Enabling Hotspot Detection and Public Health Response to the COVID-19 Pandemic.

INTRODUCTION: Public-facing maps of COVID-19 cases, hospital admissions, and deaths are commonly displayed at the state, county, and zip code levels, and low case counts are suppressed to protect confidentiality. Public health authorities are tasked with case identification, contact tracing, and canvasing for educational purposes during a pandemic. Given limited resources, authorities would benefit from the ability to tailor their efforts to a particular neighborhood or congregate living facility. METHODS: We describe the methods of building a real-time visualization of patients with COVID-19-positive tests, which facilitates timely public health response to the pandemic. We developed an interactive street-level visualization that shows new cases developing over time and resolving after 14 days of infection. Our source data included patient demographics (ie, age, race and ethnicity, and sex), street address of residence, respiratory test results, and date of test. RESULTS: We used colored dots to represent infections. The resulting animation shows where new cases developed in the region and how patterns changed over the course of the pandemic. Users can enlarge specific areas of the map and see street-level detail on residential location of each case and can select from demographic overlays and contour mapping options to see high-level patterns and associations with demographics and chronic disease prevalence as they emerge. CONCLUSIONS: Before the development of this tool, local public health departments in our region did not have a means to map cases of disease to the street level and gain real-time insights into the underlying population where hotspots had developed. For privacy reasons, this tool is password-protected and not available to the public. We expect this tool to prove useful to public health departments as they navigate not only COVID-19 pandemic outcomes but also other public health threats, including chronic diseases and communicable disease outbreaks.

Systematic analysis of SARS-CoV-2 infection of an ACE2-negative human airway cell.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike (S) variants govern transmissibility, responsiveness to vaccination, and disease severity. In a screen for new models of SARS-CoV-2 infection, we identify human H522 lung adenocarcinoma cells as naturally permissive to SARS-CoV-2 infection despite complete absence of angiotensin-converting enzyme 2 (ACE2) expression. Remarkably, H522 infection requires the E484D S variant; viruses expressing wild-type S are not infectious. Anti-S monoclonal antibodies differentially neutralize SARS-CoV-2 E484D S in H522 cells as compared to ACE2-expressing cells. Sera from vaccinated individuals block this alternative entry mechanism, whereas convalescent sera are less effective. Although the H522 receptor remains unknown, depletion of surface heparan sulfates block H522 infection. Temporally resolved transcriptomic and proteomic profiling reveal alterations in cell cycle and the antiviral host cell response, including MDA5-dependent activation of type I interferon signaling. These findings establish an alternative SARS-CoV-2 host cell receptor for the E484D SARS-CoV-2 variant, which may impact tropism of SARS-CoV-2 and consequently human disease pathogenesis.