Varying Cellular Immune Response against SARS-CoV-2 after the Booster Vaccination: A Cohort Study from Fukushima Vaccination Community Survey, Japan.

Booster vaccination reduces the incidence of severe cases and mortality related to COVID-19, with cellular immunity playing an important role. However, little is known about the proportion of the population that has achieved cellular immunity after booster vaccination. Thus, we conducted a Fukushima cohort database and assessed humoral and cellular immunity in 2526 residents and healthcare workers in Fukushima Prefecture in Japan through continuous blood collection every 3 months from September 2021. We identified the proportion of people with induced cellular immunity after booster vaccination using the T-SPOT.COVID test, and analyzed their background characteristics. Among 1089 participants, 64.3% (700/1089) had reactive cellular immunity after booster vaccination. Multivariable analysis revealed the following independent predictors of reactive cellular immunity: age < 40 years (adjusted odds ratio: 1.81; 95% confidence interval: 1.19-2.75; p-value: 0.005) and adverse reactions after vaccination (1.92, 1.19-3.09, 0.007). Notably, despite IgG(S) and neutralizing antibody titers of ≥500 AU/mL, 33.9% (349/1031) and 33.5% (341/1017) of participants, respectively, did not have reactive cellular immunity. In summary, this is the first study to evaluate cellular immunity at the population level after booster vaccination using the T-SPOT.COVID test, albeit with several limitations. Future studies will need to evaluate previously infected subjects and their T-cell subsets.

Optimization of Population-Level Testing, Contact Tracing, and Isolation in Emerging COVID-19 Outbreaks: a Mathematical Modeling Study - Tonghua City and Beijing Municipality, China, 2021-2022.

Introduction: The transmissibility of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant poses challenges for the existing measures containing the virus in China. In response, this study investigates the effectiveness of population-level testing (PLT) and contact tracing (CT) to help curb coronavirus disease 2019 (COVID-19) resurgences in China. Methods: Two transmission dynamic models (i.e. with and without age structure) were developed to evaluate the effectiveness of PLT and CT. Extensive simulations were conducted to optimize PLT and CT strategies for COVID-19 control and surveillance. Results: Urban Omicron resurgences can be controlled by multiple rounds of PLT, supplemented by CT - as long as testing is frequent. This study also evaluated the time needed to detect COVID-19 cases for surveillance under different routine testing rates. The results show that there is a 90% probability of detecting COVID-19 cases within 3 days through daily testing. Otherwise, it takes around 7 days to detect COVID-19 cases at a 90% probability level if biweekly testing is used. Routine testing applied to the age group 21-60 for COVID-19 surveillance would achieve similar performance to that applied to all populations. Discussion: Our analysis evaluates potential PLT and CT strategies for COVID-19 control and surveillance.

The Impact of Nonrandom Missingness in Surveillance Data for Population-Level Summaries: Simulation Study.

BACKGROUND: Surveillance data are essential public health resources for guiding policy and allocation of human and capital resources. These data often consist of large collections of information based on nonrandom sample designs. Population estimates based on such data may be impacted by the underlying sample distribution compared to the true population of interest. In this study, we simulate a population of interest and allow response rates to vary in nonrandom ways to illustrate and measure the effect this has on population-based estimates of an important public health policy outcome. OBJECTIVE: The aim of this study was to illustrate the effect of nonrandom missingness on population-based survey sample estimation. METHODS: We simulated a population of respondents answering a survey question about their satisfaction with their community's policy regarding vaccination mandates for government personnel. We allowed response rates to differ between the generally satisfied and dissatisfied and considered the effect of common efforts to control for potential bias such as sampling weights, sample size inflation, and hypothesis tests for determining missingness at random. We compared these conditions via mean squared errors and sampling variability to characterize the bias in estimation arising under these different approaches. RESULTS: Sample estimates present clear and quantifiable bias, even in the most favorable response profile. On a 5-point Likert scale, nonrandom missingness resulted in errors averaging to almost a full point away from the truth. Efforts to mitigate bias through sample size inflation and sampling weights have negligible effects on the overall results. Additionally, hypothesis testing for departures from random missingness rarely detect the nonrandom missingness across the widest range of response profiles considered. CONCLUSIONS: Our results suggest that assuming surveillance data are missing at random during analysis could provide estimates that are widely different from what we might see in the whole population. Policy decisions based on such potentially biased estimates could be devastating in terms of community disengagement and health disparities. Alternative approaches to analysis that move away from broad generalization of a mismeasured population at risk are necessary to identify the marginalized groups, where overall response may be very different from those observed in measured respondents.

Association between household composition and severe COVID-19 outcomes in older people by ethnicity: an observational cohort study using the OpenSAFELY platform.

BACKGROUND: Ethnic differences in the risk of severe COVID-19 may be linked to household composition. We quantified the association between household composition and risk of severe COVID-19 by ethnicity for older individuals. METHODS: With the approval of NHS England, we analysed ethnic differences in the association between household composition and severe COVID-19 in people aged 67 or over in England. We defined households by number of age-based generations living together, and used multivariable Cox regression stratified by location and wave of the pandemic and accounted for age, sex, comorbidities, smoking, obesity, housing density and deprivation. We included 2 692 223 people over 67 years in Wave 1 (1 February 2020-31 August 2020) and 2 731 427 in Wave 2 (1 September 2020-31 January 2021). RESULTS: Multigenerational living was associated with increased risk of severe COVID-19 for White and South Asian older people in both waves [e.g. Wave 2, 67+ living with three other generations vs 67+-year-olds only: White hazard ratio (HR) 1.61 95% CI 1.38-1.87, South Asian HR 1.76 95% CI 1.48-2.10], with a trend for increased risks of severe COVID-19 with increasing generations in Wave 2. There was also an increased risk of severe COVID-19 in Wave 1 associated with living alone for White (HR 1.35 95% CI 1.30-1.41), South Asian (HR 1.47 95% CI 1.18-1.84) and Other (HR 1.72 95% CI 0.99-2.97) ethnicities, an effect that persisted for White older people in Wave 2. CONCLUSIONS: Both multigenerational living and living alone were associated with severe COVID-19 in older adults. Older South Asian people are over-represented within multigenerational households in England, especially in the most deprived settings, whereas a substantial proportion of White older people live alone. The number of generations in a household, number of occupants, ethnicity and deprivation status are important considerations in the continued roll-out of COVID-19 vaccination and targeting of interventions for future pandemics.

Modeling the Virus Infection at the Population Level.

As pointed out by many researchers in the last few decades, differential equations with fractional (non-integer) order differential operators, in comparison with classical integer order ones, have apparent advantages in modeling. A Caputo fractional order system of ordinary differential equations is introduced to model the virus infection at the population level in this chapter. As well known, there are two main methods to study the dynamics of a model: qualitative analysis and numerical modeling. Here the qualitative analysis, including uniqueness, invariant set, and stability, is first presented with intuitive derivation. Then the famous genetic algorithm is introduced to numerically model the dynamics of virus infection, i.e. to adjust the parameters of the Caputo fractional model such that its solution can properly fit real data and predict future.

Modeling the population-level impact of treatment on COVID-19 disease and SARS-CoV-2 transmission.

Different COVID-19 treatment candidates are under development, and some are becoming available including two promising drugs from Merck and Pfizer. This study provides conceptual frameworks for the effects of three types of treatments, both therapeutic and prophylactic, and to investigate their population-level impact, to inform drug development, licensure, decision-making, and implementation. Different drug efficacies were assessed using an age-structured mathematical model describing SARS-CoV-2 transmission and disease progression, with application to the United States as an illustrative example. Severe and critical infection treatment reduces progression to COVID-19 severe and critical disease and death with small number of treatments needed to avert one disease or death. Post-exposure prophylaxis treatment had a large impact on flattening the epidemic curve, with large reductions in infection, disease, and death, but the impact was strongly age dependent. Pre-exposure prophylaxis treatment had the best impact and effectiveness, with immense reductions in infection, disease, and death, driven by the robust control of infection transmission. Effectiveness of both pre-exposure and post-exposure prophylaxis treatments was disproportionally larger when a larger segment of the population was targeted than a specific age group. Additional downstream potential effects of treatment, beyond the primary outcome, enhance the population-level impact of both treatments. COVID-19 treatments are an important modality in controlling SARS-CoV-2 disease burden. Different types of treatment act synergistically for a larger impact, for these treatments and vaccination.

Epidemiological Characteristics and Transmissibility for SARS-CoV-2 of Population Level and Cluster Level in a Chinese City.

Background: To date, there is a lack of sufficient evidence on the type of clusters in which severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is most likely to spread. Notably, the differences between cluster-level and population-level outbreaks in epidemiological characteristics and transmissibility remain unclear. Identifying the characteristics of these two levels, including epidemiology and transmission dynamics, allows us to develop better surveillance and control strategies following the current removal of suppression measures in China. Methods: We described the epidemiological characteristics of SARS-CoV-2 and calculated its transmissibility by taking a Chinese city as an example. We used descriptive analysis to characterize epidemiological features for coronavirus disease 2019 (COVID-19) incidence database from 1 Jan 2020 to 2 March 2020 in Chaoyang District, Beijing City, China. The susceptible-exposed-infected-asymptomatic-recovered (SEIAR) model was fitted with the dataset, and the effective reproduction number (Reff ) was calculated as the transmissibility of a single population. Also, the basic reproduction number (R0) was calculated by definition for three clusters, such as household, factory and community, as the transmissibility of subgroups. Results: The epidemic curve in Chaoyang District was divided into three stages. We included nine clusters (subgroups), which comprised of seven household-level and one factory-level and one community-level cluster, with sizes ranging from 2 to 17 cases. For the nine clusters, the median incubation period was 17.0 days [Interquartile range (IQR): 8.4-24.0 days (d)], and the average interval between date of onset (report date) and diagnosis date was 1.9 d (IQR: 1.7 to 6.4 d). At the population level, the transmissibility of the virus was high in the early stage of the epidemic (Reff = 4.81). The transmissibility was higher in factory-level clusters (R0 = 16) than in community-level clusters (R0 = 3), and household-level clusters (R0 = 1). Conclusions: In Chaoyang District, the epidemiological features of SARS-CoV-2 showed multi-stage pattern. Many clusters were reported to occur indoors, mostly from households and factories, and few from the community. The risk of transmission varies by setting, with indoor settings being more severe than outdoor settings. Reported household clusters were the predominant type, but the population size of the different types of clusters limited transmission. The transmissibility of SARS-CoV-2 was different between a single population and its subgroups, with cluster-level transmissibility higher than population-level transmissibility.

Sociodemographic changes in emergency department visits due to alcohol during COVID-19.

INTRODUCTION: Little detailed sociodemographic information is available about how alcohol use and associated health care visits have changed during COVID-19. Therefore, we assessed how rates of emergency department (ED) visits due to alcohol have changed during COVID-19 by age and sex and for individuals living in urban and rural settings and low and high-income neighborhoods. METHODS: Our cohort included 13,660,516 unique Ontario residents between the ages of 10-105. We compared rates and characteristics of ED visits due to alcohol, identified using ICD-10 codes, from March 11-August 31 2020 to the same period in the prior 3 years. We used negative binomial regressions to examine to examine changes is visits during COVID-19 after accounting for temporal and seasonal trends. RESULTS: During COVID-19, the average monthly rate of ED visits due to alcohol decreased by 17.2 % (95 % CI -22.7, -11.3) from 50.5-40.9 visits per 100,000 individuals. In contrast, the proportion of all-cause ED visits due to alcohol increased by 11.4 % (95 % CI 7.7, 15.3) from 15.0 visits to 16.3 visits per 1000 all cause ED visits. Changes in ED visits due to alcohol were similar for men in women. Decreases in visits were larger for younger adults compared to older adults and pre-COVID-19 disparities in rates of ED visits due to alcohol between urban and rural settings and low and high-income neighborhoods widened. ED visits related to harms from acute intoxication showed the largest declines during COVID-19, particularly in younger adults and urban and high-income neighborhoods. CONCLUSION: ED visits due to alcohol decreased during the first six months of COVID-19, but to a lesser extent than decreases in all-cause ED visits. Our data suggest a widening of geographic and income-based disparities in alcohol harms in Ontario during COVID-19 which may require immediate and long-term interventions to mitigate.

Population-Level Preparedness About Preventive Practices Against Coronavirus Disease 2019: A Cross-Sectional Study Among Adults in Bangladesh.

This study assessed the preparedness regarding the preventive practices toward the coronavirus disease 2019 (COVID-19) among the adult population in Bangladesh. Data were collected through an online survey with a sample size of 1,056. We constructed four variables (individual, household, economic, and community and social distancing) related to preparedness based on the principal component analysis of eight items. We employed descriptive statistics and multiple linear regression analysis. The results showed that the accuracy rate of the overall preparedness scale was 68.9%. The preparedness level related to economic, individual, household, and community and social distancing was 64.9, 77.1, 50.4, and 83.2%, respectively. However, the economic preparedness significantly varied by sex, education, occupation, attitude, and worries related to COVID-19. Individual preparedness was significantly associated with education, residence, and attitudes. The household preparedness significantly varied by education, residence, and worries, while the respondent's community and social distancing-related preparedness significantly varied by sex, region, residence, and attitude. This study implies the necessity of the coverage of financial schemes for the vulnerable group. Increased coverage of health education regarding personal hygiene targeting the less educated and rural population should be ensured.

Mass surveillance in the age of COVID-19.

Epidemiological surveillance programs such as digital contact tracing have been touted as a silver bullet that will free the American public from the strictures of social distancing, enabling a return to school, work, and socializing. This Article assesses whether and under what circumstances the United States ought to embrace such programs. Part I analyzes the constitutionality of programs like digital contact tracing, arguing that the Fourth Amendment's protection against unreasonable searches and seizures may well regulate the use of location data for epidemiological purposes, but that the legislative and executive branches have significant latitude to develop these programs within the broad constraints of the ``special needs'' doctrine elaborated by the courts in parallel circumstances. Part II cautions that the absence of a firm warrant requirement for digital contact tracing should not serve as a green light for unregulated and mass digital location tracking. In light of substantial risks to privacy, policy makers must ask hard questions about efficacy and the comparative advantages of location tracking versus more traditional means of controlling epidemic contagions, take seriously threats to privacy, tailor programs parsimoniously, establish clear metrics for determining success, and set clear plans for decommissioning surveillance programs.