tetano
Editor, Senior Moderator
Proc Natl Acad Sci U S A
. 2022 Feb 15;119(7):e2111870119.
doi: 10.1073/pnas.2111870119.
Real-time pandemic surveillance using hospital admissions and mobility data
Spencer J Fox[SUP] 1 [/SUP], Michael Lachmann[SUP] 2 [/SUP], Mauricio Tec[SUP] 3 [/SUP], Remy Pasco[SUP] 4 [/SUP], Spencer Woody[SUP] 5 [/SUP], Zhanwei Du[SUP] 6 [/SUP], Xutong Wang[SUP] 5 [/SUP], Tanvi A Ingle[SUP] 5 [/SUP], Emily Javan[SUP] 5 [/SUP], Maytal Dahan[SUP] 7 [/SUP], Kelly Gaither[SUP] 7 8 [/SUP], Mark E Escott[SUP] 9 [/SUP], Stephen I Adler[SUP] 10 [/SUP], S Claiborne Johnston[SUP] 11 [/SUP], James G Scott[SUP] 3 12 [/SUP], Lauren Ancel Meyers[SUP] 5 2 3 [/SUP]
Affiliations
Abstract
Forecasting the burden of COVID-19 has been impeded by limitations in data, with case reporting biased by testing practices, death counts lagging far behind infections, and hospital census reflecting time-varying patient access, admission criteria, and demographics. Here, we show that hospital admissions coupled with mobility data can reliably predict severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission rates and healthcare demand. Using a forecasting model that has guided mitigation policies in Austin, TX, we estimate that the local reproduction number had an initial 7-d average of 5.8 (95% credible interval [CrI]: 3.6 to 7.9) and reached a low of 0.65 (95% CrI: 0.52 to 0.77) after the summer 2020 surge. Estimated case detection rates ranged from 17.2% (95% CrI: 11.8 to 22.1%) at the outset to a high of 70% (95% CrI: 64 to 80%) in January 2021, and infection prevalence remained above 0.1% between April 2020 and March 1, 2021, peaking at 0.8% (0.7-0.9%) in early January 2021. As precautionary behaviors increased safety in public spaces, the relationship between mobility and transmission weakened. We estimate that mobility-associated transmission was 62% (95% CrI: 52 to 68%) lower in February 2021 compared to March 2020. In a retrospective comparison, the 95% CrIs of our 1, 2, and 3 wk ahead forecasts contained 93.6%, 89.9%, and 87.7% of reported data, respectively. Developed by a task force including scientists, public health officials, policy makers, and hospital executives, this model can reliably project COVID-19 healthcare needs in US cities.
Keywords: COVID-19; epidemiological data; forecasting; healthcare usage.
. 2022 Feb 15;119(7):e2111870119.
doi: 10.1073/pnas.2111870119.
Real-time pandemic surveillance using hospital admissions and mobility data
Spencer J Fox[SUP] 1 [/SUP], Michael Lachmann[SUP] 2 [/SUP], Mauricio Tec[SUP] 3 [/SUP], Remy Pasco[SUP] 4 [/SUP], Spencer Woody[SUP] 5 [/SUP], Zhanwei Du[SUP] 6 [/SUP], Xutong Wang[SUP] 5 [/SUP], Tanvi A Ingle[SUP] 5 [/SUP], Emily Javan[SUP] 5 [/SUP], Maytal Dahan[SUP] 7 [/SUP], Kelly Gaither[SUP] 7 8 [/SUP], Mark E Escott[SUP] 9 [/SUP], Stephen I Adler[SUP] 10 [/SUP], S Claiborne Johnston[SUP] 11 [/SUP], James G Scott[SUP] 3 12 [/SUP], Lauren Ancel Meyers[SUP] 5 2 3 [/SUP]
Affiliations
- PMID: 35105729
- DOI: 10.1073/pnas.2111870119
Abstract
Forecasting the burden of COVID-19 has been impeded by limitations in data, with case reporting biased by testing practices, death counts lagging far behind infections, and hospital census reflecting time-varying patient access, admission criteria, and demographics. Here, we show that hospital admissions coupled with mobility data can reliably predict severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission rates and healthcare demand. Using a forecasting model that has guided mitigation policies in Austin, TX, we estimate that the local reproduction number had an initial 7-d average of 5.8 (95% credible interval [CrI]: 3.6 to 7.9) and reached a low of 0.65 (95% CrI: 0.52 to 0.77) after the summer 2020 surge. Estimated case detection rates ranged from 17.2% (95% CrI: 11.8 to 22.1%) at the outset to a high of 70% (95% CrI: 64 to 80%) in January 2021, and infection prevalence remained above 0.1% between April 2020 and March 1, 2021, peaking at 0.8% (0.7-0.9%) in early January 2021. As precautionary behaviors increased safety in public spaces, the relationship between mobility and transmission weakened. We estimate that mobility-associated transmission was 62% (95% CrI: 52 to 68%) lower in February 2021 compared to March 2020. In a retrospective comparison, the 95% CrIs of our 1, 2, and 3 wk ahead forecasts contained 93.6%, 89.9%, and 87.7% of reported data, respectively. Developed by a task force including scientists, public health officials, policy makers, and hospital executives, this model can reliably project COVID-19 healthcare needs in US cities.
Keywords: COVID-19; epidemiological data; forecasting; healthcare usage.