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BackgroundWastewater-based epidemiology (WBE) has been deployed broadly as an early warning tool for emerging COVID-19 outbreaks. WBE can inform targeted interventions and identify communities with high transmission, enabling quick and effective response. As wastewater becomes an increasingly important indicator for COVID-19 transmission, more robust methods and metrics are needed to guide public health decision making. ObjectivesThe aim of this research was to develop and implement a mathematical framework to infer incident cases of COVID-19 from SARS-CoV-2 levels measured in wastewater. We propose a classification scheme to assess the adequacy of model training periods based on clinical testing rates and assess the sensitivity of model predictions to training periods. MethodsWe present a Bayesian deconvolution method and linear regression to estimate COVID-19 cases from wastewater data. We described an approach to characterize adequacy in testing during specific time periods and provided evidence to highlight the importance of model training periods on the projection of cases. We estimated the effective reproductive number (Re) directly from observed cases and from the reconstructed incidence of cases from wastewater. The proposed modeling framework was applied to three Northern California communities served by distinct wastewater treatment plants. ResultsBoth deconvolution and linear regression models consistently projected robust estimates of prevalent cases and Re from wastewater influent samples when assuming training periods with adequate testing. Case estimates from models that used poorer-quality training periods consistently underestimated observed cases. DiscussionWastewater surveillance data requires robust statistical modeling methods to provide actionable insight for public health decision-making. We propose and validate a modeling framework that can provide estimates of COVID-19 cases and Re from wastewater data that can be used as tool for disease surveillance including quality assessment for potential training data.
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BackgroundBy March 2021, California had one of the least equitable COVID-19 vaccine distribution programs in the US. To rectify this, Governor Newsom ordered 4 million vaccine doses be reserved for the census tracts in the lowest quartile of the Healthy Places Index (HPI). California plans to lift state-wide restrictions on June 15th, 2021, as long as test positivity and vaccine equity thresholds are met in the states most vulnerable neighborhoods. This short investigation examines current vaccine equity and forecasts where California can expect to be when the economy fully reopens. MethodsCurrent vaccine equity was investigated with simple linear regression between the county mean HPI and both single and full-dose vaccination rate. Future vaccination coverage per county were predicted using a compartmental mathematical model based on the average rate over the previous 30 days with four different rate-change scenarios. ResultsCounty mean HPI had a strong positive association with both single and full dose vaccination rates (R2: 0.716 and 0.737, respectively). We predict the overall state rate will exceed 50% fully vaccinated by June 15th if the current rates are maintained; however, the bulk of this coverage comes from the top 18 counties while the remaining 40 counties lag behind. DiscussionThe clear association between county HPI and current vaccination rates shows that California is not initiating opening plans from an equitable foundation, despite previous equity programs. If nothing changes, many of the most vulnerable counties will not be prepared to open without consequences come June 15th.
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BackgroundEfforts to protect residents in nursing homes involve non-pharmaceutical interventions, testing, and vaccine. We sought to quantify the effect of testing and vaccine strategies on the attack rate, length of the epidemic, and hospitalization. MethodsWe developed an agent-based model to simulate the dynamics of SARS-CoV-2 transmission in a nursing home with resident and staff agents. Interactions between 172 residents and 170 staff were assumed based on data from a nursing home in Los Angeles, CA. We simulated scenarios assuming different levels of non-pharmaceutical interventions, testing frequencies, and vaccine efficacy to block transmission. ResultsUnder the hypothetical scenario of widespread SARS-CoV-2 in the community, 3-day testing frequency minimized the attack rate and the time to eradicate an outbreak. Prioritization of vaccine among staff or staff and residents minimized the cumulative number of infections and hospitalization, particularly in the scenario of high probability of an introduction. Reducing the probability of a virus introduction reduced the demand on testing and vaccine to reduce infections and hospitalizations. ConclusionsImproving frequency of testing from 7-days to 3-days minimized the number of infections and hospitalizations, despite widespread community transmission. Vaccine prioritization of staff provides the best protection strategy, despite high risk of a virus introduction.
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STRUCTURED ABSTRACTO_ST_ABSImportanceC_ST_ABSCharacterization of a diverse cohort hospitalized with COVID-19 in a health care system in California is needed to further understand the impact of SARS-CoV-2 and improve patient outcomes. ObjectivesTo investigate the characteristics of patients hospitalized with COVID-19 and assess factors associated with poor outcomes. DesignPatient-level retrospective cohort study SettingUniversity of California five academic hospitals. ParticipantsPatients [≥]18 years old with a confirmed test result for SAR-CoV-2 virus hospitalized at five UC hospitals. ExposureConfirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection by positive results on polymerase chain reaction testing of a nasopharyngeal sample among patients requiring hospital admission. Main Outcomes and MeasuresAdmission to the intensive care unit, death during hospitalization, and the composite of both outcomes. ResultsOutcomes were assessed for 4,730 patients who were discharged or died during a hospitalization. A total of 846 patients were treated at UC Davis, 1,564 UC Irvine, 1,283 UC Los Angeles, 471 UC San Diego, and 566 UC San Francisco. More than 20% of patients were [≥]75 years of age (75-84: 12.3%, [≥]85: 10.5%), male (56.5%), Hispanic/Latino (45.7%), and Asian (10.3%). The most common comorbidities were hypertension (35.2%), cardiac disease (33.3%), and diabetes (24.0%). The ICU admission rate was 25.2% (1194/4730), with 7.0% (329/4730) in-hospital mortality. Among patients admitted to the ICU, 18.8% (225/1194) died; 2.9% (104/3536) died without ICU admission. The rate of the composite outcome (ICU admission and/or death) was 27.4% (1,298/4,730). While controlling for comorbidities, patients of age 75-84 (OR 1.47, 95% CI: 1.11-1.93) and 85-59 (OR 1.39, 95% CI: 1.04-1.87) were more likely to experience a composite outcome than 18-34 year-olds. Males (OR 1.39, 95% CI: 1.21-1.59), and patients identifying as Hispanic/Latino (OR 1.35, 95% CI: 1.14-1.61), and Asian (OR 1.43, 95% CI: 1.23-1.82), were also more likely to experience a composite outcome than White. Patients with 5 or more comorbidities were exceedingly likely to experience a composite outcome (OR 2.74, 95% CI: 2.32-3.25). ConclusionsMales, older patients, those with pre-existing comorbidities, and those identifying as Hispanic/Latino or Asian experienced an increased risk of ICU admission and/or death. KEY POINTSO_ST_ABSQuestionC_ST_ABSWhat are the characteristics and outcomes of patients with SARS-CoV-2 infection hospitalized at five UC Health medical centers in California? FindingsIn this retrospective case series of 4,730 patients requiring hospitalization for COVID-19 in UC Healths five medical centers, male (OR 1.41, 95% CI: 1.23-1.61), Hispanic/Latino (OR 1.35, 95% CI: 1.14-1.61), and Asian (OR 1.43, 95% CI: 1.12-1.82) were more likely to be admitted to the ICU and/or die after adjustment for age and comorbidity. ICU admission and/or death was more likely among older individuals and greater numbers of pre-existing conditions. MeaningThis study describes the experience of a large, diverse cohort of patients with COVID-19 hospitalized in five hospitals in California between December 14, 2019 and January 6, 2021.
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ObjectiveUsing a pandemic influenza model modified for COVID-19, this study investigated the degree of control over pre-symptomatic transmission that common non-pharmaceutical interventions (NPIs) would require to reduce the spread in long-term care facilities. MethodsWe created a stochastic compartmental SEIR model with Poisson-distributed transition states that compared the effect of R0, common NPIs, and isolation rates of pre-symptomatic carriers primarily on attack rate, peak cases, and timing in a 200-resident nursing home. Model sensitivity was assessed with 1st order Sobol indices. ResultsThe most rigorous NPIs decreased the peak number of infections by 4.3 and delayed the peak by 9.7 days in the absence of pre-symptomatic controls. Reductions in attack rate were not likely, even with rigorous application of all defined NPIs, unless pre-symptomatic carriers were identified and isolated at rates exceeding 76%. Attack rate was most sensitive to the pre-symptomatic isolation rate (Sobol index > 0.7) and secondarily to R0. ConclusionsCommon NPIs delayed and reduced epidemic peaks. Reducing attack rates ultimately required efficient isolation of pre-symptomatic cases, including rapid antigen tests on a nearly daily basis. This must be accounted for in testing and contact tracing plans for group living settings.
