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The trend in percent positivity metric refers to the percent of positive tests over a 7-day period. The number of positive test results is defined as the total number of first positive tests among cases over a 7-day period. It is important to note that this number excludes other positive test results for a single case. The total number of test results is calculated by adding the first positive test results and all negative tests that occurred in the 7-day period.


Systematic verification of inequalities (or equivalently, positivity of functions) underlies mathematical sciences in a universal way. With the advent of efficient and accessible numerical approaches such as semidefinite programming that can automate this task, the list of applications is broad in scope and ever-growing, encompassing classical mechanics, engineering, operations research, geometry, quantum information theory, and theoretical high energy physics. This workshop aims to bring together researchers from the theoretical high energy physics community working on the bootstrap program for conformal field theory and quantum field theory, researchers from the optimization community working on semidefinite optimization and convex algebraic geometry , as well as researchers studying positivity in various interesting problems in mathematics and related sciences. The attendees will share insights into the nature of both the physical problems and approaches to harnessing positivity.

Objective: To explore the emergence of spatial inequities in COVID-19 testing, positivity, confirmed cases, and mortality in New York, Philadelphia, and Chicago during the first 6 months of the pandemic.

Measurements: Outcomes were ZCTA-level COVID-19 testing, positivity, confirmed cases, and mortality cumulatively through the end of September 2020. Predictors were the Centers for Disease Control and Prevention Social Vulnerability Index and its 4 domains, obtained from the 2014-2018 American Community Survey. The spatial autocorrelation of COVID-19 outcomes was examined by using global and local Moran I statistics, and estimated associations were examined by using spatial conditional autoregressive negative binomial models.

Results: Spatial clusters of high and low positivity, confirmed cases, and mortality were found, co-located with clusters of low and high social vulnerability in the 3 cities. Evidence was also found for spatial inequities in testing, positivity, confirmed cases, and mortality. Specifically, neighborhoods with higher social vulnerability had lower testing rates and higher positivity ratios, confirmed case rates, and mortality rates.

Until treatment and vaccine for coronavirus disease-2019 (COVID-19) becomes widely available, other methods of reducing infection rates should be explored. This study used a retrospective, observational analysis of deidentified tests performed at a national clinical laboratory to determine if circulating 25-hydroxyvitamin D (25(OH)D) levels are associated with severe acute respiratory disease coronavirus 2 (SARS-CoV-2) positivity rates. Over 190,000 patients from all 50 states with SARS-CoV-2 results performed mid-March through mid-June, 2020 and matching 25(OH)D results from the preceding 12 months were included. Residential zip code data was required to match with US Census data and perform analyses of race/ethnicity proportions and latitude. A total of 191,779 patients were included (median age, 54 years [interquartile range 40.4-64.7]; 68% female. The SARS-CoV-2 positivity rate was 9.3% (95% C.I. 9.2-9.5%) and the mean seasonally adjusted 25(OH)D was 31.7 (SD 11.7). The SARS-CoV-2 positivity rate was higher in the 39,190 patients with "deficient" 25(OH)D values (

Each day, for each region and county, a 7-day test positivity average is calculated by dividing the sum of COVID-19 positive tests for 7 days by the sum of the total COVID-19 tests for the same 7 days, rounded to one decimal place. Whenever the test positivity average (for 7 days) increases from the previous day or is 12% or greater, it is flagged. The data is published online with a three-day lag.

The mechanism for the connection between health and positivity remains murky, but researchers suspect that people who are more positive may be better protected against the inflammatory damage of stress. Another possibility is that hope and positivity help people make better health and life decisions and focus more on long-term goals. Studies also find that negative emotions can weaken immune response.

This study suggests that antigen test positivity is more predictive of infectiousness than RT-PCR test positivity. However, false-negative antigen test results can be obtained for infectious persons, especially among those with symptoms, supporting CDC recommendations to follow negative antigen testing among symptomatic persons with RT-PCR confirmatory testing within 48 hours (14).

The empirical evidence differentiating countries that have succeeded at disease control for COVID-19 from those that have not suggests that test positivity rates are an important indicator. Countries that are successfully finding and isolating cases of COVID-19, and driving case incidence back to near zero, achieve low test positivity rates as part of their work. See this graph from Tomas Pueyo:

Low test positivity means that a lot of tests are being administered to a lot of different people, many of whom may now know they are at risk. (High test positivity rates occur when, for example, the only people being tested are those arriving at urgent care because they are worried they might be sick.) Achieving a low test positivity rate may be the result of stay-at-home orders that lower the incidence of disease in a community, thereby lowering the probability that any tested individual will receive a positive result. Or a low test positivity rate may be the result of a volume of testing wide enough to bring in asymptomatic and mild cases as well as exposed contacts, even if they are asymptomatic. Either way, a low test positivity rate reflects a high surveillance capacity and rapid case identification, supporting other disease control measures such as isolation and more comprehensive targeted hotspot testing. Knowing test positivity rate relative to the number of cases may be more important than tracking the absolute number of tests.

Importantly, test positivity rates provide valuable information only if testing is broadly accessible with uptake across all zip codes. If some zip codes are not represented in testing data, then there may be a hole in the surveillance net. The reason to look at zip codes rather than jurisdictions is that there can be great divergences in patterns of exposure and participation even at this level, deriving from uneven access to healthcare and health insurance.

However, in countries that have successfully suppressed the disease, low positivity rates indicate that risk targeting has been appropriately broad, that testing supplies are sufficiently available, and that there is broad access, not that there is a lack of targeted testing. The positivity rate should be triangulated with other measures, such as determining the percent of contacts traced, the ratio of test positives coming from targeted testing and tracing, rather than from symptomatic patients, and the range of zip codes represented in testing data.

South Korea, for example, pushed their test positivity rate down to 3% immediately after their peak caseload in late February, while concurrently ramping up their contact tracing and targeting program, and have now decreased their positivity rate to less than 1%. Similarly, New Zealand has paired a comprehensive tracing program with a consistently

While the US test positivity rate (seen in Figure 2) and more regionally-specific assessments of testing have been falling since early April, increasing test volumes must be paired with other performance indicators for test targeting and tracing, as in the successful programs abroad, to suppress the disease. At the end of the day, what matters is not simply the volume of testing but rather what is done with the knowledge gathered from the testing.

Percent positivity is not used to determine the COVID-19 Community Level, however, it is included for reference by ZIP code, city/town, and school district. This is the percentage of PCR diagnostic tests confirmed positive out of all PCR tests performed in the area selected from the most recent complete week of data.

You can view historic case rate and percent positivity data by area (city, ZIP, school district) here Version OptionsCOVID-19 DataCommunity Level for Maricopa CountyHeadlineCommunity Transmission Data for Healthcare SettingsCOVID-19 SUMMARY DASHBOARD.

Previous studies examined latitude-related differences in COVID-19 outcomes related to vitamin D [1, 5]. However, to our knowledge, only two studies investigated the direct relationship between vitamin D status and SARS-CoV-2 positivity, and these came to opposite conclusions [6, 7]. Both were based on small numbers of paired SARS-CoV-2 and 25(OH)D results, and neither involved US patients. In this study, we evaluated the association of circulating 25-hydroxyvitamin D [25(OH)D] levels, a measure of vitamin D status, with positivity for SARS-CoV-2 as assessed with nucleic acid amplification testing (NAAT).

In this retrospective, observational analysis of deidentified test results from a clinical laboratory, a Quest Diagnostics-wide unique patient identifier was used to match all results of SARS-CoV-2 testing performed March 9 through June 19, 2020, with 25(OH)D results from the preceding 12 months. Analysis was limited to one SARS-CoV-2 result per patient; patients were considered to have a positive SARS-CoV-2 result if any test result indicated positivity. When multiple 25(OH)D results were available, the most recent was selected. We excluded specimens with inconclusive results (one out of two SARS-CoV-2 targets detected) or missing residential zip code data, which are needed to assign race/ethnicity proportions and latitude. 041b061a72


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