Incorporating and addressing testing bias within estimates of epidemic dynamics for SARS-CoV-2

Lower Levels of Household Transmission of SARS-CoV-2 Omicron Variant of Concern vs Wild Type: An Interplay Between Transmissibility and Immune Status

BACKGROUND

Knowledge of SARS-CoV-2 household transmission dynamics guides infection control and vaccination measures. This household cohort study prospectively assessed the impact of the Omicron BA.2 variant and immunity on household transmission using dense saliva sampling and sequence analysis.

METHOD

Households consisting of a polymerase chain reaction-confirmed index and at least 2 household members were enrolled in March and April 2022 during the Omicron BA.2 wave in the Netherlands. SARS-CoV-2 polymerase chain reaction was performed on 10 consecutive saliva samples. Serum antibodies were measured at baseline and day 42. Household and per-person secondary attack rates (SARs) were calculated to measure transmission. Whole genome sequencing was performed for phylogenetic analysis, followed by sensitivity analysis, to correct for multiple household introductions and index definition. Results were compared with the identical, early-pandemic, preimmunization predecessor study.

RESULTS

Sixty-seven households were included, consisting of 241 individuals (median age, 33.0 years; IQR, 12.0-46.0). Maximum household SAR was 59.7% and per-person SAR 41.5%. Pediatric index cases were more likely to transmit. Transmission was negatively affected by household members' immunity. Phylogenetic analysis showed multiple introductions in 4 households. Sensitivity analysis resulted in a minimal household SAR of 51.0% and per-person SAR of 28.5%.

CONCLUSIONS

The Omicron BA.2 variant is highly transmissible within households. However, the transmission rate is lower when compared with previous studies with other SARS-CoV-2 variants, highlighting the effect of immunity. Regardless of immune status, children have a crucial role in Omicron household transmission. Intensive sampling and phylogenetic analysis are beneficial for correctly calculating transmission rates, especially during periods of minimal behavioral restrictions.

Diagnostic performance of allele-specific RT-qPCR and genomic sequencing in wastewater-based surveillance of SARS-CoV-2

Clinical genomic surveillance is regarded as the gold standard for monitoring SARS-CoV-2 variants globally. However, as the pandemic wanes, reduced testing poses a risk to effectively tracking the trajectory of these variants within populations. Wastewater-based genomic surveillance that estimates variant frequency based on its defining set of alleles derived from clinical genomic surveillance has been successfully implemented. This method has its challenges, and allele-specific (AS) RT-qPCR or RT-dPCR may instead be used as a complementary method for estimating variant prevalence. Demonstrating equivalent performance of these methods is a prerequisite for their continued application in current and future pandemics. Here, we compared single-allele frequency using AS-RT-qPCR, to single-allele or haplotype frequency estimations derived from amplicon-based sequencing to estimate variant prevalence in wastewater during emergent and prevalent periods of Delta, Omicron, and two sub-lineages of Omicron. We found that all three methods of frequency estimation were concordant and contained sufficient information to describe the trajectory of variant prevalence. We further confirmed the accuracy of these methods by quantifying the diagnostic performance through Youden's index. The Youden's index of AS-RT-qPCR was reduced during the low prevalence period of a particular variant while the same allele in sequencing was negatively influenced due to insufficient read depth. Youden's index of haplotype-based calls was negatively influenced when alleles were common between variants. Coupling AS-RT-qPCR with sequencing can overcome the shortcomings of either platform and provide a comprehensive picture to the stakeholders for public health responses.

Nowcasting methods to improve the performance of respiratory sentinel surveillance: lessons from the COVID-19 pandemic

Respiratory diseases, including influenza and coronaviruses, pose recurrent global threats. This study delves into the respiratory surveillance systems, focusing on the effectiveness of SARI sentinel surveillance for total and severe cases incidence estimation. Leveraging data from the COVID-19 pandemic in Chile, we examined 2020-2023 data (a 159-week period) comparing census surveillance results of confirmed cases and hospitalizations, with sentinel surveillance. Our analyses revealed a consistent underestimation of total cases and an overestimation of severe cases of sentinel surveillance. To address these limitations, we introduce a nowcasting model, improving the precision and accuracy of incidence estimates. Furthermore, the integration of genomic surveillance data significantly enhances model predictions. While our findings are primarily focused on COVID-19, they have implications for respiratory virus surveillance and early detection of respiratory epidemics. The nowcasting model offers real-time insights into an outbreak for public health decision-making, using the same surveillance data that is routinely collected. This approach enhances preparedness for emerging respiratory diseases by the development of practical solutions with applications in public health.

Developing scientific literacy with a cyclic independent study assisted CURE detecting SARS-CoV-2 in wastewater

The COVID-19 pandemic has exposed a high level of scientific illiteracy and mistrust that pervades the scientific and medical communities. This finding has proven the necessity of updating current methods used to expose undergraduates to research. The research in traditional course-based undergraduate research experiences (CUREs) is limited by undergraduate time constraints, skill level, and course structure, and consequently it does not attain the learning objectives or the high-impact, relevant studies achieved in graduate-level laboratories using a cyclic trainee/trainer model. Although undergraduate independent study (ISY) research more closely matches the structure and learning objectives of graduate-level research, they are uncommon as professors and universities typically view them as a significant time and resource burden with limited return. Cyclic independent study-assisted CUREs (CIS-CUREs) combine many positive aspects of ISY graduate-level research, and CUREs by pre-training ISY research lead to facilitate CURE proposal and project semesters in a cyclic model. The CIS-CURE approach allowed undergraduate students at Stetson University to perform and disseminate more rigorous, involved, long-term, and challenging research projects, such as the surveillance of SARS-CoV-2 in wastewater. In doing so, all students would have the opportunity to participate in a high-impact research project and consequently gain a more comprehensive training, reach higher levels of research dissemination, and increase their competitiveness after graduating. Together, CIS-CUREs generate graduates with higher scientific literacy and thus combat scientific mistrust in communities.

Wastewater surveillance in the COVID-19 post-emergency pandemic period: A promising approach to monitor and predict SARS-CoV-2 surges and evolution

On May 24, 2023, approximately 3.5 years into the pandemic, the World Health Organization (WHO) declared the end of the COVID-19 global health emergency. However, as there are still ∼3000 COVID-19 deaths per day in May 2023, robust surveillance systems are still warranted to return to normalcy in times of low risk and respond appropriately in times of high risk. The different phases of the pandemic have been defined by infection numbers and variants, both of which have been determined through clinical tests that are subject to many biases. Unfortunately, the end of the COVID-19 emergency threatens to exasperate these biases, thereby warranting alternative tracking methods. We hypothesized that wastewater surveillance could be used as a more accurate and comprehensive method to track SARS-CoV-2 in the post-emergency pandemic period (PEPP). SARS-CoV-2 was quantified and sequenced from wastewater between June 2022 and March 2023 to research the anticipated 2022/23 winter surge. However, in the 2022/23 winter, there was lower-than-expected SARS-CoV-2 circulation, which was hypothesized to be due to diagnostic testing biases but was confirmed by our wastewater analysis, thereby emphasizing the unpredictable nature of SARS-CoV-2 surges while also questioning its winter seasonality. Even in times of low baseline circulation, we found wastewater surveillance to be sensitive enough to detect minor changes in circulation levels ∼30-46 days prior to diagnostic tests, suggesting that wastewater surveillance may be a more appropriate early warning system to prepare for unpredictable surges in the PEPP. Furthermore, sequencing of wastewater detected variants of concern that were positively correlated with clinical samples and also provided a method to identify mutations with a high likelihood of appearing in future variants, necessary for updating vaccines and therapeutics prior to novel variant circulation. Together, these data highlight the effectiveness of wastewater surveillance in the PEPP to limit the global health burden of SARS-CoV-2 due to increases in circulation and/or viral evolution.

Rise and fall of SARS-CoV-2 variants in Rotterdam: Comparison of wastewater and clinical surveillance

Monitoring of SARS-CoV-2 in wastewater (WW) is a promising tool for epidemiological surveillance, correlating not only viral RNA levels with the infection dynamics within the population, but also to viral diversity. However, the complex mixture of viral lineages in WW samples makes tracking of specific variants or lineages circulating in the population a challenging task. We sequenced sewage samples of 9 WW-catchment areas within the city of Rotterdam, used specific signature mutations from individual SARS-CoV-2 lineages to estimate their relative abundances in WW and compared them against those observed in clinical genomic surveillance of infected individuals between September 2020 and December 2021. We showed that especially for dominant lineages, the median of the frequencies of signature mutations coincides with the occurrence of those lineages in Rotterdam's clinical genomic surveillance. This, along with digital droplet RT-PCR targeting signature mutations of specific variants of concern (VOCs), showed that several VOCs emerged, became dominant and were replaced by the next VOC in Rotterdam at different time points during the study. In addition, single nucleotide variant (SNV) analysis provided evidence that spatio-temporal clusters can also be discerned from WW samples. We were able to detect specific SNVs in sewage, including one resulting in the Q183H amino acid change in the Spike gene, that was not captured by clinical genomic surveillance. Our results highlight the potential use of WW samples for genomic surveillance, increasing the set of epidemiological tools to monitor SARS-CoV-2 diversity.

Exposure to infection when accessing groceries reveals racial and socioeconomic inequities in navigating the pandemic

Disasters often create inequitable consequences along racial and socioeconomic lines, but a pandemic is distinctive in that communities must navigate the ongoing hazards of infection exposure. We examine this for accessing essential needs, specifically groceries. We propose three strategies for mitigating risk when accessing groceries: visit grocery stores less often; prioritize generalist grocery stores; seek out stores whose clientele have lower infection rates. The study uses a unique combination of data to examine racial and socioeconomic inequities in the ability to employ these strategies in the census block groups of greater Boston, MA in April 2020, including cellphone-generated GPS records to observe store visits, a resident survey, localized infection rates, and demographic and infrastructural characteristics. We also present an original quantification of the amount of infection risk exposure when visiting grocery stores using visits, volume of visitors at each store, and infection rates of those visitors' communities. Each of the three strategies for mitigating exposure were employed in Boston, though differentially by community. Communities with more Black and Latinx residents and lower income made relatively more grocery store visits. This was best explained by differential use of grocery delivery services. Exposure and exposure per visit were higher in communities with more Black and Latinx residents and higher infection rates even when accounting for strategies that diminish exposure. The findings highlight two forms of inequities: using wealth to transfer risk to others through grocery deliveries; and behavioral segregation by race that makes it difficult for marginalized communities to avoid hazards.

Challenges and Opportunities for Global Genomic Surveillance Strategies in the COVID-19 Era

Global SARS-CoV-2 genomic surveillance efforts have provided critical data on the ongoing evolution of the virus to inform best practices in clinical care and public health throughout the pandemic. Impactful genomic surveillance strategies generally follow a multi-disciplinary pipeline involving clinical sample collection, viral genotyping, metadata linkage, data reporting, and public health responses. Unfortunately, current limitations in each of these steps have compromised the overall effectiveness of these strategies. Biases from convenience-based sampling methods can obfuscate the true distribution of circulating variants. The lack of standardization in genotyping strategies and bioinformatic expertise can create bottlenecks in data processing and complicate interpretation. Limitations and inconsistencies in clinical and demographic data collection and sharing can slow the compilation and limit the utility of comprehensive datasets. This likewise can complicate data reporting, restricting the availability of timely data. Finally, gaps and delays in the implementation of genomic surveillance data in the public health sphere can prevent officials from formulating effective mitigation strategies to prevent outbreaks. In this review, we outline current SARS-CoV-2 global genomic surveillance methods and assess roadblocks at each step of the pipeline to identify potential solutions. Evaluating the current obstacles that impede effective surveillance can improve both global coordination efforts and pandemic preparedness for future outbreaks.

Results from Canton Grisons of Switzerland suggest repetitive testing reduces SARS-CoV-2 incidence (February-March 2021)

In February 2021, in response to emergence of more transmissible SARS-CoV-2 virus variants, the Canton Grisons launched a unique RNA mass testing program targeting the labour force in local businesses. Employees were offered weekly tests free of charge and on a voluntary basis. If tested positive, they were required to self-isolate for ten days and their contacts were subjected to daily testing at work. Thereby, the quarantine of contact persons could be waved.Here, we evaluate the effects of the testing program on the tested cohorts. We examined 121,364 test results from 27,514 participants during February-March 2021. By distinguishing different cohorts of employees, we observe a noticeable decrease in the test positivity rate and a statistically significant reduction in the associated incidence rate over the considered period. The reduction in the latter ranges between 18 and 50%. The variability is partly explained by different exposures to exogenous infection sources (e.g., contacts with visiting tourists or cross-border commuters). Our analysis provides the first empirical evidence that applying repetitive mass testing to a real population over an extended period of time can prevent spread of COVID-19 pandemic. However, to overcome logistic, uptake, and adherence challenges it is important that the program is carefully designed and that disease incursion from the population outside of the program is considered and controlled.

Global Prevalence of Post COVID-19 Condition or Long COVID: A Meta-Analysis and Systematic Review

Introduction

This study aims to examine the worldwide prevalence of post COVID-19 condition, through a systematic review and meta-analysis.

Methods

PubMed, Embase, and iSearch were searched on July 5, 2021 with verification extending to March 13, 2022. Using a random effects framework with DerSimonian-Laird estimator, we meta-analyzed post COVID-19 condition prevalence at 28+ days from infection.

Results

50 studies were included, and 41 were meta-analyzed. Global estimated pooled prevalence of post COVID-19 condition was 0.43 (95% CI: 0.39,0.46). Hospitalized and non-hospitalized patients have estimates of 0.54 (95% CI: 0.44,0.63) and 0.34 (95% CI: 0.25,0.46), respectively. Regional prevalence estimates were Asia— 0.51 (95% CI: 0.37,0.65), Europe— 0.44 (95% CI: 0.32,0.56), and North America— 0.31 (95% CI: 0.21,0.43). Global prevalence for 30, 60, 90, and 120 days after infection were estimated to be 0.37 (95% CI: 0.26,0.49), 0.25 (95% CI: 0.15,0.38), 0.32 (95% CI: 0.14,0.57) and 0.49 (95% CI: 0.40,0.59), respectively. Fatigue was the most common symptom reported with a prevalence of 0.23 (95% CI: 0.17,0.30), followed by memory problems (0.14 [95% CI: 0.10,0.19]).

Discussion

This study finds post COVID-19 condition prevalence is substantial; the health effects of COVID-19 appear to be prolonged and can exert stress on the healthcare system.