References
Becker, A. D., & Grenfell, B. T. (2017). tsiR: An R package for
time-series Susceptible-Infected-Recovered models of epidemics. PLOS
ONE, 12(9), e0185528. https://doi.org/10.1371/journal.pone.0185528
Bettencourt, L. M. A., & Ribeiro, R. M. (2008). Real Time Bayesian
Estimation of the Epidemic Potential of Emerging Infectious Diseases.
PLOS ONE, 3(5), e2185. https://doi.org/10.1371/journal.pone.0002185
Bishai, D., Brenzel, L., & Padula, W. (Eds.). (2023). Handbook
of applied health economics in vaccines. Oxford University Press.
https://doi.org/10.1093/oso/9780192896087.001.0001
Bradshaw, W. J., Alley, E. C., Huggins, J. H., Lloyd, A. L., &
Esvelt, K. M. (2021). Bidirectional contact tracing could dramatically
improve COVID-19 control. Nature Communications,
12(1), 232. https://doi.org/10.1038/s41467-020-20325-7
Breban, R., Vardavas, R., & Blower, S. (2007). Theory versus Data:
How to Calculate R0? PLOS ONE, 2(3), e282. https://doi.org/10.1371/journal.pone.0000282
Cauchemez, S., Bhattarai, A., Marchbanks, T. L., Fagan, R. P., Ostroff,
S., Ferguson, N. M., Swerdlow, D., Pennsylvania H1N1 working group, the,
Sodha, S. V., Moll, M. E., Angulo, F. J., Palekar, R., Archer, W. R.,
& Finelli, L. (2011). Role of social networks in shaping disease
transmission during a community outbreak of 2009 H1N1 pandemic
influenza. Proceedings of the National Academy of Sciences,
108(7), 2825–2830. https://doi.org/10.1073/pnas.1008895108
Chitnis, N. (2017). Introduction to SEIR Models.
Christley, R. M., Mort, M., Wynne, B., Wastling, J. M., Heathwaite, A.
L., Pickup, R., Austin, Z., & Latham, S. M. (2013).
“Wrong, but Useful”: Negotiating Uncertainty
in Infectious Disease Modelling. PLOS ONE, 8(10),
e76277. https://doi.org/10.1371/journal.pone.0076277
Cori, A., Ferguson, N. M., Fraser, C., & Cauchemez, S. (2013). A new
framework and software to estimate time-varying reproduction numbers
during epidemics. American Journal of Epidemiology,
178(9), 1505–1512. https://doi.org/10.1093/aje/kwt133
Delamater, P. L., Street, E. J., Leslie, T. F., Yang, Y. T., &
Jacobsen, K. H. (2019). Complexity of the basic reproduction number
(R0) - volume 25, number 1january 2019 - emerging
infectious diseases journal - CDC. https://doi.org/10.3201/eid2501.171901
Diekmann, O., Heesterbeek, J. A. P., & Metz, J. A. J. (1990). On the
definition and the computation of the basic reproduction ratio R0 in
models for infectious diseases in heterogeneous populations. Journal
of Mathematical Biology, 28(4), 365–382. https://doi.org/10.1007/BF00178324
Driessche, P. van den. (2017). Reproduction numbers of infectious
disease models. Infectious Disease Modelling, 2(3),
288–303. https://doi.org/10.1016/j.idm.2017.06.002
Driessche, P. van den, & Watmough, J. (2002). Reproduction numbers
and sub-threshold endemic equilibria for compartmental models of disease
transmission. Mathematical Biosciences, 180(1), 29–48.
https://doi.org/10.1016/S0025-5564(02)00108-6
Du, Z., Zhang, W., Zhang, D., Yu, S., & Hao, Y. (2017). Estimating
the basic reproduction rate of HFMD using the time series SIR model in
Guangdong, China. PLOS ONE, 12(7), e0179623. https://doi.org/10.1371/journal.pone.0179623
Finkenstädt, B. F., & Grenfell, B. T. (2000). Time series modelling
of childhood diseases: A dynamical systems approach. Journal of the
Royal Statistical Society Series C: Applied Statistics,
49(2), 187–205. https://doi.org/10.1111/1467-9876.00187
Goeree, R., & Diaby, V. (2013). Introduction to health economics and
decision-making: Is economics relevant for the frontline clinician?
Best Practice & Research Clinical Gastroenterology,
27(6), 831–844. https://doi.org/10.1016/j.bpg.2013.08.016
Gostic, K. M., McGough, L., Baskerville, E. B., Abbott, S., Joshi, K.,
Tedijanto, C., Kahn, R., Niehus, R., Hay, J. A., Salazar, P. M. D.,
Hellewell, J., Meakin, S., Munday, J. D., Bosse, N. I., Sherrat, K.,
Thompson, R. N., White, L. F., Huisman, J. S., Scire, J., … Cobey, S.
(2020). Practical considerations for measuring the effective
reproductive number, Rt. PLOS Computational Biology,
16(12), e1008409. https://doi.org/10.1371/journal.pcbi.1008409
Green, W. D., Ferguson, N. M., & Cori, A. (2022). Inferring the
reproduction number using the renewal equation in heterogeneous
epidemics. Journal of The Royal Society Interface,
19(188), 20210429. https://doi.org/10.1098/rsif.2021.0429
Heeg, B. M. S., Damen, J., Buskens, E., Caleo, S., Charro, F. de, &
Hout, B. A. van. (2008). Modelling Approaches.
PharmacoEconomics, 26(8), 633–648. https://doi.org/10.2165/00019053-200826080-00002
Hilborn, R., & Mangel, M. (1997). The ecological detective:
Confronting models with data. Princeton University Press. https://www.jstor.org/stable/j.ctt24hqnx
Jit, M., & Brisson, M. (2011). Modelling the Epidemiology of
Infectious Diseases for Decision Analysis. PharmacoEconomics,
29(5), 371–386. https://doi.org/10.2165/11539960-000000000-00000
Jones, J. H. (2007). Notes on R0. California: Department of
Anthropological Sciences, 323, 1–19.
Keeling, M. J. (2006). State-of-science review: Predictive and
real-time epidemiological modelling. S9. DTI.
Keeling, M. J., & Rohani, P. (2011). Modeling Infectious
Diseases in Humans and Animals. Princeton University Press. https://doi.org/10.1515/9781400841035
Kenah, E., Lipsitch, M., & Robins, J. M. (2008). Generation interval
contraction and epidemic data analysis. Mathematical
Biosciences, 213(1), 71–79. https://doi.org/10.1016/j.mbs.2008.02.007
Kim, S.-Y., & Goldie, S. J. (2008). Cost-Effectiveness Analyses of
Vaccination Programmes. PharmacoEconomics, 26(3),
191–215. https://doi.org/10.2165/00019053-200826030-00004
Knight, J., & Mishra, S. (2020). Estimating effective reproduction
number using generation time versus serial interval, with application to
covid-19 in the greater toronto area, canada. Infectious Disease
Modelling, 5, 889–896. https://doi.org/10.1016/j.idm.2020.10.009
Kuntz, K., Sainfort, F., Butler, M., Taylor, B., Kulasingam, S.,
Gregory, S., Mann, E., Anderson, J. M., & Kane, R. L. (2013).
Decision and Simulation Modeling Alongside Systematic Reviews.
Agency for Healthcare Research; Quality (US). https://www.ncbi.nlm.nih.gov/books/NBK127478/
Meyerowitz-Katz, G., & Merone, L. (2020). A systematic review and
meta-analysis of published research data on COVID-19 infection fatality
rates. International Journal of Infectious Diseases,
101, 138–148. https://doi.org/10.1016/j.ijid.2020.09.1464
Nelson, K. E., & Williams, C. M. (2014). Infectious Disease
Epidemiology: Theory and Practice. Jones & Bartlett Publishers.
Oxford College of Emory University. (n.d.). The connection between
the poisson and binomial distributions. https://math.oxford.emory.edu/site/math117/connectingPoissonAndBinomial/
Pandit, J. J. (2020). Managing the R0 of COVID-19: mathematics fights
back. Anaesthesia, 75(12), 1643–1647. https://doi.org/10.1111/anae.15151
Park, S. W., Li, M., Metcalf, C. J. E., Grenfell, B. T., & Dushoff,
J. (2023). Immune boosting bridges leaky and polarized vaccination
models. medRxiv. https://doi.org/10.1101/2023.07.14.23292670
Pollard, A. J., & Bijker, E. M. (2021). A guide to vaccinology: from
basic principles to new developments. Nature Reviews
Immunology, 21(2), 83–100. https://doi.org/10.1038/s41577-020-00479-7
Prieto, L., & Sacristán, J. A. (2003). Problems and solutions in
calculating quality-adjusted life years (QALYs). Health and Quality
of Life Outcomes, 1(1), 80. https://doi.org/10.1186/1477-7525-1-80
Rui, J., Li, K., Wei, H., Guo, X., Zhao, Z., Wang, Y., Song, W.,
Abudunaibi, B., & Chen, T. (2024). MODELS: A six-step framework for
developing an infectious disease model. Infectious Diseases of
Poverty, 13(1), 30. https://doi.org/10.1186/s40249-024-01195-3
Sender, R., Bar-On, Y., Park, S. W., Noor, E., Dushoff, J., & Milo,
R. (2022). The unmitigated profile of COVID-19 infectiousness.
eLife, 11, e79134. https://doi.org/10.7554/eLife.79134
Severens, J. L., & Milne, R. J. (2004). Discounting Health Outcomes
in Economic Evaluation: The Ongoing Debate. Value in Health,
7(4), 397–401. https://doi.org/10.1111/j.1524-4733.2004.74002.x
Shim, E., & Galvani, A. P. (2012). Distinguishing vaccine efficacy
and effectiveness. Vaccine, 30(47), 6700–6705. https://doi.org/10.1016/j.vaccine.2012.08.045
Sittimart, M., Rattanavipapong, W., Mirelman, A. J., Hung, T. M., Dabak,
S., Downey, L. E., Jit, M., Teerawattananon, Y., & Turner, H. C.
(2024). An overview of the perspectives used in health economic
evaluations. Cost Effectiveness and Resource Allocation,
22(1), 41. https://doi.org/10.1186/s12962-024-00552-1
Smith, J. S., & Sturrock, D. T. (2023). Simio and simulation -
modeling, analysis, applications - 6th edition. https://textbook.simio.com/SASMAA6/
Texier, G., Farouh, M., Pellegrin, L., Jackson, M. L., Meynard, J.-B.,
Deparis, X., & Chaudet, H. (2016). Outbreak definition by change
point analysis: a tool for public health decision? BMC Medical
Informatics and Decision Making, 16(1), 33. https://doi.org/10.1186/s12911-016-0271-x
Turner, H. C., Archer, R. A., Downey, L. E., Isaranuwatchai, W.,
Chalkidou, K., Jit, M., & Teerawattananon, Y. (2021). An
introduction to the main types of economic evaluations used for
informing priority setting and resource allocation in healthcare: Key
features, uses, and limitations. Frontiers in Public Health,
9. https://www.frontiersin.org/articles/10.3389/fpubh.2021.722927
Vegvari, C., Abbott, S., Ball, F., Brooks-Pollock, E., Challen, R.,
Collyer, B. S., Dangerfield, C., Gog, J. R., Gostic, K. M., Heffernan,
J. M., Hollingsworth, T. D., Isham, V., Kenah, E., Mollison, D.,
Panovska-Griffiths, J., Pellis, L., Roberts, M. G., Scalia Tomba, G.,
Thompson, R. N., & Trapman, P. (2022). Commentary on the use of the
reproduction number R during the COVID-19 pandemic. Statistical
Methods in Medical Research, 31(9), 1675–1685. https://doi.org/10.1177/09622802211037079
Verani, J. R., Baqui, A. H., Broome, C. V., Cherian, T., Cohen, C.,
Farrar, J. L., Feikin, D. R., Groome, M. J., Hajjeh, R. A., Johnson, H.
L., Madhi, S. A., Mulholland, K., O’Brien, K. L., Parashar, U. D.,
Patel, M. M., Rodrigues, L. C., Santosham, M., Scott, J. A., Smith, P.
G., … Zell, E. R. (2017). Case-control vaccine effectiveness studies:
Preparation, design, and enrollment of cases and controls.
Vaccine, 35(25), 3295–3302. https://doi.org/10.1016/j.vaccine.2017.04.037
Verguet, S., Johri, M., Morris, S. K., Gauvreau, C. L., Jha, P., &
Jit, M. (2015). Controlling measles using supplemental immunization
activities: A mathematical model to inform optimal policy.
Vaccine, 33(10), 1291–1296. https://doi.org/10.1016/j.vaccine.2014.11.050
Vynnycky, E., & White, R. (2010). An Introduction to Infectious
Disease Modelling. OUP Oxford.
Wallinga, J., & Lipsitch, M. (2006). How generation intervals shape
the relationship between growth rates and reproductive numbers.
Proceedings of the Royal Society B: Biological Sciences,
274(1609), 599–604. https://doi.org/10.1098/rspb.2006.3754
Wallinga, J., & Teunis, P. (2004). Different epidemic curves for
severe acute respiratory syndrome reveal similar impacts of control
measures. American Journal of Epidemiology, 160(6),
509–516. https://doi.org/10.1093/aje/kwh255
Whitehead, S. J., & Ali, S. (2010). Health outcomes in economic
evaluation: The QALY and utilities. British Medical Bulletin,
96(1), 5–21. https://doi.org/10.1093/bmb/ldq033
World Health Organization. (2013). Correlates of vaccine-induced
protection: Methods and implications.
Zachreson, C., Tobin, R., Szanyi, J., Walker, C., Cromer, D., Shearer,
F. M., Conway, E., Ryan, G., Cheng, A., McCaw, J. M., & Geard, N.
(2023). Individual variation in vaccine immune response can produce
bimodal distributions of protection. Vaccine, 41(45),
6630–6636. https://doi.org/10.1016/j.vaccine.2023.09.025