COVID-19 Seroprevalence in Canada Modelling Waning and Boosting COVID-19 Immunity in Canada a Canadian Immunization Research Network Study

COVID-19 seroprevalence changes over time, with infection, vaccination, and waning immunity. Seroprevalence estimates are needed to determine when increased COVID-19 vaccination coverage is needed, and when booster doses should be considered, to reduce the spread and disease severity of COVID-19 infection. We use an age-structured model including infection, vaccination and waning immunity to estimate the distribution of immunity to COVID-19 in the Canadian population. This is the first mathematical model to do so. We estimate that 60–80% of the Canadian population has some immunity to COVID-19 by late Summer 2021, depending on specific characteristics of the vaccine and the waning rate of immunity. Models results indicate that increased vaccination uptake in age groups 12–29, and booster doses in age group 50+ are needed to reduce the severity COVID-19 Fall 2021 resurgence.

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Fall 2021 Resurgence and COVID-19 Seroprevalence in Canada: Modelling waning and boosting COVID-19 immunity in Canada, A Canadian Immunization Research Network Study

10.1101/2021.08.17.21262188 ◽

2021 ◽

Author(s):

David W Dick ◽

Lauren M Childs ◽

Zhilan Feng ◽

Jing Li ◽

Gergely Rost ◽

...

Keyword(s):

Large Scale ◽

Severe Disease ◽

Booster Vaccination ◽

Late Summer ◽

Research Network ◽

Canadian Population ◽

Waning Immunity ◽

Age Structured ◽

Age Structured Model ◽

Pharmaceutical Interventions

There is a threat of COVID-19 resurgence in Fall 2021 in Canada. To understand the probability and severity of this threat, quantification of the level of immunity/protection of the population is required. We use an age-structured model including infection, vaccination and waning immunity to estimate the distribution of immunity to COVID-19 in the Canadian population. By late Summer 2021, coinciding with the end of the vaccination program, we estimate that 60 - 80% of the Canadian population will have some immunity to COVID-19. Model results show that this level of immunity is not sufficient to stave off a Fall 2021 resurgence. The timing and severity of a resurgence, however, varies in magnitude given multiple factors: relaxation of non-pharmaceutical interventions such as social distancing, the rate of waning immunity, the transmissibility of variants of concern, and the protective characteristics of the vaccines against infection and severe disease. To prevent large-scale resurgence, booster vaccination and/or re-introduction of public health mitigation may be needed.

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POTENTIAL YIELD OF A ROCK SHRIMP STOCK, SICYONIA PENICILLATA IN THE NORTHERN GULF OF CALIFORNIA

Crustaceana ◽

10.1163/156854099503627 ◽

1999 ◽

Vol 72 (6) ◽

pp. 581-590 ◽

Cited By ~ 1

Author(s):

Juana Lopez-Martinez ◽

Edgar Alcantara-Razo ◽

Sergio Hernandez-Vazquez ◽

Ernesto Chavez

Keyword(s):

Gulf Of California ◽

Age Groups ◽

Potential Yield ◽

Natural Oscillations ◽

Recruitment Pattern ◽

Stock Recruitment ◽

Age Structured ◽

Stochastic Recruitment ◽

Age Structured Model ◽

Compensatory Effect

AbstractA stock of rock shrimp Sicyonia penicillata was assessed in a fishery recently opened at Bahoa Kino, Sonora, Mexico. An age-structured model with stochastic recruitment was developed, which considers growth rate, natural mortality, and fishing mortality by age. Age groups were followed year by year with a stock-recruitment Ricker function where the seasonal recruitment pattern was defined as well. Simulations might be interpreted as showing a stable population with four year cycles, reflecting a density-dependent process. In 1996, fishing intensity had an apparent compensatory effect on the stock, decreasing the amplitude of natural oscillations and maintaining the stock at a biomass level similar to the size observed in a condition of no exploitation. The stock was found currently underexploited. As a result of the seasonal accessibility and the age of first-catch fishing (adult shrimp), the stock might be capable to withstand high fishing pressure without being overexploited. Se evaluo una poblacion de camaron de roca Sicyonia penicillata, de una pesqueroa recientemente abierta en Bahoa Kino, Sonora, Mexico. Se desarrollo un modelo basado en la estructura por edades que considera reclutamiento estocastico, tasa de crecimiento, mortalidad natural y mortalidad por pesca por grupo de edad. Estos grupos de edad fueron determinados ano tras ano mediante la funcion de reclutamiento de Ricker, en los que tambien se definio el patron estacional de reclutamiento. Las simulaciones muestran una poblacion estable con ciclos de cuatro anos, que indican un proceso de densodependencia. En 1996, la intensidad de pesca tuvo un efecto compensatorio sobre la poblacion, reduciendo la amplitud de las oscilaciones naturales y manteniendo al stock en un nivel de biomasa similar al observado en la condicion sin explotacion. Se encontro que el recurso esta subexplotado. Como resultado de la accesibilidad estacional y de que la edad de primera captura corresponde a camaron adulto, el recurso soporta alta presion de pesca sin dar evidencias de sobreexplotacion.

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The effect of age and study duration on the relationship between ‘clustering’ of DNA fingerprint patterns and the proportion of tuberculosis disease attributable to recent transmission

Epidemiology and Infection ◽

10.1017/s095026880100499x ◽

2001 ◽

Vol 126 (1) ◽

pp. 43-62 ◽

Cited By ~ 23

Author(s):

E. VYNNYCKY ◽

N. NAGELKERKE ◽

M. W. BORGDORFF ◽

D. VAN SOOLINGEN ◽

J. D. A. VAN EMBDEN ◽

...

Keyword(s):

Age Groups ◽

Dna Fingerprint ◽

Older Individuals ◽

Study Duration ◽

Time Period ◽

Recent Transmission ◽

Dependent Risks ◽

Age Structured ◽

Age Structured Model ◽

The Relationship

Though it is recognized that the extent of ‘clustering’ of isolates from tuberculosis cases in a given population is related to the amount of disease attributable to recent transmission, the relationship between the two statistics is poorly understood. Given age-dependent risks of disease and the fact that a long study (e.g. spanning several years) is more likely to identify transmission-linked cases than a shorter study, both measures, and thus the relationship between them, probably depend strongly on the ages of the cases ascertained and study duration. The contribution of these factors is explored in this paper using an age-structured model which describes the introduction and transmission of M. tuberculosis strains with different DNA fingerprint patterns in The Netherlands during this century, assuming that the number of individuals contacted by each case varies between cases and that DNA fingerprint patterns change over time through random mutations, as observed in several studies.Model predictions of clustering in different age groups and over different time periods between 1993 and 1997 compare well against those observed. According to the model, the proportion of young cases with onset in a given time period who were ‘clustered’ underestimated the proportion of disease attributable to recent transmission in this age group (by up to 25% in males); for older individuals, clustering overestimated this proportion. These under- and overestimates decreased and increased respectively as the time period over which the cases were ascertained increased. These results have important implications for the interpretation of estimates of the proportion of disease attributable to recent transmission, based on ‘clustering’ statistics, as are being derived from studies of the molecular epidemiology of tuberculosis in many populations.

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Contact surveys reveal heterogeneities in age-group contributions to SARS-CoV-2 dynamics in the United States

10.1101/2021.09.25.21264082 ◽

2021 ◽

Author(s):

Taylor Chin ◽

Dennis M. Feehan ◽

Caroline O. Buckee ◽

Ayesha S. Mahmud

Keyword(s):

United States ◽

Age Groups ◽

The United States ◽

Contact Rate ◽

Age Group ◽

Contact Rates ◽

Contact Patterns ◽

Age Structured ◽

The Impact ◽

Over Time

SARS-CoV-2 is spread primarily through person-to-person contacts. Quantifying population contact rates is important for understanding the impact of physical distancing policies and for modeling COVID-19, but contact patterns have changed substantially over time due to shifting policies and behaviors. There are surprisingly few empirical estimates of age-structured contact rates in the United States both before and throughout the COVID-19 pandemic that capture these changes. Here, we use data from six waves of the Berkeley Interpersonal Contact Survey (BICS), which collected detailed contact data between March 22, 2020 and February 15, 2021 across six metropolitan designated market areas (DMA) in the United States. Contact rates were low across all six DMAs at the start of the pandemic. We find steady increases in the mean and median number of contacts across these localities over time, as well as a greater proportion of respondents reporting a high number of contacts. We also find that young adults between ages 18 and 34 reported more contacts on average compared to other age groups. The 65 and older age group consistently reported low levels of contact throughout the study period. To understand the impact of these changing contact patterns, we simulate COVID-19 dynamics in each DMA using an age-structured mechanistic model. We compare results from models that use BICS contact rate estimates versus commonly used alternative contact rate sources. We find that simulations parameterized with BICS estimates give insight into time-varying changes in relative incidence by age group that are not captured in the absence of these frequently updated estimates. We also find that simulation results based on BICS estimates closely match observed data on the age distribution of cases, and changes in these distributions over time. Together these findings highlight the role of different age groups in driving and sustaining SARS-CoV-2 transmission in the U.S. We also show the utility of repeated contact surveys in revealing heterogeneities in the epidemiology of COVID-19 across localities in the United States.

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An age-structured SEIR model for COVID-19 incidence in Dublin, Ireland with framework for evaluating health intervention cost

PLoS ONE ◽

10.1371/journal.pone.0260632 ◽

2021 ◽

Vol 16 (12) ◽

pp. e0260632

Author(s):

Fatima-Zahra Jaouimaa ◽

Daniel Dempsey ◽

Suzanne Van Osch ◽

Stephen Kinsella ◽

Kevin Burke ◽

...

Keyword(s):

Social Interactions ◽

Strong Association ◽

Age Groups ◽

Incidence Rates ◽

Age Group ◽

Seir Model ◽

Age Cohorts ◽

Intervention Cost ◽

Age Structured ◽

Predictive Approaches

Strategies adopted globally to mitigate the threat of COVID–19 have primarily involved lockdown measures with substantial economic and social costs with varying degrees of success. Morbidity patterns of COVID–19 variants have a strong association with age, while restrictive lockdown measures have association with negative mental health outcomes in some age groups. Reduced economic prospects may also afflict some age cohorts more than others. Motivated by this, we propose a model to describe COVID–19 community spread incorporating the role of age-specific social interactions. Through a flexible parameterisation of an age-structured deterministic Susceptible Exposed Infectious Removed (SEIR) model, we provide a means for characterising different forms of lockdown which may impact specific age groups differently. Social interactions are represented through age group to age group contact matrices, which can be trained using available data and are thus locally adapted. This framework is easy to interpret and suitable for describing counterfactual scenarios, which could assist policy makers with regard to minimising morbidity balanced with the costs of prospective suppression strategies. Our work originates from an Irish context and we use disease monitoring data from February 29th 2020 to January 31st 2021 gathered by Irish governmental agencies. We demonstrate how Irish lockdown scenarios can be constructed using the proposed model formulation and show results of retrospective fitting to incidence rates and forward planning with relevant “what if / instead of” lockdown counterfactuals. Uncertainty quantification for the predictive approaches is described. Our formulation is agnostic to a specific locale, in that lockdown strategies in other regions can be straightforwardly encoded using this model.

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Modelling the impact of immunization on the epidemiology of varicella zoster virus

Epidemiology and Infection ◽

10.1017/s0950268800004714 ◽

2000 ◽

Vol 125 (3) ◽

pp. 651-669 ◽

Cited By ~ 169

Author(s):

M. BRISSON ◽

W. J. EDMUNDS ◽

N. J. GAY ◽

B. LAW ◽

G. DE SERRES

Keyword(s):

First Year ◽

Varicella Zoster ◽

Vaccination Strategies ◽

Short And Long Term ◽

Catch Up ◽

Age Structured ◽

Age Structured Model ◽

The Impact ◽

Do So

The objective of this study was to develop and apply a dynamic mathematical model of VZV transmission to predict the effect of different vaccination strategies on the age-specific incidence and outcome of infection. To do so a deterministic realistic age-structured model (RAS) was used which takes account of the increased potential for transmission within school aged groups. Various vaccine efficacy scenarios, vaccine coverages and vaccination strategies were investigated and a sensitivity analysis of varicella incidence predictions to important parameters was performed. The model predicts that the overall (natural and breakthrough) incidence and morbidity of varicella would likely be reduced by mass vaccination of 12-month-old children. Furthermore, adding a catch-up campaign in the first year for 1–11 year olds seems to be the most effective strategy to reduce both varicella incidence and morbidity (in the short and long term), though with the possible detrimental effect of increasing the incidence of zoster.

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Age-Structured Model for COVID-19: Effectiveness of Social Distancing and Contact Reduction

10.21203/rs.3.rs-33480/v1 ◽

2020 ◽

Author(s):

Mark Kimathi ◽

Samuel Mwalili ◽

Viona Ojiambo ◽

Duncan Gathungu

Keyword(s):

Deterministic Model ◽

Age Groups ◽

School Closure ◽

Model Parameters ◽

Suitable Model ◽

Age Group ◽

Social Distancing ◽

First Case ◽

Age Structured ◽

The Impact

Abstract Background: Coronavirus disease 2019 is caused by severe acute respiratory syndrome coronavirus 2. The disease has spread to almost every country in the world. Kenya reported its first case on 13th of March 2020. From 16th March 2020, the country instituted various social distancing strategies to reduce the transmission and flatten the epidemic curve. These strategies include school closure, dusk-to-dawn curfew, and travel restriction across counties, especially Nairobi, Mombasa and Kwale. An age-structured compartmental model was developed to assess the impact of non-pharmaceutical interventions on severity of infections, hospital demands and deaths. Methods: The population is divided into four age-groups and for each age-group there are seven compartments, namely: susceptible , exposed, asymptomatic, mild, severe, critical, death and recovered. The contact matrices between the different ages are integrated into an age-structured deterministic model via the force of infection. This model is represented by ordinary differential equations and solved using Runge–Kutta methods, with suitable model parameters. Simulation results for the unmitigated and mitigated scenarios were depicted, for the different age-groups. Results: The 45% reduction in contacts for 60-days period resulted to between 11.5-13% reduction of infections severity and deaths, while for the 190-days period yielded between 18.8-22.7% reduction. The peak of infections in the 60-days mitigation was higher and happened about 2 months after the relaxation of mitigation as compared to that of the 190-days mitigation, which happened just a month after mitigation were relaxed. Low numbers of cases in children under 15 years was attributed to low susceptibility of persons in this age-group. High numbers of cases are reported in the 15-29 years and 30-59 years age bands since these individuals have wider interaction spheres, and they form a significant percentage of Kenya population. Conclusion: Two mitigation periods, considered in the study, resulted to reductions in severe and critical cases, attack rates, hospital and ICU bed demands, as well as deaths, with the 190-days period giving higher reductions. The study revealed the age-dependency of the key health outputs.

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Evaluation of outbreak response immunization in the control of pertussis using agent-based modeling

PeerJ ◽

10.7717/peerj.2337 ◽

2016 ◽

Vol 4 ◽

pp. e2337 ◽

Cited By ~ 5

Author(s):

Alexander Doroshenko ◽

Weicheng Qian ◽

Nathaniel D. Osgood

Keyword(s):

Public Health ◽

Vaccine Coverage ◽

Age Groups ◽

Sensitivity Analyses ◽

Age Group ◽

Outbreak Response ◽

Agent Based ◽

Waning Immunity ◽

Outbreak Response Immunization ◽

Immunization Campaign

BackgroundPertussis control remains a challenge due to recently observed effects of waning immunity to acellular vaccine and suboptimal vaccine coverage. Multiple outbreaks have been reported in different ages worldwide. For certain outbreaks, public health authorities can launch an outbreak response immunization (ORI) campaign to control pertussis spread. We investigated effects of an outbreak response immunization targeting young adolescents in averting pertussis cases.MethodsWe developed an agent-based model for pertussis transmission representing disease mechanism, waning immunity, vaccination schedule and pathogen transmission in a spatially-explicit 500,000-person contact network representing a typical Canadian Public Health district. Parameters were derived from literature and calibration. We used published cumulative incidence and dose-specific vaccine coverage to calibrate the model’s epidemiological curves. We endogenized outbreak response by defining thresholds to trigger simulated immunization campaigns in the 10–14 age group offering 80% coverage. We ran paired simulations with and without outbreak response immunization and included those resulting in a single ORI within a 10-year span. We calculated the number of cases averted attributable to outbreak immunization campaign in all ages, in the 10–14 age group and in infants. The count of cases averted were tested using Mann–WhitneyUtest to determine statistical significance. Numbers needed to vaccinate during immunization campaign to prevent a single case in respective age groups were derived from the model. We varied adult vaccine coverage, waning immunity parameters, immunization campaign eligibility and tested stronger vaccination boosting effect in sensitivity analyses.Results189 qualified paired-runs were analyzed. On average, ORI was triggered every 26 years. On a per-run basis, there were an average of 124, 243 and 429 pertussis cases averted across all age groups within 1, 3 and 10 years of a campaign, respectively. During the same time periods, 53, 96, and 163 cases were averted in the 10–14 age group, and 6, 11, 20 in infants under 1 (p< 0.001, all groups). Numbers needed to vaccinate ranged from 49 to 221, from 130 to 519 and from 1,031 to 4,903 for all ages, the 10–14 age group and for infants, respectively. Most sensitivity analyses resulted in minimal impact on a number of cases averted.DiscussionOur model generated 30 years of longitudinal data to evaluate effects of outbreak response immunization in a controlled study. Immunization campaign implemented as an outbreak response measure among adolescents may confer benefits across all ages accruing over a 10-year period. Our inference is dependent on having an outbreak of significant magnitude affecting predominantly the selected age and achieving a comprehensive vaccine coverage during the campaign. Economic evaluations and comparisons with other control measures can add to conclusions generated by our work.

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Projecting the transition of COVID-19 burden towards the young population while vaccines are rolled out: a modelling study

10.1101/2021.10.14.21265032 ◽

2021 ◽

Author(s):

Jun Cai ◽

Juan Yang ◽

Xiaowei Deng ◽

Cheng Peng ◽

Xinhua Chen ◽

...

Keyword(s):

Disease Burden ◽

Age Groups ◽

Mainland China ◽

Target Population ◽

Severe Illness ◽

Age Group ◽

Young Population ◽

Children And Young Adolescents ◽

Age Structured ◽

Roll Out

SARS-CoV-2 infection causes most cases of severe illness and fatality in older age groups. In China, over 85% of individuals aged ≥12 years have been vaccinated against COVID-19 (albeit with vaccines developed against historical lineages), while children aged 0-11 years are currently not eligible for vaccination (as of September 2021). The aim of this work is to assess whether the importation of Delta variant infections will shift the COVID-19 burden from adults to children. We developed an age-structured susceptible-infectious-removed model of SARS-CoV-2 transmission dynamics to simulate the epidemics triggered by the importation of Delta variant infections and project the age-specific incidence of SARS-CoV-2 infections, cases, hospitalisations, intensive care unit (ICU) admissions, and deaths. In the context of the vaccination programme targeting individuals aged ≥12 years (as of September 2021), and in the absence of non-pharmaceutical interventions, the importation of Delta variant infections could lead to widespread transmission and substantial disease burden in mainland China, even with vaccination coverage as high as 97% across the currently eligible age groups. The symptomatic SARS-CoV-2 infections and hospitalisation are projected to shift towards children and young adolescents, with 13% of symptomatic infections and 30% of hospitalisations occurring in those aged 0-11 years. Extending the vaccination roll-out to include children aged 3-11 years is estimated to dramatically decrease the burden of symptomatic infections and hospitalisations within this age group (54% and 81%, respectively), but would have a low impact on protecting infants (aged 0-2 years). Our findings highlight the need to strengthen vaccination efforts by simultaneously extending the target population and elevating vaccine effectiveness.

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Age-group targeted testing for COVID-19 as new prevention strategy

10.21203/rs.3.rs-31786/v1 ◽

2020 ◽

Author(s):

Ranjit Kumar Upadhyay ◽

S. Chatterjee ◽

S. Saha ◽

R.K. Azad

Keyword(s):

Human Life ◽

Age Groups ◽

Age Group ◽

Disease Dynamics ◽

Social Distancing ◽

Robust Testing ◽

Targeted Testing ◽

Age Structured ◽

Tremendous Impact ◽

Methods Of Control

Abstract Robust testing and tracing are key to fighting the menace of coronavirus disease 2019 (COVID-19). This outbreak has progressed with tremendous impact on human life, society and economy. In this paper, we propose an age-structured SIQR model to track the progression of the pandemic in India, Italy and USA, taking into account the different age-structures of these countries. We have made predictions about the disease dynamics, identified the most infected age-groups and analysed the effectiveness of social distancing measures taken in the early stages of infection. The basic reproductive ratio R0 has been numerically calculated for each country.We propose a strategy of age-targeted testing, with increased testing in the most proportionally infected age-groups. We observe a marked flattening of the infection curve upon simulating increased testing in the 15-40 year age-groups in India. Thus, we conclude that social distancing and widespread testing are effective methods of control, with emphasis on testing and identifying the hotspots of highly infected populations.

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