Epidemic Forecasting Challenge: Human Behavior

It is interesting to search and review older articles (looking for pre-COVID perspective) and realize how long we have been discussing these topics that have monopolized our current discussions in the last six months. In The Journal of Infectious Diseases, a study from 2016, Epidemic Forecasting is Messier Than Weather Forecasting: The Role of Human Behavior and Internet Data Streams in Epidemic Forecast, the authors explore the challenges of forecasting the spread of epidemics and compare and contrast with the progress of weather modeling.

The authors note in the abstract:

In the past 3 decades, the weather forecasting community has made significant advances in data collection, assimilating heterogeneous data steams into models and communicating the uncertainty of their predictions to the general public. Epidemic modelers are struggling with these same issues in forecasting the spread of emerging diseases.

Sound familiar? Here’s why, “epidemic models rely on human interactions, multiple data sources such as clinical surveillance and Internet data, and environmental or biological factors that can change the pathogen dynamics.” This reminds me of the hasty rush to a positive affirmation of test and trace (which I would not argue against) but could prove fraught with problems associated with the many and variegated variables of human behavior, requisite cooperation, and potentially unknown interactions (such as if asymptomatic superspreaders prove as accurate as is being posited). On this side point, I am not arguing against the activity of test and trace as it may be the best option we have, but the declaration of its success, given the myriad of unknowns.

The authors make a very interesting observation given what we, the entire world have experienced in the last six months, with some profound policy decisions that have been ostensibly driven by data:

Epidemic forecasting is still in its infancy and is a growing field with great potential. The challenges for accurate epidemic forecasting include data availability and emergent changes in human behavior and pathogens.

They conclude with this hopeful note which could prove prescient from four years ago, as phones and data have only increased, “there will be a parallel world, similar to that for weather forecasting, where billions of sensors will be uploading real-time information to obtain personalized disease forecasts.” Full study found here.

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