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PLoS One ; 17(4): e0267111, 2022.
Article in English | MEDLINE | ID: covidwho-1808570


BACKGROUND: Schools are primary venues of influenza amplification with secondary spread to communities. We assessed K-12 student absenteeism monitoring as a means for early detection of influenza activity in the community. MATERIALS AND METHODS: Between September 2014 and March 2020, we conducted a prospective observational study of all-cause (a-TOT), illness-associated (a-I), and influenza-like illness-associated (a-ILI) absenteeism within the Oregon School District (OSD), Dane County, Wisconsin. Absenteeism was reported through the electronic student information system. Students were visited at home where pharyngeal specimens were collected for influenza RT-PCR testing. Surveillance of medically-attended laboratory-confirmed influenza (MAI) occurred in five primary care clinics in and adjoining the OSD. Poisson general additive log linear regression models of daily counts of absenteeism and MAI were compared using correlation analysis. FINDINGS: Influenza was detected in 723 of 2,378 visited students, and in 1,327 of 4,903 MAI patients. Over six influenza seasons, a-ILI was significantly correlated with MAI in the community (r = 0.57; 95% CI: 0.53-0.63) with a one-day lead time and a-I was significantly correlated with MAI in the community (r = 0.49; 0.44-0.54) with a 10-day lead time, while a-TOT performed poorly (r = 0.27; 0.21-0.33), following MAI by six days. DISCUSSION: Surveillance using cause-specific absenteeism was feasible and performed well over a study period marked by diverse presentations of seasonal influenza. Monitoring a-I and a-ILI can provide early warning of seasonal influenza in time for community mitigation efforts.

Absenteeism , Influenza, Human , Humans , Influenza, Human/diagnosis , Influenza, Human/epidemiology , Schools , Students , Wisconsin/epidemiology
J Med Virol ; 93(3): 1568-1572, 2021 03.
Article in English | MEDLINE | ID: covidwho-1196489


The SARS-CoV-2 pandemic has led to an unprecedented demand for diagnostic tests. Many studies have modeled the efficiency gains of specimen pooling, but few have systematically evaluated the dilution effect of pooling on the sensitivity of tests. Using the frequency distribution of cycle threshold (Ct ) values of our first 838 SARS-CoV-2 positive specimens, we modeled 100 specimens on the same frequency distribution. Given this distribution, we then tested dilutions of 1:5, 1:10, and 1:50 to find the percentage of specimens positive at each Ct value with each pool size. Using the frequency distribution and the percentage of specimens positive at each Ct value, we estimate that pools of 5 lead to 93% sensitivity, pools of 10 lead to 91% sensitivity, and pools of 50 lead to 81% sensitivity. Pools of 5 and 10 lead to some specimens with Ct values of ≥32 becoming negative, while pools of 50 lead to some specimens with Ct values of ≥28 becoming negative. These sensitivity estimates can inform laboratories seeking to implement pooling approaches as they seek to balance test efficiency with sensitivity.

COVID-19 Testing/methods , COVID-19/diagnosis , Polymerase Chain Reaction/methods , SARS-CoV-2/genetics , COVID-19/virology , Diagnostic Tests, Routine/methods , Humans , Pandemics/prevention & control , RNA, Viral/genetics , Sensitivity and Specificity , Specimen Handling/methods
J Am Med Dir Assoc ; 21(1): 29-33, 2020 01.
Article in English | MEDLINE | ID: covidwho-124748


Residents of long-term care facilities (LCTFs) have high morbidity and mortality associated with acute respiratory infections (ARIs). Limited information exists on the virology of ARI in LTCFs, where virological testing is reactive. We report on findings of a surveillance feasibility substudy from a larger prospective trial of introducing rapid influenza diagnostic testing (RIDT) at 10 Wisconsin LTCFs. Any resident with symptoms consistent with ARI had a nasal swab specimen collected for RIDT by staff. Following RIDT, the residual swab was placed into viral transport medium and tested for influenza using Reverse transcription polymerase chain reaction, and for 20 pathogens using a multiplex polymerase chain reaction respiratory pathogen panel. Numbers of viruses in each of 7 categories (influenza A, influenza B, coronaviruses, human metapneumovirus, parainfluenza, respiratory syncytial virus, and rhinovirus/enterovirus) across the 3 years were compared using χ2. Totals of 160, 215, and 122 specimens were collected during 2016‒2017, 2017‒2018, and 2018‒2019, respectively. Respiratory pathogen panel identified viruses in 54.8% of tested specimens. Influenza A (19.2%), influenza B (12.6%), respiratory syncytial virus (15.9%), and human metapneumovirus (20.9%) accounted for 69% of all detections, whereas coronaviruses (17.2%), rhinovirus/enterovirus (10.5%) and parainfluenza (3.8%) were less common. The distribution of viruses varied significantly across the 3 years (χ2 = 71.663; df = 12; P < .001). Surveillance in LTCFs using nasal swabs collected for RIDT is highly feasible and yields high virus identification rates. Significant differences in virus composition occurred across the 3 study years. Simple approaches to surveillance may provide a more comprehensive assessment of respiratory viruses in LTCF settings.

Respiratory Tract Infections , Viruses , Humans , Infant , Long-Term Care , Prospective Studies , Respiratory Tract Infections/diagnosis , Respiratory Tract Infections/epidemiology , Wisconsin/epidemiology