Clustering of SARS-CoV-2 in Households in New York City: A Building-Level Analysis, March-December 2020.

OBJECTIVES: To identify the proportion of coronavirus disease 2019 (COVID-19) cases that occurred within households or buildings in New York City (NYC) beginning in March 2020 during the first stay-at-home order to determine transmission attributable to these settings and inform targeted prevention strategies. DESIGN: The residential addresses of cases were geocoded (converting descriptive addresses to latitude and longitude coordinates) and used to identify clusters of cases residing in unique buildings based on building identification number (BIN), a unique building identifier. Household clusters were defined as 2 or more cases within 2 weeks of onset or diagnosis date in the same BIN with the same unit number, last name, or in a single-family home. Building clusters were defined as 3 or more cases with onset date or diagnosis date within 2 weeks in the same BIN who do not reside in the same household. SETTING: NYC from March to December 2020. PARTICIPANTS: NYC residents with a positive SARS-CoV-2 nucleic acid amplification or antigen test result with a specimen collected during March 1, 2020, to December 31, 2020. MAIN OUTCOME MEASURE: The proportion of NYC COVID-19 cases in a household or building cluster. RESULTS: The BIN analysis identified 65 343 building and household clusters: 17 139 (26%) building clusters and 48 204 (74%) household clusters. A substantial proportion of NYC COVID-19 cases (43%) were potentially attributable to household transmission in the first 9 months of the pandemic. CONCLUSIONS: Geocoded address matching assisted in identifying COVID-19 household clusters. Close contact transmission within a household or building cluster was found in 43% of noncongregate cases with a valid residential NYC address. The BIN analysis should be utilized to identify disease clustering for improved surveillance.

A descriptive analysis of dengue in Peace Corps Volunteers, 2000-2019.

BACKGROUND: Peace Corps Volunteers (PCVs) are a unique expatriate population at risk for dengue. Previous studies examined travelers or lacked demographic information about expatriates. We examined dengue incidence among PCVs before and after deployment of an electronic medical record (EMR) to assess temporal and demographic factors. METHODS: Dengue cases within Peace Corps' Epidemiologic Surveillance System from 2000 to 2019 were identified using a standard case definition, and two timeframes were compared: pre-EMR 2000-2015 and post-EMR 2016-2019. RESULTS: Annual infections occurred in a roughly 3-year cyclic pattern from 2007 to 2019. Incidence rate decreased from 1.35 cases per 100 dengue Volunteer-years (95% CI 1.28-1.41) in 2000-2015 to 1.25 cases (95% CI 1.10-1.41) in 2016-2019. Among PCVs who served from 2016 to 2019, the majority of infections occurred in females and 20-29 year olds, and 7% were medically evacuated. Among PCVs who served from 2015 to 2019, 21% were hospitalized in-country. CONCLUSIONS: Among PCVs, a non-significant decrease in dengue incidence occurred from 2000-2015 to 2016-2019. Annual infection rates peaked every three years, offering opportunities for targeted prevention efforts. Dengue infection in PCVs appears to mimic the overall demographic of Peace Corps. Expatriates like PCVs are at an increased risk for dengue infection compared to short-term travelers.