Post by Nadica (She/Her) on Oct 24, 2024 3:51:53 GMT
Incident COVID-19 infections before Omicron in the U.S - Preprint posted Oct 21, 2024
Abstract
The timing and magnitude of COVID-19 infections are of interest to the public and to public health, but these are challenging to ascertain due to the volume of undetected asymptomatic cases and reporting delays. Accurate estimates of COVID-19 infections based on finalized data can improve understanding of the pandemic and provide more meaningful quantification of disease patterns and burden. Therefore, we retrospectively estimate daily incident infections for each U.S. state prior to Omicron. To this end, reported COVID-19 cases are deconvolved to their date of infection onset using delay distributions estimated from the CDC line list. Then, a novel serology-driven model is used to scale these deconvolved cases to account for the unreported infections. The resulting infections incorporate variant-specific incubation periods, reinfections, and waning antigenic immunity. They clearly demonstrate that the reported cases fail to reflect the full extent of disease burden in all states. Most notably, infections were severely underreported during the Delta wave, with an estimated reporting rate as low as 6.3% in New Jersey, 7.3% in Maryland, and 8.4% in Nevada. Moreover, in 44 states, fewer than 1/3 of infections appear as cases reports. Therefore, while reported cases offer a convenient proxy of disease burden, they fail to capture the full extent of infections, and can severely underestimate the true disease burden. This retrospective analysis also estimates other important quantities for every state, including variant-specific deconvolved cases, time-varying case ascertainment ratios, and infection-hospitalization ratios.
Abstract
The timing and magnitude of COVID-19 infections are of interest to the public and to public health, but these are challenging to ascertain due to the volume of undetected asymptomatic cases and reporting delays. Accurate estimates of COVID-19 infections based on finalized data can improve understanding of the pandemic and provide more meaningful quantification of disease patterns and burden. Therefore, we retrospectively estimate daily incident infections for each U.S. state prior to Omicron. To this end, reported COVID-19 cases are deconvolved to their date of infection onset using delay distributions estimated from the CDC line list. Then, a novel serology-driven model is used to scale these deconvolved cases to account for the unreported infections. The resulting infections incorporate variant-specific incubation periods, reinfections, and waning antigenic immunity. They clearly demonstrate that the reported cases fail to reflect the full extent of disease burden in all states. Most notably, infections were severely underreported during the Delta wave, with an estimated reporting rate as low as 6.3% in New Jersey, 7.3% in Maryland, and 8.4% in Nevada. Moreover, in 44 states, fewer than 1/3 of infections appear as cases reports. Therefore, while reported cases offer a convenient proxy of disease burden, they fail to capture the full extent of infections, and can severely underestimate the true disease burden. This retrospective analysis also estimates other important quantities for every state, including variant-specific deconvolved cases, time-varying case ascertainment ratios, and infection-hospitalization ratios.