Real-time pandemic surveillance using hospital admissions and mobility data

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.

Collaborative modeling key to improving outbreak response

“…There are many clear collaborative modeling successes described by Fox et al. ( 2 ). They describe the judicious use of real-time modeling whose outputs were tailored to specific needs based on conversations with city officials.…”

“…Importantly, they show very clear evidence that data on hospital admissions are strongly correlated with hospital and ICU bed use in the near future (on the scale of a week or two; see figure 1B of ref. 2 ). Not surprisingly, and likely due to changing trends in case reporting and care-seeking, case data showed substantially lower correlation and therefore were seen as a less useful “leading indicator” of future hospitalizations.…”

“…In PNAS, Fox et al. detail an important and influential collaborative modeling effort that has supported real-time public health decision-making in Austin, TX, during the COVID-19 pandemic ( 2 ). The effort described by Fox et al.…”

Novel mobility index tracks COVID-19 transmission following stay-at-home orders

“…Later it became evident that mobility may be useful as a public health surveillance tool as studies evaluated the correlation between mobility and COVID-19 diagnoses. 3 , 52 – 59 A study by Lasry et al used Safegraph mobility data as a proxy for social distancing in the metropolitan areas of Seattle, San Francisco, New York City, and New Orleans and found an association between changes in mobility (% personal mobile devices leaving home) at the state-level and COVID-19 cases during the first COVID-19 wave 9 . In all four metropolitan areas, the number of mobile devices leaving home declined from 80% (on Febraury 26 2020) to 42% in New York City, 47% in San Francisco, 52% in Seattle, and 61% in New Orleans as stay-at-home policies were implemented.…”

“…Mobility data offers several functions as a public health tool. While we focused directly on the number of COVID-19, cases, mobility can also be used to estimate and model of transmission rates 52 , 60 . Spatially explicit models of disease transmission using census data are often used to guide disease intervention decisions.…”