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

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.

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A mathematical model reveals the influence of population heterogeneity on herd immunity to SARS-CoV-2

Science ◽

10.1126/science.abc6810 ◽

2020 ◽

Vol 369 (6505) ◽

pp. 846-849 ◽

Cited By ~ 66

Author(s):

Tom Britton ◽

Frank Ball ◽

Pieter Trapman

Keyword(s):

Mathematical Model ◽

Severe Acute Respiratory Syndrome ◽

Preventive Measures ◽

Social Activity ◽

Herd Immunity ◽

Population Heterogeneity ◽

Contact Rates ◽

Mixing Rates ◽

Age Structured ◽

Induced Immunity

Despite various levels of preventive measures, in 2020, many countries have suffered severely from the coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. Using a model, we show that population heterogeneity can affect disease-induced immunity considerably because the proportion of infected individuals in groups with the highest contact rates is greater than that in groups with low contact rates. We estimate that if R0 = 2.5 in an age-structured community with mixing rates fitted to social activity, then the disease-induced herd immunity level can be ~43%, which is substantially less than the classical herd immunity level of 60% obtained through homogeneous immunization of the population. Our estimates should be interpreted as an illustration of how population heterogeneity affects herd immunity rather than as an exact value or even a best estimate.

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

10.1101/2020.04.19.20070805 ◽

2020 ◽

Cited By ~ 5

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.

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

Vaccines ◽

10.3390/vaccines8040668 ◽

2020 ◽

Vol 8 (4) ◽

pp. 668 ◽

Cited By ~ 2

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.

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The disease-induced herd immunity level for Covid-19 is substantially lower than the classical herd immunity level

10.1101/2020.05.06.20093336 ◽

2020 ◽

Cited By ~ 17

Author(s):

Tom Britton ◽

Frank Ball ◽

Pieter Trapman

Keyword(s):

Preventive Measures ◽

Reproduction Number ◽

Social Activity ◽

Herd Immunity ◽

Contact Rates ◽

Mixing Rates ◽

Disease Spreading ◽

Age Structured ◽

Induced Immunity ◽

The Basic Reproduction Number

AbstractMost countries are suffering severely from the ongoing covid-19 pandemic despite various levels of preventive measures. A common question is if and when a country or region will reach herd immunity h. The classical herd immunity level hC is defined as hC = 1−1/R0, where R0 is the basic reproduction number, for covid-19 estimated to lie somewhere in the range 2.2-3.5 depending on country and region. It is shown here that the disease-induced herd immunity level hD, after an outbreak has taken place in a country/region with a set of preventive measures put in place, is actually substantially smaller than hC. As an illustration we show that if R0 = 2.5 in an age-structured community with mixing rates fitted to social activity studies, and also categorizing individuals into three categories: low active, average active and high active, and where preventive measures affect all mixing rates proportionally, then the disease-induced herd immunity level is hD = 43% rather than hC = 1−1/2.5 = 60%. Consequently, a lower fraction infected is required for herd immunity to appear. The underlying reason is that when immunity is induced by disease spreading, the proportion infected in groups with high contact rates is greater than that in groups with low contact rates. Consequently, disease-induced immunity is stronger than when immunity is uniformly distributed in the community as in the classical herd immunity level.

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

10.1101/2021.04.07.21255024 ◽

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.

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

10.2196/preprints.22030 ◽

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.

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Forecast of COVID-19 Trend Following Countrywide Movement Control Order in Malaysia: SEIR Model Approach (Preprint)

10.2196/preprints.22304 ◽

2020 ◽

Author(s):

Aidalina Mahmud ◽

Poh Ying Lim ◽

Hayati Kadir Shahar

Keyword(s):

Mathematical Model ◽

Incidence Rate ◽

Movement Control ◽

Seir Model ◽

Infection Incidence ◽

Contact Rates ◽

Exposed Population ◽

Control Order ◽

Future Work ◽

Model Approach

BACKGROUND On March 18, 2020, the Malaysian government implemented Movement Control Order (MCO) to limit the contact rates among the population and infected individuals. OBJECTIVE The objective of this study was to forecast the trend of the COVID-19 epidemic in Malaysia in terms of its magnitude and duration. METHODS Data for this analysis was obtained from publicly available databases, from March 17 until March 27, 2020. By applying the Susceptible, Exposed, Infectious and Removed (SEIR) mathematical model and several predetermined assumptions, two analyses were carried out: without and with MCO implementation. RESULTS Without MCO, it is forecasted that it would take 18 days to reach the peak of infection incidence. The incidence rate would plateau at day 80 and end by day 94, with 43% of the exposed population infected. With the implementation of the MCO, it is forecasted that new cases of infection would peak at day 25, plateau at day 90 and end by day 100. At its peak, the infection could affect up to about 40% of the exposed population. CONCLUSIONS It is forecasted that the COVID-19 epidemic in Malaysia will subside soon after the mid-year of 2020. Although the implementation of MCO can flatten the epidemiological curve, it also prolongs the duration of the epidemic. The MCO can result in several unfavorable consequences in economic and psychosocial aspects. A future work of an exit plan for the MCO should also be devised and implemented gradually. The exit plan raises several timely issues of re-infection resurgence after MCO are lifted.

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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

Medical Sciences ◽

10.3390/medsci9030053 ◽

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.

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