Many schools and universities have seen a significant increase in the spread of COVID-19. As such, a number of non-pharmaceutical interventions have been proposed including distancing requirements, surveillance testing, and updating ventilation systems. Unfortunately, there is limited guidance for which policy or set of policies are most effective for a specific school system. We develop a novel approach to model the spread of SARS-CoV-2 quanta in a closed classroom environment that extends traditional transmission models that assume uniform mixing through air recirculation by including the local spread of quanta from a contagious source. In addition, the behavior of students with respect to guideline compliance was modeled through an agent-based simulation. Estimated infection rates were on average lower using traditional transmission models compared to our approach. Further, we found that although ventilation changes were effective at reducing mean transmission risk, it had much less impact than distancing practices. Duration of the class was an important factor in determining the transmission risk. For the same total number of semester hours for a class, delivering lectures more frequently for shorter durations was preferable to less frequently with longer durations. Finally, as expected, as the contact tracing level increased, more infectious students were identified and removed from the environment and the spread slowed, though there were diminishing returns. These findings can help provide guidance as to which school-based policies would be most effective at reducing risk and can be used in a cost/comparative effectiveness estimation study given local costs and constraints.
Background The electronic health record (EHR), utilized to apply statistical methodology, assists provider decision-making, including during the care of chronic kidney disease (CKD) patients. When estimated glomerular filtration (eGFR) decreases, the rate of that change adds meaning to a patient’s single eGFR and may represent severity of renal injury. Since the cumulative sum chart technique (CUSUM), often used in quality control and surveillance, continuously checks for change in a series of measurements, we selected this statistical tool to detect clinically relevant eGFR decreases and developed CUSUMGFR. Methods In a retrospective analysis we applied an age adjusted CUSUMGFR, to signal identification of eventual ESKD patients prior to diagnosis date. When the patient signaled by reaching a specified threshold value, days from CUSUM signal date to ESKD diagnosis date (earliness days) were measured, along with the corresponding eGFR measurement at the signal. Results Signaling occurred by CUSUMGFR on average 791 days (se = 12 days) prior to ESKD diagnosis date with sensitivity = 0.897, specificity = 0.877, and accuracy = .878. Mean days prior to ESKD diagnosis were significantly greater in Black patients (905 days) and patients with hypertension (852 days), diabetes (940 days), cardiovascular disease (1027 days), and hypercholesterolemia (971 days). Sensitivity and specificity did not vary by sociodemographic and clinical risk factors. Conclusions CUSUMGFR correctly identified 30.6% of CKD patients destined for ESKD when eGFR was > 60 ml/min/1.73 m2 and signaled 12.3% of patients that did not go on to ESKD (though almost all went on to later-stage CKD). If utilized in an EHR, signaling patients could focus providers’ efforts to slow or prevent progression to later stage CKD and ESKD.
Co-infection of COVID-19 and other respiratory pathogens, including influenza virus family, has been of importance since the beginning of the recent pandemic. As the upcoming flu season arrives in countries with ongoing COVID-19 epidemic, the need for preventive policy actions becomes more critical. We present a joint compartmental SEIRS-SIRS model for the co-circulation of SARS-CoV-2 and influenza and discuss the characteristics of the model, such as the basic reproduction number (R0) and cases of death and recovery. We implemented the model using 2020 to early 2021 data derived from global healthcare organizations and studied the impact of interventions and policy actions such as vaccination, quarantine, and public education. The VENSIM simulation of the model resulted in R0 = 7.5, which is higher than what was reported for the COVID-19 pandemic. Vaccination against COVID-19 dramatically slowed its spread and the co-infection of both diseases significantly, while other types of interventions had a limited impact on the co-dynamics of the diseases given our assumptions. These findings can help provide guidance as to which preventive policies would be most effective at the time of concurrent epidemics, and contributes to the literature as a novel model to simulate and analyze the co-circulation of respiratory pathogens in a compartmental setting that can further be used to study the co-infection of COVID-19 or similar respiratory infections with other diseases.
Background The electronic health record (EHR), utilized to apply statistical methodology, assists provider decision-making, including during the care of chronic kidney disease (CKD) patients. When estimated glomerular filtration (eGFR) decreases, the rate of that change adds meaning to a patient’s single eGFR and may represent severity of renal injury. Since the cumulative sum chart technique (CUSUM), often used in quality control and surveillance, continuously checks for change in a series of measurements, we selected this statistical tool to detect clinically relevant eGFR decreases and developed CUSUMGFR.Methods In a retrospective analysis we applied CUSUMGFR to signal identification of eventual ESKD patients prior to diagnosis date. When the patient signaled by reaching a specified threshold CUSUMGFR value, days from CUSUMGFR signal date to ESKD diagnosis date were measured, along with the corresponding eGFR measurement at the signal. Results Signaling occurred 790 days prior to ESKD diagnosis date with sensitivity of 0.830 and specificity of 0.910. Mean days prior to ESKD diagnosis were significantly greater in Black patients (875 ), and in patients with hypertension (849 ), diabetes (940 ), cardiovascular disease (1037 ), and hypercholesterolemia (971 ). Sensitivity and specificity did not vary by sociodemographic and clinical risk factors.Conclusions CUSUMGFR correctly identified nearly 25% of CKD patients destined for ESKD when eGFR was > 60 ml/min/1.73 m2. If utilized in an EHR, signaling patients could focus providers’ efforts to slow or prevent progression to ESKD.
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