Disease Modeling References

A comprehensive bibliography organized by project, documenting the epidemiological literature, mathematical methods, and data sources that informed each modeling study.

General Methodology

Foundational texts and methods applicable across multiple projects.

• Brauer, F., van den Driessche, P., Wu, J (Eds.) (2008). Mathematical Epidemiology . Springer.
  Comprehensive textbook on compartmental modeling, parameter estimation, and epidemic theory.
• Storn, R., Price, K. (1997). Differential Evolution - A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces . Journal of Global Optimization, 11, 341-359.
  Global optimization algorithm used for parameter estimation in the Cuba HIV/AIDS model.

Cuban HIV/AIDS Model

Compartmental modeling of contact tracing effectiveness (1986-2000).

• De Arazoza, H., Lounes, R. (2002). A non-linear model for a sexually transmitted disease with contact tracing . IMA Journal of Mathematics Applied in Medicine and Biology, 19, 221-234.
  Primary data source: annual HIV cases, AIDS cases, and deaths in Cuba from 1986-2000.

Senegal Yellow Fever Outbreak (2002)

SEIR model with vaccination intervention and parameter estimation.

• Barnet, E. D. (2007). Yellow fever: epidemiology and prevention . Clinical Infectious Diseases, 44(6). 850-856.
  Overview of yellow fever epidemiology and transmission dynamics.
• Garske, T., Van Kerkhove, M. D., Yactayo, S., Ronveaux, O., Lewis, R. F., Staples, J. E., ... & Ferguson, N. M. (2014). Yellow Fever in Africa: estimating the burden of disease and impact of mass vaccination from outbreak and serological data . PLoS Medicine, 11(5), e1001638.
  Infectious period estimates and vaccination impact assessment methodology.
• Monath, T. P. (2012). Review of the risks and benefits of yellow fever vaccination including some new analyses . Expert Review of Vaccines, 11(4), 427-448.
  Vaccine efficacy data and safety profile.
• Monath, T. P. & Vasconcelos, P. F. (2015). Yellow Fever . Journal of Clinical Virology, 64, 160-173.
  Comprehensive review of clinical parameters including incubation period (6 days).
• Staples, J. E., Gershman, M., & Fischer, M. (2010). Yellow fever vaccine: recommendations of the Advisory Committee on Immunization Practices (ACIP) . MMWR Recommendations and Reports, 59(RR-7), 1-27.
  CDC vaccine guidelines and efficacy standards (≥95%).
• World Health Organization (2023). Yellow Fever . WHO Fact Sheets.
  Current epidemiology, outbreak data, and prevention strategies.

Measles Agent-Based Model in Schools

Individual-level simulation of measles transmission with spatial structure and vaccination clustering.

Epidemiological Parameters

• Centers for Disease Control and Prevention (CDC). Measles (Rubeola): For Healthcare Professionals . CDC Website.
  MMR vaccine efficacy (97% with 2 doses), infectious period (8 days), clinical parameters.
• Guerra, F. M., Bolotin, S., Lim, G., Heffernan, J., Deeks, S. L., Li, Y., & Crowcroft, N. S. (2017). The basic reproduction number (R₀) of measles: a systematic review . The Lancet Infectious Diseases, 17(12), e420-e428.
  Systematic review of R₀ estimates (12-18), latent period (~10 days), transmissibility parameters.
• Phadke, V. K., Bednarczyk, R. A., Salmon, D. A., & Omer, S. B. (2016). Association Between Vaccine Refusal and Vaccine-Preventable Diseases in the United States . JAMA, 315(11), 1149-1158.
  Evidence for vaccine refusal clustering and outbreak risk in undervaccinated communities.
• World Health Organization (2017). Measles vaccines: WHO position paper – April 2017 . Weekly Epidemiological Record, 92(17), 205-228.
  WHO vaccine efficacy standards, global epidemiology context, herd immunity thresholds.

Contact Network Studies

• Read, J. M., Edmunds, W. J., Riley, S., Lessler, J., & Cummings, D. A. (2012). Close encounters of the infectious kind: methods to measure social mixing behaviour . Epidemiology & Infection, 140(12), 2117-2130.
  Contact pattern measurement methodology, within-classroom vs between-classroom mixing ratios.
• Stehlé, J., Voirin, N., Barrat, A., Cattuto, C., Isella, L., Pinton, J. F., ... & Vanhems, P. (2011). High-resolution measurements of face-to-face contact patterns in a primary school . PLOS ONE, 6(8), e23176.
  Empirical contact network data from primary schools: ~20 within-classroom contacts/day, contact duration distributions.

Agent-Based Modeling Methodology

• Ferguson, N. M., Cummings, D. A., Fraser, C., Cajka, J. C., Cooley, P. C., & Burke, D. S. (2006). Strategies for mitigating an influenza pandemic . Nature, 442(7101), 448-452.
  Large-scale ABM implementation for pandemic planning, school closure intervention modeling.
• Halloran, M. E., Longini Jr, I. M., Nizam, A., & Yang, Y. (2002). Containing bioterrorist smallpox . Science, 298(5597), 1428-1432.
  Foundational work on ABM for vaccine-preventable diseases, intervention strategy evaluation.
• Kerr, C. C., Stuart, R. M., Mistry, D., Abeysuriya, R. G., Rosenfeld, K., Hart, G. R., ... & Klein, D. J. (2021). Covasim: an agent-based model of COVID-19 dynamics and interventions . PLOS Computational Biology, 17(7), e1009149.
  Modern ABM framework demonstrating network structure, stochastic dynamics, and intervention testing.
• Willem, L., Verelst, F., Bilcke, J., Hens, N., & Beutels, P. (2017). Lessons from a decade of individual-based models for infectious disease transmission: a systematic review (2006-2015) . BMC Infectious Diseases, 17(1), 612.
  Comprehensive review of ABM best practices, validation approaches, and methodological standards.

Behavioral Economics of Childhood Vaccination

SEIR-V compartmental model with game-theoretic decision-making and cost-effectiveness analysis.

Game Theory & Vaccination Behavior

• Bauch, C. T., & Earn, D. J. D. (2004). Vaccination and the theory of games . Proceedings of the National Academy of Sciences, 101(36), 13391-13394.
  Foundational paper on game-theoretic vaccination models; demonstrates Nash equilibrium below herd immunity threshold due to free-rider problem.
• Bauch, C. T. (2005). Imitation dynamics predict vaccinating behaviour . Proceedings of the Royal Society B: Biological Sciences, 272(1573), 1669-1675.
  Extends static game theory with dynamic learning; shows how vaccine coverage oscillates over time through social imitation.
• Bauch, C. T., & Bhattacharyya, S. (2012). Evolutionary game theory and social learning can determine how vaccine scares unfold . PLOS Computational Biology, 8(4), e1002452.
  Validates behavior-incidence models against UK pertussis and MMR vaccine scares; demonstrates strategic interactions drive vaccinating behavior.

Economic Analysis & Nash vs. Utilitarian Optima

• Galvani, A. P., Reluga, T. C., & Chapman, G. B. (2007). Long-standing influenza vaccination policy is in accord with individual self-interest but not with the utilitarian optimum . Proceedings of the National Academy of Sciences, 104(13), 5692-5697.
  Key paper comparing Nash equilibrium (individual self-interest) vs. utilitarian optimum (social welfare); shows CDC policy aligns with individual behavior rather than optimal public health outcomes.
• World Health Organization (2008). WHO Guide for standardization of economic evaluations of immunisation programmes . WHO Document Production Services.
  Standard methodology for vaccine cost-effectiveness analysis; defines ICER calculation, discount rates (3%), and GDP-based thresholds.
• Robinson, L. A., Hammitt, J. K., Chang, A. Y., & Resch, S. (2017). Understanding and improving the one and three times GDP per capita cost-effectiveness thresholds . PLOS ONE, 11(12), e0168512.
  Systematic review of DALY-based cost-effectiveness studies; validates WHO threshold methodology and discount rate standards.

Vaccine Hesitancy Framework

• MacDonald, N. E., & SAGE Working Group on Vaccine Hesitancy (2015). Vaccine hesitancy: Definition, scope and determinants . Vaccine, 33(34), 4161-4164.
  WHO SAGE official definition of vaccine hesitancy; introduces the "3 C's" model (Confidence, Complacency, Convenience).
• World Health Organization (2014). Report of the SAGE Working Group on Vaccine Hesitancy . WHO Strategic Advisory Group of Experts on Immunization.
  Comprehensive determinants matrix categorizing contextual, individual/group, and vaccine-specific influences on hesitancy.
• Dubé, E., Laberge, C., Guay, M., Bramadat, P., Roy, R., & Bettinger, J. A. (2013). Vaccine hesitancy: an overview . Human Vaccines & Immunotherapeutics, 9(8), 1763-1773.
  Review of psychological and social factors driving vaccine hesitancy; discusses risk perception, trust, and information processing.

Measles Epidemiology & Economics

• Guerra, F. M., Bolotin, S., Lim, G., Heffernan, J., Deeks, S. L., Li, Y., & Crowcroft, N. S. (2017). The basic reproduction number (R₀) of measles: a systematic review . The Lancet Infectious Diseases, 17(12), e420-e428.
  Systematic review establishing R₀ range (12-18), latent period (~10 days), infectious period (~8 days); critical for SEIR-V parameterization.
• Centers for Disease Control and Prevention (CDC). Measles (Rubeola): For Healthcare Professionals . CDC Website.
  Vaccine efficacy (93% first dose, 97% two doses), case fatality rate (0.1-0.2% in developed countries), complication rates.
• Zhou, F., Shefer, A., Wenger, J., Messonnier, M., Wang, L. Y., Lopez, A., ... & Seward, J. F. (2014). Economic evaluation of the routine childhood immunization program in the United States, 2009 . Pediatrics, 133(4), 577-585.
  Cost-benefit analysis showing $13.50 saved per $1 spent on MMR vaccine; source for vaccination and treatment cost parameters.
• Atwell, J. E., Van Otterloo, J., Zipprich, J., Winter, K., Harriman, K., Salmon, D. A., ... & Omer, S. B. (2013). Nonmedical vaccine exemptions and pertussis in California, 2010 . JAMA Pediatrics, 167(10), 929-934.
  Demonstrates link between vaccine refusal clustering and outbreak risk; motivates spatial heterogeneity in behavioral model.

DALYs & Health Impact Metrics

• Global Burden of Disease Study 2019. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019 . The Lancet, 396(10258), 1204-1222.
  Source for disability weights: measles mild (0.051), moderate (0.133), severe (0.280), complications (0.400).
• Salomon, J. A., Haagsma, J. A., Davis, A., de Noordhout, C. M., Polinder, S., Havelaar, A. H., ... & Vos, T. (2015). Disability weights for the Global Burden of Disease 2013 study . The Lancet Global Health, 3(11), e712-e723.
  Methodology for deriving disability weights through population surveys; validates GBD weight assignments.