Skip to main content Accessibility help
×
Hostname: page-component-857557d7f7-nhjpk Total loading time: 0 Render date: 2025-12-09T07:19:57.940Z Has data issue: false hasContentIssue false

Chapter 6 - Modeling Behavior During a Pandemic Using HIV as an Historical Analogy

from Part III - Health as Human Capital

Published online by Cambridge University Press:  28 November 2025

Victor Chernozhukov
Affiliation:
Massachusetts Institute of Technology
Johannes Hörner
Affiliation:
Yale University, Connecticut
Eliana La Ferrara
Affiliation:
Harvard University, Massachusetts
Iván Werning
Affiliation:
Massachusetts Institute of Technology
Get access

Summary

Every 5 years, the World Congress of the Econometric Society brings together scholars from around the world. Leading scholars present state-of-the-art overviews of their areas of research, offering newcomers access to key research in economics. Advances in Economics and Econometrics: Twelfth World Congress consists of papers and commentaries presented at the Twelfth World Congress of the Econometric Society. This two-volume set includes surveys and interpretations of key developments in economics and econometrics, and discussions of future directions for a variety of topics, covering both theory and application. The first volume addresses such topics as contract theory, industrial organization, health and human capital, as well as racial justice, while the second volume includes theoretical and applied papers on climate change, time-series econometrics, and causal inference. These papers are invaluable for experienced economists seeking to broaden their knowledge or young economists new to the field.

Information

Type
Chapter
Information
Advances in Economics and Econometrics
Twelfth World Congress
, pp. 176 - 212
Publisher: Cambridge University Press
Print publication year: 2025

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Book purchase

Temporarily unavailable

References

Aadland, D., Finnoff, D. C., and Huang, K. X. D. (2013). “Syphilis cycles,” The B.E. Journal of Economic Analysis and Policy, 14(1), 297348.10.1515/bejeap-2012-0060CrossRefGoogle Scholar
Acemoglu, D., Chernozhukov, V., Werning, I., and Whinston, M. D. (2020). “A multi-risk SIR model with optimally targeted lockdown.” NBER Working Paper No. 27102.Google Scholar
Adda, J. (2007). “Behavior towards health risks: An empirical study using the ‘mad cow’ crisis as an experiment,” Journal of Risk and Uncertainty, 35(3), 285305.10.1007/s11166-007-9026-5CrossRefGoogle Scholar
Adda, J. (2016). “Economic activity and the spread of viral diseases: Evidence from high frequency data,” Quarterly Journal of Economics, 131(2), 891941.10.1093/qje/qjw005CrossRefGoogle Scholar
Adhvaryu, A. (2014). “Learning, misallocation, and technology adoption: Evidence from new malaria therapy in Tanzania,” The Review of Economic Studies, 81(4), 13311365.10.1093/restud/rdu020CrossRefGoogle ScholarPubMed
Aguirregabiria, V., Gu, J., Luo, Y., and Mira, P. (2021). “Diffusion of Covid-19 in social and production networks: Simulation evidence from a dynamic model,” Annals of Economics and Statistics, 142, 179210.10.15609/annaeconstat2009.142.0179CrossRefGoogle Scholar
Ahuja, A., Athey, S., Baker, A., Budish, E., Castillo, J. C., Glennerster, R., et al. (2021). “Preparing for a pandemic: Accelerating vaccine availability.” NBER Working Paper No. 28492.Google Scholar
Allcott, H., Boxell, L., Conway, J., Gentzkow, M., Thaler, M., and Yang, D. Y. (2020). “Polarization and public health: Partisan differences in social distancing during the coronavirus pandemic,” Journal of Public Economics, 191, 104254.10.1016/j.jpubeco.2020.104254CrossRefGoogle Scholar
Alvarez, F. E., Argente, D., and Lippi, F. (2020). “A simple planning problem for COVID-19 lockdown.” NBER Working Paper No. 26981.Google Scholar
Andersen, M. (2020). “Early evidence on social distancing in response to COVID-19 in the United States.” SSRN Working Paper 3569368.Google Scholar
Arcidiacono, P., Sieg, H., and Sloan, F. (2007). “Living rationally under the volcano? An empirical analysis of heavy drinking and smoking,” International Economic Review, 48(1), 3765.10.1111/j.1468-2354.2007.00417.xCrossRefGoogle Scholar
Asher, J. (2018). “Forecasting Ebola with a regression transmission model,” Epidemics, 22, 5055.10.1016/j.epidem.2017.02.009CrossRefGoogle ScholarPubMed
Ashraf, B. N. (2020). “Socioeconomic conditions, government interventions and health outcomes during COVID-19,” Covid Economics, 37, 141162.Google Scholar
Atkeson, A. G., Kopecky, K., and Zha, T. (2020). “Behavior and the transmission of Covid-19.” Mimeo, University of California Los Angeles.Google Scholar
Atkeson, A. G., Kopecky, K., and Zha, T. (2021). “Behavior and the transmission of Covid-19,” AEA Papers and Proceedings, 111, 356360.10.1257/pandp.20211064CrossRefGoogle Scholar
Barrios, J. M., and Hochberg, Y. (2020). “Risk perception through the lens of politics in the time of the COVID-19 pandemic.” NBER Working Paper No. 27008.Google Scholar
Bauch, C. T. (2005). “Imitation dynamics predict vaccinating behaviour,” Proceedings of the Royal Society, Series B, 272(1573), 16691675.Google ScholarPubMed
Beigel, J. H., Tomashek, K. M., Dodd, L. E., Mehta, A. K., Zingman, B. S., Kalil, A. C., et al. (2020). “Remdesivir for the treatment of Covid-19 – final report,” New England Journal of Medicine, 383(19), 18131826.10.1056/NEJMoa2007764CrossRefGoogle ScholarPubMed
Berube, A., and Bateman, N. (2020). “Who are the workers already impacted by the COVID-19 recession?Metropolitan Policy Program Covid-19 Analysis, Brookings Institute.Google Scholar
Bhatraju, P. K., Ghassemieh, B. J., Nichols, M., Kim, R., Jerome, K. R., Nalla, A. K., et al. (2020). “Covid-19 in critically ill patients in the Seattle region – case series,” New England Journal of Medicine, 382(21), 20122022.10.1056/NEJMoa2004500CrossRefGoogle ScholarPubMed
Boppart, T., Harmenberg, K., Hassler, J., Krusell, P., and Olsson, J. (2020). Confronting Epidemics: The Need for Epi-econ IAMs. Stockholm: Konjunkturinstitutet.Google Scholar
Brooks, L. C., Ray, E. L., Bien, J., Bracher, J., Rumack, A., Tibshirani, R. J., and Reich, N. G. (2020). “Comparing ensemble approaches for short-term probabilistic COVID-19 forecasts in the U.S.” International Institute of Forecasters.Google Scholar
Brotherhood, L., Kircher, P., Santos, C., and Tertilt, M. (2020). “An economic model of the Covid-19 pandemic with young and old agents: Behavior, testing and policies.” Discussion Paper 175, Collaborative Research Center Transregio 224.Google Scholar
Card, D. (1999). “The causal effect of education on earnings.” In Ashenfelter, O. C. and Card, D. (eds.), Handbook of Labor Economics, vol. 3. Amsterdam: Elsevier, pp. 1801–1863.Google Scholar
Cawley, J., and Ruhm, C. J. (2011). “The economics of risky health behaviors.” In Pauly, M. V., McGuire, T. G., and Barros, P. P. (eds.), Handbook of Health Economics, vol. 2. Amsterdam: Elsevier, pp. 95–199.Google Scholar
Centers for Disease Control and Prevention (2014). “CDC announces winner of the ‘Predict the Influenza Season Challenge’.” https://www.cdc.gov/flu/news/predict-flu-challenge-winner.htm.Google Scholar
Centers for Disease Control and Prevention (2020). “FluSight: Flu forecasting.” https://www.cdc.gov/flu/weekly/flusight/index.html.Google Scholar
Chan, T. Y., and Hamilton, B. H. (2006). “Learning, private information, and the economic evaluation of randomized experiments,” Journal of Political Economy, 114(6), 9971040.10.1086/508239CrossRefGoogle Scholar
Chan, T. Y., Hamilton, B. H., and Papageorge, N. W. (2016). “Health, risky behaviour and the value of medical innovation for infectious disease,” Review of Economic Studies, 83(4), 14651510.10.1093/restud/rdv053CrossRefGoogle Scholar
Chang, R., Martínez, H., and Velasco, A. (2021). “Pandemics, incentives, and economic policy: A dynamic model.” NBER Working Paper No. 28636.Google Scholar
Chernozhukov, V., Kasaha, H., and Schrimpf, P. (2021). “Causal impact of masks, policies, behavior on early COVID-19 pandemic in the U.S,” Journal of Econometrics, 220(1), 2362.10.1016/j.jeconom.2020.09.003CrossRefGoogle ScholarPubMed
Chiou, L., and Tucker, C. (2020). “Social distancing, internet access and inequality.” NBER Working Paper No. 26982.Google Scholar
Chowell, G., Hincapie-Palacio, D., Ospina, J., Pell, B., Tariq, A., Dahal, S., et al. (2016). “Using phenomenological models to characterize transmissibility and forecast patterns and final burden of Zika epidemics,” Public Library of Science Currents, 8.Google ScholarPubMed
Crawford, G. S., and Shum, M. (2005). “Uncertainty and learning in pharmaceutical demand,” Econometrica, 73(4), 11371173.10.1111/j.1468-0262.2005.00612.xCrossRefGoogle Scholar
Cronin, C. J., Forsstrom, M. P., and Papageorge, N. W. (2020). “What good are treatment effects without treatment? Mental health and the reluctance to use talk therapy.” NBER Working Paper No. 27711.Google Scholar
Currie, J. (2009). “Healthy, wealthy, and wise: Socioeconomic status, poor health in childhood, and human capital development,” Journal of Economic Literature, 47(1), 87122.10.1257/jel.47.1.87CrossRefGoogle Scholar
Darden, M. (2017). “Smoking, expectations, and health: A dynamic stochastic model of lifetime smoking behavior,” Journal of Political Economy, 125(5), 14651522.10.1086/693394CrossRefGoogle Scholar
Darden, M. E., Dowdy, D., Gardner, L., Hamilton, B. H., Kopecky, K., Marx, M., Papageorge, N. W., et al. (2022). “Modeling to inform economy-wide pandemic policy: Bringing epidemiologists and economists together,” Health Economics, 31(7), 12911295.10.1002/hec.4527CrossRefGoogle ScholarPubMed
Dave, D., Friedson, A. I., Matsuzawa, K., and Sabia, J. J. (2021). “When do Shelter-in-place orders fight Covid-19 best? Policy heterogeneity across states and adoption time”, Economic Inquiry, 59(1), 2952.10.1111/ecin.12944CrossRefGoogle ScholarPubMed
Dee, T. S. (2004). “Are there civic returns to education?,” Journal of Public Economics, 88(9), 16971720.10.1016/j.jpubeco.2003.11.002CrossRefGoogle Scholar
DeLuca, S., Papageorge, N. W., and Kalish, E. (2020). “The unequal cost of social distancing.” Johns Hopkins University Coronavirus Resource Center. https://coronavirus.jhu.edu/from-our-experts/the-unequal-cost-of-social-distancing.Google Scholar
Dowd, J. B., Andriano, L., Brazel, D. M., Rotondi, V., Block, P., Ding, X., et al. (2020). “Demographic science aids in understanding the spread and fatality rates of COVID-19,” Proceedings of the National Academy of Sciences, 117(18), 96969698.10.1073/pnas.2004911117CrossRefGoogle ScholarPubMed
Eichenbaum, M. S., Rebelo, S., and Trabandt, M. (2021a). “The macroeconomics of epidemics.” NBER Working Paper No. 26882.Google Scholar
Eichenbaum, M. S., Rebelo, S., and Trabandt, M. (2021b). “The macroeconomics of testing and quarantining.” NBER Working Paper No. 27104.Google Scholar
Farboodi, M., Jarosch, G., and Shimer, R. (2020). “Internal and external effects of social distancing in a pandemic.” NBER Working Paper No. 27059.Google Scholar
Felsenstein, S., Herbert, J. A., McNamara, P. S., and Hedrich, C. M. (2020). “COVID-19: Immunology and treatment options,” Clinical Immunology, 215, 108448.10.1016/j.clim.2020.108448CrossRefGoogle ScholarPubMed
Fenichel, E. P., Castillo-Chavez, C., Ceddia, M. G., Chowell, G., Gonzalez Parra, P. A., Hickling, G. J., et al. (2011). “Adaptive human behavior in epidemiological models,” Proceedings of the National Academy of Sciences, 108(15), 63066311.10.1073/pnas.1011250108CrossRefGoogle ScholarPubMed
Fernández, R., Parsa, S., and Viarengo, M. (2019). “Coming out in America: AIDS, politics, and cultural change.” NBER Working Paper No. 25697.Google Scholar
Friedson, A. I., McNichols, D., Sabia, J. J., and Dave, D. (2020). “Did California’s Shelter-in-place order work? Early coronavirus-related public health effects.” NBER Working Paper No. 26992.Google Scholar
Funk, S., Bansal, S., Bauch, C. T., Eames, K. T. D., Edmunds, W. J., Galvani, A. P., and Klepac, P. (2015). “Nine challenges in incorporating the dynamics of behaviour in infectious diseases models,” Epidemics, 10, 2125.10.1016/j.epidem.2014.09.005CrossRefGoogle ScholarPubMed
Funk, S., Camacho, A., Kucharski, A. J., Eggo, R. M., and Edmunds, W. J. (2018). “Real-time forecasting of infectious disease dynamics with a stochastic semi-mechanistic model,” Epidemics, 22, 5661.10.1016/j.epidem.2016.11.003CrossRefGoogle ScholarPubMed
Galeotti, A., and Rogers, B. W. (2013). “Strategic immunization and group structure,” American Economic Journal: Microeconomics, 5(2), 132.Google Scholar
Gayle, H., Foege, W., Brown, L., and Kahn, B. (eds.) (2020). Framework for Equitable Allocation of COVID-19 Vaccine. Washington, DC: The National Academies Press.10.17226/25917CrossRefGoogle Scholar
Gersovitz, M., and Hammer, J. S. (2004). “The economical control of infectious diseases,” The Economic Journal, 114(492), 127.10.1046/j.0013-0133.2003.0174.xCrossRefGoogle Scholar
Gilleskie, D. B. (1998). “A dynamic stochastic model of medical care use and work absence,” Econometrica, 66(1), 145.10.2307/2998539CrossRefGoogle Scholar
Godlonton, S., Munthali, A., and Thornton, R. (2016). “Responding to risk: Circumcision, information, and HIV prevention,” Review of Economics and Statistics, 98(2), 333i–349.10.1162/REST_a_00516CrossRefGoogle Scholar
Goodkin-Gold, M., Kremer, M., Snyder, C. M., and Williams, H. L. (2020). “Optimal vaccine subsidies for endemic and epidemic diseases.” NBER Working Paper No. 28085.Google Scholar
Gould, E., and Kassa, M. (2020). “Young workers hit hard by the COVID-19 economy.” White Paper, Economic Policy Institute.Google Scholar
Greenwood, J., Kircher, P., Santos, C., and Tertilt, M. (2019). “An equilibrium model of the African HIV/AIDS epidemic,” Econometrica, 87(4), 10811113.10.3982/ECTA11530CrossRefGoogle Scholar
Grossman, M. (1972). “On the concept of health capital and the demand for health,” Journal of Political Economy, 80(2), 223255.10.1086/259880CrossRefGoogle Scholar
Grossman, M. (2006). “Education and nonmarket outcomes.” In Hanushek, E. A. and Welch, F. (eds.), Handbook of the Economics of Education. Amsterdam: Elsevier, pp. 577–633.Google Scholar
Gupta, S., Nguyen, T. D., Lozano Rojas, F., Raman, S., Lee, B., Bento, A., et al. (2020). “Tracking public and private response to the Covid-19 epidemic: Evidence from state and local government actions.” NBER Working Paper No. 27027.Google Scholar
Hall, R. E., Jones, C. I., and Klenow, P. J. (2020). “Trading off consumption and COVID-19 deaths.” NBER Working Paper No. 27340.Google Scholar
Hamilton, B. H., Hincapié, A., Miller, R. A., and Papageorge, N. W. (2018). “Innovation and diffusion of medical treatment.” NBER Working Paper No. 24577.Google Scholar
Hamilton, B. H., Hincapié, A., Kalish, E. C., and Papageorge, N. W. (2020a). “Innovation and health disparities during a pandemic: The case of HIV.” Mimeo, Johns Hopkins University.Google Scholar
Hamilton, B. H., Papageorge, N. W., and Zahn, M. V. (2020b). “Modeling an infectious disease from an individual dynamic discrete choice perspective.” Mimeo, Johns Hopkins University.Google Scholar
Hongru, D., Zahn, M., Loo, S., Alleman, T., Truelove, S., Patenaude, B., et al. (2024). “Modeling dynamic disease-behavior feedbacks for improved epidemic prediction and response.” medRxiv 2024-11.Google Scholar
Hsiang, S., Allen, D., Annan-Phan, S., Bell, K., Bolliger, I., Chong, T., et al. (2020). “The effect of large-scale anti-contagion policies on the COVID-19 pandemic,” Nature, 584(7820), 262267.10.1038/s41586-020-2404-8CrossRefGoogle ScholarPubMed
Ilin, C., Annan-Phan, S. E., Hui Tai, X., Mehra, S., Hsiang, S. M., and Blumenstock, J. E. (2020). “Public mobility data enables COVID-19 forecasting and management at local and global scales.” NBER Working Paper No. 28120.Google Scholar
Jentsch, P., Anand, M., and Bauch, C. T. (2020). “Prioritising COVID-19 vaccination in changing social and epidemiological landscapes.” Working Paper 2020.09.i25.20201889, medRxiv.10.1101/2020.09.25.20201889CrossRefGoogle Scholar
Joint United Nations Programme on HIV and AIDS (2020). “Fact sheet – World AIDS Day 2020.” Global HIV and AIDS Statistics.Google Scholar
Kaiser Family Foundation (2019). “The HIV/AIDS epidemic in the United States: The basics.” https://www.kff.org/hivaids/fact-sheet/the-hivaids-epidemic-in-the-united-states-the-basics/.Google Scholar
Kermack, W. O., and McKendrick, A. G. (1927). “A contribution to the mathematical theory of epidemics,” Proceedings of the Royal Society of London, Series A, 115(772), 700721.Google Scholar
Labgold, K., Hamid, S., Shah, S., Gandhi, N. R., Chamberlain, A., Khan, F., et al. (2021). “Estimating the unknown: Greater racial and ethnic disparities in COVID-19 burden after accounting for missing race and ethnicity data,” Epidemiology, 32(2), 157161.10.1097/EDE.0000000000001314CrossRefGoogle ScholarPubMed
Li, X.-Z., Yang, J., and Martcheva, M. (2020). Age Structured Epidemic Modeling. Berlin: Springer.10.1007/978-3-030-42496-1CrossRefGoogle Scholar
Lighter, J., Phillips, M., Hochman, S., Sterling, S., Johnson, D., Francois, F., and Stachel, A. (2020). “Obesity in patients younger than 60 years is a risk factor for COVID-19 hospital admission,” Clinical Infectious Diseases, 71(15), 896897.10.1093/cid/ciaa415CrossRefGoogle ScholarPubMed
Lochner, L., and Moretti, E. (2004). “The effect of education on crime: Evidence from prison inmates, arrests, and self-reports,” American Economic Review, 94(1), 155189.10.1257/000282804322970751CrossRefGoogle Scholar
Machin, S., Marie, O., and S. Vuji´c (2011). “The crime reducing effect of education,” The Economic Journal, 121(552), 463484.10.1111/j.1468-0297.2011.02430.xCrossRefGoogle Scholar
Manfredi, P., and D’Onofrio, A. (2013). Modeling the Interplay Between Human Behavior and the Spread of Infectious Diseases. Cham: Springer Science and Business Media.10.1007/978-1-4614-5474-8CrossRefGoogle Scholar
Martcheva, M. (2015). An Introduction to Mathematical Epidemiology. Berlin: Springer.10.1007/978-1-4899-7612-3CrossRefGoogle Scholar
McAdams, D. (2020). “Nash SIR: An economic-epidemiological model of strategic behavior during a viral epidemic,” Covid Economics, 16, 115134.Google Scholar
McLaren, J., and Wang, S. (2020). “Effects of reduced workplace presence on Covid-19 deaths: An instrumental-variables approach.” NBER Working Paper No. 28275.Google Scholar
Murray, E. J. (2020). “Epidemiology’s time of need: COVID-19 calls for epidemicrelated economics,” Journal of Economic Perspectives, 34(4), 105120.10.1257/jep.34.4.105CrossRefGoogle Scholar
Papageorge, N. W. (2016). “Why medical innovation is valuable: Health, human capital, and the labor market,” Quantitative Economics, 7(3), 671725.10.3982/QE459CrossRefGoogle Scholar
Papageorge, N. W., Pauley, G. C., Cohen, M., Wilson, T. E., Hamilton, B. H., and Pollak, R. A. (2019). “Health, human capital and domestic violence,” Journal of Human Resources, 56(4), 9971030.10.3368/jhr.56.4.1115-7543R5CrossRefGoogle ScholarPubMed
Papageorge, N. W., Zahn, M. V., Belot, M., van den Broek-Altenburg, E., Choi, S., Jamison, J. C., and Tripodi, E. (2021). “Socio-demographic factors associated with self-protecting behavior during the Covid-19 pandemic,” Journal of Population Economics, 34(2), 691738.10.1007/s00148-020-00818-xCrossRefGoogle ScholarPubMed
Poulson, M., Geary, A., Annesi, C., Allee, L., Kenzik, K., Sanchez, S., et al. (2021). “National disparities in COVID-19 outcomes between black and white Americans,” Journal of the National Medical Association, 113(2), 125132.10.1016/j.jnma.2020.07.009CrossRefGoogle ScholarPubMed
Ravi, K. (2020). “Ethnic disparities in COVID-19 mortality: Are comorbidities to blame?,” The Lancet, 396(10243), 22.10.1016/S0140-6736(20)31423-9CrossRefGoogle ScholarPubMed
Ray, E. L., Wattanachit, N., Niemi, J., Hannan Kanji, A., House, K., Cramer, E. Y., et al. (2020). “Ensemble forecasts of coronavirus disease 2019 (COVID-19) in the U.S.” Working Paper 2020.08.19.20177493, medRxiv.10.1101/2020.08.19.20177493CrossRefGoogle Scholar
Roosa, K., Lee, Y., Luo, R., Kirpich, A., Rothenberg, R., Hyman, J. M., et al. (2020). “Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020,” Infectious Disease Modelling, 5, 256263.10.1016/j.idm.2020.02.002CrossRefGoogle Scholar
Rozenfeld, Y., Beam, J., Maier, H., Haggerson, W., Boudreau, K., Carlson, J., and Medows, R. (2020). “A model of disparities: Risk factors associated with COVID-19 infection,” International Journal for Equity in Health, 19(1), 110.10.1186/s12939-020-01242-zCrossRefGoogle Scholar
Serkez, Y. (2020). “The magic number for reducing infections and keeping businesses open,” New York Times. https://www.nytimes.com/interactive/2020/12/16/opinion/coronavirus-shutdown-strategies.html.Google Scholar
Simonov, A., Sacher, S. K., Dubé, J.-P. H., and Biswas, S. (2020). “The persuasive effect of Fox News: Non-compliance with social distancing during the Covid-19 pandemic.” NBER Working Paper No. 27237.Google Scholar
Smith, A. A., Fridling, J., Ibhrahim, D., and P. S. Porter Jr. (2020). “Identifying patients at greatest risk of mortality due to COVID-19: A New England perspective,” Western Journal of Emergency Medicine, 21(4), 785.Google ScholarPubMed
Smith, J. M., and Price, G. R. (1973). “The logic of animal conflict,” Nature, 246(5427), 1518.10.1038/246015a0CrossRefGoogle Scholar
Taylor, P. D., and Jonker, L. B. (1978). “Evolutionary stable strategies and game dynamics,” Mathematical Biosciences, 40(1–2), 145156.10.1016/0025-5564(78)90077-9CrossRefGoogle Scholar
Varghese, B., Maher, J. E., Peterman, T. A., Branson, B. M., and Steketee, R. W. (2002). “Reducing the risk of sexual HIV transmission: Quantifying the per-act risk for HIV on the basis of choice of partner, sex act, and condom use,” Sexually Transmitted Diseases, 29(1), 3843.10.1097/00007435-200201000-00007CrossRefGoogle ScholarPubMed
White, E. (1991). States of Desire: Travels in Gay America. New York: Plume Books.Google Scholar
Wright, A. L., Sonin, K., Driscoll, J., and Wilson, J. (2020). “Poverty and economic dislocation reduce compliance with COVID-19 shelter-in-place protocols,” Journal of Economic Behavior & Organization, 180, 544554.10.1016/j.jebo.2020.10.008CrossRefGoogle ScholarPubMed
Wu, C., Chen, X., Cai, Y., Xia, J., Zhou, X., Xu, S., et al. (2020a). “Risk factors associated with acute respiratory distress syndrome and death in patients with coronavirus disease 2019 pneumonia in Wuhan, China,” Journal of the American Medical Association Internal Medicine, 180(7), 934943.Google Scholar
Wu, R., Wang, L., Dina Kuo, H.-C., Shannar, A., Peter, R., Chou, P. J., et al. (2020b). “An update on current therapeutic drugs treating COVID-19,” Current Pharmacology Reports, 6(3), 5670.10.1007/s40495-020-00216-7CrossRefGoogle Scholar
Yang, J., Martcheva, M., and Chen, Y. (2016). “Imitation dynamics of vaccine decision-making behaviours based on the game theory,” Journal of Biological Dynamics, 10(1), 3158.10.1080/17513758.2015.1099749CrossRefGoogle ScholarPubMed
Zhang, R., Li, Y., Zhang, A. L., Wang, Y., and Molina, M. J. (2020). “Identifying airborne transmission as the dominant route for the spread of COVID-19,” Proceedings of the National Academy of Sciences, 117(26), 1485714863.10.1073/pnas.2009637117CrossRefGoogle Scholar
Zhu, N., Zhang, D., Wang, W., Li, X., Yang, B., Song, J., et al. (2020). “A novel coronavirus from patients with pneumonia in China, 2019,” New England Journal of Medicine, 382(8), 727733.10.1056/NEJMoa2001017CrossRefGoogle ScholarPubMed

Accessibility standard: Inaccessible, or known limited accessibility

Why this information is here

This section outlines the accessibility features of this content - including support for screen readers, full keyboard navigation and high-contrast display options. This may not be relevant for you.

Accessibility Information

The PDF of this book is known to have missing or limited accessibility features. We may be reviewing its accessibility for future improvement, but final compliance is not yet assured and may be subject to legal exceptions. If you have any questions, please contact accessibility@cambridge.org.

Content Navigation

Table of contents navigation
Allows you to navigate directly to chapters, sections, or non‐text items through a linked table of contents, reducing the need for extensive scrolling.
Index navigation
Provides an interactive index, letting you go straight to where a term or subject appears in the text without manual searching.

Save book to Kindle

To save this book to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×