SARS-CoV-2 variants of concern Alpha and Delta show increased viral load in saliva.. [PMID: 35194615 PMCID: PMC8863157 DOI: 10.1101/2022.02.10.22270797]

An epidemiological modeling framework to inform institutional-level response to infectious disease outbreaks: a Covid-19 case study

Institutions have an enhanced ability to implement tailored mitigation measures during infectious disease outbreaks. However, macro-level predictive models are inefficient for guiding institutional decision-making due to uncertainty in local-level model input parameters. We present an institutional-level modeling toolkit used to inform prediction, resource procurement and allocation, and policy implementation at Clemson University throughout the Covid-19 pandemic. Through incorporating real-time estimation of disease surveillance and epidemiological measures based on institutional data, we argue this approach helps minimize uncertainties in input parameters presented in the broader literature and increases prediction accuracy. We demonstrate this through case studies at Clemson and other university settings during the Omicron BA.1 and BA.4/BA.5 variant surges. The input parameters of our toolkit are easily adaptable to other institutional settings during future health emergencies. This methodological approach has potential to improve public health response through increasing the capability of institutions to make data-informed decisions that better prioritize the health and safety of their communities while minimizing operational disruptions.

Key Considerations during the Transition from the Acute Phase of the COVID-19 Pandemic: A Narrative Review

The COVID-19 pandemic has been met with an unprecedented response from the scientific community, leading to the development, investigation, and authorization of vaccines and antivirals, ultimately reducing the impact of SARS-CoV-2 on global public health. However, SARS-CoV-2 is far from being eradicated, continues to evolve, and causes substantial health and economic burdens. In this narrative review, we posit essential points on SARS-CoV-2 and its responsible management during the transition from the acute phase of the COVID-19 pandemic. As discussed, despite Omicron (sub)variant(s) causing clinically milder infections, SARS-CoV-2 is far from being a negligible pathogen. It requires continued genomic surveillance, particularly if one considers that its future (sub)lineages do not necessarily have to be milder. Antivirals and vaccines remain the essential elements in COVID-19 management. However, the former could benefit from further development and improvements in dosing, while the seasonal administration of the latter requires simplification to increase interest and tackle vaccine hesitancy. It is also essential to ensure the accessibility of COVID-19 pharmaceuticals and vaccines in low-income countries and improve the understanding of their use in the context of the long-term goals of SARS-CoV-2 management. Regardless of location, the primary role of COVID-19 awareness and education must be played by healthcare workers, who directly communicate with patients and serve as role models for healthy behaviors.

An epidemiological modeling framework to inform institutional-level response to infectious disease outbreaks: A Covid-19 case study

Institutions have an enhanced ability to implement tailored mitigation measures during infectious disease outbreaks. However, macro-level predictive models are inefficient for guiding institutional decision-making due to uncertainty in local-level model input parameters. We present an institutional-level modeling toolkit used to inform prediction, resource procurement and allocation, and policy implementation at Clemson University throughout the Covid-19 pandemic. Through incorporating real-time estimation of disease surveillance and epidemiological measures based on institutional data, we argue this approach helps minimize uncertainties in input parameters presented in the broader literature and increases prediction accuracy. We demonstrate this through case studies at Clemson and other university settings during the Omicron BA.1 and BA.4/BA.5 variant surges. The input parameters of our toolkit are easily adaptable to other institutional settings during future health emergencies. This methodological approach has potential to improve public health response through increasing the capability of institutions to make data-informed decisions that better prioritize the health and safety of their communities while minimizing operational disruptions.

Size distribution and relationship of airborne SARS-CoV-2 RNA to indoor aerosol in hospital ward environments

Aerosol particles proved to play a key role in airborne transmission of SARS-CoV-2 viruses. Therefore, their size-fractionated collection and analysis is invaluable. However, aerosol sampling in COVID departments is not straightforward, especially in the sub-500-nm size range. In this study, particle number concentrations were measured with high temporal resolution using an optical particle counter, and several 8 h daytime sample sets were collected simultaneously on gelatin filters with cascade impactors in two different hospital wards during both alpha and delta variants of concern periods. Due to the large number (152) of size-fractionated samples, SARS-CoV-2 RNA copies could be statistically analyzed over a wide range of aerosol particle diameters (70-10 µm). Our results revealed that SARS-CoV-2 RNA is most likely to exist in particles with 0.5-4 µm aerodynamic diameter, but also in ultrafine particles. Correlation analysis of particulate matter (PM) and RNA copies highlighted the importance of indoor medical activity. It was found that the daily maximum increment of PM mass concentration correlated the most with the number concentration of SARS-CoV-2 RNA in the corresponding size fractions. Our results suggest that particle resuspension from surrounding surfaces is an important source of SARS-CoV-2 RNA present in the air of hospital rooms.

Predicting COVID-19 Infected Individuals in a Defined Population from Wastewater RNA Data

Wastewater surveillance of SARS-CoV-2 RNA has become an important tool for tracking the presence of the virus and serving as an early indicator for the onset of rapid transmission. Nevertheless, wastewater data are still not commonly used to predict the number of infected individuals in a sewershed. The main objective of this study was to calibrate a susceptible-exposed-infectious-recovered (SEIR) model using RNA copy rates in sewage (i.e., gene copies per liter times flow rate) and the number of SARS-CoV-2 saliva-test-positive infected individuals in a university student population that was subject to repeated weekly testing during the Spring 2021 semester. A strong correlation was observed between the RNA copy rates and the number of infected individuals. The parameter in the SEIR model that had the largest impact on calibration was the maximum shedding rate, resulting in a mean value of 7.72 log10 genome copies per gram of feces. Regressing the saliva-test-positive infected individuals on predictions from the SEIR model based on the RNA copy rates yielded a slope of 0.87 (SE=0.11), which is statistically consistent with a 1:1 relationship between the two. These findings demonstrate that wastewater surveillance of SARS-CoV-2 can be used to estimate the number of infected individuals in a sewershed.

Evaluating methodological approaches to assess the severity of infection with SARS-CoV-2 variants: scoping review and applications on Belgian COVID-19 data

Background

Differences in the genetic material of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants may result in altered virulence characteristics. Assessing the disease severity caused by newly emerging variants is essential to estimate their impact on public health. However, causally inferring the intrinsic severity of infection with variants using observational data is a challenging process on which guidance is still limited. We describe potential limitations and biases that researchers are confronted with and evaluate different methodological approaches to study the severity of infection with SARS-CoV-2 variants.

Methods

We reviewed the literature to identify limitations and potential biases in methods used to study the severity of infection with a particular variant. The impact of different methodological choices is illustrated by using real-world data of Belgian hospitalized COVID-19 patients.

Results

We observed different ways of defining coronavirus disease 2019 (COVID-19) disease severity (e.g., admission to the hospital or intensive care unit versus the occurrence of severe complications or death) and exposure to a variant (e.g., linkage of the sequencing or genotyping result with the patient data through a unique identifier versus categorization of patients based on time periods). Different potential selection biases (e.g., overcontrol bias, endogenous selection bias, sample truncation bias) and factors fluctuating over time (e.g., medical expertise and therapeutic strategies, vaccination coverage and natural immunity, pressure on the healthcare system, affected population groups) according to the successive waves of COVID-19, dominated by different variants, were identified. Using data of Belgian hospitalized COVID-19 patients, we were able to document (i) the robustness of the analyses when using different variant exposure ascertainment methods, (ii) indications of the presence of selection bias and (iii) how important confounding variables are fluctuating over time.

Conclusions

When estimating the unbiased marginal effect of SARS-CoV-2 variants on the severity of infection, different strategies can be used and different assumptions can be made, potentially leading to different conclusions. We propose four best practices to identify and reduce potential bias introduced by the study design, the data analysis approach, and the features of the underlying surveillance strategies and data infrastructure.

Omicron Variant Generates a Higher and More Sustained Viral Load in Nasopharynx and Saliva Than the Delta Variant of SARS-CoV-2

The Omicron variant of SARS-CoV-2 spreads more easily than earlier variants, possibly as a result of a higher viral load in the upper respiratory tract and oral cavity. Hence, we investigated whether the Omicron variant generates a higher viral load than that of the Delta variant in saliva and nasopharynx. Both specimens were collected from 52 Omicron and 17 Delta cases at two time points one week apart and analyzed by qRT-PCR. Viral load was measured as 10 log RNA genome copies per 1000 human cells according to the WHO reference standard. We found that Omicron cases carried a higher viral load and had more sustained viral shedding compared to the Delta cases, especially in the nasopharynx.