Raude, J., McColl, K., Flamand, C. & Apostolidis, T. Understanding health behavior changes in response to outbreaks: findings from a longitudinal study of a large epidemic of mosquito-borne disease. Soc. Sci. Med. 230, 184–193 (2019).
Google Scholar
Kapiriri, L. & Ross, A. The politics of disease epidemics: a comparative analysis of the SARS, Zika, and Ebola outbreaks. Glob. Soc. Welf. 7, 33–45 (2020).
Google Scholar
Lewis, M. The economics of epidemics. Georget. J. Int. Aff. 2, 25–31 (2001).
Gelfand M. J. et al. The relationship between cultural tightness–looseness and COVID-19 cases and deaths: a global analysis. Lancet Planet. Health https://doi.org/10.1016/S2542-5196(20)30301-6 (2021).
Marston, C., Renedo, A. & Miles, S. Community participation is crucial in a pandemic. Lancet 395, 1676–1678 (2020).
Google Scholar
Shultz, J. M. et al. The role of fear-related behaviors in the 2013–2016 West Africa Ebola virus disease outbreak. Curr. Psychiatry Rep. 18, 104 (2016).
Google Scholar
Abramowitz, S. et al. The opposite of denial: social learning at the onset of the Ebola emergency in Liberia. J. Health Commun. 22, 59–65 (2017).
Google Scholar
Lee, C., Ayers, S. L. & Kronenfeld, J. J. The association between perceived provider discrimination, healthcare utilization and health status in racial and ethnic minorities. Ethn. Dis. 19, 330–337 (2009).
Google Scholar
Fenton, J. J., Jerant, A. F., Bertakis, K. D. & Franks, P. The cost of satisfaction: a national study of patient satisfaction, health care utilization, expenditures, and mortality. Arch. Intern. Med. 172, 405–411 (2012).
Google Scholar
Carter, S. E. et al. Barriers and enablers to treatment-seeking behavior and causes of high-risk practices in Ebola: a case study from Sierra Leone. J. Health Commun. 22, 31–38 (2017).
Google Scholar
Kretzschmar, M. Disease modeling for public health: added value, challenges, and institutional constraints. J. Public Health Policy 41, 39–51 (2020).
Google Scholar
Brauer, F. Mathematical epidemiology: past, present, and future. Infect. Dis. Model 2, 113–127 (2017).
Google Scholar
Chowell, G., Sattenspiel, L., Bansal, S. & Viboud, C. Mathematical models to characterize early epidemic growth: a review. Phys. Life Rev. 18, 66–97 (2016).
Google Scholar
Polonsky, J. A. et al. Outbreak analytics: a developing data science for informing the response to emerging pathogens. Philos. Trans. R. Soc. Lond. B Biol. Sci. 374, 20180276 (2019).
Google Scholar
Longini, I. M. Jr et al. Containing pandemic influenza at the source. Science 309, 1083–1087 (2005).
Google Scholar
Zhang, Q. et al. Spread of Zika virus in the Americas. Proc. Natl Acad. Sci. USA. 114, E4334–E4343 (2017).
Google Scholar
Chretien, J.-P., Riley, S. & George, D. B. Mathematical modeling of the West Africa Ebola epidemic. eLife 4, e09186 (2015).
Google Scholar
Chowell, G. & Nishiura, H. Transmission dynamics and control of Ebola virus disease (EVD): a review. BMC Med. 12, 196 (2014).
Google Scholar
Adam, D. Special report: the simulations driving the world’s response to COVID-19. Nature 580, 316–318 (2020).
Google Scholar
Siegenfeld, A. F., Taleb, N. N. & Bar-Yam, Y. Opinion: what models can and cannot tell us about COVID-19. Proc. Natl Acad. Sci. USA 117, 16092–16095 (2020).
Google Scholar
Manfredi, P. & D’Onofrio, A., eds. Modeling the Interplay Between Human Behavior and the Spread of Infectious Diseases (Springer-Verlag, 2013).
Philipson, T. in Handbook of Health Economics (eds Culyer, A. and Newhouse, J.) Vol. 1, Ch. 33, 1761–1799 (Elsevier, 2000).
Abramowitz, S. A., Hipgrave, D. B., Witchard, A. & Heymann, D. L. Lessons from the West Africa Ebola epidemic: a systematic review of epidemiological and social and behavioral science research priorities. J. Infect. Dis. 218, 1730–1738 (2018).
Google Scholar
Bedford, J. et al. Application of social science in the response to Ebola, Equateur Province, Democratic Republic of the Congo/Application des sciences sociales dans la riposte a la maladie a virus Ebola, province de l’Equateur, Republique democratique du Congo. Wkly Epidemiological Rec. 94, 19–24 (2019).
Norton, A. et al. A living mapping review for COVID-19 funded research projects: six-month update [version 3; peer review: 2 approved]. Wellcome Open Res. 5, 209 (2021).
Google Scholar
Pedi, D. et al. The development of standard operating procedures for social mobilization and community engagement in sierra leone during the West Africa Ebola outbreak of 2014-2015. J. Health Commun. 22, 39–50 (2017).
Google Scholar
RCCE Collective Service. Operational guide for engaging communities in contact tracing World Health Organization (2021); https://apps.who.int/iris/bitstream/handle/10665/341553/WHO-2019-nCoV-Contact_tracing-Community_engagement-2021.1-eng.pdf?sequence=1
Cellules d’Analyses en Sciences Sociales (CASS). Social Science Support for COVID-19: Lessons Learned Brief 3 7 (2020); https://www.unicef.org/drcongo/media/4131/file/CASS-Brief3-recommendations.pdf
Xepapadeas, A. The spatial dimension in environmental and resource economics. Environ. Dev. Econ. 15, 747–758 (2010).
Google Scholar
Reed, M. S. et al. What is social learning? Ecol. Soc. 15, r1 (2010).
Google Scholar
Kermack, W. O., McKendrick, A. G. & Walker, G. T. A contribution to the mathematical theory of epidemics. Proc. R. Soc. Lond. A 115, 700–721 (1927).
Google Scholar
Influenza in a boarding school. Brit. Med. J. 1, 587–587 (1978).
Funk, S., Salathé, M. & Jansen, V. A. A. Modelling the influence of human behaviour on the spread of infectious diseases: a review. J. R. Soc. Interface 7, 1247–1256 (2010).
Google Scholar
Eksin, C., Paarporn, K. & Weitz, J. S. Systematic biases in disease forecasting-the role of behavior change. Epidemics 27, 96–105 (2019).
Google Scholar
Bedford, J. et al. A new twenty-first century science for effective epidemic response. Nature 575, 130–136 (2019).
Google Scholar
Verelst, F., Willem, L. & Beutels, P. Behavioural change models for infectious disease transmission: a systematic review (2010-2015). J. R. Soc. Interface https://doi.org/10.1098/rsif.2016.0820 (2016).
Weston, D., Hauck, K. & Amlôt, R. Infection prevention behaviour and infectious disease modelling: a review of the literature and recommendations for the future. BMC Public Health 18, 336 (2018).
Google Scholar
Gersovitz, M. The economics of infection control. Annu. Rev. Resour. Econ. 3, 277–296 (2011).
Google Scholar
Perrings, C. et al. Merging economics and epidemiology to improve the prediction and management of infectious disease. Ecohealth 11, 464–475 (2014).
Google Scholar
Althouse, B. M., Bergstrom, T. C. & Bergstrom, C. T. Evolution in health and medicine Sackler colloquium: a public choice framework for controlling transmissible and evolving diseases. Proc. Natl Acad. Sci. USA 107, 1696–1701 (2010).
Google Scholar
Ward, C. J. Influenza vaccination campaigns: is an ounce of prevention worth a pound of cure? Am. Econ. J. Appl. Econ. 6, 38–72 (2014).
Google Scholar
Fenichel, E. P. Economic considerations for social distancing and behavioral based policies during an epidemic. J. Health Econ. 32, 440–451 (2013).
Google Scholar
Acemoglu, D., Chernozhukov, V., Werning, I. & Whinston, M. D. Optimal Targeted Lockdowns in a Multi-Group SIR Model Working Paper 27102 (National Bureau of Economic Research, 2020); https://doi.org/10.3386/w27102
Ahituv, A., Hotz, V. J. & Philipson, T. The responsiveness of the demand for condoms to the local prevalence of AIDS. J. Hum. Resour. 31, 869–897 (1996).
Google Scholar
Kremer, M. Integrating behavioral choice into epidemiological models of AIDS. Q. J. Econ. 111, 549–573 (1996).
Google Scholar
Justwan, F., Baumgaertner, B., Carlisle, J. E., Carson, E. & Kizer, J. The effect of trust and proximity on vaccine propensity. PLoS ONE 14, e0220658 (2019).
Google Scholar
Chen, F. H. Rational behavioral response and the transmission of STDs. Theor. Popul. Biol. 66, 307–316 (2004).
Google Scholar
Geoffard, P.-Y. & Philipson, T. Rational epidemics and their public control. Int. Econ. Rev. 37, 603–624 (1996).
Google Scholar
Fenichel, E. P. et al. Adaptive human behavior in epidemiological models. Proc. Natl Acad. Sci. USA 108, 6306 (2011).
Google Scholar
Morin, B. R., Fenichel, E. P. & Castillo-Chavez, C. SIR dynamics with economically driven contact rates. Nat. Resour. Model. 26, 505–525 (2013).
Google Scholar
Fenichel, E. P., Kuminoff, N. V. & Chowell, G. Skip the trip: air travelers’ behavioral responses to pandemic influenza. PLoS ONE 8, e58249 (2013).
Google Scholar
Hung, Y. W. et al. Impact of a free care policy on the utilisation of health services during an Ebola outbreak in the Democratic Republic of Congo: an interrupted time-series analysis. BMJ Glob. Health 5, e002119 (2020).
Google Scholar
Modeling Anthropogenic Effects in the Spread of Infectious Diseases (MASpread) Project. EcoServices: Disease Risks. Arizona State University (Accessed 17 April 2021); http://ecoservices.asu.edu/Diseaserisks/DRindex.html
Morris, M. Network Epidemiology: A Handbook for Survey Design and Data Collection (OUP, 2004).
Meyers, L. Contact network epidemiology: bond percolation applied to infectious disease prediction and control. Bull. Am. Math. Soc. 44, 63–86 (2007).
Google Scholar
Pastor-Satorras, R., Castellano, C., Van Mieghem, P. & Vespignani, A. Epidemic processes in complex networks. Rev. Mod. Phys. 87, 925–979 (2015).
Google Scholar
Wang, Z. et al. Statistical physics of vaccination. Phys. Rep. 664, 1–113 (2016).
Google Scholar
Cohen, R., Havlin, S. & Ben-Avraham, D. Efficient immunization strategies for computer networks and populations. Phys. Rev. Lett. 91, 247901 (2003).
Google Scholar
Salathé, M. & Jones, J. H. Dynamics and control of diseases in networks with community structure. PLoS Comput. Biol. 6, e1000736 (2010).
Google Scholar
Hébert-Dufresne, L., Allard, A., Young, J.-G. & Dubé, L. J. Global efficiency of local immunization on complex networks. Sci. Rep. 3, 2171 (2013).
Google Scholar
Rosenblatt, S. F., Smith, J. A., Gauthier, G. R. & Hébert-Dufresne, L. Immunization strategies in networks with missing data. PLoS Comput. Biol. 16, e1007897 (2020).
Google Scholar
Funk, S., Gilad, E., Watkins, C. & Jansen, V. A. A. The spread of awareness and its impact on epidemic outbreaks. Proc. Natl Acad. Sci. USA 106, 6872–6877 (2009).
Google Scholar
Funk, S. & Jansen, V. A. A. Interacting epidemics on overlay networks. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 81, 036118 (2010).
Google Scholar
Hébert-Dufresne, L., Mistry, D. & Althouse, B. M. Spread of infectious disease and social awareness as parasitic contagions on clustered networks. Phys. Rev. Res. 2, 033306 (2020).
Google Scholar
Marceau, V., Noël, P.-A., Hébert-Dufresne, L., Allard, A. & Dubé, L. J. Modeling the dynamical interaction between epidemics on overlay networks. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 84, 026105 (2011).
Google Scholar
Fu, F., Christakis, N. A. & Fowler, J. H. Dueling biological and social contagions. Sci. Rep. 7, 43634 (2017).
Google Scholar
Granell, C., Gómez, S. & Arenas, A. Competing spreading processes on multiplex networks: awareness and epidemics. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 90, 012808 (2014).
Google Scholar
Fan, C.-J. et al. Effect of individual behavior on the interplay between awareness and disease spreading in multiplex networks. Phys. A 461, 523–530 (2016).
Google Scholar
Scatà, M., Di Stefano, A., Liò, P. & La Corte, A. The impact of heterogeneity and awareness in modeling epidemic spreading on multiplex networks. Sci. Rep. 6, 37105 (2016).
Google Scholar
Wang, W. et al. Suppressing disease spreading by using information diffusion on multiplex networks. Sci. Rep. 6, 29259 (2016).
Google Scholar
Zheng, C., Xia, C., Guo, Q. & Dehmer, M. Interplay between SIR-based disease spreading and awareness diffusion on multiplex networks. J. Parallel Distrib. Comput. 115, 20–28 (2018).
Google Scholar
Gross, T. & Blasius, B. Adaptive coevolutionary networks: a review. J. R. Soc. Interface 5, 259–271 (2008).
Google Scholar
Gross, T. & Sayama, H. in Adaptive Networks: Theory, Models and Applications (eds Gross, T. & Sayama, H.) 1–8 (Springer, 2009).
Wang, Z., Andrews, M. A., Wu, Z.-X., Wang, L. & Bauch, C. T. Coupled disease–behavior dynamics on complex networks: a review. Phys. Life Rev. 15, 1–29 (2015).
Google Scholar
Valdez, L. D., Macri, P. A. & Braunstein, L. A. Intermittent social distancing strategy for epidemic control. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 85, 036108 (2012).
Google Scholar
Tunc, I., Shkarayev, M. S. & Shaw, L. B. Epidemics in adaptive social networks with temporary link deactivation. J. Stat. Phys. 151, 355–366 (2013).
Google Scholar
Epstein, J. M., Parker, J., Cummings, D. & Hammond, R. A. Coupled contagion dynamics of fear and disease: mathematical and computational explorations. PLoS ONE 3, e3955 (2008).
Google Scholar
Kiss, I. Z., Cassell, J., Recker, M. & Simon, P. L. The impact of information transmission on epidemic outbreaks. Math. Biosci. 225, 1–10 (2010).
Google Scholar
Gross, T., D’Lima, C. J. D. & Blasius, B. Epidemic dynamics on an adaptive network. Phys. Rev. Lett. 96, 208701 (2006).
Google Scholar
Zanette, D. H. & Risau-Gusmán, S. Infection spreading in a population with evolving contacts. J. Biol. Phys. 34, 135–148 (2008).
Google Scholar
Marceau, V., Noël, P.-A., Hébert-Dufresne, L., Allard, A. & Dubé, L. J. Adaptive networks: coevolution of disease and topology. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 82, 036116 (2010).
Google Scholar
Shaw, L. B. & Schwartz, I. B. Enhanced vaccine control of epidemics in adaptive networks. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 81, 046120 (2010).
Google Scholar
Althouse, B. M. & Hébert-Dufresne, L. Epidemic cycles driven by host behaviour. J. R. Soc. Interface https://doi.org/10.1098/rsif.2014.0575 (2014).
Scarpino, S. V., Allard, A. & Hébert-Dufresne, L. The effect of a prudent adaptive behaviour on disease transmission. Nat. Phys. 12, 1042–1046 (2016).
Google Scholar
Shaw, L. B. & Schwartz, I. B. Fluctuating epidemics on adaptive networks. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 77, 066101 (2008).
Google Scholar
Sayama, H. et al. Modeling complex systems with adaptive networks. Comput. Math. Appl. 65, 1645–1664 (2013).
Google Scholar
Do, A.-L., Rudolf, L. & Gross, T. Patterns of cooperation: fairness and coordination in networks of interacting agents. N. J. Phys. 12, 063023 (2010).
Google Scholar
Van Segbroeck, S., Santos, F. C., Lenaerts, T. & Pacheco, J. M. Selection pressure transforms the nature of social dilemmas in adaptive networks. N. J. Phys. 13, 013007 (2011).
Google Scholar
Zhan, X.-X. et al. Coupling dynamics of epidemic spreading and information diffusion on complex networks. Appl. Math. Comput. 332, 437–448 (2018).
Google Scholar
Hatfield, E., Cacioppo, J. T. & Rapson, R. L. Emotional contagion. Curr. Dir. Psychol. Sci. 2, 96–100 (1993).
Google Scholar
Epstein, J. M. Agent_Zero: Toward Neurocognitive Foundations for Generative Social Science (Princeton Univ. Press, 2014).
Barton, C. M. et al. Call for transparency of COVID-19 models. Science 368, 482–483 (2020).
Google Scholar
Hammond, R., Ornstein, J. T., Purcell, R., Haslam, M. D., & Kasman, M. Modeling robustness of COVID-19 containment policies. Preprint at OSF https://doi.org/10.31219/osf.io/h5ua7 (2021).
Cooley, P. C. et al. The model repository of the models of infectious disease agent study. IEEE Trans. Inf. Technol. Biomed. 12, 513–522 (2008).
Google Scholar
Eubank, S. et al. Modelling disease outbreaks in realistic urban social networks. Nature 429, 180–184 (2004).
Google Scholar
Burke, D. S. et al. Individual-based computational modeling of smallpox epidemic control strategies. Acad. Emerg. Med. 13, 1142–1149 (2006).
Google Scholar
Ferguson, N. M. et al. Strategies for mitigating an influenza pandemic. Nature 442, 448–452 (2006).
Google Scholar
Germann, T. C., Kadau, K., Longini, I. M. Jr & Macken, C. A. Mitigation strategies for pandemic influenza in the United States. Proc. Natl Acad. Sci. USA 103, 5935–5940 (2006).
Google Scholar
Longini, I. M. Jr et al. Containing a large bioterrorist smallpox attack: a computer simulation approach. Int. J. Infect. Dis. 11, 98–108 (2007).
Google Scholar
Hammond, R. A. Considerations and Best Practices in Agent-Based Modeling to Inform Policy (National Academies Press, 2015).
Wallace, R et al. Assessing the Use of Agent-Based Models for Tobacco Regulation (National Academies Press, 2015).
Pedro, S. A. et al. Conditions for a second wave of COVID-19 due to interactions between disease dynamics and social processes. Front. Phys. 8, 574514 (2020).
Google Scholar
Walters, C. E., Meslé, M. M. I. & Hall, I. M. Modelling the global spread of diseases: a review of current practice and capability. Epidemics 25, 1–8 (2018).
Google Scholar
Li, Y., Lawley, M. A., Siscovick, D. S., Zhang, D. & Pagán, J. A. Agent-based modeling of chronic diseases: a narrative review and future research directions. Prev. Chronic Dis. 13, E69 (2016).
Google Scholar
Weston, D., Ip, A. & Amlôt, R. Examining the application of behaviour change theories in the context of infectious disease outbreaks and emergency response: a review of reviews. BMC Public Health 20, 1483 (2020).
Google Scholar
Ripoll, S., Gercama, I., Jones, T. & Wilkinson, A. Social Science in Epidemics: Ebola Virus Disease Lessons Learned Background Report, UNICEF, IDS & Anthrologica https://opendocs.ids.ac.uk/opendocs/handle/20.500.12413/14160 (Institute of Development Studies, 2018).
DuBois, M., Wake, C., Sturridge, S. & Bennett, C. The Ebola Response in West Africa: Exposing the Politics and Culture of International Aid (Overseas Development Institute, 2015).
Hird, T. et al. Lessons From Ebola Affected Communities: Being Prepared for Future Health Crises (Africa All Party Parliamentary Group, 2016).
WHO. Report of the Ebola Interim Assessment Panel—July 2015 (2020).
Ashworth, H. C., Dada, S., Buggy, C. & Lees, S. The importance of developing rigorous social science methods for community engagement and behavior change during outbreak response. Disaster Med. Public Health Prep. 1–6 (2020).
Wenham, C. et al. Women are most affected by pandemics—lessons from past outbreaks. Nature 583, 194–198 (2020).
Google Scholar
Schwartz, D. A., Anoko, J. N. & Abramowitz, S. A. Pregnant in the Time of Ebola: Women and Their Children in the 2013-2015 West African Epidemic (Springer International Publishing, 2019).
Moore, M. D. Historicising ‘containment and delay’: COVID-19, the NHS and high-risk patients. Wellcome Open Res. 5, 130 (2020).
Google Scholar
Marcis, F. L., Enria, L., Abramowitz, S., Saez, A.-M. & Faye, S. L. B. Three acts of resistance during the 2014–16 West Africa Ebola epidemic. J. Humanitarian Aff. 1, 23–31 (2019).
Google Scholar
Parker, M., Hanson, T. M., Vandi, A., Babawo, L. S. & Allen, T. Ebola and public authority: saving loved ones in Sierra Leone. Med. Anthropol. 38, 440–454 (2019).
Google Scholar
Vinck, P., Pham, P. N., Bindu, K. K., Bedford, J. & Nilles, E. J. Institutional trust and misinformation in the response to the 2018–19 Ebola outbreak in North Kivu, DR Congo: a population-based survey. Lancet Infect. Dis. 19, 529–536 (2019).
Google Scholar
Ripoll, S., Gercama, I. & Jones, T. Rapid Appraisal of Key Health-Seeking Behaviours in Epidemics. SSHAP Practical Approaches brief 5, UNICEF, IDS & Anthrologica https://opendocs.ids.ac.uk/opendocs/handle/20.500.12413/15430 (Institute of Development Studies, 2020).
Bielicki, J. A. et al. Monitoring approaches for health-care workers during the COVID-19 pandemic. Lancet Infect. Dis. 20, e261–e267 (2020).
Google Scholar
Chu, I. Y.-H., Alam, P., Larson, H. J. & Lin, L. Social consequences of mass quarantine during epidemics: a systematic review with implications for the COVID-19 response. J. Travel Med. 27, taaa192 (2020).
Google Scholar
R&D Good Participatory Practice for COVID-19 Clinical Trials: a Toolbox (World Health Organization, 2020); https://www.who.int/publications/m/item/r-d-good-participatory-practice-for-covid-19-clinical-trials-a-toolbox
Hankins, C. Good Participatory Practice Guidelines for Trials of Emerging (and Re-emerging) Pathogens That are Likely to Cause Severe Outbreaks in the Near Future and For Which Few or No Medical Countermeasures Exist (GPP-EP) (WHO, 2016).
Sigfrid, L. et al. Addressing challenges for clinical research responses to emerging epidemics and pandemics: a scoping review. BMC Med. 18, 190 (2020).
Google Scholar
Gobat, N. H. et al. Talking to the people that really matter about their participation in pandemic clinical research: a qualitative study in four European countries. Health Expect. 21, 387–395 (2018).
Google Scholar
Richards, P. et al. Social pathways for ebola virus disease in rural Sierra Leone, and some implications for containment. PLoS Negl. Trop. Dis. 9, e0003567 (2015).
Google Scholar
Jalloh, M. F. et al. National survey of Ebola-related knowledge, attitudes and practices before the outbreak peak in Sierra Leone: August 2014. BMJ Glob. Health 2, e000285 (2017).
Google Scholar
Bedford, J. Social science and behavioral data compilation, DRC Ebola outbreak, November 2018 – February 2019. Social Science in Humanitarian Action and GOARN Research Social Science Group (2019); https://opendocs.ids.ac.uk/opendocs/bitstream/handle/20.500.12413/14144/SSHAP_data_compilation_brief_November_2018_updated.pdf
Pinchoff, J. et al. Evidence-based process for prioritizing positive behaviors for promotion: Zika prevention in Latin America and the Caribbean and applicability to future health emergency responses. Glob. Health Sci. Pr. 7, 404–417 (2019).
Google Scholar
Guirguis, S., Obregon, R., Coleman, M., Hickler, B. & SteelFisher, G. Placing human behavior at the center of the fight to eradicate polio: lessons learned and their application to other life-saving interventions. J. Infect. Dis. 216, S331–S336 (2017).
Google Scholar
Research Guides: Social Science Data Resources: COVID-19 https://guides.library.yale.edu/covid19impacts (Accessed 17 April 2021).
Rohan, H., Bausch, D. G. & Blanchet, K. Action not justification: how to use social science to improve outbreak response. PLoS Blogs (2018); https://collectionsblog.plos.org/action-not-justification-how-to-use-social-science-to-improve-outbreak-response/
Bardosh, K. et al. Towards People-Centred Epidemic Preparedness and Response: From Knowledge to Action (Wellcome/DFID, 2019).
UNICEF Minimum Quality Standards and Indicators for Community Engagement. Guidance Towards High Quality, Evidence-Based Community Engagement in The Development and Humanitarian Contexts. (2020); https://www.unicef.org/mena/reports/community-engagement-standards
Hennessey Lavery, S. et al. The community action model: a community-driven model designed to address disparities in health. Am. J. Public Health 95, 611–616 (2005).
Google Scholar
Boyce, M. R. & Katz, R. Community health workers and pandemic preparedness: current and prospective roles. Front. Public Health 7, 62 (2019).
Google Scholar
Baggio, O. Real-Time Ebola Community Feedback Mechanism (SSHAP Case Study 10, UNICEF, IDS and Anthrologica, 2020).
Collective Communication and Community Engagement in Humanitarian Action: How to Guide for Leaders and Responders (CDAC Network, 2019).
Ackerman Gulaid, L. & Kiragu, K. Lessons learnt from promising practices in community engagement for the elimination of new HIV infections in children by 2015 and keeping their mothers alive: summary of a desk review. J. Int. AIDS Soc. 15, 17390 (2012).
Google Scholar
Gilmore, B. et al. Community engagement for COVID-19 prevention and control: a rapid evidence synthesis. BMJ Glob. Health 5, e003188 (2020).
Google Scholar
O’Mara-Eves, A. et al. The effectiveness of community engagement in public health interventions for disadvantaged groups: a meta-analysis. BMC Public Health 15, 129 (2015).
Google Scholar
Milton, B., Attree, P., French, B., Povall, S. L. & Popay, J. The impact of community engagement on health and social outcomes: a systematic review. 47, 316–334 (2011).
Abramowitz, S. et al. Data Sharing in Public Health Emergencies: Anthropological and Historical Perspectives on Data Sharing During the 2014-2016 Ebola Epidemic and the 2016 Yellow Fever Epidemic (Wellcome Trust, 2018); https://www.glopid-r.org/wp-content/uploads/2019/07/data-sharing-in-public-health-emergencies-yellow-fever-and-ebola.pdf
Bedson, J. et al. Community engagement in outbreak response: lessons from the 2014-2016 Ebola outbreak in Sierra Leone. BMJ Glob. Health 5, e002145 (2020).
Google Scholar
Jalloh, M. Design and implementation of an integrated digital system for community engagement and community-based surveillance during the 2014-2016 Ebola outbreak in Sierra Leone. BMJ Global Health 5, e003936 (2020).
Google Scholar
McComas, K. A. Defining moments in risk communication research: 1996-2005. J. Health Commun. 11, 75–91 (2006).
Google Scholar
Glik, D. C. Risk communication for public health emergencies. Annu. Rev. Public Health 28, 33–54 (2007).
Google Scholar
WHO General Information on Risk Communication (2015).
Tworek, H., Beacock, I. & Ojo, E. Democratic health communications during Covid-19: a RAPID response (UBC Centre for the Study of Democratic Institutions, 2020); https://democracy.arts.ubc.ca/2020/09/14/covid-19/
Winters, M. et al. Risk communication and ebola-specific knowledge and behavior during 2014-2015 outbreak, Sierra Leone. Emerg. Infect. Dis. 24, 336–344 (2018).
Google Scholar
Novetta. Social Media Analysis of ‘Tu vois Les Retombées’ Facebook Page (Insecurity Insight, 2020); http://insecurityinsight.org/wp-content/uploads/2020/06/Social-Media-Analysis-Novetta-June-2020.pdf
Ghenai, A. & Mejova, Y. Catching Zika Fever: Application of Crowdsourcing and Machine Learning for Tracking Health Misinformation on Twitter. Preprint at arXiv https://arxiv.org/abs/1707.03778 (2017).
Taggart, T., Grewe, M. E., Conserve, D. F., Gliwa, C. & Roman Isler, M. Social media and HIV: a systematic review of uses of social media in HIV communication. J. Med. Internet Res. 17, e248 (2015).
Google Scholar
Smith, R. D. Responding to global infectious disease outbreaks: lessons from SARS on the role of risk perception, communication and management. Soc. Sci. Med. 63, 3113–3123 (2006).
Google Scholar
Li, C. et al. Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China, 2020. Euro Surveill. 25, 2000199 (2020).
Google Scholar
Lu, Y. & Zhang, L. Social media WeChat infers the development trend of COVID-19. J. Infect. 81, e82–e83 (2020).
Google Scholar
Effenberger, M. et al. Association of the COVID-19 pandemic with Internet search volumes: a Google TrendsTM Analysis. Int. J. Infect. Dis. 95, 192–197 (2020).
Google Scholar
Gallotti, R., Valle, F., Castaldo, N., Sacco, P. & De Domenico, M. Assessing the risks of ‘infodemics’ in response to COVID-19 epidemics. Nat. Hum. Behav. 4, 1285–1293 (2020).
Google Scholar
Bhattacharjee, S. & Dotto, C. Case study: understanding the impact of polio vaccine disinformation in Pakistan. First Draft (20 February 2020); https://firstdraftnews.org/long-form-article/first-draft-case-study-understanding-the-impact-of-polio-vaccine-disinformation-in-pakistan/
Krause, N. M., Freiling, I., Beets, B. & Brossard, D. Fact-checking as risk communication: the multi-layered risk of misinformation in times of COVID-19. J. Risk Res. 23, 1052–1059 (2020).
Google Scholar
Eysenbach, G. Infodemiology and infoveillance: framework for an emerging set of public health informatics methods to analyze search, communication and publication behavior on the Internet. J. Med. Internet Res. 11, e11 (2009).
Google Scholar
Eysenbach, G. Infodemiology: the epidemiology of (mis)information. Am. J. Med. 113, 763–765 (2002).
Google Scholar
Islam, M. S. et al. COVID-19-related infodemic and its impact on public health: a global social media analysis. Am. J. Trop. Med. Hyg. 103, 1621–1629 (2020).
Google Scholar
Funk, S. et al. Nine challenges in incorporating the dynamics of behaviour in infectious diseases models. Epidemics 10, 21–25 (2015).
Google Scholar
Davis, P. K., O’Mahony, A., Gulden, T. R., Sieck, K. & Osoba, O. A. Priority Challenges for Social and Behavioral Research and Its Modeling (RAND, 2018).
WHO Guidance For Managing Ethical Issues In Infectious Disease Outbreaks (2016).
Bruine de Bruin, W., Parker, A. M., Galesic, M. & Vardavas, R. Reports of social circles’ and own vaccination behavior: a national longitudinal survey. Health Psychol. 38, 975–983 (2019).
Google Scholar
Facebook. COVID-19 Interactive Map & Dashboard (Accessed 14 April 2020); https://dataforgood.facebook.com/covid-survey/?region=WORLD
Pruyt, E., Auping, W. L. & Kwakkel, J. H. Ebola in west Africa: model-based exploration of social psychological effects and interventions: Ebola in West Africa. Syst. Res. Behav. Sci. 32, 2–14 (2015).
Google Scholar
Schmidt-Hellerau, K. et al. Homecare for sick family members while waiting for medical help during the 2014-2015 Ebola outbreak in Sierra Leone: a mixed methods study. BMJ Glob. Health 5, e002732 (2020).
Google Scholar
Baggio, O. Case Study, Real-Time Ebola Community Feedback Mechanism (Social Science in Humanitarian Action, 2020); https://core.ac.uk/download/pdf/326024204.pdf
WHO, UNICEF and IFRC. The Collective Service (2020); https://www.who.int/teams/risk-communication/the-collective-service
WHO. COVID-19 Knowledge Hub (2020); https://extranet.who.int/goarn/COVID19Hub
Giles-Vernick, T. et al. A new social sciences network for infectious threats. Lancet Infect. Dis. 19, 461–463 (2019).
Google Scholar
Preventive Health Survey (Facebook, 2020); https://dataforgood.fb.com/tools/preventive-health-survey/
COVID-19 Community Mobility Reports (Google, 2020).
Badr, H. S. et al. Association between mobility patterns and COVID-19 transmission in the USA: a mathematical modelling study. Lancet Infect. Dis. 20, 1247–1254 (2020).
Google Scholar
WHO. Early AI-supported Response with Social Listening (2020); https://whoinfodemic.citibeats.com/?cat=fYJ1oBNEUQtfbExrkGvsyr
WHO. Ebola or Marburg Case Investigation and Recording Sheet (16 June 2020); https://www.who.int/publications/m/item/ebola-or-marburg-case-investigation-and-recording-sheet
CDC. Investigating a COVID-19 Case (2020); https://www.cdc.gov/coronavirus/2019-ncov/php/contact-tracing/contact-tracing-plan/investigating-covid-19-case.html
WHO. Disease Case Investigation Forms (Accessed 14 April 2021); https://www.who.int/emergencies/outbreak-toolkit/data-collection-standards/disease-case-investigation-forms
Social Science Support for COVID-19: Lessons Learned Brief 1 (Cellule D’analyse en Sciences Sociales, 2020).
Rivers, C., Pollett, S. & Viboud, C. The opportunities and challenges of an Ebola modeling research coordination group. PLoS Negl. Trop. Dis. 14, e0008158 (2020).
Google Scholar
WHO. Global Health Observatory (Accessed 14 April 2021); https://www.who.int/data/gho
Data Portal (RCCE Collective Service: Risk Communication and Community Engagement, 2020); https://www.rcce-collective.net/data/
Richards, P. Ebola: How a People’s Science Helped End an Epidemic (Zed Books, 2016).
Social Science in Humanitarian Action Platform Social Science in Humanitarian Action, Key Considerations: Engaging Twa communities in Equateur Province (2018).
Heesterbeek, H. et al. Modeling infectious disease dynamics in the complex landscape of global health. Science 347, aaa4339 (2015).
Google Scholar
Skrip, L., Fallah, M. P., Bedson, J., Hébert-Dufresne, L. & Althouse, B. M. Coordinated support for local action: a modeling study of strategies to facilitate behavior adoption in urban poor communities of Liberia for sustained COVID-19 suppression. Preprint at medRxiv https://doi.org/10.1101/2020.08.11.20172031 (2020).
Online Database of Training on Social Dimensions of Infectious Threats (Sonar Global, Accessed 14 April 2021); https://www.sonar-global.eu/trainings/
OpenWHO. https://openwho.org (Accessed 14 April 2021).
Gwynn, S. Access to Research in the Global South: Reviewing the Evidence (International Network for the Availability of Scientific Publications, 2019).
Urassa, M. et al. Cross-cultural research must prioritize equitable collaboration. Nat. Hum. Behav. https://doi.org/10.1038/s41562-021-01076-x (2021).
Bonino, F., Jean, I. & Knox-Clarke, P. Closing the Loop: Effective Feedback in Humanitarian Contexts (ALNAP/ODI, 2014).
Metcalf, C. J. E., Edmunds, W. J. & Lessler, J. Six challenges in modelling for public health policy. Epidemics 10, 93–96 (2015).
Google Scholar
Cobey, S. Modeling infectious disease dynamics. Science 368, 713–714 (2020).
Google Scholar
Ordway, D.-M. Epidemiological Models: 10 Things to Know About Coronavirus Research (Harvard Kennedy School, 2020); https://journalistsresource.org/tip-sheets/research/epidemiological-models-coronavirus/
Knight, G. M. et al. Bridging the gap between evidence and policy for infectious diseases: how models can aid public health decision-making. Int. J. Infect. Dis. 42, 17–23 (2016).
Google Scholar
Source: Ecology - nature.com
