Outbreak investigations use data from interviews, healthcare providers, laboratories and surveillance systems. However, integrated use of data from multiple sources requires a patchwork of software that present challenges in usability, interoperability, confidentiality, and cost. Rapid integration, visualization and analysis of data from multiple sources can guide effective public health interventions. We developed MicrobeTrace to facilitate rapid public health responses by overcoming barriers to data integration and exploration in molecular epidemiology. MicrobeTrace is a web-based, client-side, JavaScript application (https://microbetrace.cdc.gov) that runs in Chromium-based browsers and remains fully operational without an internet connection. Using publicly available data, we demonstrate the analysis of viral genetic distance networks and introduce a novel approach to minimum spanning trees that simplifies results. We also illustrate the potential utility of MicrobeTrace in support of contact tracing by analyzing and displaying data from an outbreak of SARS-CoV-2 in South Korea in early 2020. MicrobeTrace is developed and actively maintained by the Centers for Disease Control and Prevention. Users can email microbetrace@cdc.gov for support. The source code is available at https://github.com/cdcgov/microbetrace.

MotivationOutbreak investigations use data from interviews, healthcare providers, laboratories and surveillance systems. However, integrated use of data from multiple sources requires a patchwork of software that present challenges in usability, interoperability, confidentiality, and cost. Rapid integration, visualization and analysis of data from multiple sources can guide effective public health interventions.ResultsWe developed MicrobeTrace to facilitate rapid public health responses by overcoming barriers to data integration and exploration in molecular epidemiology. Using publicly available HIV sequences and other data, we demonstrate the analysis of viral genetic distance networks and introduce a novel approach to minimum spanning trees that simplifies results. We also illustrate the potential utility of MicrobeTrace in support of contact tracing by analyzing and displaying data from an outbreak of SARS-CoV-2 in South Korea in early 2020.Availability and ImplementationMicrobeTrace is a web-based, client-side, JavaScript application (https://microbetrace.cdc.gov) that runs in Chromium-based browsers and remains fully-operational without an internet connection. MicrobeTrace is developed and actively maintained by the Centers for Disease Control and Prevention. The source code is available at https://github.com/cdcgov/microbetrace.Contactells@cdc.gov

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How well can social scientists predict societal change, and what processes underlie their predictions? To answer these questions, we ran two forecasting tournaments testing accuracy of predictions of societal change in domains commonly studied in the social sciences: ideological preferences, political polarization, life satisfaction, sentiment on social media, and gender-career and racial bias. Following provision of historical trend data on the domain, social scientists submitted pre-registered monthly forecasts for a year (Tournament 1; N=86 teams/359 forecasts), with an opportunity to update forecasts based on new data six months later (Tournament 2; N=120 teams/546 forecasts). Benchmarking forecasting accuracy revealed that social scientists’ forecasts were on average no more accurate than simple statistical models (historical means, random walk, or linear regressions) or the aggregate forecasts of a sample from the general public (N=802). However, scientists were more accurate if they had scientific expertise in a prediction domain, were interdisciplinary, used simpler models, and based predictions on prior data.

Background The 2019 CDC Threats Report lists extended spectrum β-lactamase (ESBL) producing Enterobacterales as a serious health threat. While the clinical epidemiology of uncomplicated urinary tract infection (uUTI) has remained stable, there has been a notable increase in antimicrobial resistance (AMR) among community-acquired uUTIs. Urine cultures are seldom ordered for uUTI as treatment is often empiric; local surveillance data may therefore be lacking. The study objective was to determine the prevalence and geographic distribution of AMR in urine E. coli isolates from females in the US outpatient setting. Methods A retrospective cross-sectional study of E. coli ambulatory urine isolates identified from females (≥ 12 years of age) at 296 facilities, with ≥ 1 quarter of data in 2019 (BD Insights Research Database, Franklin Lakes, NJ). Initial isolates representing each distinct susceptibility pattern within 30 days of index urine were included. E. coli isolates were identified as not-susceptible (NS) if intermediate/resistant to trimethoprim-sulfamethoxazole (TMP-SMX), fluoroquinolone (FQ), nitrofurantoin (NFT), ESBL+ (by commercial panels or intermediate/resistant to ceftriaxone, cefotaxime, ceftazidime or cefepime), and multi-drug resistant, defined as NS to ≥ 2 or ≥ 3 of FQ, TMP-SMX, NFT or ESBL+. Logistic regression models were used to evaluate resistance prevalence and variation across US census regions. Results Of 267,524 non-duplicate E. coli isolates evaluated, 25.1% (67,189) were TMP-SMX NS, 20.3% (54,359) were FQ NS, 7.3% (19,576) were ESBL+, 3.5% (9,453) were NFT NS, 14.0% (37,328) were NS to ≥ 2 drugs and 4.0% (10,814) were NS to ≥ 3 drugs. For all phenotypes, there was significant variation in resistance across census regions (all P< 0.001) with the highest in the East South Central region and lowest in the New England region of the US (Table). The figure shows regional prevalence of ESBL+ E. coli in 2019. Table. Antimicrobial resistance data from 30-day non-duplicate urine E. coli isolates in females ≥12 years old in 2019, by US census region. Figure. Heat map of the overall US geographic distribution of ESBL+ E. coli (30-day non-duplicate urine isolates) from females across 296 acute care facilities in 2019. Conclusion The 2019 prevalence of AMR in non-duplicate ambulatory E. coli urine isolates was notable: TMP-SMX NS and FQ NS were > 20%. In addition, there were significant regional differences in resistance, with the highest in the East South Central region of the US, for all NS phenotypes. These analyses inform, and may optimize, empiric treatment of uUTI and patient outcomes. Disclosures Vikas Gupta, PharmD, BCPS, Becton, Dickinson and Company (Employee, Shareholder)GlaxoSmithKline plc. (Other Financial or Material Support, Funding) Aruni Mulgirigama, MBBS, GlaxoSmithKline plc. (Employee, Shareholder) Ashish V. Joshi, PhD, GlaxoSmithKline plc. (Employee, Shareholder) Nicole Scangarella-Oman, MS, GlaxoSmithKline plc. (Employee, Shareholder) Kalvin Yu, MD, Becton, Dickinson and Company (Employee)GlaxoSmithKline plc. (Other Financial or Material Support, Funding) Anthony Boyles, MSc, Becton, Dickinson and Company (Employee)GlaxoSmithKline plc. (Other Financial or Material Support, Funding) Fanny S. Mitrani-Gold, MPH, GlaxoSmithKline plc. (Employee, Shareholder)