology, but category theory lets us go further and treat these diagrams as mathematical entities in their own right. In this chapter we use communicable disease models created with our software, StockFlow.jl, to explain the benefits of the categorical approach. We first explain the category of stock-flow diagrams and note the clear separation between the syntax of these diagrams and their semantics, demonstrating three examples of semantics already implemented in the software: ODEs, causal loop diagrams, and system structure diagrams. We then turn to two methods for building large stock-flow diagrams from smaller ones in a modular fashion: composition and stratification. Finally, we introduce the open-source ModelCollab software for diagram-based collaborative modeling. The graphical user interface of this web-based software lets modelers take advantage of the ideas discussed here without any knowledge of their categorical foundations.},
	language = {en},
	urldate = {2024-05-01},
	booktitle = {Mathematics of {Public} {Health}: {Mathematical} {Modelling} from the {Next} {Generation}},
	publisher = {Springer International Publishing},
	author = {Baez, John C. and Li, Xiaoyan and Libkind, Sophie and Osgood, Nathaniel D. and Redekopp, Eric},
	editor = {David, Jummy and Wu, Jianhong},
	year = {2023},
	doi = {10.1007/978-3-031-40805-2_8},
	pages = {175--207}
}

@article{baez2017compositional,
  title={A compositional framework for reaction networks},
  author={Baez, John C and Pollard, Blake S},
  journal={Reviews in Mathematical Physics},
  volume={29},
  number={09},
  pages={1750028},
  year={2017},
  publisher={World Scientific}
}

@article{balabdaoui2020age,
  title={Age-stratified discrete compartment model of the {COVID-19} epidemic with application to {Switzerland}},
  author={Balabdaoui, Fadoua and Mohr, Dirk},
  journal={Scientific reports},
  volume={10},
  number={1},
  pages={1--12},
  year={2020},
  publisher={Nature Publishing Group}
}

@article{chang2022stochastic,
  title={A Stochastic Multi-Strain {SIR} Model with Two-Dose Vaccination Rate},
  author={Chang, Yen-Chang and Liu, Ching-Ti},
  journal={Mathematics},
  volume={10},
  number={11},
  pages={1804},
  year={2022},
  publisher={MDPI}
}

@article{currie2020simulation,
  title={How simulation modelling can help reduce the impact of {COVID-19}},
  author={Currie, Christine SM and Fowler, John W and Kotiadis, Kathy and Monks, Thomas and Onggo, Bhakti Stephan and Robertson, Duncan A and Tako, Antuela A},
  journal={Journal of Simulation},
  volume={14},
  number={2},
  pages={83--97},
  year={2020},
  publisher={Taylor \& Francis}
}

@misc{enserink2020covid,
  title={With {COVID-19}, modeling takes on life and death importance},
  author={Enserink, Martin and Kupferschmidt, Kai},
  year={2020},
  publisher={American Association for the Advancement of Science}
}

@article{fields2021age,
  title={Age-stratified transmission model of {COVID-19} in {Ontario} with human mobility during pandemic's first wave},
  author={Fields, R and Humphrey, L and Flynn-Primrose, D and Mohammadi, Z and Nahirniak, M and Thommes, EW and Cojocaru, MG},
  journal={Heliyon},
  volume={7},
  number={9},
  pages={e07905},
  year={2021},
  publisher={Elsevier}
}

@article{fong2018seven,
  title={Seven sketches in compositionality: An invitation to applied category theory},
  author={Fong, Brendan and Spivak, David I},
  journal={arXiv preprint arXiv:1803.05316},
  year={2018}
}

@article{friston2020dynamic,
  title={Dynamic causal modelling of {COVID-19}},
  author={Friston, Karl J and Parr, Thomas and Zeidman, Peter and Razi, Adeel and Flandin, Guillaume and Daunizeau, Jean and Hulme, Ollie J and Billig, Alexander J and Litvak, Vladimir and Moran, Rosalyn J and others},
  journal={Wellcome open research},
  volume={5},
  year={2020},
  publisher={The Wellcome Trust}
}

@article{gog2002dynamics,
  title={Dynamics and selection of many-strain pathogens},
  author={Gog, Julia R and Grenfell, Bryan T},
  journal={Proceedings of the National Academy of Sciences},
  volume={99},
  number={26},
  pages={17209--17214},
  year={2002},
  publisher={National Acad Sciences}
}

 @misc{algebraicjulia,
  author       = {Micah Halter and
                  Andrew Baas and
                  Evan Patterson and
                  James and
                  Tyler Hanks and
                  Pietro Monticone},
  title        = {{AlgebraicJulia}/{AlgebraicPetri.jl}: v0.7.2},
  month        = jul,
  year         = 2022,
  publisher    = {Zenodo},
  version      = {v0.7.2},
  doi          = {10.5281/zenodo.6928476},
  url          = {https://doi.org/10.5281/zenodo.6928476}
}

@article{koyama2020emergence,
  title={Emergence of drift variants that may affect {COVID-19} vaccine development and antibody treatment},
  author={Koyama, Takahiko and Weeraratne, Dilhan and Snowdon, Jane L and Parida, Laxmi},
  journal={Pathogens},
  volume={9},
  number={5},
  pages={324},
  year={2020},
  publisher={MDPI}
}

@article{kryazhimskiy2007state,
  title={On state-space reduction in multi-strain pathogen models, with an application to antigenic drift in influenza {A}},
  author={Kryazhimskiy, Sergey and Dieckmann, Ulf and Levin, Simon A and Dushoff, Jonathan},
  journal={PLoS Computational Biology},
  volume={3},
  number={8},
  pages={e159},
  year={2007},
  publisher={Public Library of Science San Francisco, USA}
}

@article{lavielle2020extension,
  title={Extension of a {SIR} model for modelling the propagation of {COVID-19} in several countries.},
  author={Lavielle, Marc and Faron, Matthieu and Zeitoun, Jean-David and others},
  journal={medRxiv},
  year={2020},
  publisher={Cold Spring Harbor Laboratory Press}
}

@article{leontitsis2021seahir,
  title={{SEAHIR}: A specialized compartmental model for {COVID-19}},
  author={Leontitsis, Alexandros and Senok, Abiola and Alsheikh-Ali, Alawi and Al Nasser, Younus and Loney, Tom and Alshamsi, Aamena},
  journal={International journal of environmental research and public health},
  volume={18},
  number={5},
  pages={2667},
  year={2021},
  publisher={MDPI}
}

@article{libkind2021operadic,
  title={Operadic modeling of dynamical systems: mathematics and computation},
  author={Libkind, Sophie and Baas, Andrew and Patterson, Evan and Fairbanks, James},
  journal={arXiv preprint arXiv:2105.12282},
  year={2021}
}

@article{Libkind2022an,
  doi = {10.48550/ARXIV.2203.16345},
  url = {https://arxiv.org/abs/2203.16345},
  author = {Libkind, Sophie and Baas, Andrew and Halter, Micah and Patterson, Evan and Fairbanks, James},
  title = {An Algebraic Framework for Structured Epidemic Modeling},
  publisher = {arXiv},
  journal = {arXiv preprint arXiv:2203.16345},
  year = {2022},
  copyright = {Creative Commons Attribution 4.0 International}
}

@article{lofgren2014mathematical,
  title={Mathematical models: A key tool for outbreak response},
  author={Lofgren, Eric T and Halloran, M Elizabeth and Rivers, Caitlin M and Drake, John M and Porco, Travis C and Lewis, Bryan and Yang, Wan and Vespignani, Alessandro and Shaman, Jeffrey and Eisenberg, Joseph NS and others},
  journal={Proceedings of the National Academy of Sciences},
  volume={111},
  number={51},
  pages={18095--18096},
  year={2014},
  publisher={National Acad Sciences}
}

@article{massonis2021structural,
  title={Structural identifiability and observability of compartmental models of the {COVID-19} pandemic},
  author={Massonis, Gemma and Banga, Julio R and Villaverde, Alejandro F},
  journal={Annual reviews in control},
  volume={51},
  pages={441--459},
  year={2021},
  publisher={Elsevier}
}

@article{mcbryde2020role,
  title={Role of modelling in {COVID-19} policy development},
  author={McBryde, Emma S and Meehan, Michael T and Adegboye, Oyelola A and Adekunle, Adeshina I and Caldwell, Jamie M and Pak, Anton and Rojas, Diana P and Williams, Bridget M and Trauer, James M},
  journal={Paediatric respiratory reviews},
  volume={35},
  pages={57--60},
  year={2020},
  publisher={Elsevier}
}

@book{roberts2009applied,
  title={Applied Combinatorics},
  author={Roberts, Fred S and Tesman, Barry},
  year={2009},
  edition = {2},
  publisher={Taylor and Francis}
}


@article{vasireddy2021review,
  title={Review of {COVID-19} variants and {COVID-19} vaccine efficacy: what the clinician should know?},
  author={Vasireddy, Deepa and Vanaparthy, Rachana and Mohan, Gisha and Malayala, Srikrishna Varun and Atluri, Paavani},
  journal={Journal of Clinical Medicine Research},
  volume={13},
  number={6},
  pages={317},
  year={2021},
  publisher={Elmer Press}
}


@book{walter1999compartmental,
  title={Compartmental modeling with networks},
  author={Walter, Gilbert G and Contreras, Martha},
  year={1999},
  publisher={Springer Science \& Business Media}
}

@article{williams2021localization,
  title={Localization, epidemic transitions, and unpredictability of multistrain epidemics with an underlying genotype network},
  author={Williams, Blake JM and St-Onge, Guillaume and H{\'e}bert-Dufresne, Laurent},
  journal={PLoS Computational Biology},
  volume={17},
  number={2},
  pages={e1008606},
  year={2021},
  publisher={Public Library of Science San Francisco, CA USA}
}

@article{worden2017products,
  title={Products of compartmental models in epidemiology},
  author={Worden, Lee and Porco, Travis C},
  journal={Computational and Mathematical Methods in Medicine},
  volume={2017},
  pages = {e8613878},
  doi = {10.1155/2017/8613878},
  year={2017},
  publisher={Hindawi}
}

@article{savageau1988introduction,
  title={Introduction to {S}-systems and the underlying power-law formalism},
  author={Savageau, Michael A},
  journal={Mathematical and Computer Modelling},
  volume={11},
  pages={546--551},
  year={1988},
  publisher={Elsevier}
}


@article{voit1988recasting,
  title={Recasting nonlinear models as {S}-systems},
  author={Voit, Eberhard O},
  journal={Mathematical and Computer Modelling},
  volume={11},
  pages={140--145},
  year={1988},
  publisher={Elsevier}
}

@article{voit1990s,
  title={S-system modelling of endemic infections},
  author={Voit, EO},
  journal={Computers \& Mathematics with Applications},
  volume={20},
  number={4-6},
  pages={161--173},
  year={1990},
  publisher={Elsevier}
}

@article{dietz1995structured,
  title = {A Structured Epidemic Model Incorporating Geographic Mobility among Regions},
  author = {Dietz, Klaus and Sattenspiel, Lisa},
  year = {1995},
  month = aug,
  journal = {Mathematical Biosciences},
  volume = {128},
  number = {1-2},
  pages = {71--91},
  doi = {10.1016/0025-5564(94)00068-B},
  urldate = {2010-01-01}
}


@misc{mohammadi2023importation,
	title = {Importation models for travel-related {SARS}-{CoV}-2 cases reported in {Newfoundland} and {Labrador} during the {COVID}-19 pandemic},
	copyright = {© 2023, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NoDerivs 4.0 International), CC BY-ND 4.0, as described at http://creativecommons.org/licenses/by-nd/4.0/},
	url = {https://www.medrxiv.org/content/10.1101/2023.06.08.23291136v1},
	doi = {10.1101/2023.06.08.23291136},
	abstract = {During the COVID-19 pandemic there was substantial variation between countries in the severity of the travel restrictions implemented suggesting a need for better importation models. Data to evaluate the accuracy of importation models is available for the Canadian province of Newfoundland and Labrador (NL; September 2020 to June 2021) as arriving travelers were frequently tested for SARS-CoV-2 and travel-related cases were reported. Travel volume to NL was estimated from flight data, and travel declaration forms completed at entry to Canada, and at entry to NL during the pandemic. We found that during the pandemic travel to NL decreased by 82\%, the percentage of travelers arriving from Québec decreased (from 14 to 4\%), and from Alberta increased (from 7 to 17\%). We derived and validated an epidemiological model predicting the number of travelers testing positive for SARS-CoV-2 after arrival in NL, but found that statistical models with less description of SARS-CoV-2 epidemiology, and with parameters fitted from the validation data more accurately predicted the daily number of travel-related cases reported in NL originating from Canada (R2 = 0.55, ΔAICc = 137). Our results highlight the importance of testing travelers and reporting travel-related cases as these data are needed for importation models to support public health decisions.
Significance Importation models consider epidemiology and inbound travel volumes to predict the arrival rate of infected travelers to a jurisdiction, and are used to guide travel restriction recommendations during outbreaks. A limitation of many importation models are that they are not validated with ‘real world’ data. Using data from the province of Newfoundland and Labrador (NL), Canada, we develop an importation model that considers travel volumes estimated from travel declaration forms completed by arriving travelers, and SARS-CoV-2 epidemiology. We validate the model using the number of travel-related cases reported from September 2020 to May 2021 in NL. Our results highlight the importance of reporting travel-related cases, and the need for detailed travel and epidemiological data to formulate reliable importation models.},
	language = {en},
	urldate = {2023-06-27},
	publisher = {medRxiv},
	author = {Mohammadi, Zahra and Cojocaru, Monica and Arino, Julien and Hurford, Amy},
	month = jun,
	year = {2023},
	note = {Pages: 2023.06.08.23291136},
	file = {Full Text PDF:/home/bolker/Documents/zotero_new/storage/KHQC4QVI/Mohammadi et al. - 2023 - Importation models for travel-related SARS-CoV-2 c.pdf:application/pdf},
}


@article{gharouni2022testing,
	title = {Testing and {Isolation} {Efficacy}: {Insights} from a {Simple} {Epidemic} {Model}},
	volume = {84},
	issn = {0092-8240, 1522-9602},
	shorttitle = {Testing and {Isolation} {Efficacy}},
	url = {https://link.springer.com/10.1007/s11538-022-01018-2},
	doi = {10.1007/s11538-022-01018-2},
	abstract = {Abstract
            
              Testing individuals for pathogens can affect the spread of epidemics. Understanding how individual-level processes of sampling and reporting test results can affect community- or population-level spread is a dynamical modeling question. The effect of testing processes on epidemic dynamics depends on factors underlying implementation, particularly testing intensity and on whom testing is focused. Here, we use a simple model to explore how the individual-level effects of testing might directly impact population-level spread. Our model development was motivated by the COVID-19 epidemic, but has generic epidemiological and testing structures. To the classic SIR framework we have added a
              per capita
              testing intensity, and compartment-specific testing weights, which can be adjusted to reflect different testing emphases—surveillance, diagnosis, or control. We derive an analytic expression for the relative reduction in the basic reproductive number due to testing, test-reporting and related isolation behaviours. Intensive testing and fast test reporting are expected to be beneficial at the community level because they can provide a rapid assessment of the situation, identify hot spots, and may enable rapid contact-tracing. Direct effects of fast testing at the individual level are less clear, and may depend on how individuals’ behaviour is affected by testing information. Our simple model shows that under some circumstances both increased testing intensity and faster test reporting can
              reduce
              the effectiveness of control, and allows us to explore the conditions under which this occurs. Conversely, we find that focusing testing on infected individuals always acts to increase effectiveness of control.},
	language = {en},
	number = {6},
	urldate = {2022-05-13},
	journal = {Bulletin of Mathematical Biology},
	author = {Gharouni, Ali and Abdelmalek, Fred M. and Earn, David J. D. and Dushoff, Jonathan and Bolker, Benjamin M.},
	month = jun,
	year = {2022},
	pages = {66},
	file = {Gharouni et al. - 2022 - Testing and Isolation Efficacy Insights from a Si.pdf:/home/bolker/Documents/zotero_new/storage/VX96RMZE/Gharouni et al. - 2022 - Testing and Isolation Efficacy Insights from a Si.pdf:application/pdf},
}

@article{grenande85,
  author =        {B. T. Grenfell and R. M. Anderson},
  journal =       {Journal of Hygiene (Cambridge)},
  pages =         {419--436},
  title =         {The estimation of age-related rates of infection from
                   case notifications and serological data},
  volume =        {95},
  year =          {1985},
}

@article{andemay85,
  author =        {R. M. Anderson and R. M. May},
  journal =       {Journal of Hygiene (Cambridge)},
  pages =         {365--436},
  title =         {Age-related changes in the rate of disease
                   transmission: implications for the design of
                   vaccination programmes},
  volume =        {94},
  year =          {1985},
}

@book{andemaybook,
  author =        {Roy M. Anderson and Robert M. May},
  publisher =     {Oxford University Press},
  title =         {Infectious Diseases of Humans: Dynamics and Control},
  year =          {1992},
}


@article{LeviEarn22,
   Author = {Zachary Levine and David J. D. Earn},
   Title = {Face Masking and {COVID-19}: {Potential} effects of variolation on transmission dynamics},
   Journal = {Journal of the Royal Society of London, Interface},
   Volume = {19},
   Pages = {20210781},
   doi = {10.1098/rsif.2021.0781},
   url = {https://dx.doi.org/10.1098/rsif.2021.0781},
   Year = {2022}
}


@incollection{Earn2008,
	address = {Berlin, Heidelberg},
	title = {A {Light} {Introduction} to {Modelling} {Recurrent} {Epidemics}},
	isbn = {978-3-540-78911-6},
	url = {https://doi.org/10.1007/978-3-540-78911-6_1},
	abstract = {Epidemics of many infectious diseases occur periodically. Why?},
	language = {en},
	urldate = {2024-04-26},
	booktitle = {Mathematical {Epidemiology}},
	publisher = {Springer},
	author = {Earn, David J. D.},
	editor = {Brauer, Fred and van den Driessche, Pauline and Wu, Jianhong},
	year = {2008},
	doi = {10.1007/978-3-540-78911-6_1},
	keywords = {Basic Reproduction Number, Epidemic Model, Infectious Period, Phase Portrait, York City},
	pages = {3--17}
}

@article{xiaMeasles2004,
	title = {Measles {Metapopulation} {Dynamics}: {A} {Gravity} {Model} for {Epidemiological} {Coupling} and {Dynamics}},
	volume = {164},
	shorttitle = {Measles {Metapopulation} {Dynamics}},
	doi = {10.1086/422341},
	number = {2},
	journal = {American Naturalist},
	author = {Xia, Y. and Bjørnstad, O. N. and Grenfell, B. T.},
	year = {2004},
	pages = {267--281},
	file = {Google Scholar Linked Page:/home/bolker/Documents/zotero_new/storage/D5TUV2MR/422341.html:text/html},
}