Providing Real-Time Insight During Public Health Emergencies

During public health emergencies, decision-makers need to quantify the risk to the public, delineate priorities with a clear and narrow focus, and maintain flexibility in considering options. During outbreak responses, modelers are asked to estimate the size of populations at risk for disease or death and the potential impact of interventions on both the timing and public health burden of an outbreak (Figure). By facilitating dialogue about what data are available and what data are needed to answer these questions, modelers can aid decision-makers as an outbreak situation evolves (11). Framing and addressing such questions via models helps leadership understand the appropriate size, type, time frame, and scale of resources needed to deploy interventions to maximize their impact. For example, one model produced during the Ebola virus disease (Ebola) response predicted the likelihood of the spread of Ebola from districts with reported Ebola cases to specific districts and neighboring countries with no reported cases. This forecast of geographic spread of Ebola allowed decision-makers to prioritize where to direct resources to improve surveillance (12).

To provide insight, modelers often must extract and combine useful information from diverse data sources, including traditional surveillance data, laboratory data, and social media, and collate them into meaningful information. Early in the West African Ebola epidemic, researchers at the University of Texas at Austin and Yale University used a combination of viral sequence data and case counts reported on the Sierra Leone Ministry of Health Facebook page to estimate the rate of spread and the clustered nature of Ebola transmission (13). During the 2009 H1N1 influenza pandemic, CDC modelers provided leaders, policy makers, and the public with near real-time modeled estimates of cases, hospitalizations, and deaths, corrected for underreporting (14,15). Before sufficient epidemiologic data existed, the modeled data allowed public health officials to more readily appreciate the magnitude of the disease transmission and understand the dynamic of the pandemic as risk patterns changed over time. Knowing where influenza is spreading in near real time and anticipating the timing and severity of the peak can improve clinical practice by facilitating plans for hospital and laboratory surge capacity and the implementation of pharmaceutical and nonpharmaceutical interventions (16). This insight gives decision-makers more flexibility to match resources to needs during public health emergencies.

Modelers have provided critical support for emergency response activities by estimating the size and potential growth of outbreaks before large amounts of data were available, assessing the potential impact of interventions, identifying important data needs (e.g., value of what is known, value of what is not known, prioritization of data collection), and developing simple decision-support tools for broad dissemination (11)