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

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2337 ◽  
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.

scholarly journals Estimating age-specific vaccine effectiveness using data from a large measles outbreak in Berlin, Germany, 2014/15: evidence for waning immunity

2019 ◽  
Vol 24 (17) ◽  
Author(s):  
Julia Bitzegeio ◽  
Shannon Majowicz ◽  
Dorothea Matysiak-Klose ◽  
Daniel Sagebiel ◽  
Dirk Werber
Keyword(s):  
Scenario Analysis ◽  
Vaccine Coverage ◽  
Screening Method ◽  
Age Groups ◽  
Vaccine Effectiveness ◽  
Natural Immunity ◽  
Age Group ◽  
Measles Outbreak ◽  
Waning Immunity ◽  
Using Data

Background Measles elimination is based on 95% coverage with two doses of a measles-containing vaccine (MCV2), high vaccine effectiveness (VE) and life-long vaccine-induced immunity. Longitudinal analysis of antibody titres suggests existence of waning immunity, but the relevance at the population-level is unknown. Aim We sought to assess presence of waning immunity by estimating MCV2 VE in different age groups (2–5, 6–15, 16–23, 24–30 and 31–42 years) in Berlin. Methods We conducted a systematic literature review on vaccination coverage and applied the screening-method using data from a large measles outbreak (2014/15) in Berlin. Uncertainty in input variables was incorporated by Monte Carlo simulation. In a scenario analysis, we estimated the proportion vaccinated with MCV2 in those 31-42 years using VE of the youngest age group, where natural immunity was deemed negligible. Results Of 773 measles cases (median age: 20 years), 40 had received MCV2. Average vaccine coverage per age group varied (32%–88%). Estimated median VE was  > 99% (95% credible interval (CrI): 98.6–100) in the three youngest age groups, but lower (90.9%, 95% CrI: 74.1–97.6) in the oldest age group. In the scenario analysis, the estimated proportion vaccinated was 98.8% (95% CrI: 96.5–99.8). Conclusion VE for MCV2 was generally high, but lower in those aged 31-42 years old. The estimated proportion with MCV2 should have led to sufficient herd immunity in those aged 31-42 years old. Thus, lower VE cannot be fully explained by natural immunity, suggesting presence of waning immunity.

scholarly journals Predicting Age Groups of Reddit Users Based on Posting Behavior and Metadata: Classification Model Development and Validation (Preprint)

2020 ◽  
Author(s):  
Robert Chew ◽  
Caroline Kery ◽  
Laura Baum ◽  
Thomas Bukowski ◽  
Annice Kim ◽  
...  
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Social Media ◽  
Age Groups ◽  
Demographic Characteristics ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Age Group ◽  
Adult Group ◽  
Linguistic Patterns

BACKGROUND Social media are important for monitoring perceptions of public health issues and for educating target audiences about health; however, limited information about the demographics of social media users makes it challenging to identify conversations among target audiences and limits how well social media can be used for public health surveillance and education outreach efforts. Certain social media platforms provide demographic information on followers of a user account, if given, but they are not always disclosed, and researchers have developed machine learning algorithms to predict social media users’ demographic characteristics, mainly for Twitter. To date, there has been limited research on predicting the demographic characteristics of Reddit users. OBJECTIVE We aimed to develop a machine learning algorithm that predicts the age segment of Reddit users, as either adolescents or adults, based on publicly available data. METHODS This study was conducted between January and September 2020 using publicly available Reddit posts as input data. We manually labeled Reddit users’ age by identifying and reviewing public posts in which Reddit users self-reported their age. We then collected sample posts, comments, and metadata for the labeled user accounts and created variables to capture linguistic patterns, posting behavior, and account details that would distinguish the adolescent age group (aged 13 to 20 years) from the adult age group (aged 21 to 54 years). We split the data into training (n=1660) and test sets (n=415) and performed 5-fold cross validation on the training set to select hyperparameters and perform feature selection. We ran multiple classification algorithms and tested the performance of the models (precision, recall, F1 score) in predicting the age segments of the users in the labeled data. To evaluate associations between each feature and the outcome, we calculated means and confidence intervals and compared the two age groups, with 2-sample t tests, for each transformed model feature. RESULTS The gradient boosted trees classifier performed the best, with an F1 score of 0.78. The test set precision and recall scores were 0.79 and 0.89, respectively, for the adolescent group (n=254) and 0.78 and 0.63, respectively, for the adult group (n=161). The most important feature in the model was the number of sentences per comment (permutation score: mean 0.100, SD 0.004). Members of the adolescent age group tended to have created accounts more recently, have higher proportions of submissions and comments in the r/teenagers subreddit, and post more in subreddits with higher subscriber counts than those in the adult group. CONCLUSIONS We created a Reddit age prediction algorithm with competitive accuracy using publicly available data, suggesting machine learning methods can help public health agencies identify age-related target audiences on Reddit. Our results also suggest that there are characteristics of Reddit users’ posting behavior, linguistic patterns, and account features that distinguish adolescents from adults.

scholarly journals Tracking the dynamics and allocating tests for COVID-19 in real-time: An acceleration index with an application to French age groups and départements

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252443
Author(s):  
Christelle Baunez ◽  
Mickael Degoulet ◽  
Stéphane Luchini ◽  
Patrick A. Pintus ◽  
Miriam Teschl
Keyword(s):  
Public Health ◽  
Real Time ◽  
Age Groups ◽  
Population Based ◽  
National Average ◽  
Health Policies ◽  
Age Group ◽  
Actual Distribution ◽  
Public Health Policies ◽  
Using Data

An acceleration index is proposed as a novel indicator to track the dynamics of COVID-19 in real-time. Using data on cases and tests in France for the period between the first and second lock-downs—May 13 to October 25, 2020—our acceleration index shows that the pandemic resurgence can be dated to begin around July 7. It uncovers that the pandemic acceleration was stronger than national average for the [59–68] and especially the 69 and older age groups since early September, the latter being associated with the strongest acceleration index, as of October 25. In contrast, acceleration among the [19–28] age group was the lowest and is about half that of the [69–78]. In addition, we propose an algorithm to allocate tests among French “départements” (roughly counties), based on both the acceleration index and the feedback effect of testing. Our acceleration-based allocation differs from the actual distribution over French territories, which is population-based. We argue that both our acceleration index and our allocation algorithm are useful tools to guide public health policies as France might possibly enter a third lock-down period with indeterminate duration.

The Epidemiology of Māori Suicide in Aotearoa/New Zealand

2000 ◽  
Vol 12 ◽  
pp. 1-12
Author(s):  
Nicole M. Coupe
Keyword(s):  
Public Health ◽  
New Zealand ◽  
Suicide Rate ◽  
Age Groups ◽  
Public Health Problem ◽  
Epidemiological Data ◽  
Public Health Issue ◽  
Major Public Health Problem ◽  
Age Group ◽  
Aotearoa New Zealand

AbstractSuicide is a Māori Public Health Issue. Suicide rates in Aotearoa/New Zealand are amongst the highest in OECD countries in the 15-24 year age group and second only to Hungary in other age groups (WHO, 1996; Disley & Coggan, 1996). Suicide is the leading cause of death for young people under the age of 25 years in Aotearoa/New Zealand and a major public health problem (Coggan, 1997). Approximatel, 540 New Zealanders kill themselves each year (Rose, Hatcher, & Koelmeyer, 1999). The total Māori suicide rate (per 100 000) increased to 17.5 in 1997, compared to non-Māori (13.1), and the Māori youth suicide rate (33.9) far exceeded the equivalent non-Māori rate (24.3), reflecting the disparity between Māori and non-Māori (Ministry of Health, 1997). This paper aims to present epidemiological data on Māori suicide and then use the existing literature to discuss possible reasons for the high Māori rate.

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 Adequacy of public health communications and health propaganda on the prevention of coronavirus disease in Jiangsu province: a community-based cross-sectional study

2020 ◽  
Author(s):  
Jiaojiao Fei ◽  
Hongyan Cheng ◽  
Yanhua Li ◽  
Weifei Gao ◽  
Guanqun Dai ◽  
...  
Keyword(s):  
Public Health ◽  
Age Groups ◽  
Jiangsu Province ◽  
Mediating Effect ◽  
Urban Residents ◽  
Age Group ◽  
Knowledge Level ◽  
Health Communications ◽  
Cross Sectional ◽  
Effective Response

Abstract Background: To measure the public’s awareness of COVID-19 and evaluate the adequacy of public health communications and health propaganda on the prevention of coronavirus disease in Jiangsu province.Methods: We made an electronic questionnaire and launched the survey during February 12 to March 12, 2020. Respondents were randomly selected and recruited from thirteen cities in Jiangsu province. An opportunistic sampling approach was also used to recruit new participants or members in the same household through referrals from existing participants. Data was collected through the “Questionnaire Star” system. SPSS24.0 version was used for data statistical analysis.Result：The effective response rate of completing questionnaire was 97.14% (2650/2728). Compared with traditional media such as TV (51.43%) and newspaper (14.91%), participants were more willing to choose new media such as websites (71.17%) and social platforms such as We-Chat (73.96%) to obtained health information. Chi-square test showed that women (54.14% vs. 48.49%), the 20-50 age group (24.22% vs. 22.94%, 32.69% vs. 31.40%, 28.92 vs. 27.77%) and urban residents (61.42% vs. 59.85%) had higher COVID-19 preventive knowledge level, urban residents had better attitude (60.29% vs. 59.85%), women (53.53% vs. 51.51%), the 30-50 age group (33.14% vs. 31.40%, 29.00% vs. 27.77%)), urban residents (61.50% vs. 59.85%) had good behavior. Multivariate logistic analysis showed that gender (females vs. males, OR=2.226, OR 95%CI: 1.346-3.682, P<0.001), age groups (<50 vs. >50 years old: OR=0.689, OR 95%CI: 0.561-0.847, P<0.001), areas (urban vs. suburban: OR=0.359, OR 95%CI: 0.219-0.588, P<0.001), knowledge level (high vs. low: OR=1.259: OR 95%CI: 1.188-1.335, P<0.001), and attitude (good vs. bad: OR=0.462 OR 95%CI: 0.342-0.626, P<0.001) were associated with good behaviors. The moderating effect and mediating effect shows that attitude mediates the influence of knowledge on behavior. (The 95% interval does not include the number 0(OR95% CI:0.002-0.013). All means of health propaganda can modulate the influence between knowledge and behavior (P<0.001).Conclusions: Providing adequacy of health propaganda and public health communications on the prevention of coronavirus disease makes the public fully understand the knowledge of COVID-19 and lead them to take preventive actions.

scholarly journals Community and Campus COVID-19 Risk Uncertainty Under University Reopening Scenarios: Model-Based Analysis (Preprint)

2020 ◽  
Author(s):  
James Benneyan ◽  
Christopher Gehrke ◽  
Iulian Ilies ◽  
Nicole Nehls
Keyword(s):  
Public Health ◽  
Grey Literature ◽  
Sensitivity Analyses ◽  
Base Case ◽  
Agent Based ◽  
University Campuses ◽  
College And University ◽  
Effective Interventions ◽  
College And University Campuses ◽  
Risk Communities

BACKGROUND Significant uncertainty has existed about the safety of reopening college and university campuses before the COVID-19 pandemic is better controlled. Moreover, little is known about the effects that on-campus students may have on local higher-risk communities. OBJECTIVE We aimed to estimate the range of potential community and campus COVID-19 exposures, infections, and mortality under various university reopening plans and uncertainties. METHODS We developed campus-only, community-only, and campus × community epidemic differential equations and agent-based models, with inputs estimated via published and grey literature, expert opinion, and parameter search algorithms. Campus opening plans (spanning fully open, hybrid, and fully virtual approaches) were identified from websites and publications. Additional student and community exposures, infections, and mortality over 16-week semesters were estimated under each scenario, with 10% trimmed medians, standard deviations, and probability intervals computed to omit extreme outliers. Sensitivity analyses were conducted to inform potential effective interventions. RESULTS Predicted 16-week campus and additional community exposures, infections, and mortality for the base case with no precautions (or negligible compliance) varied significantly from their medians (4- to 10-fold). Over 5% of on-campus students were infected after a mean of 76 (SD 17) days, with the greatest increase (first inflection point) occurring on average on day 84 (SD 10.2 days) of the semester and with total additional community exposures, infections, and mortality ranging from 1-187, 13-820, and 1-21 per 10,000 residents, respectively. Reopening precautions reduced infections by 24%-26% and mortality by 36%-50% in both populations. Beyond campus and community reproductive numbers, sensitivity analysis indicated no dominant factors that interventions could primarily target to reduce the magnitude and variability in outcomes, suggesting the importance of comprehensive public health measures and surveillance. CONCLUSIONS Community and campus COVID-19 exposures, infections, and mortality resulting from reopening campuses are highly unpredictable regardless of precautions. Public health implications include the need for effective surveillance and flexible campus operations.

scholarly journals Increased mask adherence after important politician infected with COVID-19

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0261398
Author(s):  
Deborah A. Cohen ◽  
Meghan Talarowski ◽  
Olaitan Awomolo ◽  
Bing Han ◽  
Stephanie Williamson ◽  
...  
Keyword(s):  
Public Health ◽  
Age Groups ◽  
City Council ◽  
Age Group ◽  
Health Departments ◽  
Systematic Observation ◽  
Public Health Departments ◽  
Before And After ◽  
Race Ethnicity ◽  
The City

Objectives To quantify changes in adherence to mask and distancing guidelines in outdoor settings in Philadelphia, PA before and after President Trump announced he was infected with COVID-19. Methods We used Systematic Observation of Masking Adherence and Distancing (SOMAD) to assess mask adherence in parks, playgrounds, and commercial streets in the 10 City Council districts in Philadelphia PA. We compared adherence rates between August and September 2020 and after October 2, 2020. Results Disparities in mask adherence existed by age group, gender, and race/ethnicity, with females wearing masks correctly more often than males, seniors having higher mask use than other age groups, and Asians having higher adherence than other race/ethnicities. Correct mask use did not increase after the City released additional mask guidance in September but did after Oct 2. Incorrect mask use also decreased, but the percentage not having masks at all was unchanged. Conclusions Vulnerability of leadership appears to influence population behavior. Public health departments likely need more resources to effectively and persuasively communicate critical safety messages related to COVID-19 transmission.

Sign in / Sign up

Export Citation Format

Share Document