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. 2021 Jun;24(2):320-329.
doi: 10.1007/s10729-020-09525-1. Epub 2020 Oct 28.

Aligning SARS-CoV-2 indicators via an epidemic model: application to hospital admissions and RNA detection in sewage sludge

Edward H Kaplan  1   2   3 , Dennis Wang  4 , Mike Wang  5 , Amyn A Malik  6 , Alessandro Zulli  7 , Jordan Peccia  7
Affiliations

Aligning SARS-CoV-2 indicators via an epidemic model: application to hospital admissions and RNA detection in sewage sludge

Edward H Kaplan et al. Health Care Manag Sci. 2021 Jun.

Abstract

Ascertaining the state of coronavirus outbreaks is crucial for public health decision-making. Absent repeated representative viral test samples in the population, public health officials and researchers alike have relied on lagging indicators of infection to make inferences about the direction of the outbreak and attendant policy decisions. Recently researchers have shown that SARS-CoV-2 RNA can be detected in municipal sewage sludge with measured RNA concentrations rising and falling suggestively in the shape of an epidemic curve while providing an earlier signal of infection than hospital admissions data. The present paper presents a SARS-CoV-2 epidemic model to serve as a basis for estimating the incidence of infection, and shows mathematically how modeled transmission dynamics translate into infection indicators by incorporating probability distributions for indicator-specific time lags from infection. Hospital admissions and SARS-CoV-2 RNA in municipal sewage sludge are simultaneously modeled via maximum likelihood scaling to the underlying transmission model. The results demonstrate that both data series plausibly follow from the transmission model specified and provide a 95% confidence interval estimate of the reproductive number R0 ≈ 2.4 ± 0.2. Sensitivity analysis accounting for alternative lag distributions from infection until hospitalization and sludge RNA concentration respectively suggests that the detection of viral RNA in sewage sludge leads hospital admissions by 3 to 5 days on average. The analysis suggests that stay-at-home restrictions plausibly removed 89% of the population from the risk of infection with the remaining 11% exposed to an unmitigated outbreak that infected 9.3% of the total population.

Keywords: COVID-19; COVID-19 hospital admissions; Epidemic indicators; Probability model; SARS-CoV-2; Sewage sludge viral RNA concentration; Wastewater epidemiology.

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Figures

Fig. 1
Fig. 1
Model-scale infection indicators (all units in infections per person per unit time): SARS-CoV-2 incidence (solid line), sludge viral load (dashed line), hospital admissions (dotted line)
Fig. 2
Fig. 2
Daily COVID-19 hospital admissions: observed data (solid line), model-based expected value (dashed line), 95% prediction interval limits (dotted line)
Fig. 3
Fig. 3
SARS-CoV-2 RNA Copies x 105 / ml Sludge: observed data (solid line), model-based expected value (dashed line), 95% prediction interval limits (dotted line)
Fig. 4
Fig. 4
a Hospitalization lag density functions indicating short (mean 12 days), Lewnard et al (mean 13.5 days), and long (mean 15 days) lags. b Forward generation time densities indicating early transmission (Park et al) and late transmission (Li et al) of infection
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