Infectious diseases and resistance to antimicrobial agents grow as threats to human and animal welfare worldwide and drain limited resources. To contain and manage infecting microbes and their resistance genes, we need to know where they are and where they are going. Tens of thousands of microbiology laboratories worldwide reporting results daily to healthcare providers should be viewed as infectious disease sensors, rich sentinel windows into the world's evolving microbial populations. To support the collection, integration, and interpretation of data from disparate data sources, our team at the WHO Collaborating Centre for Surveillance of Antimicrobial Resistance has developed and disseminated the WHONET software for 24 years in collaboration with the World Health Organization. WHONET is the leading free public health software to support laboratory-based surveillance of antimicrobial resistance, and supports surveillance activities in over 110 countries and 2500 hospital, public health, food, and animal laboratories. In our current grant, we have successfully modernized the legacy desktop version of WHONET into a secure, multi-tiered web architecture for data management, analysis, and presentation. With this more mature platform, a grant renewal would permit the expansion of WHONET's scope and impact in a number of areas: 1) data capture from new molecular technologies rapidly transforming routine microbiological practices; 2) improved tools for recognition, geo-tracking, and automated caregiver, infection preventionist, and public health alerts of microbial subtypes from both classic and new molecular phenotyping methods; 3) additional security options for data access and transfer; and 4) pilot implementation in partner networks. The improved definition of strain phenotypes will leverage classical statistical and newer data mining techniques offered by the integration of EpiInfo, R, and Weka routines into the WHONET analysis module. In the past few years, an exciting new direction for WHONET development has been the targeted expansion of capabilities to support laboratory-based public health reporting of healthcare-associated infections, most notably the MDRO/CDI module of the CDC National Healthcare Safety Network (NHSN). This has required the addition of new data feeds for patient movements (Admission/Discharge/Transfer) and selective clinical data elements. U.S. and international infection prevention colleagues increasingly request the inclusion within WHONET of additional infection reporting modules, especially CLABSI, CAUTI, and SSI, which we will accomplish with this application. Microbes move - between people, hospitals, animals, continents - and evolve. Through automated, real-time mobilization and vigilance of a rich yet largely untapped resource - the cumulative efforts of the world's microbiology laboratories - our goal is the development of free solutions in the public sector that can be applied worldwide to mobilize, share, and interpret these data to guide timely and effective public health action.

Public Health Relevance

The WHONET software developed by the WHO Collaborating Centre for Surveillance of Antimicrobial Resistance is the leading free public health software for electronic laboratory-based surveillance of antimicrobial resistance and supports surveillance activities in over 2500 healthcare facilities in over 110 countries linking a largely untapped a resource - the cumulative efforts of the world's microbiology laboratories - to prompt and effective public health action. We propose further development of the WHONET data capture, analysis, and presentation capabilities which leverage new molecular-based technologies rapidly entering clinical diagnostic practice; new analytical features for recognition and tracking of microbial subpopulations; and new web-based visualization tools for management of geo-referenced data. The goal is provision at local, national, and global levels of timely, actionable alerts on evolving threats to caregivers, infection preventionists, and public health agencies responsible for the detection and containment of the successive epidemics of resistant genes and germs that are building the world's growing antimicrobial resistance burden.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM103525-09
Application #
9020976
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Ravichandran, Veerasamy
Project Start
2008-08-01
Project End
2017-02-28
Budget Start
2016-03-01
Budget End
2017-02-28
Support Year
9
Fiscal Year
2016
Total Cost
Indirect Cost
Name
Brigham and Women's Hospital
Department
Type
DUNS #
030811269
City
Boston
State
MA
Country
United States
Zip Code
Natale, Alessandra; Stelling, John; Meledandri, Marcello et al. (2017) Use of WHONET-SaTScan system for simulated real-time detection of antimicrobial resistance clusters in a hospital in Italy, 2012 to 2014. Euro Surveill 22:
Park, Rachel; O'Brien, Thomas F; Huang, Susan S et al. (2016) Statistical detection of geographic clusters of resistant Escherichia coli in a regional network with WHONET and SaTScan. Expert Rev Anti Infect Ther 14:1097-1107
Chang, Hsiao-Han; Cohen, Ted; Grad, Yonatan H et al. (2015) Origin and proliferation of multiple-drug resistance in bacterial pathogens. Microbiol Mol Biol Rev 79:101-16
O'Brien, Thomas F; Stelling, John (2014) The world's microbiology laboratories can be a global microbial sensor network. Biomedica 34 Suppl 1:9-15
Siedner, Mark J; Galar, Alicia; Guzmán-Suarez, Belisa B et al. (2014) Cefepime vs other antibacterial agents for the treatment of Enterobacter species bacteremia. Clin Infect Dis 58:1554-63
Viñas, María R; Tuduri, Ezequiel; Galar, Alicia et al. (2013) Laboratory-based prospective surveillance for community outbreaks of Shigella spp. in Argentina. PLoS Negl Trop Dis 7:e2521
Galar, Alicia; Kulldorff, Martin; Rudnick, Wallis et al. (2013) Biochemical phenotypes to discriminate microbial subpopulations and improve outbreak detection. PLoS One 8:e84313
Stelling, J; Yih, W K; Galas, M et al. (2010) Automated use of WHONET and SaTScan to detect outbreaks of Shigella spp. using antimicrobial resistance phenotypes. Epidemiol Infect 138:873-83