Thu Sep 26 14:00:00 UTC 2024: ## New Methodology Reveals Widespread Undiagnosed COVID-19 Infections During First Wave
**San Francisco, CA** – A groundbreaking new methodology developed by researchers at HEC Montréal and Université de Montréal reveals a shockingly high number of undiagnosed COVID-19 infections across U.S. states during the first wave of the pandemic. The study, published in PLOS ONE, analyzed daily testing data from late March to early April 2020, utilizing a novel approach that corrects observed positivity rates for non-random sampling to estimate overall population infection rates.
The researchers, David Benatia, Raphael Godefroy, and Joshua Lewis, found that for every identified case nationwide, there were an estimated 12 total infections in the population. This implies that the number of actual infections was significantly higher than the number of diagnosed cases, especially in states with lower testing capacity.
“This methodology can provide policymakers with estimates of disease prevalence across different geographic units during an emerging outbreak,” explained lead author Joshua Lewis. “Had the approach been applied to earlier testing data in March 2020, it would have revealed widespread undocumented community transmission, which may have led policymakers to enact earlier and more aggressive public health interventions.”
The study’s findings align with results from seroprevalence surveys, alternative approaches to measuring COVID-19 infections, and total excess mortality during the first wave of the pandemic. This strong correlation validates the new methodology and underscores the importance of accurate and timely information on population infection rates during outbreaks.
The methodology utilizes a simple selection model to link the observed positivity rate among tested individuals to overall population infection rate. By analyzing the relationship between the positivity rate and the size of the tested population, the researchers were able to estimate the extent of selection bias, which occurs when testing is not randomly allocated across the population.
The study highlights the limitations of relying solely on reported case counts to gauge the true extent of an outbreak. “Differences in state-level policies towards COVID-19 testing may mask important differences in underlying disease prevalence,” said Lewis. “The methodology can provide a clearer picture of the true scope of the pandemic and help policymakers make more informed decisions.”
The researchers acknowledge that their study has limitations, including dependence on the quality of diagnostic testing and assumptions about the consistency of sample selection processes. Nonetheless, the BGL methodology offers a valuable tool for policymakers seeking to track real-time population infection rates during emerging outbreaks.
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