RESUMO
Clinical presentation, outcomes, and duration of COVID-19 has ranged dramatically. While some individuals recover quickly, others suffer from persistent symptoms, collectively known as post-acute sequelae of SAR-CoV-2 (PASC). Most PASC research has focused on hospitalized COVID-19 patients with moderate to severe disease. We used data from a diverse population-based cohort of Arizonans to estimate prevalence of various symptoms of PASC, defined as experiencing at least one symptom 30 days or longer. There were 303 non-hospitalized individuals with a positive lab-confirmed COVID-19 test who were followed for a median of 61 days (range 30-250). COVID-19 positive participants were mostly female (70%), non-Hispanic white (68%), and on average 44 years old. Prevalence of PASC at 30 days post-infection was 68.7% (95%CI 63.4, 73.9). The most common symptoms were fatigue (37.5%), shortness-of-breath (37.5%), brain fog (30.8%), and stress (30.8%). The median number of symptoms was 3 (range 1-20). Amongst 157 participants with longer follow-up ([≥]60 days), PASC prevalence was 77.1%.
RESUMO
Accurate diagnosis of potential SARS-CoV-2 infections by symptoms is one strategy for continuing global surveillance, particularly in low-resource communities. We conducted a prospective, population-based cohort study, the Arizona CoVHORT, among Arizona residents to elucidate the symptom profile of laboratory-confirmed COVID-19 participants(16.2%) compared to laboratory-confirmed negative(22.4%) and untested general population participants(61.4%). Among the 1514 study participants, those who were COVID-19 positive were more likely to be Hispanic(33.5%) and more likely to report obesity > 30 kg/m2(34.7%) compared to COVID-19 negative participants(19.2%; 31.0%) and untested CoVHORT participants(13.8%; 23.8%). Of the 245 laboratory-confirmed COVID-19 cases, 15.0% reported having had no symptoms. Of those that did report symptoms, the most commonly-reported first symptoms were sore throat(19.0%), headache(15.5%), cough(12.7%), runny nose/cold-like symptoms(12.1%), and fatigue(12.0%). In adjusted logistic regression models, COVID-19 positive participants were more likely than negative participants to experience loss of taste and smell(OR:35.7; 95% CI 18.4-69.5); bone or nerve pain(OR:17.9; 95% CI 6.7-47.4), vomiting(OR:10.8; 95% CI 3.1-37.5), nausea(OR:10.5; 95% CI 5.5-19.9), and headache(OR:8.4; 95% CI 5.6-12.8). When comparing confirmed COVID-19 cases with confirmed negative or untested participants, the pattern of symptoms that discriminates SARS-CoV-2 infection from those arising from other potential circulating pathogens may differ from general reports of symptoms among cases alone.
RESUMO
Most Bluetooth-based exposure notification apps use three binary classifications to recommend quarantine following SARS-CoV-2 exposure: a window of infectiousness in the transmitter, [≥]15 minutes duration, and Bluetooth attenuation below a threshold. However, Bluetooth attenuation is not a reliable measure of distance, and infection risk is not a binary function of distance, nor duration, nor timing. We model uncertainty in the shape and orientation of an exhaled virus-containing plume and in inhalation parameters, and measure uncertainty in distance as a function of Bluetooth attenuation. We calculate expected dose by combining this with estimated infectiousness based on timing relative to symptom onset. We calibrate an exponential dose-response curve based on infection probabilities of household contacts. The probability of current or future infectiousness, conditioned on how long post-exposure an exposed individual has been symptom-free, decreases during quarantine, with shape determined by incubation periods, proportion of asymptomatic cases, and asymptomatic shedding durations. It can be adjusted for negative test results using Bayes Theorem. We capture a 10-fold range of risk using 6 infectiousness values, 11-fold range using 3 Bluetooth attenuation bins, [~]6-fold range from exposure duration given the 30 minute duration cap imposed by the Google/Apple v1.1, and [~]11-fold between the beginning and end of 14 day quarantine. Public health authorities can either set a threshold on initial infection risk to determine 14-day quarantine onset, or on the conditional probability of current and future infectiousness conditions to determine both quarantine and duration.
