In 2018–2019, the Ebola virus disease emergency in the Democratic Republic of Congo continues to prompt fresh reflections on how to optimize epidemic response1. ‘Lessons learned’ from previous outbreaks often focus on the development and deployment of medical countermeasures, such as vaccines and antivirals. However, additional tools can aid in outbreak response. Mathematical and statistical modeling has repeatedly proven to be a valuable resource in 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 in contrast to research and development of medical countermeasures, few efforts have been coordinated to improve optimization of modeling and other outbreak data analyses during public health emergencies caused by emerging infectious diseases.

Models played a prominent role in the 2014–2016 Ebola epidemic in West Africa. During the first half of the outbreak, public health operations were complicated by situational awareness limited by sparse data availability from the affected countries4. Reports from the ground indicated that the outbreak was growing quickly, but uncertainty about the current and future trajectory of the epidemic slowed mobilization of the public health response4. Faced with that uncertainty, decision makers incorporated information from infectious disease models, with early forecasts indicating that incidence would continue to grow rapidly unless aggressive interventions were implemented. For example, a forecast generated by the CDC predicted up to 1.4 million Ebola cases with no additional interventions or changes in community behavior5. These forecasts likely contributed to the acceleration of the international response and provided guidance for how resources might be effectively deployed.

Nevertheless, the integration of those analyses into the decision-making cycle for the Ebola 2014–2016 epidemic was not seamless, a pattern repeated across many recent outbreaks, including Zika6. Reasons for this vary. Modeling and outbreak data analysis efforts typically occur in silos with limited communication of methods and data between model developers and end users. Modeling “cross talk” across stakeholders within and between countries is also typically limited, often occurring within a landscape of legal and ethical uncertainty. Specifically, the ethics of performing research using surveillance and health data7, limited knowledge of what types of questions models can help inform, data sharing restrictions8, and the incentive in academia to quickly publish modeling results in peer-reviewed journals contribute to a complex collaborative environment with different and sometimes conflicting stakeholder goals and priorities.

To remedy these challenges, we propose the establishment of ‘outbreak science’ as an inter-disciplinary field to improve the implementation of models and critical data analyses in epidemic response. This new track of outbreak science describes the functional use of models, clinical knowledge, laboratory results, data science, statistics, and other advanced analytical methods to specifically support public health decision making between and during outbreak threats. Outbreak scientists work with decision makers to turn outbreak data into actionable information for decisions about how to anticipate the course of an outbreak, allocate scarce resources, and prioritize and implement public health interventions. Here, we make three specific recommendations to get the most out of modeling efforts during outbreaks and epidemics. Together these recommendations constitute the foundation of an integrative field that is “outbreak science”: