Infectious disease modeling has played a prominent role in recent outbreaks, yet integrating these analyses into public health decision-making has been challenging. We recommend establishing ‘outbreak science’ as an inter-disciplinary field to improve applied epidemic modeling

Biggerstaff, Matthew

Brett-Major, David

Chretien, Jean-Paul

Jarman, Richard G.

Johansson, Michael A.

Maljkovic Berry, Irina

Meyer, Diane

Morton, Lindsay

Pavlin, Julie A.

Pollett, Simon

Reich, Nicholas G.

Riley, Steven

Rivers, Caitlin

Snyder, Michael R.

Woodward, Alexandra

English

University of Nebraska Medical Center Research: DigitalCommons@UNMC

University of Nebraska Medical Center DigitalCommons@UNMC Journal Articles: Epidemiology Epidemiology 2019 Using "outbreak science" to strengthen the use of models during epidemics. Caitlin Rivers Jean-Paul Chretien Steven Riley Julie A. Pavlin Alexandra Woodward See next page for additional authors Follow this and additional works at: https://digitalcommons.unmc.edu/coph_epidem_articles  Part of the Epidemiology Commons Authors Caitlin Rivers, Jean-Paul Chretien, Steven Riley, Julie A. Pavlin, Alexandra Woodward, David Brett-Major, Irina Maljkovic Berry, Lindsay Morton, Richard G. Jarman, Matthew Biggerstaff, Michael A. Johansson, Nicholas G. Reich, Diane Meyer, Michael R. Snyder, and Simon Pollett COMMENTUsing “outbreak science” tostrengthen the use of models duringepidemicsCaitlin Rivers1, Jean-Paul Chretien2, Steven Riley3, Julie A. Pavlin4,Alexandra Woodward5,6, David Brett-Major7, Irina Maljkovic Berry 8,Lindsay Morton5,6,9, Richard G. Jarman8, Matthew Biggerstaff10,Michael A. Johansson 11, Nicholas G. Reich 12, Diane Meyer1,Michael R. Snyder1 & Simon Pollett7,8,13Infectious disease modeling has played a prominent role in recent outbreaks, yetintegrating these analyses into public health decision-making has been chal-lenging. We recommend establishing ‘outbreak science’ as an inter-disciplinaryfield to improve applied epidemic modeling.The enduring threat of epidemics and the promise of modelsIn 2018–2019, the Ebola virus disease emergency in the Democratic Republic of Congo continuesto prompt fresh reflections on how to optimize epidemic response1. ‘Lessons learned’ fromprevious outbreaks often focus on the development and deployment of medical counter-measures, such as vaccines and antivirals. However, additional tools can aid in outbreakresponse. Mathematical and statistical modeling has repeatedly proven to be a valuable resourcein targeting outbreak response needs, and can inform the effective use of vaccines2, antivirals3,and other countermeasures (e.g. school closures and social distancing). Despite this, and incontrast to research and development of medical countermeasures, few efforts have beencoordinated to improve optimization of modeling and other outbreak data analyses duringpublic health emergencies caused by emerging infectious diseases.Models played a prominent role in the 2014–2016 Ebola epidemic in West Africa. During thefirst half of the outbreak, public health operations were complicated by situational awarenesslimited by sparse data availability from the affected countries4. Reports from the ground indi-cated that the outbreak was growing quickly, but uncertainty about the current and futuretrajectory of the epidemic slowed mobilization of the public health response4. Faced with thathttps://doi.org/10.1038/s41467-019-11067-2 OPEN1 Johns Hopkins Center for Health Security, Baltimore, MD 21202, USA. 2Department of Defense, Fort Detrick, MD 21702, USA. 3MRC Centre for GlobalInfectious Disease Analysis, School of Public Health, Imperial College, London, UK. 4National Academies of Sciences, Engineering, and Medicine, Washington,DC 20006, USA. 5Cherokee Nation Strategic Programs, Tulsa, OK 74116, USA. 6Global Emerging Infections Surveillance, Armed Forces Health SurveillanceBranch, Silver Spring, MD 20904, USA. 7Department of Preventive Medicine & Biostatistics, Uniformed Services University, Bethesda, MD 20814, USA. 8ViralDiseases Branch, Walter Reed Army Institute of Research, Silver Spring, MD 20910, USA. 9Department of Global Health, Milken Institute School of PublicHealth, George Washington University, Washington, DC 20037, USA. 10 Influenza Division, Centers for Disease Control & Prevention, Atlanta, GA 30333,USA. 11Division of Vector-Borne Diseases, Centers for Disease Control & Prevention, Atlanta, PR, USA. 12Department of Biostatistics and Epidemiology,University of Massachusetts Amherst School of Public Health and Health Sciences, Amherst, MA 01003, USA. 13Marie Bashir Institute for Infectious Diseases& Biosecurity, University of Sydney, Sydney, NSW, Australia. Correspondence and requests for materials should be addressed to C.R. (email: crivers6@jhu.edu)NATURE COMMUNICATIONS |         (2019) 10:3102 | https://doi.org/10.1038/s41467-019-11067-2 | www.nature.com/naturecommunications 11234567890():,;uncertainty, decision makers incorporated information frominfectious disease models, with early forecasts indicating thatincidence would continue to grow rapidly unless aggressiveinterventions were implemented. For example, a forecast gener-ated by the CDC predicted up to 1.4 million Ebola cases with noadditional interventions or changes in community behavior5.These forecasts likely contributed to the acceleration of theinternational response and provided guidance for how resourcesmight be effectively deployed.Nevertheless, the integration of those analyses into thedecision-making cycle for the Ebola 2014–2016 epidemic was notseamless, a pattern repeated across many recent outbreaks,including Zika6. Reasons for this vary. Modeling and outbreakdata analysis efforts typically occur in silos with limited com-munication of methods and data between model developers andend users. Modeling “cross talk” across stakeholders within andbetween countries is also typically limited, often occurring withina landscape of legal and ethical uncertainty. Specifically, the ethicsof performing research using surveillance and health data7, lim-ited knowledge of what types of questions models can helpinform, data sharing restrictions8, and the incentive in academiato quickly publish modeling results in peer-reviewed journalscontribute to a complex collaborative environment with differentand sometimes conflicting stakeholder goals and priorities.To remedy these challenges, we propose the establishment of‘outbreak science’ as an inter-disciplinary field to improve theimplementation of models and critical data analyses in epidemicresponse. This new track of outbreak science describes thefunctional use of models, clinical knowledge, laboratory results,data science, statistics, and other advanced analytical methodsto specifically support public health decision making between andduring outbreak threats. Outbreak scientists work with decisionmakers to turn outbreak data into actionable informationfor decisions about how to anticipate the course of an outbreak,allocate scarce resources, and prioritize and implement publichealth interventions. Here, we make three specific recommen-dations to get the most out of modeling efforts duringoutbreaks and epidemics. Together these recommendationsconstitute the foundation of an integrative field that is “outbreakscience”:1: Be prepared: Establish functional model capability andfoster relationships before the emergency beginsIn many of the recent communicable disease public healthemergencies, a suite of academic and government modelersproduced important analyses, but few were specifically tasked andfunded to do so in a coordinated, functional fashion. The reactive(rather than prospective) mobilization of modelers and compu-tational epidemiologists in public health emergencies is not new.Rapid development and deployment of infectious disease modelswas important during the 2009 influenza pandemic, chikungunya,and SARS9–11. Models helped decision makers to understand thelikely trajectory of the outbreaks, the risk of international spread,and the potential impact of interventions. Although identified asinfluential at the time, few if any investments in permanentcapabilities to integrate such analyses into the decision-makingprocess have followed.However, a few examples of successful partnerships betweenmodelers and decision makers may serve as inspiration. Smallteams exist in agencies in the US government such as at CDC,BARDA and NIH. Similar examples exist in the UK withexpertise within government agencies and from academic entities,for instance, Imperial College and the London School of Hygieneand Tropical Medicine.Leveraging these promising examples (not an exhaustive list),we therefore recommend that epidemic modeling capability beenhanced and honed not during rapidly-evolving public healthemergencies, but rather between major epidemics. Such approa-ches can leverage historical datasets and/or be developed usingroutine communicable disease data. An excellent example of thisis the Epidemic Prediction Initiative (EPI) [https://predict.cdc.gov], which has built a framework for open forecasting andtransparent evaluation and facilities participation through stan-dardized data and forecast formats and community building12.2: People and computation: Develop connections betweenmodel developers and model usersCurrently, limited information is available on how those on thefrontlines of public health perceive and use models in epidemicdecision-making. Understanding the functional relationshipbetween the public health end-users and model developers iscritical to improving capacity during outbreaks. A recent pilotstudy (n= 29 respondents) in Australia suggests a disconnectbetween model end-users and model developers, with overallinfrequent use of modeling in public health practice due to factorssuch as a lack of in-house modeling expertise, variable trust and/or comprehension of model outputs, and uncertainty regardingwhether models could answer the most important operationalquestions on the most relevant temporal and spatial scale13.Similarly, state and local health officials in the US reportedreluctance in translating pandemic influenza epidemic modelsinto public health action, despite the respondents’ familiarity withsuch models14. More recently, a limited evaluation of 39 influenzapublic health practitioners from three US public health organi-zations indicated that approximately one in five had directcommunication with modelers, 67% felt that models should bemore relevant to public health questions, and 59% wanted morefrequent communication with model developers15.But this relationship must also be a two-way street. Withoutopportunities to gain experience, modelers do not necessarilyunderstand or anticipate the needs and preference of decisionmakers. Improved communication and collaboration between thetwo stakeholder groups would improve the quality and utility ofmodels and the decisions they support. This suggestion is notnovel, yet the need clearly remains in 201913,15. To improve theseconnections, we advocate systematically measuring perceptions ofboth model end-users and model developers to identify what arethe key barriers to functional model development and imple-mentation. For example, stakeholder surveys, focus groups andother qualitative data can identify whether the public healthpriorities as interpreted by model developers are aligned withend-users, whether end-users trust or prefer particular models (ormodelers) over others, and whether major gaps are present inend-user modeling literacy and/or model developercommunication.3: Modelers on the front-lines: Formalize ‘outbreak science’by building capacity and supporting training and practiceopportunitiesDeveloping and implementing epidemic models under the “fog ofwar” is an enterprise far removed from the controlled conven-tional academic setting of epidemiology and biostatistics. We callfor cross-cutting outbreak science fellowship programs in whichepidemic modelers partner with experts from other disciplinesincluding clinicians, basic scientists, computational biologists,veterinarians, entomologists, epidemiologists, environmental sci-entists, and those in operational public health. Ideally, theseprograms would involve rotations in the field to experiencechallenges like rapidly changing information, varying quality ofCOMMENT NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-11067-22 NATURE COMMUNICATIONS |         (2019) 10:3102 | https://doi.org/10.1038/s41467-019-11067-2 | www.nature.com/naturecommunicationsdata, critical inter-personal relationships between and withininstitutions, and evolving public health priorities. The CDC’sEpidemic Intelligence Service program, a good model for trainingpublic health practitioners in applied epidemiology, could serve asa template for a similar program that provides academic modelersand other stakeholders with training in applied infectious diseasemodeling. Furthermore, training programs of this kind canfacilitate research opportunities, as called for in recommendation#2, that investigate relationships and processes between modelersand end users, document current challenges to model imple-mentation during epidemic response, and identify best practicesneeded to promote integration of modeling into public health.Conclusion: Invest in functional modeling capability beforethe emergency beginsTo make outbreak modeling a more effective component ofdecision-making during outbreaks requires a paradigm shift. Tobe successful, building working relationships is as necessary asbuilding quantitative modeling capacity. Rather than waiting forthe next pandemic, we should use opportunities between pan-demics to fine tune model development and implementation andformally measure and redress any disconnect between modeldevelopers and model-users.Finally, we see great potential for a new track of epidemicmodeling we have dubbed ‘outbreak science’ to improve publichealth preparedness response. With sufficient investment in thedevelopment and growth of the field, those trained in ‘outbreakscience’ will become vital contributors in helping public healthprofessionals navigate the many difficult decisions that come withinfectious disease emergencies. Additionally, they will serve as akey link between quantitative modelers and public health decisionmakers in an era of increasing epidemic threats.Received: 21 December 2018 Accepted: 3 June 2019References1. Nkengasong, J. N. & Onyebujoh, P. Response to the Ebola virus diseaseoutbreak in the Democratic Republic of the Congo. Lancet 391, 2395–2398(2018).2. Bellan, S. E. et al. Statistical power and validity of Ebola vaccine trials in SierraLeone: a simulation study of trial design and analysis. LANCET Infect. Dis. 15,703–710 (2015).3. Gani, R. et al. Potential Impact of Antiviral Drug Use during InfluenzaPandemic. Emerg. Infect. Dis. 11, 1355–1362 (2005).4. Ebola Response, W. H. O. Team. Ebola Virus Disease in West Africa — TheFirst 9 Months of the Epidemic and Forward Projections. N. Engl. J. Med. 371,1481–1495 (2014).5. Meltzer, M. et al. Estimating the Future Number of Cases in the EbolaEpidemic — Liberia and Sierra Leone, 2014–2015. MMWR Surveill Summ. 63,pp. 1–14, (2014).6. Pollett, S. Zika virus forecasting and prediction studies: a systematic reviewand evaluation of their utility during a global health emergency. BioArxiv[Preprint] (2018), https://doi.org/10.1101/634832.7. Saenz, C. Zika virus: ethics preparedness for old and new challenges. LancetGlob. Health 4, e686 (2016).8. Chretien, J. P., Rivers, C. M. & Johansson, M. A. Make Data Sharing Routineto Prepare for Public Health Emergencies. PLOS Med. 13, e1002109 (2016).9. Glasser, J. W., Hupert, N., McCauley, M. M. & Hatchett, R. Modeling andpublic health emergency responses: lessons from SARS. Epidemics 3, 32–37(2011).10. Johansson, M. A., Powers, A. M., Pesik, N., Cohen, N. J. & Staples, J. E.Nowcasting the Spread of Chikungunya Virus in the Americas. PLOS ONE 9,e104915 (2014).11. Lee, B. Y., Haidari, L. A. & Lee, M. S. Modelling during an emergency:the 2009 H1N1 influenza pandemic. Clin. Microbiol. Infect. 19, 1014–1022(2013).12. Biggerstaff, M. et al. Results from the Centers for Disease Control andPrevention’s Predict the 2013–2014 Influenza Season Challenge. BMC Infect.Dis. 16, 357 (2016).13. Muscatello, D. J. et al. Translation of real-time infectious disease modelinginto routine public health practice. Emerg. Infect. Dis. 23, pp. e1–e6,(2017).14. Rosenfeld, L. A. et al. Use of computer modeling for emergency preparednessfunctions by local and state health officials: a needs assessment. J. PublicHealth Manag. Pract. 15, 96–104 (2009).15. Doms, C., Kramer, S. C. & Shaman, J. Assessing the Use of Influenza Forecastsand Epidemiological Modeling in Public Health Decision Making in theUnited States. Sci. Rep. 8, (2018).AcknowledgementsThe views expressed in this article are those of the authors and do not necessarily reflectthe official policy or position of the Department of the Army, Department of Defense northe U.S. Government. Several of the authors are US Government Employees. This workwas prepared as part of their official duties. Title 17 U.S.C. § 105 provides that ‘Copyrightprotection under this title is not available for any work of the United States Government.’Title 17 U.S.C. §101 defines a U.S. Government work as a work prepared by a militaryservice member or employee of the U.S. Government as part of that person’s officialduties. The contents of this publication are the sole responsibility of the authors and donot necessarily reflect the views, assertions, opinions or policies of the Uniformed Ser-vices University or the Department of Defense. Mention of trade names, commercialproducts, or organizations does not imply endorsement by the U.S. Government. Morethan one author is an employee of the U.S. Government and as such under the provisionsof 17 U.S.C. 105, copyright protection is not available for this workAuthor contributionsC.R. and S.P. conceived of and drafted the manuscript. J.P.C., S. R., J.A.P, A.W., D.B.M.,I.M.B., L.M., R.G.J., M.B., M.A.J., N.G.R., D.M., and M.R.S. contributed formative ideasand recommendations, and assisted with drafting and editing of the manuscript.Additional informationCompeting interests: The authors declare no competing interests.Reprints and permission information is available online at http://npg.nature.com/reprintsandpermissions/Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.Open Access This article is licensed under a Creative CommonsAttribution 4.0 International License, which permits use, sharing,adaptation, distribution and reproduction in any medium or format, as long as you giveappropriate credit to the original author(s) and the source, provide a link to the CreativeCommons license, and indicate if changes were made. The images or other third partymaterial in this article are included in the article’s Creative Commons license, unlessindicated otherwise in a credit line to the material. If material is not included in thearticle’s Creative Commons license and your intended use is not permitted by statutoryregulation or exceeds the permitted use, you will need to obtain permission directly fromthe copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.© The Author(s) 2019NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-11067-2 COMMENTNATURE COMMUNICATIONS |         (2019) 10:3102 | https://doi.org/10.1038/s41467-019-11067-2 | www.nature.com/naturecommunications 3

Using  outbreak science  to strengthen the use of models during epidemics.

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Using outbreak science to strengthen the use of models during epidemics.

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