Analysis of mobility data to build contact networks for COVID-19.

As social distancing policies and recommendations went into effect in response to COVID-19, people made rapid changes to the places they visit. These changes are clearly seen in mobility data, which records foot traffic using location trackers in cell phones. While mobility data is often used to extract the number of customers that visit a particular business or business type, it is the frequency and duration of concurrent occupancy at those sites that governs transmission. Understanding the way people interact at different locations can help target policies and inform contact tracing and prevention strategies. This paper outlines methods to extract interactions from mobility data and build networks that can be used in epidemiological models. Several measures of interaction are extracted: interactions between people, the cumulative interactions for a single person, and cumulative interactions that occur at particular businesses. Network metrics are computed to identify structural trends which show clear changes based on the timing of stay-at-home orders. Measures of interaction and structural trends in the resulting networks can be used to better understand potential spreading events, the percent of interactions that can be classified as close contacts, and the impact of policy choices to control transmission

Prioritizing vaccination based on analysis of community networks

Abstract Many countries that had early access to COVID-19 vaccines implemented vaccination strategies that prioritized health care workers and the elderly. As barriers to access eased, vaccine prioritization strategies have been relaxed. However, these strategies are still an important tool for decision makers to manage new variants, plan for future booster shots, or stage mass vaccinations. This paper explores the impact of vaccine prioritization strategies using networks that represent communities with different demographics and connectivity. The impact of vaccination is compared to non-medical intervention to reduce transmission. Several sources of uncertainty are considered, including vaccine willingness and mask effectiveness. This paper finds that while prioritization strategies can have a large impact on reducing deaths and peak hospitalization, selecting the best strategy depends on community characteristics and the desired objective. Additionally, in some cases random vaccination performs as well as more targeted prioritization strategies. Understanding these trade-offs is important when planning vaccine distribution

Evaluating a Standardized Measure of Healthcare Personnel Influenza Vaccination

BackgroundMethods of measuring influenza vaccination of healthcare personnel (HCP) vary substantially, as do the groups of HCP that are included in any given set of measurements. Thus, comparison of vaccination rates across healthcare facilities is difficult.PurposeThe goal of the study was to determine the feasibility of implementing a standardized measure for reporting HCP influenza vaccination data in various types of healthcare facilities.MethodsA total of 318 facilities recruited in four U.S. jurisdictions agreed to participate in the evaluation, including hospitals, long-term care facilities, dialysis clinics, ambulatory surgery centers, and physician practices. HCP in participating facilities were categorized as employees, credentialed non-employees, or other non-employees using standard definitions. Data were gathered using cross-sectional web-based surveys completed at three intervals between October 2010 and May 2011; data were analyzed in February 2012.Results234 facilities (74%) completed all three surveys. Most facilities could report on-site employee vaccination; almost one third could not provide complete data on HCP vaccinated outside the facility, contraindications, or declinations, primarily due to missing non-employee data. Inability to determine vaccination status of credentialed and other non-employees was cited as a major barrier to measure implementation by 24% and 27% of respondents, respectively.ConclusionsUsing the measure to report employee vaccination status was feasible for most facilities; tracking non-employee HCP was more challenging. Based on evaluation findings, the measure was revised to limit the types of non-employees included. Although the revised measure is less comprehensive, it is more likely to produce valid vaccination coverage estimates. Use of this standardized measure can inform quality improvement efforts and facilitate comparison of HCP influenza vaccination among facilities

Evaluating a Standardized Measure of Healthcare Personnel Influenza Vaccination

BACKGROUND: Methods of measuring influenza vaccination of healthcare personnel (HCP) vary substantially, including which groups of HCP are included in measurements. Thus, comparison of vaccination rates across healthcare facilities is difficult. PURPOSE: The goal of the study was to determine the feasibility of implementing a standardized measure for reporting HCP influenza vaccination data in various types of healthcare facilities. METHODS: A total of 318 facilities recruited in four U.S. jurisdictions agreed to participate in the evaluation, including hospitals, long-term care facilities, dialysis clinics, ambulatory surgery centers, and physician practices. HCP in participating facilities were categorized as employees, credentialed non-employees, or other non-employees using standard definitions. Data were gathered using cross-sectional web-based surveys completed at three intervals between October 2010 and May 2011 and analyzed in February 2012. RESULTS: 234 facilities (74%) completed all three surveys. Most facilities could report on-site employee vaccination; almost one third could not provide complete data on HCP vaccinated outside the facility, contraindications, or declinations, primarily due to missing non-employee data. Inability to determine vaccination status of credentialed and other non-employees was cited as a major barrier to measure implementation by 24% and 27% of respondents, respectively. CONCLUSIONS: Using the measure to report employee vaccination status was feasible for most facilities; tracking non-employee HCP was more challenging. Based on evaluation findings, the measure was revised to limit the types of non-employees included. Although the revised measure is less comprehensive, it is more likely to produce valid vaccination coverage estimates. Use of this standardized measure can inform quality improvement efforts and facilitate comparison of HCP influenza vaccination among facilities