The current global outbreaks of Monkeypox is a unique infectious disease in the way it seems to be transmitting: it has been observed to be highly concentrated in communities of men who have sex with men (MSM) through pair formation, and also provides immunity. This framework of mostly close, prolonged contact spreading a disease that admits immunity after infection is unlike similar infections which either offer little to no immunity post-infection or are lifelong infections. This creates the need for a new model framework that incorporates pair formation structure with recovery. While seemingly a straight forward model, we show how new dynamics arise from the combination of pair formation and recovery that are not present in a standard model with recovery and also not present in a pair formation model without recovery. We see that the combination of these two properties allows for waves of infection that are not seen in a standard SIR model. These dynamics suggest that outbreaks of monkeypox around the world may require special attention from public health. We also derive a reproduction number for this model and estimate the reproduction number of human monkeypox to be ≈2.3 using global and Canadian data. The expression derived for R0 can help estimate key parameters for diseases transmission and public health interventions and compare to equivalent models without pair formation.

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BackgroundHealthcare-associated infections (HAI) are a significant cause of morbidity and mortality for patients and continue to be an area of focus for public health programs. In the era of mandatory reporting, hospital infection prevention and control (IPC) departments are responsible for HAI data collection and management. Enumeration of infection and denominator data is often a manual and time-intensive process, which increases the potential for errors. In 2014, IPC and data analytics departments partnered to optimize data collection/reporting through the creation of a QlikView™ application, the Infection Control Dashboard (ICD).MethodsICD was developed through an iterative process from 2014 to 2015 at a quaternary care children’s hospital and is comprised of infection data from the hospital electronic surveillance system and electronic medical record software. The first release was May 2014. Iterations included development of statistical process control charts and filters to view data by date, unit, pathogen, HAI type, and patient details. ICD was finalized in May 2015 and refreshes daily for numerator and denominator data to identify actionable information in close to real-time. Time spent on data collection/reporting was tracked and compared pre- and post-ICD implementation.ResultsPost-implementation, time spent on external reporting decreased from 12 to 6 hours monthly and shifted from data collating to validation. Over 12 months, IPC received an average of 25 (mean 25.5, range 16–29) data requests per month. Using ICD, average time spent per data pull decreased from 80 to 27 minutes, saving more than 22 hours per month. Additional real-time applications included standard data displays for internal sharing and tracking infection rates by type, location, or department. ICD also allowed for internal review of detailed denominator data, facilitating validation between internally and externally reported data.ConclusionDevelopment of an automated data visualization tool improved HAI data management and reporting, streamlined workflow, and increased employee productivity. Use of this type of tool in IPC programs can improve data quality and enable departments to focus on targeted interventions in near-real time based on data trends.Disclosures All authors: No reported disclosures.

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