Posted on

Modeling the influence of COVID superspreading events

Study: Exploring the Role of Superspreading Events in SARS-CoV-2 Outbreaks. Image Credit: StockTom / Shutterstock

In a recent study posted to the medRxiv* preprint server, University of Kansas researchers assessed the effect of superspreading events (SSEs) on the United States (US) severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak dynamics.

Study: Exploring the Role of Superspreading Events in SARS-CoV-2 Outbreaks. Image Credit: StockTom / ShutterstockStudy: Exploring the Role of Superspreading Events in SARS-CoV-2 Outbreaks. Image Credit: StockTom / Shutterstock

Background

SARS-CoV-2, the novel coronavirus that emerged in late December 2019, has quickly swept over the globe, resulting in over 546 million illnesses and more than 6.3 million fatalities thus far. Coronavirus disease 2019 (COVID-19) has strained the US healthcare network, with several hospitals exceeding or nearing capacity and few limiting services. 

Governments at the state and national levels have responded by issuing guidelines and regulations for decreasing SARS-CoV-2 transmission, including social-distancing directives, mask mandates, stay-at-home instructions, and restrictions on big gatherings. However, insufficient adherence and compliance by the population have affected the efficiency of these laws and regulations, encouraging SSEs, which have assisted the SARS-CoV-2 transmission.

About the study

In the present study, the researchers developed a continuous-time Markov chain (CTMC) model to examine the impact of SSEs on the dynamics of the SARS-CoV-2 outbreak in the US. The authors defined SSEs as social or public events that lead to numerous SARS-CoV-2 infections over a short period.

The current research sought to determine the effect of SSEs compared to non-SSEs on COVID-19 outbreak dynamics, the efficacy of hospitalization and quarantine as containment methods for SSE relative to non-SSE-dominated outbreaks, and the impact of quarantine violation on the efficacy of quarantine for SSE compared to non-SSE-dominated outbreaks.

The investigators simulated a CTMC model for SARS-CoV-2 spread utilizing Gillespie’s direct algorithm under three distinct scenarios: 1) neither hospitalization nor quarantine; 2) quarantine, hospitalization, premature hospital discharge, and quarantine violation; and 3) hospitalization and quarantine but not premature hospital discharge or quarantine violation. They also alter the rate of quarantine violations under realistic hospitalization and quarantine (RHQ) scenarios.

Results

The study results demonstrated that the SARS-CoV-2 outbreaks with SSE dominance were often more variable yet less severe and more prone to extinction than outbreaks without SSE dominance. The authors observed this after eliminating hospitalization and quarantine conditions or upon the inclusion of hospitalization, quarantine, early hospital discharge, and quarantine breach. 

However, the severity of the most catastrophic SSE-dominated outbreaks was higher than the most severe outbreaks without SSE dominance, despite most SSE-dominated outbreaks being less severe. Upon the inclusion of quarantine and hospitalization, while excluding quarantine breach and premature hospital discharge, SARS-CoV-2 outbreaks dominated by SSE were more susceptible to extinction than outbreaks without SSE dominance but were more severe and less variable.

Upon the inclusion of quarantine, hospitalization, premature hospital discharge, and halved quarantine breach, outbreaks dominated by SSE were comparable to when quarantine and hospitalization were included, but quarantine breach and premature hospital discharge were excluded. Besides, when quarantine breach was doubled outbreaks were similar to when quarantine and hospitalization were excluded.

Quarantine and hospitalization were more potent at regulating outbreaks dominated by SSE than those without SSE dominance in all scenarios. Similarly, quarantine breaches and premature hospital discharge were significant for outbreaks dominated by SSE.

SSE-dominated outbreaks were extremely improbable to become extinct when quarantine and hospitalization were excluded. They were moderately unlikely to become extinct when quarantine, hospitalization, premature hospital discharge, and quarantine violation were included. Furthermore, they were highly plausible to become extinct when hospitalization and quarantine were included, but quarantine breach and premature hospital discharge were excluded. 

Moreover, SSE-dominated outbreaks were more likely to become extinct when quarantine violations were halved. However, outbreaks dominated by SSE were less likely to become extinct when quarantine breaches were doubled.

Conclusions

Altogether, the study findings showed that COVID-19 outbreaks dominated by SSE differ noticeably from non-SSE-dominated outbreaks in their severity, variability, and chances of extinction. They also vary, albeit more low-key, from outbreaks dominated by superspreading individuals (SI). The possibility of hospitalization or quarantine and the likelihood of premature hospital discharge or violation of quarantine significantly impact the dynamics of SSE-dominated outbreaks.

Hospitalization and quarantine were substantially effective preventative interventions for COVID-19 outbreaks dominated by SSE. Nevertheless, premature hospital discharge and breach of the quarantine drastically diminished their efficacy. Besides, the team assessed control techniques using the probability of extinction.

The present findings have significant public health consequences, necessitating SARS-CoV-2 modelers must: 1) assess the contribution of SSEs or SIs to COVID-19 spread; and 2) differentiate between SSEs, SIs, and non-SIs/non-SSEs in their models. More research into the combined and individual effects of SSEs and SIs on SARS-CoV-2 outbreak dynamics and the efficacy of control strategies for various kinds of outbreaks were required to guide eradication and containment initiatives.

*Important notice

medRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.

Posted on

Managing Adverse Events with Superior Combination Therapies in RCC

Managing Adverse Events with Superior Combination Therapies in RCC

In an interview with Targeted OncologyTM, Thomas Hutson, MD, PharmD, director of the urologic oncology program for Texas Oncology Baylor University Medical Center, discusses the challenges that naturally come when adding therapies to treatment, such as the use of lenvatinib (Lenvima) and pembrolizumab (Keytruda) in patients with renal cell carcinoma (RCC). 

According to Hutson, with combination therapies, there will naturally be more adverse events (AEs) to manage than with monotherapy, but efficacy from the combination treatment will increase. Therefore, Hutson describes strategies, such as dose reduction, to manage additional AEs and make sure patients continue to see a superior benefit on a combination than they would with monotherapy.

The phase 3 CLEAR study (NCT02811861) demonstrated that the combinations of lenvatinib and pembrolizumab or lenvatinib and everolimus (Afinitor) were superior to sunitinib (Sutent) in patients with advanced RCC for progression-free survival, objective response rate, and overall survival.

However, nearly all patients in the study and combination therapy experienced treatment-related AEs with 67.3% of patients treated with lenvatinib plus pembrolizumab compared with 49.7% in the sunitinib arm that led to dose reductions. Hutson says being proactive about dose reductions in this patient population allows patients to still experience the superior benefits without having to lose those benefits as clinicians manage AEs. He discusses empowering the patient to be a part of these decisions to help find the best dose for them and how the health-related quality of life is also still favorable with combination therapies.  

TRANSCRIPTION:

0:08 | As we start adding on therapies, there is going to be an addition of [AEs] and that’s just the way it is. Again, I think the surprise has been that if you can work through the AEs with the patient and optimize the dose. There is a dose response effect for people that as the higher the dose, the more chance of benefit, but also the more toxicity, so they go hand in hand.

0:34 | So, it’s trying to optimize and individualize the dose for the patients sitting in front of you, using the standard for how we dose people, which is starting off at full dose, allowing AEs to declare themselves, then lowering the dose and optimizing it to try to keep that highest dose intensity. Sometimes that is just taking breaks periodically and allowing them to stay at the same dose, but saying, “Hey, when that AE gets to that point, that it’s really impacting your quality of life, take a break for a couple days, when it gets better go back on it.” Patients like that they have power, they’re in control of that, and if that doesn’t happen then go into the lower dose. So that kind of strategy is important.

1:18 | As we combine therapies, we’re going to have added to have additive toxicity. Where we can feel comfortable and point your patients too and say, “Hey, if we can get this to work out and if we can find this right dose for you, then the data we have so far from the trials to health-related quality-of-life data, you’re actually going to start feeling better as your tumor gets smaller.” The health-related quality of life data shows us that, that even though there’s more overall AEs with the combinations, the health-related quality of life is improving over the comparator drug that had less AEs.

Posted on

Health events amongst pregnant females after COVID-19 vaccination

Study: Safety of COVID-19 vaccines in pregnancy: a Canadian National Vaccine Safety (CANVAS) Network study. Image Credit: Huseyin Eren Obuz/Shutterstock

In a recent study posted to the medRxiv* preprint server, researchers evidenced that messenger ribonucleic acid (mRNA)-based coronavirus disease 2019 (COVID-19) vaccines are safe in pregnancy, with lower rates of significant adverse event following immunization (AEFIs) in pregnant women than non-pregnant females.

Study: Safety of COVID-19 vaccines in pregnancy: a Canadian National Vaccine Safety (CANVAS) Network study. Image Credit: Huseyin Eren Obuz/Shutterstock
Study: Safety of COVID-19 vaccines in pregnancy: a Canadian National Vaccine Safety (CANVAS) Network study. Image Credit: Huseyin Eren Obuz/Shutterstock

Background

Multiple research works have published positive recommendations for mRNA-based COVID-19 vaccines in pregnancy, based on the evidence of high efficacy in pre-authorization clinical trials. However, in the absence of a contemporaneous control group to enable comparison with background rates of AEFIs and comparisons based solely on historical rates of AEFIs, apprehensions surrounding the safety of mRNA vaccines during pregnancy are still lurking around.

The Canadian National Vaccine Safety (CANVAS) Network, established during the 2009 influenza pandemic, has been monitoring COVID-19 vaccine safety in Canada since the vaccine rollout in December 2020 to provide rapid, real-time safety data.

The CANVAS actively follow-up individuals with significant health events and actively enrolls control group(s) to enable comparisons with unvaccinated individuals in a similar time frame.

About the study

In the present study, researchers recruited pregnant and non-pregnant females aged 15-49 years, as of 4 November 2021, under the ‘vaccinated’ and ‘control’ cohorts in Canada to evaluate the safety profile of mRNA-based COVID-19 vaccines.

The females in the vaccinated cohort had received the first dose of a vaccine within seven days before enrolling for the study. They had an active email address and telephone number and could communicate in English or French. They reported the occurrence of AEFIs over an email after seven days following each dose of the COVID-19 vaccine and at seven months after their first vaccine dose. The control group participants were unvaccinated and reported significant health events that occurred seven days, 28 days, and six months after enrolling in the study.

All the participants had to report injection site reactions; however, only those who indicated having a significant health event had to provide further details.

The researchers analyzed two types of exposures for the study analysis:

  1. vaccination status among pregnant people;
  2. pregnancy status among vaccinated people.

Two endpoints were analyzed, including ‘significant’ and ‘serious’ health events, including common and uncommon symptoms following the first and second doses of COVID-19 vaccines. The former is defined as a new or worsening of a health event sufficient to cause work/school absenteeism or medical consultation in the previous seven days, and the latter describes any event resulting in hospitalization.

Likewise, they analyzed three vaccine groups:

  1. BNT162b2,
  2. mRNA-1273, and
  3. any mRNA vaccine.

They also examined associations between the outcomes and the exposures, using two sets of univariate/multivariate (MV) logistic regression models. When fitting MV models, they adjusted known or expected covariates such as age group, prior COVID-19 infection, and trimester of pregnancy, as appropriate.

Lastly, they conducted two sensitivity analyses to evaluate the robustness of the findings.

Study findings

Significant health events were lower in pregnant people than in age-matched non-pregnant vaccine recipients. Among pregnant females, AEFI was higher in those who received the second dose of the mRNA-1273 vaccine. However, there was no difference in AEFIs after either dose of the BNT162b2 vaccine.

Initial clinical trials of the mRNA-1273 and BNT162b2 vaccines have reported relatively high rates of AEFIs compared with most routinely used vaccines, including higher rates for dose two than dose one.

The current study analysis revealed similar patterns among pregnant females. Although the analysis specifically quantified the significant and serious AEFI rates in this population for each of the mRNA vaccines, the lower rate of significant AEFIs among pregnant people, compared with vaccinated non-pregnant females, revealed interesting insights.

During pregnancy, dynamic immunologic adaptations occur, for instance, a skewed response towards a T helper cell 2 (Th2)-dominant state. Since mRNA vaccines have specifically elicited a Th1-biased immune response, the Th2-bias during pregnancy may be partially responsible for this lower rate of significant AEFIs.

Conclusions

Considering the high rate of complications related to COVID-19 in pregnancy, it is crucial to maximize vaccine coverage in this at-high risk population for the protection of both the pregnant female and her young infant. Immunized mothers pass on antigen-specific immunoglobulin G (IgG) antibodies against SARS-CoV-2 via placenta or breast milk.

Overall, the study data appropriately informed about the reactogenicity of COVID-19 vaccines during pregnancy. This information should be considered alongside effectiveness and immunogenicity data to make appropriate recommendations about the best use of COVID-19 vaccines in pregnancy. The long-term data from this cohort following a six-month follow-up, when available, could also prove quite useful.  Similar data from countries where the ChAdOx-S vaccines are used could provide a complete overview of the safety of COVID-19 vaccines in pregnancy.

In the future, research studies should identify whether the observed reduced reactogenicity of non-COVID-19 mRNA vaccines in pregnant people in this study is a feature of the vaccine platform or these specific vaccines.

*Important notice

medRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.