Browsing by Author "Javan, Emily"

Now showing 1 - 7 of 7

Estimating the unseen emergence of COVID-19 in the US
(2020-11) Javan, Emily; Fox, Spencer J.; Meyers, Lauren Ancel
Heterogeneous burden of the COVID-19 pandemic in central Texas
(2021-01) Javan, Emily; Kushnereit, Emily; Betke, Briana; Woody, Spencer; Pasco, Remy; Pierce, Kelly; Johnson, Kaitlyn; Johnson-León, Maureen; Lachmann, Michael; Fox, Spencer J.; Meyers, Lauren Ancel
The heterogeneous burden of the COVID-19 pandemic within and across US cities has been linked to myriad risk factors including occupation, socioeconomic status, and race [1-3]. Here we use fine-grain, anonymized hospitalization data to estimate the heterogeneous impact of the COVID-19 pandemic on Austin, Texas across age groups and ZIP codes. We provide estimates for (1) the percent of the population infected as of January 11, 2021 and (2) the reporting rate of infections, and relate these estimates to the CDC Social Vulnerability Index (SVI) for each ZIP
Mapping COVID Data with R
(2020-09-25) Javan, Emily
Real-time pandemic surveillance using hospital admissions and mobility data
(PNAS, 2022-02) Fox, Spencer J.; Lachmann, Michael; Tec, Mauricio; Pasco, Remy; Woody, Spencer; Du, Zhanwei; Wang, Xutong; Ingle, Tanvi; Javan, Emily; Dahan, Maytal; Gaither, Kelly; Escott, Mark E.; Adler, Stephen I.; Johnston, S. Claiborne; Scott, James; Meyers, Lauren Ancel
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 health-care 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 to0.77) 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.
Spatial distribution of COVID-19 infections and vaccinations in Austin, Texas
(2021-04) Woody, Spencer; Javan, Emily; Johnson, Kaitlyn; Pasco, Remy; Johnson-León, Maureen; Lachmann, Michael; Fox, Spencer J.; Meyers, Lauren Ancel
In this report, we estimate the spatial distribution of SARS-CoV-2 infections and vaccine administration across Austin, TX. We find marked geographic differences in these outcomes. In particular, ZIP codes on the western side of the city tend to have higher vaccine coverage and lower estimated cumulative infections than ZIP codes on the eastern side of the city. These differences mirror disparities in social vulnerability, as measured by the CDC's social vulnerability index (SVI), which tends to be higher in eastern ZIP codes than in western ZIP codes.
The unseen and pervasive threat of COVID-19 throughout the US
(2020-04) Javan, Emily; Fox, Spencer J.; Meyers, Lauren Ancel
Using the COVID-19 to influenza ratio to estimate early pandemic spread in Wuhan, China and Seattle, US
(EclinicalMedicine, 2020-08) Du, Zhanwei; Javan, Emily; Nugent, Ciara; Cowling, Benjamin J.; Meyers, Lauren Ancel
Background: Pandemic SARS-CoV-2 was first reported in Wuhan, China on December 31, 2019. Twenty-one days later, the US identified its first case a man who had traveled from Wuhan to the state of Washington. Recent studies in the Wuhan and Seattle metropolitan areas retrospectively tested samples taken from patients with COVID-like symptoms. In the Wuhan study, there were 4 SARS-CoV-2 positives and 7 influenza positives out of 26 adults outpatients who sought care for influenza-like-illness at two central hospitals prior to January 12, 2020. The Seattle study reported 25 SARS-CoV-2 positives and 442 influenza positives out of 2353 children and adults who reported acute respiratory illness prior to March 9, 2020. Here, we use these findings to extrapolate the early prevalence of symptomatic COVID-19 in Wuhan and Seattle. Methods: For each city, we estimate the ratio of COVID-19 to influenza infections from the retrospective testing data and estimate the age-specific prevalence of influenza from surveillance reports during the same time period. Combining these, we approximate the total number of symptomatic COVID-19 infections. Findings: In Wuhan, there were an estimated 1386 [95% CrI: 420 3793] symptomatic cases over 30 of COVID- 19 between December 30, 2019 and January 12, 2020. In Seattle, we estimate that 2268 [95% CrI: 498, 6069] children under 18 and 4367 [95% CrI: 2776, 6526] adults were symptomatically infected between February 24 and March 9, 2020. We also find that the initial pandemic wave in Wuhan likely originated with a single infected case who developed symptoms sometime between October 26 and December 13, 2019; in Seattle, the seeding likely occurred between December 25, 2019 and January 15, 2020. Interpretation: The spread of COVID-19 in Wuhan and Seattle was far more extensive than initially reported. The virus likely spread for months in Wuhan before the lockdown. Given that COVID-19 appears to be overwhelmingly mild in children, our high estimate for symptomatic pediatric cases in Seattle suggests that there may have been thousands more mild cases at the time.