Longitudinal and Quantitative Fecal Shedding Dynamics of SARS-CoV-2, Pepper Mild Mottle Virus and CrAssphage (preprint)

Wastewater-based epidemiology (WBE) emerged during the COVID-19 pandemic as a scalable and broadly applicable method for community-level monitoring of infectious disease burden, though the lack of high-quality, longitudinal fecal shedding data of SARS-CoV-2 and other viruses limits the interpretation and applicability of wastewater measurements. In this study, we present longitudinal, quantitative fecal shedding data for SARS-CoV-2 RNA, as well as the commonly used fecal indicators Pepper Mild Mottle Virus (PMMoV) RNA and crAss-like phage (crAssphage) DNA. The shedding trajectories from 48 SARS-CoV-2 infected individuals suggest a highly individualized, dynamic course of SARS-CoV-2 RNA fecal shedding, with individual measurements varying from below limit of detection to 2.79x10^6 gene copies/mg - dry mass of stool (gc/mg-dw). Of individuals that contributed at least 3 samples covering a range of at least 15 of the first 30 days after initial acute symptom onset, 77.4% had at least one positive SARS-CoV-2 RNA stool sample measurement. We detected PMMoV RNA in at least one sample from all individuals and in 96% (352/367) of samples overall; and measured crAssphage DNA above detection limits in 80% (38/48) of individuals and 48% (179/371) of samples. Median shedding values for PMMoV and crAssphage nucleic acids were 1x10^5 gc/mg-dw and 1.86x10^3 gc/mg-dw, respectively. These results can be used to inform and build mechanistic models to significantly broaden the potential of WBE modeling and to provide more accurate insight into SARS-CoV-2 prevalence estimates.

Variant-specific burden of SARS-CoV-2 in Michigan: March 2020 through November 2021 (preprint)

Accurate estimates of total burden of SARS-CoV-2 are needed to inform policy, planning and response. We sought to quantify SARS-CoV-2 cases, hospitalizations, and deaths by age in Michigan. COVID-19 cases reported to the Michigan Disease Surveillance System were multiplied by age and time-specific adjustment factors to correct for under-detection. Adjustment factors were estimated in a model fit to incidence data and seroprevalence estimates. Age-specific incidence of SARS-CoV-2 hospitalization, death, and vaccination, and variant proportions were estimated from publicly available data. We estimated substantial under-detection of infection that varied by age and time. Accounting for under-detection, we estimate cumulative incidence of infection in Michigan reached 75% by mid-November 2021, and over 87% of Michigan residents were estimated to have had [≥]1 vaccination dose and/or previous infection. Comparing pandemic waves, the relative burden among children increased over time. Adults [≥]80 years were more likely to be hospitalized or die if infected in fall 2020 than if infected during later waves. Our results highlight the ongoing risk of periods of high SARS-CoV-2 incidence despite widespread prior infection and vaccination. This underscores the need for long-term planning for surveillance, vaccination, and other mitigation measures amidst continued response to the acute pandemic.

Containment of a multi-index B.1.1.7 outbreak on a university campus through a genomically-informed public health response (preprint)

The first cluster of SARS-CoV-2 cases with lineage B.1.1.7 in the state of Michigan was identified through intensive university-led surveillance sampling and targeted sequencing. A collaborative investigation and response was conducted by the local and state health departments, and the campus and athletic medicine COVID-19 response teams, using S-gene target failure screening and rapid genomic sequencing to inform containment strategies. A total of 50 cases of B.1.1.7-lineage SARS-CoV-2 were identified in this outbreak, which was due to three coincident introductions of B.1.1.7-lineage SARS-CoV-2, all of which were genetically distinct from lineages which later circulated in the broader community. This investigation demonstrates the successful implementation of a genomically-informed outbreak response which can be extended to university campuses and other settings at high risk for rapid emergence of new variants.

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US (preprint)

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. In 2020, the COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized hundreds of thousands of specific predictions from more than 50 different academic, industry, and independent research groups. This manuscript systematically evaluates 23 models that regularly submitted forecasts of reported weekly incident COVID-19 mortality counts in the US at the state and national level. One of these models was a multi-model ensemble that combined all available forecasts each week. The performance of individual models showed high variability across time, geospatial units, and forecast horizons. Half of the models evaluated showed better accuracy than a naive baseline model. In combining the forecasts from all teams, the ensemble showed the best overall probabilistic accuracy of any model. Forecast accuracy degraded as models made predictions farther into the future, with probabilistic accuracy at a 20-week horizon more than 5 times worse than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks. f

SARS-CoV-2 surveillance in decedents in a large, urban medical examiner's office (preprint)

Background: SARS-CoV-2 has become a global pandemic. Given the challenges in implementing widespread SARS-CoV-2 testing, there is increasing interest in alternative surveillance strategies. Methods: We tested nasopharyngeal swabs from 821 decedents in the Wayne County Medical Examiner's office for SARS-CoV-2. All decedents were assessed by a COVID-19 checklist, and decedents flagged by the checklist (237) were preferentially tested. A random sample of decedents not flagged by the checklist were also tested (584). We statistically analyzed the characteristics of decedents (age, sex, race, and manner of death), differentiating between those flagged by the checklist and not and between those SARS-CoV-2 positive and not. Results: Decedents were more likely to be male (70% vs 48%) and Black (55% vs 36%) than the catchment population. Seven-day average percent positivity among flagged decedents closely matched the trajectory of percent positivity in the catchment population, particularly during the peak of the outbreak (March and April). After a lull in May to mid-June, new positive tests in late June coincided with increased case detection in the catchment. We found large racial disparities in test results: despite no statistical difference in the racial distribution between those flagged and not, SARS-CoV-2 positive decedents were substantially more likely to be Black (89% vs 51%). SARS-CoV-2 positive decedents were also more likely to be older and to have died of natural causes, including of COVID-19 disease. Conclusions: Disease surveillance through medical examiners and coroners could supplement other forms of surveillance and may serve as a possible early outbreak warning sign.