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

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.

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

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.

scholarly journals SARS-CoV-2 transmission across age groups in France and implications for control

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Cécile Tran Kiem ◽  
Paolo Bosetti ◽  
Juliette Paireau ◽  
Pascal Crépey ◽  
Henrik Salje ◽  
...  
Keyword(s):  
Young Adults ◽  
Age Groups ◽  
Deep Understanding ◽  
Transmission Dynamics ◽  
Age Group ◽  
Older Individuals ◽  
Targeted Intervention ◽  
Social Distancing ◽  
Intervention Measures ◽  
Better Than

AbstractThe shielding of older individuals has been proposed to limit COVID-19 hospitalizations while relaxing general social distancing in the absence of vaccines. Evaluating such approaches requires a deep understanding of transmission dynamics across ages. Here, we use detailed age-specific case and hospitalization data to model the rebound in the French epidemic in summer 2020, characterize age-specific transmission dynamics and critically evaluate different age-targeted intervention measures in the absence of vaccines. We find that while the rebound started in young adults, it reached individuals aged ≥80 y.o. after 4 weeks, despite substantial contact reductions, indicating substantial transmission flows across ages. We derive the contribution of each age group to transmission. While shielding older individuals reduces mortality, it is insufficient to allow major relaxations of social distancing. When the epidemic remains manageable (R close to 1), targeting those most contributing to transmission is better than shielding at-risk individuals. Pandemic control requires an effort from all age groups.

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

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.

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

PLoS ONE ◽  
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.

scholarly journals The average peak time and intensity of seasonal influenza may vary by age: a study of laboratory confirmed influenza

2020 ◽  
Vol 21 (5) ◽  
pp. 170-176
Author(s):  
Bhikhari P Tharu
Keyword(s):  
Seasonal Influenza ◽  
Human Life ◽  
Parametric Method ◽  
Age Groups ◽  
Respiratory Illness ◽  
Peak Time ◽  
Age Group ◽  
Calendar Time ◽  
Control And Prevention ◽  
The Usa

Background: Seasonal influenza (SI) is an acute respiratory illness that exerts a severe impact on human life year-round. Yet, very few studies have been conducted to investigate its peak timing for different age groups. Objective: To evaluate the average peak calendar time and intensity for the incidence of SI for different age groups. Methods: The study uses laboratory-confirmed Influenza data from the Centers for Disease Control and Prevention (CDC) of the USA with age groups 2, 11, 34, 57 and 65 years during 2009–2018 for the analysis. A non-parametric method of estimation of a circular probability distribution called likelihood cross-validation method has been utilised. Results: The average peak date of incidence for age groups 2 and 11 is around the last week of December. However, the date shifts to the last week of January to the first week of February for other groups. Age groups 65 and 2 years experienced the most severe impact among all. Discussion: The average peak time for SI incidence is between the last week of December to January with a single peak time for every age group. However, the incidence seems to develop an additional moderate peak time for age group 65.

scholarly journals Assessment of Social Distancing for Controlling COVID-19 in Korea: An Age-Structured Modeling Approach

2020 ◽  
Vol 17 (20) ◽  
pp. 7474
Author(s):  
Yongin Choi ◽  
James Slghee Kim ◽  
Heejin Choi ◽  
Hyojung Lee ◽  
Chang Hyeong Lee
Keyword(s):  
Mathematical Model ◽  
Age Groups ◽  
Epidemiological Data ◽  
The Elderly ◽  
Model Parameters ◽  
Social Distancing ◽  
First Case ◽  
Age Structured ◽  
Epidemiological Characteristics ◽  
The Impact

The outbreak of the novel coronavirus disease 2019 (COVID-19) occurred all over the world between 2019 and 2020. The first case of COVID-19 was reported in December 2019 in Wuhan, China. Since then, there have been more than 21 million incidences and 761 thousand casualties worldwide as of 16 August 2020. One of the epidemiological characteristics of COVID-19 is that its symptoms and fatality rates vary with the ages of the infected individuals. This study aims at assessing the impact of social distancing on the reduction of COVID-19 infected cases by constructing a mathematical model and using epidemiological data of incidences in Korea. We developed an age-structured mathematical model for describing the age-dependent dynamics of the spread of COVID-19 in Korea. We estimated the model parameters and computed the reproduction number using the actual epidemiological data reported from 1 February to 15 June 2020. We then divided the data into seven distinct periods depending on the intensity of social distancing implemented by the Korean government. By using a contact matrix to describe the contact patterns between ages, we investigated the potential effect of social distancing under various scenarios. We discovered that when the intensity of social distancing is reduced, the number of COVID-19 cases increases; the number of incidences among the age groups of people 60 and above increases significantly more than that of the age groups below the age of 60. This significant increase among the elderly groups poses a severe threat to public health because the incidence of severe cases and fatality rates of the elderly group are much higher than those of the younger groups. Therefore, it is necessary to maintain strict social distancing rules to reduce infected cases.

scholarly journals Vaccination Prioritization Strategies for COVID-19 in Korea: A Mathematical Modeling Approach

2021 ◽  
Vol 18 (8) ◽  
pp. 4240
Author(s):  
Yongin Choi ◽  
James Slghee Kim ◽  
Jung Eun Kim ◽  
Heejin Choi ◽  
Chang Hyeong Lee
Keyword(s):  
Age Groups ◽  
Epidemiological Data ◽  
The Elderly ◽  
Age Group ◽  
Elderly Age ◽  
Social Distancing ◽  
Vaccination Strategies ◽  
Effective Age ◽  
Incidence And Mortality ◽  
Age Dependent

Coronavirus disease 2019 (COVID-19) vaccination has recently started worldwide. As the vaccine supply will be limited for a considerable period of time in many countries, it is important to devise the effective vaccination strategies that reduce the number of deaths and incidence of infection. One of the characteristics of COVID-19 is that the symptom, severity, and mortality of the disease differ by age. Thus, when the vaccination supply is limited, age-dependent vaccination priority strategy should be implemented to minimize the incidences and mortalities. In this study, we developed an age-structured model for describing the transmission dynamics of COVID-19, including vaccination. Using the model and actual epidemiological data in Korea, we estimated the infection probability for each age group under different levels of social distancing implemented in Korea and investigated the effective age-dependent vaccination strategies to reduce the confirmed cases and fatalities of COVID-19. We found that, in a lower level of social distancing, vaccination priority for the age groups with the highest transmission rates will reduce the incidence mostly, but, in higher levels of social distancing, prioritizing vaccination for the elderly age group reduces the infection incidences more effectively. To reduce mortalities, vaccination priority for the elderly age group is the best strategy in all scenarios of levels of social distancing. Furthermore, we investigated the effect of vaccine supply and efficacy on the reduction in incidence and mortality.

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

Vaccines ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 17
Author(s):  
David W. Dick ◽  
Lauren Childs ◽  
Zhilan Feng ◽  
Jing Li ◽  
Gergely Röst ◽  
...  
Keyword(s):  
Age Groups ◽  
Late Summer ◽  
Research Network ◽  
Age Group ◽  
Canadian Population ◽  
Waning Immunity ◽  
Age Structured ◽  
Changes Over Time ◽  
Age Structured Model ◽  
Do So

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.

scholarly journals Effects of Lockdowns And Its Impacts On Age-Specific Transmission Dynamics of SARS-Cov-2 In Georgia, USA

2022 ◽  
Author(s):  
Max SY Lau ◽  
Carol Liu ◽  
Aaron Siegler ◽  
Patrick Sullivan ◽  
Lance A. Waller ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Preventive Measures ◽  
Age Groups ◽  
Vaccination Rate ◽  
Transmission Dynamics ◽  
Transmission Model ◽  
Mortality Data ◽  
Average Risk ◽  
Age Group ◽  
Social Distancing

Abstract Social distancing measures are effective in reducing overall community transmission but much remains unknown about how they have impacted finer-scale dynamics. In particular, much is unknown about how changes of contact patterns and other behaviors including adherence to social distancing, induced by these measures, may have impacted finer-scale transmission dynamics among different age groups. In this paper, we build a stochastic age-specific transmission model to systematically characterize the degree and variation of age-specific transmission dynamics, before and after lifting the lockdown in Georgia, USA. We perform Bayesian (missing-) data-augmentation model inference, leveraging reported age-specific case, seroprevalence and mortality data. We estimate that community-level transmissibility was reduced to 41.2% with 95% CI [39%, 43.8%] of the pre-lockdown level in about a week of the announcement of the shelter-in-place order. Although it subsequently increased after the lockdown was lifted, it only bounced back to 62% [58%, 67.2%] of the pre-lockdown level after about a month. We also find that during the lockdown susceptibility to infection increases with age. Specifically, relative to the oldest age group (>65+), susceptibility for the youngest age group (0-17 years) is 0.13 [0.09, 0.18], and it increases to 0.53 [0.49, 0.59] for 18-44 and 0.75 [0.68, 0.82] for 45- 64. More importantly, our results reveal clear changes of age-specific susceptibility (defined as average risk of getting infected during an infectious contact incorporating age-dependent behavioral factors) after the lockdown was lifted, with a trend largely consistent with reported age-specific adherence levels to social distancing and preventive measures. Specifically, the older groups (>45) (with the highest levels of adherence) appear to have the most significant reductions of susceptibility (e.g., post-lockdown susceptibility reduced to 31.6% [29.3%, 34%] of the estimate before lifting the lockdown for the 65+ group). Finally, we find heterogeneity in case reporting rates among different age groups, with the lowest rate occurring among the 0-18 group (9.7% [6.4%, 19%]). Our results provide a more fundamental understanding of the impacts of stringent lockdown measures, and finer evidence that other social distancing and preventive measures may be effective in reducing SARS-CoV-2 transmission. These results may be exploited to guide more effective implementations of these measures in many current settings (with low vaccination rate globally and emerging variants) and in future potential outbreaks of novel pathogens.

scholarly journals The monetary value of human life losses associated with COVID-19 in Turkey

2020 ◽  
Vol 2 ◽  
pp. 44
Author(s):  
Joses Muthuri Kirigia ◽  
Rose Nabi Deborah Karimi Muthuri ◽  
Lenity Honesty Kainyu Nkanata
Keyword(s):  
Life Expectancy ◽  
Human Life ◽  
Age Groups ◽  
International Health ◽  
Years Of Life Lost ◽  
Age Group ◽  
Health Coverage ◽  
Discount Rates ◽  
Monetary Value ◽  
National Life

Background:  This study aimed to appraise the monetary value of human life losses associated with COVID-19 in Turkey. To our knowledge, it is the first study in Turkey to value human life losses associated with COVID-19.  Methods: A human capital approach (HCA) model was applied to estimate the total monetary value of the 4,807 human lives lost in Turkey (TMVHL) from COVID-19 by 15 June 2020. The TMVHL equals the sum of monetary values of human lives lost (MVHL) across nine age groups. The MVHL accruing to each age group is the sum of the product of discount factor, years of life lost, net GDP per capita, and the number of COVID-19 deaths in an age group. The HCA model was re-calculated five times assuming discount rates of 3%, 5%, and 10% with a national life expectancy of 78.45 years; and the world highest life expectancy of 87.1 years and global life expectancy of 72 years with 3% discount rate. Results: The 4807 human life losses from COVID-19 had a TMVHL of Int$1,098,469,122; and a mean of Int$228,514 per human life. Reanalysis with 5% and 10% discount rates, holding national life expectancy constant, reduced the TMVHL by Int$167,248,319 (15.2%) and Int$ 429,887,379 (39%), respectively. Application of the global life expectancy reduced the TMVHL by 36.4%, and use of world highest life expectancy increased TMVHL by 69%. However, the HCA captures only the economic production losses incurred as a result of years of life lost. It ignores non-market contributions to social welfare and the adverse effects of economic activities. Conclusions: Additional investment is needed to bridge the persisting gaps in International Health Regulations capacities, Universal Health Coverage, and safely managed water and sanitation services.

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