Mathematical modeling identifies optimal dosing schedules for COVID-19 vaccines to minimize breakthrough infections

While the development of different vaccines has slowed the dissemination of SARS-CoV-2, the occurrence of breakthrough infections continues to fuel the pandemic. As a strategy to secure at least partial protection, with a single dose of a given COVID-19 vaccine to maximum possible fraction of the population, delayed administration of subsequent doses (or boosters) has been implemented in many countries. However, waning immunity and emergence of new variants of SARS-CoV-2 suggest that such measures may jeopardize the attainment of herd immunity due to intermittent lapses in protection. Optimizing vaccine dosing schedules could thus make the difference between periodic occurrence of breakthrough infections or effective control of the pandemic. To this end, we have developed a mechanistic mathematical model of adaptive immune response to vaccines and demonstrated its applicability to COVID-19 mRNA vaccines as a proof-of-concept for future outbreaks. The model was thoroughly calibrated against multiple clinical datasets involving immune response to SARS-CoV-2 infection and mRNA vaccines in healthy and immunocompromised subjects (cancer patients undergoing therapy); the model showed robust clinical validation by accurately predicting neutralizing antibody kinetics, a correlate of vaccine-induced protection, in response to multiple doses of mRNA vaccines. Importantly, we estimated population vulnerability to breakthrough infections and predicted tailored vaccination dosing schedules to maximize protection and thus minimize breakthrough infections, based on the immune status of a sub-population. We have identified a critical waiting window for cancer patients (or, immunocompromised subjects) to allow recovery of the immune system (particularly CD4+ T-cells) for effective differentiation of B-cells to produce neutralizing antibodies and thus achieve optimal vaccine efficacy against variants of concern, especially between the first and second doses. Also, we have obtained optimized dosing schedules for subsequent doses in healthy and immunocompromised subjects, which vary from the CDC-recommended schedules, to minimize breakthrough infections. The developed modeling tool is based on generalized adaptive immune response to antigens and can thus be leveraged to guide vaccine dosing schedules during future outbreaks.

Real World Evidence of Effectiveness of COVID-19 Vaccines and Anti SARS-CoV-2 Monoclonal Antibodies Against Post-Acute Sequelae of SARS-CoV-2 Infection

BackgroundWe evaluated the effectiveness of COVID-19 vaccines and monoclonal antibodies (mAb) against Post-Acute Sequelae of SARS-CoV-2 infection (PASC), an emerging public health problem. Methods and FindingsIn a retrospective cohort study, we identified patients with clinically significant PASC using a COVID-19 specific, electronic medical record-based surveillance and outcomes registry from an 8-hospital tertiary healthcare system in the greater Houston metropolitan (primary analyses). Analyses were then replicated across a global research network database. We included all adults (>= 18) who survived beyond 28-days of their index infection. PASC was defined as experiencing constitutional (palpitations, malaise / fatigue, headache) or systemic (sleep disorder, shortness of breath, mood / anxiety disorders, cough, and cognitive impairment) symptoms beyond 28-day post-infection period. Instances of PASC were excluded if the symptoms were present pre-COVID or if they resolved within four weeks of initial infection. We fit multivariable logistic regression models and report estimated likelihood of PASC associated with vaccination or mAb treatment as adjusted odds ratios (aOR) with 95% confidence intervals (CI). Primary analyses included 53,239 subjects (54.9% female), of whom 5,929, 11.1% (CI: 10.9 - 11.4), experienced PASC. Both, vaccinated breakthrough cases (vs. unvaccinated) and mAb treated patients (vs. untreated) had lower likelihoods for developing PASC, aOR (CI): 0.58 (0.52, 0.66), and 0.77 (0.69, 0.86), respectively. Vaccination was associated with decreased odds of developing all constitutional and systemic symptoms except for taste and smell changes. For all symptoms, vaccination was associated with lower likelihood of experiencing PASC compared to mAb treatment. Replication analysis found almost identical frequency of PASC (11.2%) and similar protective effects against PASC for the COVID-19 vaccine: aOR (CI) 0.25 (0.21 - 0.30) and mAb treatment: 0.62 (0.59 - 0.66). DiscussionAlthough both COVID-19 vaccines and mAbs decreased the likelihood of PASC, at present, vaccination is the most effective tool to potentially prevent long-term clinical and socio-economic consequences of COVID-19.

Do Pandemics Obey the Elliott Wave Principle of Financial Markets?

The Elliott Wave principle is a time-honored, oft-used method for predicting variations in the financial markets. It is based on the notion that human emotions drive financial decisions. In the fight against COVID-19, human emotions are similarly decisive, for instance in that they determine ones willingness to be vaccinated, and/or to follow preventive measures including the wearing of masks, the application of social distancing protocols, and frequent handwashing. On this basis, we postulated that the Elliott Wave Principle may similarly be used to predict the future evolution of the COVID-19 pandemic. We demonstrated that this method reproduces the data pattern especially well for USA (daily new cases). Potential scenarios were then extrapolated, from the best-case corresponding to a rapid, full vaccination of the population, to the utterly disastrous case of slow vaccination, and poor adherence to preventive protocols.

Innate immunity plays a key role in controlling viral load in COVID-19: mechanistic insights from a whole-body infection dynamics model

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a pathogen of immense public health concern. Efforts to control the disease have only proven mildly successful, and the disease will likely continue to cause excessive fatalities until effective preventative measures (such as a vaccine) are developed. To develop disease management strategies, a better understanding of SARS-CoV-2 pathogenesis and population susceptibility to infection are needed. To this end, physiologically-relevant mathematical modeling can provide a robust in silico tool to understand COVID-19 pathophysiology and the in vivo dynamics of SARS-CoV-2. Guided by ACE2-tropism (ACE2 receptor dependency for infection) of the virus, and by incorporating cellular-scale viral dynamics and innate and adaptive immune responses, we have developed a multiscale mechanistic model for simulating the time-dependent evolution of viral load distribution in susceptible organs of the body (respiratory tract, gut, liver, spleen, heart, kidneys, and brain). Following calibration with in vivo and clinical data, we used the model to simulate viral load progression in a virtual patient with varying degrees of compromised immune status. Further, we conducted global sensitivity analysis of model parameters and ranked them for their significance in governing clearance of viral load to understand the effects of physiological factors and underlying conditions on viral load dynamics. Antiviral drug therapy, interferon therapy, and their combination was simulated to study the effects on viral load kinetics of SARS-CoV-2. The model revealed the dominant role of innate immunity (specifically interferons and resident macrophages) in controlling viral load, and the impotance of timing when initiating therapy following infection. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=118 SRC="FIGDIR/small/20215335v1_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@54d70eorg.highwire.dtl.DTLVardef@1f2f0ecorg.highwire.dtl.DTLVardef@a71f28org.highwire.dtl.DTLVardef@1eeaeb8_HPS_FORMAT_FIGEXP M_FIG C_FIG

Prevalence of SARS-CoV-2 infection among asymptomatic healthcare workers in greater Houston: a cross-sectional analysis of surveillance data from a large healthcare system

ObjectiveTo determine the prevalence of SARS-CoV-2 infection among asymptomatic COVID-19 facing and non-COVID-19 facing Healthcare Workers (HCWs), with varying job categories across different hospitals. DesignCross-sectional analysis of a healthcare system surveillance program that included asymptomatic clinical (COVID-19 facing and non-COVID-19 facing), and non-clinical HCWs. A convenience sample of asymptomatic community residents (CR) was also tested. Proportions and 95% confidence Intervals (CI) of SARS-CoV-2 positive HCWs are reported. Proportional trend across HCW categories was tested using Chi Square trend test. Logistic regression model-based likelihood estimates of SARS-CoV-2 prevalence among HCWs with varying job functions and across different hospitals are reported as adjusted odds ratios (aOR) and CI. SettingHealthcare system comprising one tertiary care academic medical center and six large community hospitals across Greater Houston and a community sample. Participants2,872 self-reported asymptomatic adult (> 18 years) HCWs and CRs. ExposureClinical HCWs in COVID-19 and non-COVID-19 units, non-Clinical HCWs, and CRs. Job categories of Nursing, Providers, Allied Health, Support, and Administration / Research. Seven hospitals in the healthcare system. Main OutcomesPositive reverse transcriptase polymerized chain reaction (RT-PCR) test for SARS-CoV-2 ResultsAmong 2,872 asymptomatic HCWs and CRs, 3.9% (CI: 3.2 - 4.7) tested positive for SARS-CoV-2. Mean (SD) age was 40.9 (11.7) years and 73% were females. Among COVID-19 facing HCWs 5.4% (CI: 4.5 - 6.5) were positive, whereas 0.6% (CI: 0.2 - 1.7%) of non COVID-19 facing HCWs and none of the non-clinical HCWs or CRs were positive (Ptrend < 0.001). Among COVID-19 facing HCWs, SARS-CoV-2 positivity was similar for all job categories (p = 0.74). However, significant differences in positivity were observed across hospitals. Conclusions and RelevanceAsymptomatic HCWs with COVID-19 patient exposure had a higher rate of SARS-CoV-2 positive testing than those not routinely exposed to COVID-19 patients and those not engaged in patient care. Among HCWs with routine COVID-19 exposure, all job types had relatively similar infection rates. These data can inform hospital surveillance and infection control practices for patient-facing job classifications and suggest that general environmental exposure within hospitals is not a significant source of asymptomatic SARS-CoV-2 infection. What is already known on this topicO_LIA sizeable proportion of individuals who contract the novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) can remain largely asymptomatic. C_LIO_LIThough such individuals may not develop symptoms, they continue to shed enough viral particles to trigger positive reverse transcriptase polymerized chain reaction (RT PCR) test for SARS-CoV-2 C_LIO_LIPrior reports on proportion of asymptomatic SARS-CoV-2 individuals are highly variable with positivity ranging across < 1% to 36% C_LIO_LIAsymptomatic SARS-CoV-2 infection among healthcare workers is specifically critical to understand C_LI What this study addsO_LIThis study demonstrates that overall rate of SARS-CoV-2 infection among asymptomatic healthcare workers in a large healthcare system of a metropolitan city in the United States was 3.9% C_LIO_LIThe rate of SARS-CoV-2 infection among healthcare workers who provided direct care to COVID-19 patients was 5.4% whereas it was 0.6% among those healthcare workers who did not provide direct care to COVID-19 patients C_LIO_LIThere was no difference in SARS-CoV-2 positivity rate for different job categories of healthcare workers who provided direct care to COVID-19 patients C_LI

Racial and Ethnic Disparities in SARS-CoV-2 Pandemic: Analysis of a COVID-19 Observational Registry for a Diverse U.S. Metropolitan Population

IntroductionData on race and ethnic susceptibility to SARS-CoV-2 infection are limited. We analyzed socio-demographic factors associated with higher likelihood of SARS-CoV-2 infection and explore mediating pathways for race disparities in the SARS-CoV-2 pandemic. MethodsCross sectional analysis of COVID-19 Surveillance and Outcomes Registry (CURATOR), which captures data for a large healthcare system comprising of one central tertiary care, seven large community hospitals, and an expansive ambulatory / emergency care network in the Greater Houston area. Nasopharyngeal samples for individuals inclusive of all ages, races, ethnicities and sex were tested for SARS-CoV-2. We analyzed, socio-demographic (age, sex, race, ethnicity, household income, residence population density) and comorbidity (hypertension, diabetes, obesity, cardiac disease) factors. Multivariable logistic regression models were fitted to provide adjusted Odds Ratios (aOR), 95% confidence intervals (CI) for likelihood of positive SARS-CoV-2 test. Structural Equation Modeling (SEM) framework was utilized to explore three mediation pathways (low income, high population density, high comorbidity burden) for association between African American race and SARS-CoV-2 infection. ResultsAmong 4,513 tested individuals, 754 (16.7%) tested positive. Overall mean (SD) age was 50.6 (18.9) years, 62% females and 26% were African American. African American race was associated with lower socio-economic status, higher comorbidity burden, and population density residence. In the fully adjusted model, African American race (vs. White; aOR, CI: 1.84, 1.49-2.27) and Hispanic ethnicity (vs. non-Hispanic; aOR, CI: 1.70, 1.35-2.14) had a higher likelihood of infection. Older individuals and males were also at a higher risk of SARS-CoV-2 infection. The SEM framework demonstrated a statistically significant (p = 0.008) indirect effect of African American race on SARS-CoV-2 infection mediated via a pathway that included residence in densely populated zip code. ConclusionsThere is strong evidence of race and ethnic disparities in the SARS-CoV-2 pandemic potentially mediated through unique social determinants of health. O_LSTStrengths and limitations of this studyC_LSTO_LIOne of the first studies to systematically evaluate race and ethnic disparities in susceptibility to SARS-CoV-2 infection, while accounting for multiple sociodemographic characteristics and comorbidities C_LIO_LIStudy population represents a large and diverse metropolitan of the U.S. with data from one of the largest healthcare providers across the greater metropolitan area C_LIO_LIStudy evaluates potential mediation pathways for race disparities and demonstrates that residence in areas with high population density may mediate race disparities in susceptibility to SARS-CoV-2 infection C_LIO_LISingle center study with limited information about true burden of comorbidity and lifestyle factors C_LI