Characterizing features of outbreak duration for novel SARS-CoV-2 variants of concern (preprint)

Characterizing the dynamics of epidemic trajectories is critical to understanding the potential impacts of emerging outbreaks and to designing appropriate mitigation strategies. As the COVID-19 pandemic evolves, however, the emergence of SARS-CoV-2 variants of concern has complicated our ability to assess in real-time the potential effects of imminent outbreaks, such as those presently caused by the Omicron variant. Here, we report that SARS-CoV-2 outbreaks across regions exhibit strain-specific times from onset to peak, specifically for Delta and Omicron variants. Our findings may facilitate real-time identification of peak medical demand and may help fine-tune ongoing and future outbreak mitigation deployment efforts.

The Evolving Roles of US Political Partisanship and Social Vulnerability in the COVID-19 Pandemic from February 2020 - February 2021 (preprint)

The COVID-19 pandemic has had intense, heterogeneous impacts on different communities and geographies in the United States. We explore county level associations between COVID-19 attributed deaths and social, demographic, vulnerability, and political variables to develop a better understanding of the evolving roles these variables play in relation to mortality. We focus on the role of political variables, as captured by support for either the Republican or Democrat presidential candidates in the 2020 elections and the stringency of state-wide governor mandates, during three non-overlapping time periods between February 2020 and February 2021. We find that during the first three months of the pandemic, Democratic-leaning and internationally-connected urban counties were affected. During subsequent months (between May and September, 2020), Republican counties with high percentages of Hispanic and Black populations were most hardly hit. Importantly, in the time period between October 2020 and February 2021, when the effectiveness of non-pharmaceutical interventions, such as social distancing and wearing masks indoors, had been well-established. During this period, we find that Republican-leaning counties experienced up to 3 times higher death rates than Democratic-leaning counties, even after controlling for multiple social vulnerability factors. We also find that Republican-leaning counties in states with less strict mandates experienced the most severe outbreaks. Our findings suggest that ideologies promoted by prominent political actors may not align with efforts to mitigate the impact of the ongoing pandemic and prevent deaths.

Population Impact of SARS-CoV-2 Variants with Enhanced Transmissibility and/or Partial Immune Escape (preprint)

SARS-CoV-2 variants of concern exhibit varying degrees of transmissibility and, in some cases, escape from infection- and vaccine-induced immunity. Much effort has been devoted to measuring these phenotypes, but predicting their impact on the course of the pandemic – especially that of immune escape – remains a challenge. Here, we use a mathematical model to simulate the dynamics of wildtype and variant strains of SARS-CoV-2 in the context of vaccine rollout and nonpharmaceutical interventions. We show that variants with enhanced transmissibility easily rise to high frequency, whereas partial immune escape, on its own, often fails to do so. However, when these phenotypes are combined, enhanced transmissibility can carry the variant to high frequency, at which point partial immune escape may limit the ability of vaccination to control the epidemic. Our findings suggest that moderate immune escape poses a low risk unless combined with a substantial increase in transmissibility.Funding: MB and BPT were supported by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health under award number R01AI128344. RK, ML and WPH were supported by the U.S. National Cancer Institute SeroNet cooperative agreement U01CA261277.Declaration of Interests: RK discloses consulting fees from Partners In Health and the PanAmerican Health Organization. ML received funding through his institution from US CDC, NIH, and UK National Institute for Health Research, and Pfizer, and consulting fees or honoraria from Merck,Sanofi Pasteur, Janssen, and Bristol Myers Squibb. He is a member of the Scientific Advisory Board for CEPI, the Coalition for Epidemic Preparedness Innovations. WPH serves on the Advisory Board of Biobot Analytics and has received compensation for expert witness testimony on the course of the SARS-CoV-2 pandemic. All others have nothing to disclose.

Metrics to relate COVID-19 wastewater data to clinical testing dynamics (preprint)

Wastewater surveillance has emerged as a useful tool in the public health response to the COVID-19 pandemic. While wastewater surveillance has been applied at various scales to monitor population-level COVID-19 dynamics, there is a need for quantitative metrics to interpret wastewater data in the context of public health trends. We collected 24-hour composite wastewater samples from March 2020 through May 2021 from a Massachusetts wastewater treatment plant and measured SARS-CoV-2 RNA concentrations using RT-qPCR. We show that the relationship between wastewater viral titers and COVID-19 clinical cases and deaths varies over time. We demonstrate the utility of three new metrics to monitor changes in COVID-19 epidemiology: (1) the ratio between wastewater viral titers and clinical cases (WC ratio), (2) the time lag between wastewater and clinical reporting, and (3) a transfer function between the wastewater and clinical case curves. We find that the WC ratio increases after key events, providing insight into the balance between disease spread and public health response. We also find that wastewater data preceded clinically reported cases in the first wave of the pandemic but did not serve as a leading indicator in the second wave, likely due to increased testing capacity. These three metrics could complement a framework for integrating wastewater surveillance into the public health response to the COVID-19 pandemic and future pandemics.

SARS-CoV-2 Sequence Characteristics of COVID-19 Persistence and Reinfection (preprint)

BackgroundBoth SARS-CoV-2 reinfection and persistent infection have been described, but a systematic assessment of mutations is needed. We assessed sequences from published cases of COVID-19 reinfection and persistence, characterizing the hallmarks of reinfecting sequences and the rate of viral evolution in persistent infection. MethodsA systematic review of PubMed was conducted to identify cases of SARS-CoV-2 reinfection and persistent infection with available sequences. Amino acid changes in the reinfecting sequence were compared to both the initial and contemporaneous community variants. Time-measured phylogenetic reconstruction was performed to compare intra-host viral evolution in persistent COVID-19 to community-driven evolution. ResultsFourteen reinfection and five persistent infection cases were identified. Reports of reinfection cases spanned a broad distribution of ages, baseline health status, reinfection severity, and occurred as early as 1.5 months or >8 months after the initial infection. The reinfecting viral sequences had a median of 9 amino acid changes with enrichment of changes in the S, ORF8 and N genes. The number of amino acid changes did not differ by the severity of reinfection and reinfecting variants were similar to the contemporaneous sequences circulating in the community. Patients with persistent COVID-19 demonstrated more rapid accumulation of mutations than seen with community-driven evolution with continued viral changes during convalescent plasma or monoclonal antibody treatment. ConclusionsSARS-CoV-2 reinfection does not require an unusual set of circumstances in the host or virus, while persistent COVID-19 is largely described in immunosuppressed individuals and is associated with accelerated viral evolution as measured by clock rates.

Optimal SARS-CoV-2 vaccine allocation using real-time seroprevalence estimates in Rhode Island and Massachusetts (preprint)

As three SARS-CoV-2 vaccines come to market in Europe and North America in the winter of 2020-2021, distribution networks will be in a race against a major epidemiological wave of SARS-CoV-2 that began in autumn 2020. Rapid and optimized vaccine allocation is critical during this time. With 95% efficacy reported for two of the vaccines, near-term public health needs require that distribution is prioritized to the elderly, health-care workers, teachers, essential workers, and individuals with co-morbidities putting them at risk of severe clinical progression. Here, we evaluate various age-based vaccine distributions using a validated mathematical model based on current epidemic trends in Rhode Island and Massachusetts. We allow for varying waning efficacy of vaccine-induced immunity, as this has not yet been measured. We account for the fact that known COVID-positive cases may not be included in the first round of vaccination. And, we account for current age-specific immune patterns in both states. We find that allocating a substantial proportion ( > 75%) of vaccine supply to individuals over the age of 70 is optimal in terms of reducing total cumulative deaths through mid-2021. As we do not explicitly model other high mortality groups, this result on vaccine allocation applies to all groups at high risk of mortality if infected. Our analysis confirms that for an easily transmissible respiratory virus, allocating a large majority of vaccinations to groups with the highest mortality risk is optimal. Our analysis assumes that health systems during winter 2020-2021 have equal staffing and capacity to previous phases of the SARS-CoV-2 epidemic; we do not consider the effects of understaffed hospitals or unvaccinated medical staff. Vaccinating only seronegative individuals avoids redundancy in vaccine use on individuals that may already be immune, and will result in 1% to 2% reductions in cumulative hospitalizations and deaths by mid-2021. Assuming high vaccination coverage ( > 28%) and no major relaxations in distancing, masking, gathering size, or hygiene guidelines between now and spring 2021, our model predicts that a combination of vaccination and population immunity will lead to low or near-zero transmission levels by the second quarter of 2021.

SARS-CoV-2 titers in wastewater are higher than expected from clinically confirmed cases (preprint)

Wastewater surveillance may represent a complementary approach to measure the presence and even prevalence of infectious diseases when the capacity for clinical testing is limited. Moreover, aggregate, population-wide data can help inform modeling efforts. We tested wastewater collected at a major urban treatment facility in Massachusetts and found the presence of SARS-CoV-2 at high titers in the period from March 18 - 25 using RT-qPCR. We then confirmed the identity of the PCR product by direct DNA sequencing. Viral titers observed were significantly higher than expected based on clinically confirmed cases in Massachusetts as of March 25. The reason for the discrepancy is not yet clear, however, and until further experiments are complete, these data do not necessarily indicate that clinical estimates are incorrect. Our approach is scalable and may be useful in modeling the SARS-CoV-2 pandemic and future outbreaks.