scholarly journals Epidemiological impact of SARS-CoV-2 vaccination: mathematical modeling analyses

2020 ◽  
Author(s):  
Monia Makhoul ◽  
Houssein H. Ayoub ◽  
Hiam Chemaitelly ◽  
Shaheen Seedat ◽  
Ghina R Mumtaz ◽  
...  
Keyword(s):  
Decision Making ◽  
Disease Progression ◽  
Vaccine Development ◽  
Severe Disease ◽  
Herd Immunity ◽  
Social Contact ◽  
Population Level ◽  
Contact Rate ◽  
Disease Case ◽  
Vaccine Impact

AbstractBackgroundSeveral SARS-CoV-2 vaccine candidates are currently in the pipeline. This study aims to inform SARS-CoV-2 vaccine development, licensure, decision-making, and implementation by determining key preferred vaccine product characteristics and associated population-level impact.MethodsVaccination impact was assessed at various efficacies using an age-structured mathematical model describing SARS-CoV-2 transmission and disease progression, with application for China.ResultsA prophylactic vaccine with efficacy against acquisition (VES) of ≥70% is needed to eliminate this infection. A vaccine with VES <70% will still have a major impact, and may control the infection if it reduces infectiousness or infection duration among those vaccinated who acquire the infection, or alternatively if supplemented with a moderate social-distancing intervention (<20% reduction in contact rate), or complemented with herd immunity. Vaccination is cost-effective. For a vaccine with VES of 50%, number of vaccinations needed to avert one infection is only 2.4, one severe disease case is 25.5, one critical disease case is 33.2, and one death is 65.1. Gains in effectiveness are achieved by initially prioritizing those ≥60 years. Probability of a major outbreak is virtually zero with a vaccine with VES ≥70%, regardless of number of virus introductions. Yet, an increase in social contact rate among those vaccinated (behavior compensation) can undermine vaccine impact.ConclusionsEven a partially-efficacious vaccine can offer a fundamental solution to control SARS-CoV-2 infection and at high cost-effectiveness. In addition to the primary endpoint on infection acquisition, developers should assess natural history and disease progression outcomes and/or proxy biomarkers, since such secondary endpoints may prove critical in licensure, decision-making, and vaccine impact.

scholarly journals Epidemiological Impact of SARS-CoV-2 Vaccination: Mathematical Modeling Analyses

Vaccines ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 668 ◽  
Author(s):  
Monia Makhoul ◽  
Houssein H. Ayoub ◽  
Hiam Chemaitelly ◽  
Shaheen Seedat ◽  
Ghina R. Mumtaz ◽  
...  
Keyword(s):  
Mathematical Modeling ◽  
Vaccine Development ◽  
Severe Disease ◽  
Herd Immunity ◽  
Social Contact ◽  
Population Level ◽  
Contact Rate ◽  
Disease Case ◽  
Major Outbreak ◽  
Vaccine Impact

This study aims to inform SARS-CoV-2 vaccine development/licensure/decision-making/implementation, using mathematical modeling, by determining key preferred vaccine product characteristics and associated population-level impacts of a vaccine eliciting long-term protection. A prophylactic vaccine with efficacy against acquisition (VES) ≥70% can eliminate the infection. A vaccine with VES <70% may still control the infection if it reduces infectiousness or infection duration among those vaccinated who acquire the infection, if it is supplemented with <20% reduction in contact rate, or if it is complemented with herd-immunity. At VES of 50%, the number of vaccinated persons needed to avert one infection is 2.4, and the number is 25.5 to avert one severe disease case, 33.2 to avert one critical disease case, and 65.1 to avert one death. The probability of a major outbreak is zero at VES ≥70% regardless of the number of virus introductions. However, an increase in social contact rate among those vaccinated (behavior compensation) can undermine vaccine impact. In addition to the reduction in infection acquisition, developers should assess the natural history and disease progression outcomes when evaluating vaccine impact.

scholarly journals Development and validation of a clinical score to estimate progression to severe or critical state in COVID-19 pneumonia hospitalized patients

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Francisco Gude ◽  
Vanessa Riveiro ◽  
Nuria Rodríguez-Núñez ◽  
Jorge Ricoy ◽  
Óscar Lado-Baleato ◽  
...  
Keyword(s):  
Decision Making ◽  
Predictive Model ◽  
Disease Progression ◽  
Clinical Laboratory ◽  
Severe Disease ◽  
Invasive Mechanical Ventilation ◽  
Risk Scores ◽  
Brier Score ◽  
Operating Characteristics ◽  
Early Decision

AbstractThe prognosis of a patient with COVID-19 pneumonia is uncertain. Our objective was to establish a predictive model of disease progression to facilitate early decision-making. A retrospective study was performed of patients admitted with COVID-19 pneumonia, classified as severe (admission to the intensive care unit, mechanic invasive ventilation, or death) or non-severe. A predictive model based on clinical, laboratory, and radiological parameters was built. The probability of progression to severe disease was estimated by logistic regression analysis. Calibration and discrimination (receiver operating characteristics curves and AUC) were assessed to determine model performance. During the study period 1152 patients presented with SARS-CoV-2 infection, of whom 229 (19.9%) were admitted for pneumonia. During hospitalization, 51 (22.3%) progressed to severe disease, of whom 26 required ICU care (11.4); 17 (7.4%) underwent invasive mechanical ventilation, and 32 (14%) died of any cause. Five predictors determined within 24 h of admission were identified: Diabetes, Age, Lymphocyte count, SaO2, and pH (DALSH score). The prediction model showed a good clinical performance, including discrimination (AUC 0.87 CI 0.81, 0.92) and calibration (Brier score = 0.11). In total, 0%, 12%, and 50% of patients with severity risk scores ≤ 5%, 6–25%, and > 25% exhibited disease progression, respectively. A risk score based on five factors predicts disease progression and facilitates early decision-making according to prognosis.

scholarly journals Age-targeted dose allocation can halve COVID-19 vaccine requirements

2020 ◽  
Author(s):  
Michael T. Meehan ◽  
Daniel G. Cocks ◽  
Jamie M. Caldwell ◽  
James M. Trauer ◽  
Adeshina I. Adekunle ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Severe Disease ◽  
Herd Immunity ◽  
Population Level ◽  
Morbidity And Mortality ◽  
Effective Strategy ◽  
Risk Of Infection ◽  
Middle Aged ◽  
Contact Rates ◽  
Age Structured

ABSTRACTIn anticipation of COVID-19 vaccine deployment, we use an age-structured mathematical model to investigate the benefits of optimizing age-specific dose allocation to suppress the transmission, morbidity and mortality of SARS-CoV-2 and the associated disease, COVID-19. To minimize transmission, we find that the highest priority individuals across 179 countries are typically those between 30 and 59 years of age because of their high contact rates and higher risk of infection and disease. Conversely, morbidity and mortality are initially most effectively reduced by targeting 60+ year olds who are more likely to experience severe disease. However, when population-level coverage is sufficient — such that herd immunity can be achieved through targeted dose allocation — prioritizing middle-aged individuals becomes the most effective strategy to minimize hospitalizations and deaths. For each metric considered, we show that optimizing the allocation of vaccine doses can more than double their effectiveness.

scholarly journals Improved diagnosis of SARS-CoV-2 by using Nucleoprotein and Spike protein fragment 2 in quantitative dual ELISA tests

2021 ◽  
Author(s):  
Carolina De Marco Verissimo ◽  
Carol O'Brien ◽  
Jesus Lopez Corrales ◽  
Amber Dorey ◽  
Krystyna Cwiklinski ◽  
...  
Keyword(s):  
Severe Disease ◽  
Herd Immunity ◽  
Population Level ◽  
The Novel ◽  
Antibody Testing ◽  
Protein Fragment ◽  
Epidemiological Tool ◽  
Novel Coronavirus ◽  
Antibody Levels ◽  
Antibody Tests

The novel Coronavirus, SARS-CoV-2, is the causative agent of the 2020 worldwide coronavirus pandemic. Antibody testing is useful for diagnosing historic infections of a disease in a population. These tests are also a helpful epidemiological tool for predicting how the virus spreads in a community, relating antibody levels to immunity and for assessing herd immunity. In the present study, SARS-CoV-2 viral proteins were recombinantly produced and used to analyse serum from individuals previously exposed, or not, to SARS-CoV-2. The nucleocapsid (Npro) and Spike subunit 2 (S2Frag) proteins were identified as highly immunogenic, although responses to the former were generally greater. These two proteins were used to develop two quantitative ELISA assays that when used in combination resulted in a highly reliable diagnostic test. Npro and S2Frag-ELISAs could detect at least 10% more true positive COVID-19 cases than the commercially available ARCHITECT test (Abbott). Moreover, our quantitative ELISAs also show that specific antibodies to SARS-CoV-2 proteins tend to wane rapidly even in patients that had developed severe disease. As antibody tests complement COVID-19 diagnosis and determine population-level surveillance during this pandemic, the alternative diagnostic we present in this study could play a role in controlling the spread of the virus.

Personalized stratification of back to work risk amidst COVID-19: A machine learning approach (Preprint)

2020 ◽  
Author(s):  
Carson Lam ◽  
Jacob Calvert ◽  
Gina Barnes ◽  
Emily Pellegrini ◽  
Anna Lynn-Palevsky ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Learning Algorithm ◽  
Severe Disease ◽  
High Specificity ◽  
Population Level ◽  
The United States ◽  
Health Condition ◽  
Available P ◽  
Severe Illness

BACKGROUND In the wake of COVID-19, the United States has developed a three stage plan to outline the parameters to determine when states may reopen businesses and ease travel restrictions. The guidelines also identify subpopulations of Americans that should continue to stay at home due to being at high risk for severe disease should they contract COVID-19. These guidelines were based on population level demographics, rather than individual-level risk factors. As such, they may misidentify individuals at high risk for severe illness and who should therefore not return to work until vaccination or widespread serological testing is available. OBJECTIVE This study evaluated a machine learning algorithm for the prediction of serious illness due to COVID-19 using inpatient data collected from electronic health records. METHODS The algorithm was trained to identify patients for whom a diagnosis of COVID-19 was likely to result in hospitalization, and compared against four U.S policy-based criteria: age over 65, having a serious underlying health condition, age over 65 or having a serious underlying health condition, and age over 65 and having a serious underlying health condition. RESULTS This algorithm identified 80% of patients at risk for hospitalization due to COVID-19, versus at most 62% that are identified by government guidelines. The algorithm also achieved a high specificity of 95%, outperforming government guidelines. CONCLUSIONS This algorithm may help to enable a broad reopening of the American economy while ensuring that patients at high risk for serious disease remain home until vaccination and testing become available.

SARS-CoV-2 Biology Insights, Part IV.Antibody-Dependent Enhancement (ADE) and the tricky “Herd Immunity”: narrative review (Preprint)

2020 ◽  
Author(s):  
Laura Lafon-Hughes
Keyword(s):  
Viral Infection ◽  
Viral Entry ◽  
Neutralizing Antibodies ◽  
Vaccine Development ◽  
Whooping Cough ◽  
Common Knowledge ◽  
Herd Immunity ◽  
Life Quality ◽  
Host Cells ◽  
System P

BACKGROUND It is common knowledge that vaccination has improved our life quality and expectancy since it succeeded in achieving almost eradication of several diseases including chickenpox (varicella), diphtheria, hepatitis A and B, measles, meningococcal, mumps, pneumococcal, polio, rotavirus, rubella, tetanus and whooping cough (pertussis) Vaccination success is based on vaccine induction of neutralizing antibodies that help fight the infection (e.g. by a virus), preventing the disease. Conversely, Antibody-dependent enhancement (ADE) of a viral infection occurs when anti-viral antibodies facilitate viral entry into host cells and enhance viral infection in these cells. ADE has been previously studied in Dengue and HIV viruses and explains why a second infection with Dengue can be lethal. As already reviewed in Part I and Part II, SARS-Cov-2 shares with HIV not only 4 sequences in the Spike protein but also the capacity to attack the immune system. OBJECTIVE As HIV presents ADE, we wondered whether this was also the case regarding SARS-CoV-2. METHODS A literature review was done through Google. RESULTS SARS-CoV-2 presents ADE. As SARS, which does not have the 4 HIV-like inserts, has the same property, ADE would not be driven by the HIV-like spike sequences. CONCLUSIONS ADE can explain the failure of herd immunity-based strategies and will also probably hamper anti-SARS-CoV-2 vaccine development. As reviewed in Part I, there fortunately are promising therapeutic strategies for COVID-19, which should be further developed. In the meantime, complementary countermeasures to protect mainly the youth from this infection are presented to be discussed in Part V Viewpoint.

scholarly journals Are We Paving the Way to Dig Out of the “Pandemic Hole”? A Narrative Review on SARS-CoV-2 Vaccination: From Animal Models to Human Immunization

2021 ◽  
Vol 9 (3) ◽  
pp. 53
Author(s):  
Giuseppe Tardiolo ◽  
Pina Brianti ◽  
Daniela Sapienza ◽  
Pia dell’Utri ◽  
Viviane Di Dio ◽  
...  
Keyword(s):  
Animal Models ◽  
Viral Vector ◽  
Herd Immunity ◽  
Population Level ◽  
Narrative Review ◽  
Therapeutic Interventions ◽  
Preclinical Research ◽  
Inactivated Vaccines ◽  
Preclinical And Clinical Studies ◽  
Social Upheaval

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a new pathogen agent causing the coronavirus infectious disease (COVID-19). This novel virus originated the most challenging pandemic in this century, causing economic and social upheaval internationally. The extreme infectiousness and high mortality rates incentivized the development of vaccines to control this pandemic to prevent further morbidity and mortality. This international scenario led academic scientists, industries, and governments to work and collaborate strongly to make a portfolio of vaccines available at an unprecedented pace. Indeed, the robust collaboration between public systems and private companies led to resolutive actions for accelerating therapeutic interventions and vaccines mechanism. These strategies contributed to rapidly identifying safe and effective vaccines as quickly and efficiently as possible. Preclinical research employed animal models to develop vaccines that induce protective and long-lived immune responses. A spectrum of vaccines is worldwide under investigation in various preclinical and clinical studies to develop both individual protection and safe development of population-level herd immunity. Companies employed and developed different technological approaches for vaccines production, including inactivated vaccines, live-attenuated, non-replicating viral vector vaccines, as well as acid nucleic-based vaccines. In this view, the present narrative review provides an overview of current vaccination strategies, taking into account both preclinical studies and clinical trials in humans. Furthermore, to better understand immunization, animal models on SARS-CoV-2 pathogenesis are also briefly discussed.

scholarly journals The hemocyte counts as a potential biomarker for predicting disease progression in COVID-19: a retrospective study

2020 ◽  
Vol 58 (7) ◽  
pp. 1106-1115 ◽  
Author(s):  
Yufen Zheng ◽  
Ying Zhang ◽  
Hongbo Chi ◽  
Shiyong Chen ◽  
Minfei Peng ◽  
...  
Keyword(s):  
Risk Factors ◽  
Platelet Count ◽  
Disease Progression ◽  
Neutrophil Count ◽  
Calibration Curve ◽  
Lymphocyte Count ◽  
Severe Disease ◽  
Clinical Impact ◽  
Predictive Tool ◽  
Potential Biomarker

AbstractObjectivesIn December 2019, there was an outbreak of coronavirus disease 2019 (COVID-19) in Wuhan, China, and since then, the disease has been increasingly spread throughout the world. Unfortunately, the information about early prediction factors for disease progression is relatively limited. Therefore, there is an urgent need to investigate the risk factors of developing severe disease. The objective of the study was to reveal the risk factors of developing severe disease by comparing the differences in the hemocyte count and dynamic profiles in patients with severe and non-severe COVID-19.MethodsIn this retrospectively analyzed cohort, 141 confirmed COVID-19 patients were enrolled in Taizhou Public Health Medical Center, Taizhou Hospital, Zhejiang Province, China, from January 17, 2020 to February 26, 2020. Clinical characteristics and hemocyte counts of severe and non-severe COVID patients were collected. The differences in the hemocyte counts and dynamic profiles in patients with severe and non-severe COVID-19 were compared. Multivariate Cox regression analysis was performed to identify potential biomarkers for predicting disease progression. A concordance index (C-index), calibration curve, decision curve and the clinical impact curve were calculated to assess the predictive accuracy.ResultsThe data showed that the white blood cell count, neutrophil count and platelet count were normal on the day of hospital admission in most COVID-19 patients (87.9%, 85.1% and 88.7%, respectively). A total of 82.8% of severe patients had lymphopenia after the onset of symptoms, and as the disease progressed, there was marked lymphopenia. Multivariate Cox analysis showed that the neutrophil count (hazard ratio [HR] = 4.441, 95% CI = 1.954–10.090, p = 0.000), lymphocyte count (HR = 0.255, 95% CI = 0.097–0.669, p = 0.006) and platelet count (HR = 0.244, 95% CI = 0.111–0.537, p = 0.000) were independent risk factors for disease progression. The C-index (0.821 [95% CI, 0.746–0.896]), calibration curve, decision curve and the clinical impact curve showed that the nomogram can be used to predict the disease progression in COVID-19 patients accurately. In addition, the data involving the neutrophil count, lymphocyte count and platelet count (NLP score) have something to do with improving risk stratification and management of COVID-19 patients.ConclusionsWe designed a clinically predictive tool which is easy to use for assessing the progression risk of COVID-19, and the NLP score could be used to facilitate patient stratification management.

scholarly journals THU0070 DEFINING SYNOVIAL SIGNATURES IN THE RAT CIA MODEL: WHAT CAN WE LEARN ABOUT RA PROGRESSION?

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 247.2-248
Author(s):  
D. Ruelas ◽  
R. LI ◽  
C. Franci ◽  
V. Lira ◽  
D. Lopez ◽  
...  
Keyword(s):  
Disease Progression ◽  
Synovial Tissue ◽  
Current Knowledge ◽  
Severe Disease ◽  
Unmet Need ◽  
Poor Response ◽  
Multiple Time ◽  
Combination Therapies ◽  
Course Of Disease ◽  
Time Point

Background:Patients showing inadequate or no response to current therapies represent a key unmet need in rheumatoid arthritis (RA). To address this, novel or combination therapies are of high clinical interest. Identification of novel therapeutic targets requires a greater understanding of the pathogenic molecular drivers in the RA synovium. However, our current knowledge of human molecular patterns that emerge as a result of disease progression is complicated by patient-to-patient heterogeneity and access to synovial tissue.Objectives:Here we use the current knowledge of human synovial heterogeneity to conduct a longitudinal study of global molecular responses in the rat collagen-induced arthritis (CIA) model to better understand synovial biology, improve the preclinical modeling of human disease, and discover novel targets for RA.Methods:A rat CIA model was performed as previously described.1RNA-Seq was performed on 56 knee synovial tissues collected at multiple time points throughout the course of disease. Differential gene expression was determined at each individual time point and longitudinally with disease progression. Published human synovial datasets were used to categorize these genes into myeloid, lymphoid, fibroid, and low inflammatory signatures.2Differentially expressed genes (DEGs) at each time point were compared to human synovial datasets of RA patients before and after treatment. In addition, we compared disease-driven genes in CIA to genes in RA patients that are unchanged following therapy to identify possible combination therapies.Results:Disease pathology in the rat CIA natural history study progressed as expected: significant decreases were seen in body weight, as well as increases in ankle diameter, paw weight, and histopathology scores of joints in collagen-injected vs noninjected rats. There were 1900 DEGs identified between diseased and naïve rats over the course of disease, representing disease-induced gene signatures (Fig. 1). Comparing these DEGs to reported human RA synovial signatures, both the lymphoid and myeloid signatures were found to be highly upregulated. Interestingly, there were no significant DEGs representing the human fibroid and low inflammatory synovial signatures identified in the CIA rat model. This suggests that the rat CIA model most closely models RA patients with an immune synovial phenotype. In addition, we examined the overlap between disease-driven genes in CIA and genes in RA patients that are unchanged following therapy to identify signaling pathways that may be of utility in combination therapy. Of genes that were upregulated in CIA, 94% of genes that mapped to extracellular matrix-receptor pathways remained unchanged in the synovial tissue of RA patients following tocilizumab treatment.Conclusion:Previous studies have shown that nearly 30% of treatment-naïve early RA patients exhibit a strong fibroid phenotype that correlates with less severe disease and a relatively poor response to disease-modifying anti-rheumatic drugs.3These data indicate that the synovial biology associated with such patients (fibroid or pauci-immune) is not well captured in CIA, the most common preclinical RA model. To assess potential new therapies targeting these patients, it will be necessary to develop alternative animal models with more intact fibroid signatures. In addition to these findings, we also characterized the global molecular changes that occur with disease progression in the CIA rat and made a comparison to RA patients on treatment, providing an overall understanding of disease-relevant pathways in the synovium that may point to possible combination therapies.References:[1]Trentham DE, et al.J Exp Med. 1977;146(3):857-868.[2]Dennis G Jr, et al.Arthritis Res Ther. 2014;16(2):R90.[3]Humby F, et al.Ann Rheum Dis. 2019;78(6):761-772.Disclosure of Interests:Debbie Ruelas Employee of: Gilead, Ruidong Li Employee of: Gilead, Christian Franci Employee of: Gilead, Victor Lira Employee of: Gilead, David Lopez Employee of: Gilead, Li Li Employee of: Gilead, Gundula Min-Oo Employee of: Gilead, Julie A. Di Paolo Employee of: Gilead

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