CDC Grand Rounds: Modeling and Public Health Decision-Making

Abstract

Mathematical models incorporate various data sources and advanced computational techniques to portray real-world disease transmission and translate the basic science of infectious diseases into decision-support tools for public health. Unlike standard epidemiologic methods that rely on complete data, modeling is needed when there are gaps in data. By combining diverse data sources, models can fill gaps when critical decisions must be made using incomplete or limited information. They can be used to assess the effect and feasibility of different scenarios and provide insight into the emergence, spread, and control of disease. During the past decade, models have been used to predict the likelihood and magnitude of infectious disease outbreaks, inform emergency response activities in real time (1), and develop plans and preparedness strategies for future events, the latter of which proved invaluable during outbreaks such as severe acute respiratory syndrome and pandemic influenza (2-6). Ideally, modeling is a multistep process that involves communication between modelers and decision-makers, allowing them to gain a mutual understanding of the problem to be addressed, the type of estimates that can be reliably generated, and the limitations of the data. As models become more detailed and relevant to real-time threats, the importance of modeling in public health decision-making continues to grow. Predicting the Likelihood, Timing, and Magnitude of Infectious Disease OutbreaksFederal agencies and academic partners are working to produce models with short-and long-term projections of when and where outbreaks will occur (7). For example, the "Predict the Influenza Season" challenge, started in 2013, moved influenza forecasting forward by engaging the scientific community to develop innovative and cost-effective methods to predict influenza activity and to more clearly identify areas of uncertainty in forecasting flu activity (8). This ongoing project encourages participants to predict the timing, peak, and intensity of influenza seasons by combining social media data (e.g., Twitter, internet search data, web surveys, etc.) and data from CDC's routine influenza surveillance systems (9). As part of the Influenza Virologic Surveillance Right Size project, a public health-academic partnership developed models that determine the minimum weekly number of specimens to be screened per public health laboratory to efficiently detect emerging viruses and select strains for inclusion in the next seasonal influenza vaccine (10). Providing Real-Time Insight During Public Health EmergenciesDuring 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 dat...

Effectiveness of workplace social distancing measures in reducing influenza transmission: a systematic review

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Predicting the hotspots of age-adjusted mortality rates of lower respiratory infection across the continental United States: Integration of GIS, spatial statistics and machine learning algorithms

“…According to Fischer et al [ 45 ], advanced computational models can translate the occurrence of infectious diseases into decision-support tools. Unlike traditional models, machine learning algorithms can quantify the association between infectious disease and explanatory variables, even with incomplete or noisy data [ 26 ] in a shorter time period and less costs.…”

Translation of Real-Time Infectious Disease Modeling into Routine Public Health Practice

“…Similar to our study, the US respondents in the CDC study recognized challenges involved in modeling: the limited knowledge on modeling and the need to train personnel; scaling to locallevel circumstances and populations, including vulnerable populations and at-risk groups; the need to access modeling capabilities on personal computers; and the need to prepare gap analyses ( 35 ). The lack of modeling capacity in the public health sector continues to be recognized in the United States ( 40 ). …”

“…Formal development or acquisition of information systems that meet the needs of a business activity requires a series of interacting organizational, human resource, technical, and project management processes ( 45 ). Software needs to be continually updated and maintained to conform to changing operating systems, data formats, and user requirements ( 40 ). Critical success factors for the implementation of public sector information systems include commitment to providing authority and resources to the acquisition, a clear statement of the project mission, quality of project planning and scheduling, and availability of the necessary technology and suitably qualified technical personnel ( 46 ).…”