Abstract
BackgroundThe threat of surging COVID-19 cases prompted many hospitals in the United States to preemptively suspend elective procedures throughout the pandemic. Utilizing samples from a large hospital in Los Angeles, we sought to determine if temporal trends in SARS-CoV-2 Cycle threshold (Ct) values (proxy for viral RNA loads) were predictive for the number of future COVID-19 cases.MethodsNasopharyngeal specimens on symptomatic patients and asymptomatic admissions were tested using the Xpert Xpress SARS-CoV-2 and SARS-CoV-2/Flu/RSV assays (Cepheid). Ct values for all SARS-CoV-2 detections between October 2020 to March 2021 were compiled for analysis.ResultsA total of 2,114 SARS-CoV-2-positive samples were included. The number of tests performed per week increased dramatically in December peaking the first week of January before returning to pre-surge numbers by Mid-February. Ct values fell during this same period with values in December and January (25.6±7.8 and 27±7.9, respectively) significantly lower than those of the other months (30±9.3 to 37.7±6.3). Average weekly Ct values for all patients were significantly, negatively correlated with the number of tests run the following week (R= -0.71, P< 0.001) and two weeks later (R= -0.75, P< 0.0001). Ct values for patients who were asymptomatic at the time of testing most strongly correlated with total number of tests performed one month later (R= -0.86, P< 0.0001). Average weekly Ct values and number of test run As cases (light grey) increased during December and January, there was a significant decrease in Ct values (dark grey) during that same time period.Average Ct values are a leading indicator of cases Average weekly Ct values for all patients (light grey) were significantly, negatively correlated with the number of tests run the following week (R= -0.71, P<0.001) and two weeks later (R= -0.75, P<0.0001). Ct values for patients who were asymptomatic at the time of testing (dark grey) most strongly correlated with total number of tests performed one month later (R= -0.86, P<0.0001).ConclusionLower Ct values, representing higher levels of viral RNA, have been associated with risk of intubation and infectivity. During the winter surge, we observed significantly lower Ct values suggesting that the increased transmission and morbidity of COVID-19 was temporarily associated with higher viral loads. Interestingly, Ct values for asymptomatic patients were most strongly associated with number of cases observed 1 months in the future, suggesting that asymptomatic viral load may be a leading indicator for forthcoming outbreaks. Given this association, Ct values may be a useful tool for predicting regional outbreaks of COVID-19 and more judicious cessation of elective procedures.Disclosures All Authors: No reported disclosures
Highlights
Healthcare-associated transmission of SARS-CoV-2 is relatively rare and may be difficult to quantify
Lack of appropriate respiratory hygiene led to SARS-CoV-2 transmission during a single hemodialysis session, based on clinical and genomic epidemiology
Cycle threshold (Ct) values for patients who were asymptomatic at the time of testing most strongly correlated with total number of tests performed one month later (R= -0.86, P< 0.0001)
Summary
Healthcare-associated transmission of SARS-CoV-2 is relatively rare and may be difficult to quantify. The threat of surging COVID-19 cases prompted many hospitals in the United States to preemptively suspend elective procedures throughout the pandemic. Utilizing samples from a large hospital in Los Angeles, we sought to determine if temporal trends in SARS-CoV-2 Cycle threshold (Ct) values (proxy for viral RNA loads) were predictive for the number of future COVID-19 cases. At the beginning of the COVID-19 pandemic there were many questions about vulnerability and data reporting among African countries. We previously found that policymakers in Cameroon value region-specific risk maps for emerging diseases. We created regional vulnerability indices for COVID-19 in Cameroon. We aimed to compare how these predictions related to reported COVID-19 cases in Cameroon and whether additional African countries had available data to assess vulnerability for COVID-19.
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