Study compares H1N1 and COVID-19 spread across metropolitan areas in the U.S.

Public health researchers at Columbia University Mailman School of Public Health used computer modeling to reconstruct how the 2009 H1N1 flu pandemic and the 2020 COVID-19 pandemic unfolded in the U.S. The findings highlight the rapid spread of pandemic respiratory pathogens and the challenges of early outbreak containment. The study, published in the journal Proceedings of the National Academy of Sciences, is the first to comprehensively compare the spatial transmission of the last two respiratory pandemics in the U.S. at the metropolitan scale. 

In the U.S., the 2009 H1N1 flu pandemic was responsible for 274,304 hospitalizations and 12,469 deaths, and the 2020 COVID-19 pandemic has so far led to 1.2 million confirmed deaths.

The researchers set out understand the geographic spread of the two pandemics to inform strategies to prevent future pandemics. They applied detailed data on the dynamics of the two infectious diseases to a computer model to simulate their spread using known patterns of air travel and commuting, as well as potential superspreading events. They focused on over three hundred metropolitan areas in the U.S.

In the simulations, both pandemics were widely circulating in most of the metro areas within weeks, before government interventions or early case detection. While the specific transmission pathways across locations were different for the last two pandemics, the spatial expansion was driven by several shared transmission hubs such as the New York and Atlanta metropolitan areas. Their spread was largely driven by air travel rather than commuting, though random dynamics introduced substantial uncertainty in transmission routes, which makes it hard to predict where the outbreaks will happen in real time.

The rapid and uncertain spread of the 2009 H1N1 flu and 2020 COVID-19 pandemics underscores the challenges for timely detection and control. Expanding wastewater surveillance coverage coupled with effective infection control could potentially slow the initial spread of future pandemics." 

Sen Pei, PhD, study's senior author, assistant professor of environmental health sciences, Columbia Mailman School

 Many studies have pointed to the benefits of wastewater surveillance programs. The new study further underscores the benefit of expanding wastewater surveillance for pandemic preparedness.

Beyond reconstructing the historical spread of the last two pandemics, the study also provides a generalizable framework to infer early epidemic dynamics that may be applied to other pathogens. While mobility, particularly air travel, is a key driver of pandemic spread, the researchers caution that other factors also play a role, including community demographics, school schedules, winter holidays, and weather conditions.

The study's first author is Renquan Zhang, Dalian University of Technology, Dalian, China. Additional authors include Rui Deng and Sitong Liu, Dalian University of Technology; Qing Yao and Jeffrey Shaman, Columbia University; Bryan T. Grenfell, Princeton; and Cécile Viboud, National Institutes of Health.

For more than a decade, Jeffrey Shaman and colleagues, including Sen Pei, have developed and refined methods to understand and simulate the spread of infectious diseases, including the flu, COVID-19, and others. Their real-time forecasts anticipate the rate and geographic spread of an outbreak, as well as the timing of its peak, to guide public health responses.

Shaman and Columbia University disclose partial ownership of SK Analytics. Other authors declare no competing interests.

This study was supported by funding from the National Natural Science Foundation of China (12371516), U.S. National Science Foundation (DMS-2229605), the Centers for Disease Control and Prevention (U01CK000592, 75D30122C14289), National Institute of Allergy and Infectious Diseases (R01AI163023), Princeton Catalysis Initiative, Princeton Precision Health, and High Meadows Environmental Institute. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the U.S. National Institutes of Health, Centers for Disease Control and Prevention, or Department of Health and Human Services.