In recent years, public health forecasting has evolved into a complex field that combines epidemiological research with computational modeling techniques. Traditional data-driven approaches often run the risk of overlooking critical human factors in decision-making processes. A groundbreaking project led by Thomas McAndrew, an Assistant Professor at Lehigh University’s College of Health, aims to address this challenge through a blend of innovative forecasting methods. Funded by the National Science Foundation (NSF), this initiative titled "IHBEM: Enhancing Influenza Forecasting Through an Integrated Platform for User-Generated Temporal Forecasts" strives to refine public health decision-making in the context of infectious diseases.
In its essence, McAndrew’s project seeks to illuminate the thought processes and cultural norms embraced by public health practitioners. By thoroughly understanding how epidemiologists and infectious disease clinicians make decisions, the research aims to uncover the data and models they routinely rely upon, alongside the barriers they encounter. This introspective exploration of human factors is essential for developing more effective forecasting methodologies that incorporate both data and human insights.
The second phase of McAndrew’s initiative focuses on creating a sophisticated software platform designed to serve as a central repository for information that can guide decision-making. This unique technology will facilitate a dynamic human judgment platform, capable of collecting and analyzing public opinions and forecasts regarding infectious diseases. Unlike traditional methods, which may suffer from biases, this platform aims to provide a clear, systematic overview of the diverse forecasts produced by users.
A particularly innovative aspect of this platform is its dual functionality. It features both a public-facing component, encouraging citizen engagement in public health discourse, and a private component tailored for professionals in the field. This creates a space where the general public can present questions and contribute forecasts related to critical health matters. The platform’s design allows users to not only see the number of forecasts submitted but also access individual rationales behind those forecasts, enriching the overall discourse in the process.
In an intriguing twist, McAndrew is integrating a temporal element into his ensemble forecasting approach. Instead of merely evaluating forecasts at a single time point, this project will assess the accuracy of individual predictions over time, utilizing a weighted methodology that values continuous users’ contributions. Such a layered analysis could yield insights into how public perception and expert predictions can evolve based on changing circumstances.
Beyond just exploring human judgments, the project raises an exciting question: Can citizen scientists outperform advanced computational models? A significant portion of McAndrew’s research involves comparing the forecasts generated by non-expert contributors to those produced by sophisticated statistical models within the scope of the CDC’s FluSight Ensemble. This initiative, which integrates forecasts from multiple predictive models, challenges conventional wisdom about the supremacy of machines in data-based predictions.
Past research has suggested that human forecasters can achieve performance levels comparable to their computational counterparts. McAndrew’s enthusiasm about this paradox underscores the wider implications of his research—not just for the realm of infectious diseases, but for public health forecasting efforts as a whole. The project aims to reinforce the notion that human intuition and experience hold significant value in contexts often dominated by hard data.
The breadth of McAndrew’s expertise spans computational epidemiology and infectious disease forecasting, positioning him uniquely to spearhead this project. By harnessing the collective insights of human judgment to enhance standard epidemic models, he hopes to pave new pathways for innovation in health outcomes. At the heart of his mission is a desire to empower public health officials, providing them with the necessary tools to generate forecasts that can rival those of computational models.
Collaborating alongside McAndrew is an interdisciplinary team dedicated to amplifying the project’s impact. Rochelle Frounfelker, another professor in the College of Health, will investigate the intricacies of public health decision-making processes. Their combined expertise, alongside contributions from Shaun Truelove of the Johns Hopkins Bloomberg School of Public Health, enriches the interdisciplinary nature of the research and expands its potential applicability.
Ultimately, the anticipated outcomes of this project extend far beyond mere statistical comparisons. McAndrew envisions that improving the evidence-based decision-making capabilities of public health officials will yield tangible benefits, including a reduction in morbidity and mortality rates associated with infectious diseases. By encouraging public health professionals to acknowledge and utilize their inherent decision-making capabilities, he hopes to foster a more nuanced and dynamic approach to forecasting in public health.
Lehigh University’s Center for Catastrophe Modeling and Resilience will also support this endeavor, weaving its expertise into the development of effective prediction models for both infectious diseases and broader biological phenomena. McAndrew’s work exemplifies how recent advances in technology and collaboration can lead to significant improvements in public health outcomes, offering hope for a future where health metrics are driven by a deeper understanding of both data and human intuition.
In summary, McAndrew’s project represents a notable shift in the paradigm of public health forecasting. By highlighting the interplay between human judgment and computational modeling, it opens new avenues for better predictive methods that account for the complexities of decision-making in the face of infectious diseases. As researchers like McAndrew seek innovative solutions to ongoing public health challenges, the potential for citizen involvement in health forecasting highlights the importance of community engagement and shared knowledge, propelling public health into a new era where both human insight and computational prowess can coexist and thrive.
Subject of Research: Enhancing Influenza Forecasting
Article Title: Harnessing Human Intuition: A New Era of Influenza Forecasting
News Publication Date: October 2023
Web References: Lehigh University Faculty Page, Center for Catastrophe Modeling and Resilience
References: National Science Foundation, CDC’s FluSight Ensemble
Image Credits: Lehigh University
Keywords: Public Health, Infectious Diseases, Flu Forecasting, Computational Models, Epidemiology, Citizen Science, Public Health Decision-Making, Temporal Forecasting, Human Judgment, Ensemble Forecasting, Health Outcomes, Public Engagement.
