Characterizing Responses to COVID-19 Vaccine Promotion on TikTok.

PURPOSE: The Alabama Department of Public Health (ADPH) sponsored a TikTok contest to improve vaccination rates among young people. This analysis sought to advance understanding of COVID-19 vaccine perceptions among ADPH contestants and TikTok commenters. APPROACH: This exploratory content analysis characterized sentiment and imagery in the TikTok videos and comments. Videos were coded by two reviewers and engagement metrics were collected for each video. SETTING: Publicly available TikTok videos entered into ADPH's contest with the hashtags #getvaccinatedAL and #ADPH between July 16 - August 6, 2021. PARTICIPANTS: ADPH contestants (n = 44) and TikTok comments (n = 502). METHOD: A content analysis was conducted; videos were coded by two reviewers and engagement metrics was collected for each video (e.g., reason for vaccination, content, type of vaccination received). Video comments were analyzed using VADER, a lexicon and rule-based sentiment analysis tool). RESULTS: Of 44 videos tagged with #getvaccinatedAL and #ADPH, 37 were related to the contest. Of the 37 videos, most cited family/friends and civic duty as their reason to get the COVID-19 vaccine. Videos were shared an average of 9 times and viewed 977 times. 70% of videos had comments, ranging from 0-61 (mean 44). Words used most in positively coded comments included, "beautiful," "smiling face emoji with 3 hearts," "masks," and "good.;" whereas words used most in negatively coded comments included "baby," "me," "chips," and "cold." CONCLUSION: Understanding COVID-19 vaccine sentiment expressed on social media platforms like TikTok can be a powerful tool and resource for public health messaging.

Defining the pediatric response to SARS-CoV-2 variants.

The global population has been severely affected by the coronavirus disease 2019 (COVID-19) pandemic, however, with older age identified as a risk factor, children have been underprioritized. This article discusses the factors contributing to the less severe response observed in children following infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), including, differing viral entry receptor expression and immune responses. It also discusses how emerging and future variants could present a higher risk to children, including those with underlying comorbidities, in developing severe disease. Furthermore, this perspective discusses the differential inflammatory markers between critical and non-critical cases, as well as discussing the types of variants that may be more pathogenic to children. Importantly, this article highlights where more research is urgently required, in order to protect the most vulnerable of our children.

MORTALITY TRENDS IN CROATIA IN THE FIRST TWO DECADES OF THE 21st CENTURY

Negative demographic trends in Croatia (natural decrease, negative net migration and population aging) are increasingly influencing socio-economic development of the country. Already in early 21st century, the long term decrease of live births and the increase of deaths were recognized as destabilizing factors of population development in Croatia. After the Croatian accession to the EU, the concerns regarding future demographic development of the country raised even more due to intensive emigration to other EU countries, which coincided with the historically low birth rates and high death rates. The focus of this paper is on mortality trends in Croatia in the first two decades of the 21st century. In this period, mortality in Croatia was influenced by different socio-economic, demographic, and epidemiological factors. Given the lack of recent papers dealing with mortality in Croatia, the main aim of this paper is to provide an overview of the changes in selected mortality indicators and contribute to the discussion on recent mortality trends in Croatia. The results of this research indicate that Croatia experienced some positive changes regarding mortality (increase of life expectancy at birth and decrease of infant mortality rates in the first period, in particular), but, some of the trends are not favourable, particularly the changes in the causes of death. Although improvements were observed regarding the share of deaths caused by the diseases of the circulatory system, there was a notable increase in deaths caused by the endocrine, nutritional and metabolic diseases which can be attributed to the unhealthy lifestyle and various behavioural factors.

Direct and indirect mortality impacts of the COVID-19 pandemic in the United States, March 1, 2020 to January 1, 2022.

Excess mortality studies provide crucial information regarding the health burden of pandemics and other large-scale events. Here, we use time series approaches to separate the direct contribution of SARS-CoV-2 infection on mortality from the indirect consequences of the pandemic in the United States. We estimate excess deaths occurring above a seasonal baseline from March 1, 2020 to January 1, 2022, stratified by week, state, age, and underlying mortality condition (including COVID-19 and respiratory diseases; Alzheimer's disease; cancer; cerebrovascular diseases; diabetes; heart diseases; and external causes, which include suicides, opioid overdoses, and accidents). Over the study period, we estimate an excess of 1,065,200 (95% Confidence Interval (CI) 909,800-1,218,000) all-cause deaths, of which 80% are reflected in official COVID-19 statistics. State-specific excess death estimates are highly correlated with SARS-CoV-2 serology, lending support to our approach. Mortality from 7 of the 8 studied conditions rose during the pandemic, with the exception of cancer. To separate the direct mortality consequences of SARS-CoV-2 infection from the indirect effects of the pandemic, we fit generalized additive models (GAM) to age- state- and cause-specific weekly excess mortality, using covariates representing direct (COVID-19 intensity) and indirect pandemic effects (hospital intensive care unit (ICU) occupancy and measures of interventions stringency). We find that 84% (95% CI 65-94%) of all-cause excess mortality can be statistically attributed to the direct impact of SARS-CoV-2 infection. We also estimate a large direct contribution of SARS-CoV-2 infection (≥67%) on mortality from diabetes, Alzheimer's, heart diseases, and in all-cause mortality among individuals over 65 years. In contrast, indirect effects predominate in mortality from external causes and all-cause mortality among individuals under 44 years, with periods of stricter interventions associated with greater rises in mortality. Overall, on a national scale, the largest consequences of the COVID-19 pandemic are attributable to the direct impact of SARS-CoV-2 infections; yet, the secondary impacts dominate among younger age groups and in mortality from external causes. Further research on the drivers of indirect mortality is warranted as more detailed mortality data from this pandemic becomes available.

The contribution of population age-sex structure to the excess mortality estimates of 2020-2021 in Denmark, Finland, Iceland, Norway, and Sweden.

The Nordic countries offer an ideal case study of the COVID-19 pandemic due to their comparability, high data quality, and variable mitigations. We investigated the age- and sex-specific mortality patterns during 2020-2021 for the five Nordic countries and analysed the total age- and sex-adjusted excess deaths, ratios of actual to expected death rates, and age-standardized excess death estimates. We assessed excess deaths using several time periods and sensitivity tests, and 42 sex and age groups. Declining pre-pandemic age-specific death rates reflected improving health demographics. These affect the expected death estimates and should be accounted for in excess mortality models. Denmark had the highest death rates both before and during the pandemic, whereas in 2020 Sweden had the largest mortality increase. The age-standardized mortality of Denmark, Iceland and Norway was lowest in 2020. 2021 was one of the lowest mortality years for all Nordic countries. The total excess deaths in 2020-2021 were dominated by 70-89-year-olds, were not identified in children, and were more pronounced among men than women. Sweden had more excess deaths in 2020 than in 2021, whereas Finland, Norway and Denmark had the opposite. Our study provides new details on Nordic sex- and age-specific mortality during the first two years of the pandemic and shows that several metrics are important to enable a full understanding and comparison of the pandemic mortality.

Age-specific contribution of contacts to transmission of SARS-CoV-2 in Germany.

Current estimates of pandemic SARS-CoV-2 spread in Germany using infectious disease models often do not use age-specific infection parameters and are not always based on age-specific contact matrices of the population. They also do usually not include setting- or pandemic phase-based information from epidemiological studies of reported cases and do not account for age-specific underdetection of reported cases. Here, we report likely pandemic spread using an age-structured model to understand the age- and setting-specific contribution of contacts to transmission during different phases of the COVID-19 pandemic in Germany. We developed a deterministic SEIRS model using a pre-pandemic contact matrix. The model was optimized to fit age-specific SARS-CoV-2 incidences reported by the German National Public Health Institute (Robert Koch Institute), includes information on setting-specific reported cases in schools and integrates age- and pandemic period-specific parameters for underdetection of reported cases deduced from a large population-based seroprevalence studies. Taking age-specific underreporting into account, younger adults and teenagers were identified in the modeling study as relevant contributors to infections during the first three pandemic waves in Germany. For the fifth wave, the Delta to Omicron transition, only age-specific parametrization reproduces the observed relative and absolute increase in pediatric hospitalizations in Germany. Taking into account age-specific underdetection did not change considerably how much contacts in schools contributed to the total burden of infection in the population (up to 12% with open schools under hygiene measures in the third wave). Accounting for the pandemic phase and age-specific underreporting is important to correctly identify those groups of the population in which quarantine, testing, vaccination, and contact-reduction measures are likely to be most effective and efficient. Age-specific parametrization is also highly relevant to generate informative age-specific output for decision makers and resource planers.

Estimating the Impact of the COVID-19 Pandemic on Rising Trends in Drug Overdose Mortality in the United States, 2018-2021.

BACKGROUND: During the COVID-19 pandemic, social and economic disruption such as social isolation, job and income losses, and increased psychological distress, may have contributed to the increase in drug-overdose mortality. This study aims to measure the impact of the pandemic on monthly trends in drug-overdose mortality in the United States. METHODS: We used the 2018-2020 final and 2021 provisional monthly deaths from the National Vital Statistics System and monthly population estimates from the Census Bureau to compute monthly mortality rates by age, sex, and race/ethnicity. We use log-linear regression models to estimate monthly percent increases in mortality rates from January 2018 through November 2021. RESULTS: The age-adjusted drug-overdose mortality rate among individuals aged ≥15 years increased by 30% between 2019 (70,459 deaths) and 2020 (91,536 deaths). During January 2018-November 2021, the monthly drug-overdose mortality rate increased by 2.05% per month for Blacks, 2.25% for American Indians/Alaska Natives, 1.96% for Hispanics, 1.33% for Asian/Pacific Islanders, and 0.96% for non-Hispanic Whites. Average monthly increases in mortality were most marked among those aged 15-24 and 35-44 years. CONCLUSIONS: The COVID-19 pandemic had a substantial impact on the rising trends in drug-overdose mortality during the peak months in 2020 and 2021.

COVID-19 pandemic vaccination strategies of early 2021 based on behavioral differences between residents of Tokyo and Osaka, Japan.

BACKGROUND: During the fourth COVID-19 wave in Japan, marked differences became apparent in the scale of the epidemic between metropolitan Tokyo in eastern Japan and Osaka prefecture in western Japan. METHODS: Public epidemic data were analyzed, with performance of mathematical simulations using simplified SEIR models. RESULTS: The increase in the number of infected persons per 100,000 population during the fourth wave of expansion was greater in Osaka than in Tokyo. The basic reproduction number in Osaka was greater than in Tokyo. Particularly, the number of infected people in their 20 s increased during the fourth wave: The generation-specific reproduction number for people in their 20 s was higher than for people of other generations. Both Tokyo and Osaka were found to have strong correlation between the increase in the number of infected people and the average number of people using the main downtown stations at night. Simulations showed vaccination of people in their 60 s and older reduced the number of infected people among the high-risk elderly population in the fourth wave. However, age-specific vaccination of people in their 20 s reduced the number of infected people more than vaccination of people in their 60 s and older. CONCLUSIONS: Differences in the epidemic between Tokyo and Osaka are explainable by different behaviors of the most socially active generation. When vaccine supplies are adequate, priority should be assigned to high-risk older adults, but if vaccine supplies are scarce, simulation results suggest consideration of vaccinating specific groups among whom the epidemic is spreading rapidly.

Booster Vaccination Strategies for "Living With COVID-19".

Although the primary and secondary vaccination rates in Korea account for over 75% of the total population, confirmed cases of COVID-19 are dramatically increasing due to immune waning and the Omicron variant. Therefore, it is urgent to evaluate the effectiveness of booster vaccination strategies for living with COVID-19. In this work, we have developed an age-specific mathematical model with eight age groups and included age-specific comorbidities to evaluate the effectiveness of age-specific vaccination prioritization strategies to minimize morbidity and mortality. Furthermore, we have investigated the impacts of age-specific vaccination strategies for different vaccine supplies and non-pharmaceutical intervention levels during two periods: (1) when vaccine supply was insufficient and (2) after the emergence of the omicron variant. During the first period, the best option was to vaccinate the 30-49 year age group and the group with comorbidities to minimize morbidity and mortality, respectively. However, a booster vaccination should prioritize the 30-49 year age group to promote both minimal morbidity and mortality. Critical factors, such as vaccination speed, vaccine efficacy, and non-pharmaceutical interventions (NPIs), should be considered for effective vaccination prioritization as well. Primary, secondary vaccinations, and a booster shot vaccinations require different age prioritization strategies under different vaccination rates, vaccine efficacies, and NPI levels.

Impact of changes in protective behaviors and out-of-household activities by age on COVID-19 transmission and hospitalization in Chicago, Illinois.

PURPOSE: Even with an efficacious vaccine, protective behaviors (social distancing, masking) are essential for preventing COVID-19 transmission and could become even more important if current or future variants evade immunity from vaccines or prior infection. METHODS: We created an agent-based model representing the Chicago population and conducted experiments to determine the effects of varying adult out-of-household activities (OOHA), school reopening, and protective behaviors across age groups on COVID-19 transmission and hospitalizations. RESULTS: From September-November 2020, decreasing adult protective behaviors and increasing adult OOHA both substantially impacted COVID-19 outcomes; school reopening had relatively little impact when adult protective behaviors and OOHA were maintained. As of November 1, 2020, a 50% reduction in young adult (age 18-40) protective behaviors resulted in increased latent infection prevalence per 100,000 from 15.93 (IQR 6.18, 36.23) to 40.06 (IQR 14.65, 85.21) and 19.87 (IQR 6.83, 46.83) to 47.74 (IQR 18.89, 118.77) with 15% and 45% school reopening. Increasing adult (age ≥18) OOHA from 65% to 80% of prepandemic levels resulted in increased latent infection prevalence per 100,000 from 35.18 (IQR 13.59, 75.00) to 69.84 (IQR 33.27, 145.89) and 38.17 (IQR 15.84, 91.16) to 80.02 (IQR 30.91, 186.63) with 15% and 45% school reopening. Similar patterns were observed for hospitalizations. CONCLUSIONS: In areas without widespread vaccination coverage, interventions to maintain adherence to protective behaviors, particularly among younger adults and in out-of-household settings, remain a priority for preventing COVID-19 transmission.

Age-Specific Characteristics of Adult and Pediatric Respiratory Viral Infections: A Retrospective Single-Center Study.

This study aimed to identify age-specific characteristics of respiratory viral infections. Hospitalized patients with confirmed viral respiratory infections were included in the sample. The patients were divided into the pediatric group (<19 years old) and the adult group (≥19 years old). The groups were then subdivided based on age: 0-6, 7-12, 13-18, 19-49, 50-64, and ≥65 years old. These groups were compared to evaluate the differences in the pattern of respiratory viral infections. Among a total of 4058 pediatric patients (mean age 3.0 ± 2.9 years, n = 1793 females), 2829 (48.9%) had mono-infections, while 1229 (51.1%) had co-infections. Co-infections were the most common in the 0-6-year-old group (31.6%). Among 1550 adult patients (mean age 70.2 ± 15.3 years, n = 710 females), 1307 (85.6%) had mono-infections and 243 (14.4%) had co-infections. Co-infections were most common in the ≥65-year-old group (16.8%). Viral infection and co-infection rates decreased with age in pediatric patients but increased with increasing age in adults. In pediatric patients, the rates of viral infections and co-infections were high; the rate of co-infections was higher in younger patients. In adult patients, the rates of viral infections and co-infections were lower than those in pediatric patients; the rate of co-infections was higher in older patients.

Age-Specific Transmissibility Change of COVID-19 and Associations With Breathing Air Volume, Preexisting Immunity, and Government Response.

Background: The comprehensive impacts of diverse breathing air volumes and preexisting immunity on the host susceptibility to and transmission of COVID-19 at various pandemic stages have not been investigated. Methods: We classified the US weekly COVID-19 data into 0-4, 5-11, 12-17, 18-64, and 65+ age groups and applied the odds ratio (OR) of incidence between one age group and the 18-64 age group to delineate the transmissibility change. Results: The changes of incidence ORs between May, 2020 and November, 2021 were 0.22-0.66 (0-4 years), 0.20-1.34 (5-11 years), 0.39-1.04 (12-17 years), and 0.82-0.73 (65+ years). The changes could be explained by age-specific preexisting immunity including previous infection and vaccination, as well as volumes of breathing air. At the early pandemic, the ratio that 0-4-year children exhaled one-fifth of air and discharge a similar ratio of viruses was closely associated with incidence OR between two age groups. While, after a rollout of pandemic and vaccination, the much less increased preexisting immunity in children resulted in rapidly increased OR of incidence. The ARIMA model predicted the largest increase of relative transmissibility in 6 coming months in 5-11-year children. Conclusions: The volume of breathing air may be a notable factor contributing to the infectivity of COVID-19 among different age groups of patients. This factor and the varied preexisting greatly shape the transmission of COVID-19 at different periods of pandemic among different age groups of people.

Factors associated with hospital admission and severe outcomes for older patients with COVID-19.

BACKGROUND: Morbidity and death due to coronavirus disease 2019 (COVID-19) experienced by older adults in nursing homes have been well described, but COVID-19's impact on community-living older adults is less studied. Similarly, the previous ambulatory care experience of such patients has rarely been considered in studies of COVID-19 risks and outcomes. METHODS: To investigate the relationship of advanced age (65+), on risk factors associated with COVID-19 outcomes in community-living elders, we identified an electronic health records cohort of older patients aged 65+ with laboratory-confirmed COVID-19 with and without an ambulatory care visit in the past 24 months (n = 47,219) in the New York City (NYC) academic medical institutions and the NYC public hospital system from January 2020 to February 2021. The main outcomes are COVID-19 hospitalization; severe outcomes/Intensive care unit (ICU), intubation, dialysis, stroke, in-hospital death), and in-hospital death. The exposures include demographic characteristics, and those with ambulatory records, comorbidities, frailty, and laboratory results. RESULTS: The 31,770 patients with an ambulatory history had a median age of 74 years; were 47.4% male, 24.3% non-Hispanic white, 23.3% non-Hispanic black, and 18.4% Hispanic. With increasing age, the odds ratios and attributable fractions of sex, race-ethnicity, comorbidities, and biomarkers decreased except for dementia and frailty (Hospital Frailty Risk Score). Patients without ambulatory care histories, compared to those with, had significantly higher adjusted rates of COVID-19 hospitalization and severe outcomes, with strongest effect in the oldest group. CONCLUSIONS: In this cohort of community-dwelling older adults, we provided evidence of age-specific risk factors for COVID-19 hospitalization and severe outcomes. Future research should explore the impact of frailty and dementia in severe COVID-19 outcomes in community-living older adults, and the role of engagement in ambulatory care in mitigating severe disease.

The optimal vaccination strategy to control COVID-19: a modeling study in Wuhan City, China.

BACKGROUND: Reaching optimal vaccination rates is an essential public health strategy to control the coronavirus disease 2019 (COVID-19) pandemic. This study aimed to simulate the optimal vaccination strategy to control the disease by developing an age-specific model based on the current transmission patterns of COVID-19 in Wuhan City, China. METHODS: We collected two indicators of COVID-19, including illness onset data and age of confirmed case in Wuhan City, from December 2, 2019, to March 16, 2020. The reported cases were divided into four age groups: group 1, ≤ 14 years old; group 2, 15 to 44 years old; group 3, 44 to 64 years old; and group 4, ≥ 65 years old. An age-specific susceptible-exposed-symptomatic-asymptomatic-recovered/removed model was developed to estimate the transmissibility and simulate the optimal vaccination strategy. The effective reproduction number (Reff) was used to estimate the transmission interaction in different age groups. RESULTS: A total of 47 722 new cases were reported in Wuhan City from December 2, 2019, to March 16, 2020. Before the travel ban of Wuhan City, the highest transmissibility was observed among age group 2 (Reff = 4.28), followed by group 2 to 3 (Reff = 2.61), and group 2 to 4 (Reff = 1.69). China should vaccinate at least 85% of the total population to interrupt transmission. The priority for controlling transmission should be to vaccinate 5% to 8% of individuals in age group 2 per day (ultimately vaccinated 90% of age group 2), followed by 10% of age group 3 per day (ultimately vaccinated 90% age group 3). However, the optimal vaccination strategy for reducing the disease severity identified individuals ≥ 65 years old as a priority group, followed by those 45-64 years old. CONCLUSIONS: Approximately 85% of the total population (nearly 1.2 billion people) should be vaccinated to build an immune barrier in China to safely consider removing border restrictions. Based on these results, we concluded that 90% of adults aged 15-64 years should first be vaccinated to prevent transmission in China.

Priority and age specific vaccination algorithm for the pandemic diseases: a comprehensive parametric prediction model.

BACKGROUND: There have been several destructive pandemic diseases in the human history. Since these pandemic diseases spread through human-to-human infection, a number of non-pharmacological policies has been enforced until an effective vaccine has been developed. In addition, even though a vaccine has been developed, due to the challenges in the production and distribution of the vaccine, the authorities have to optimize the vaccination policies based on the priorities. Considering all these facts, a comprehensive but simple parametric model enriched with the pharmacological and non-pharmacological policies has been proposed in this study to analyse and predict the future pandemic casualties. METHOD: This paper develops a priority and age specific vaccination policy and modifies the non-pharmacological policies including the curfews, lockdowns, and restrictions. These policies are incorporated with the susceptible, suspicious, infected, hospitalized, intensive care, intubated, recovered, and death sub-models. The resulting model is parameterizable by the available data where a recursive least squares algorithm with the inequality constraints optimizes the unknown parameters. The inequality constraints ensure that the structural requirements are satisfied and the parameter weights are distributed proportionally. RESULTS: The results exhibit a distinctive third peak in the casualties occurring in 40 days and confirm that the intensive care, intubated, and death casualties converge to zero faster than the susceptible, suspicious, and infected casualties with the priority and age specific vaccination policy. The model also estimates that removing the curfews on the weekends and holidays cause more casualties than lifting the restrictions on the people with the chronic diseases and age over 65. CONCLUSION: Sophisticated parametric models equipped with the pharmacological and non-pharmacological policies can predict the future pandemic casualties for various cases.

Inequality in mortality between Black and White Americans by age, place, and cause and in comparison to Europe, 1990 to 2018.

Although there is a large gap between Black and White American life expectancies, the gap fell 48.9% between 1990 and 2018, mainly due to mortality declines among Black Americans. We examine age-specific mortality trends and racial gaps in life expectancy in high- and low-income US areas and with reference to six European countries. Inequalities in life expectancy are starker in the United States than in Europe. In 1990, White Americans and Europeans in high-income areas had similar overall life expectancy, while life expectancy for White Americans in low-income areas was lower. However, since then, even high-income White Americans have lost ground relative to Europeans. Meanwhile, the gap in life expectancy between Black Americans and Europeans decreased by 8.3%. Black American life expectancy increased more than White American life expectancy in all US areas, but improvements in lower-income areas had the greatest impact on the racial life expectancy gap. The causes that contributed the most to Black Americans' mortality reductions included cancer, homicide, HIV, and causes originating in the fetal or infant period. Life expectancy for both Black and White Americans plateaued or slightly declined after 2012, but this stalling was most evident among Black Americans even prior to the COVID-19 pandemic. If improvements had continued at the 1990 to 2012 rate, the racial gap in life expectancy would have closed by 2036. European life expectancy also stalled after 2014. Still, the comparison with Europe suggests that mortality rates of both Black and White Americans could fall much further across all ages and in both high-income and low-income areas.

Impact of school reopening on pandemic spread: A case study using an agent-based model for COVID-19.

This article examines the impact of partial/full reopening of school/college campuses on the spread of a pandemic using COVID-19 as a case study. The study uses an agent-based simulation model that replicates community spread in an urban region of U.S.A. via daily social mixing of susceptible and infected individuals. Data representing population demographics, SARS-CoV-2 epidemiology, and social interventions guides the model's behavior, which is calibrated and validated using data reported by the government. The model indicates a modest but significant increase (8.15%) in the total number of reported cases in the region for a complete (100%) reopening compared to keeping schools and colleges fully virtual. For partial returns of 75% and 50%, the percent increases in the number of reported cases are shown to be small (2.87% and 1.26%, respectively) and statistically insignificant. The AB model also predicts that relaxing the stringency of the school safety protocol for sanitizing, use of mask, social distancing, testing, and quarantining and thus allowing the school transmission coefficient to double may result in a small increase in the number of reported infected cases (2.14%). Hence for pandemic outbreaks from viruses with similar characteristics as for SARS-CoV-2, keeping the schools and colleges open with a modest campus safety protocol and in-person attendance below a certain threshold may be advisable.

Estimating age-specific COVID-19 fatality risk and time to death by comparing population diagnosis and death patterns: Australian data.

BACKGROUND: Mortality is a key component of the natural history of COVID-19 infection. Surveillance data on COVID-19 deaths and case diagnoses are widely available in the public domain, but they are not used to model time to death because they typically do not link diagnosis and death at an individual level. This paper demonstrates that by comparing the unlinked patterns of new diagnoses and deaths over age and time, age-specific mortality and time to death may be estimated using a statistical method called deconvolution. METHODS: Age-specific data were analysed on 816 deaths among 6235 cases over age 50 years in Victoria, Australia, from the period January through December 2020. Deconvolution was applied assuming logistic dependence of case fatality risk (CFR) on age and a gamma time to death distribution. Non-parametric deconvolution analyses stratified into separate age groups were used to assess the model assumptions. RESULTS: It was found that age-specific CFR rose from 2.9% at age 65 years (95% CI:2.2 - 3.5) to 40.0% at age 95 years (CI: 36.6 - 43.6). The estimated mean time between diagnosis and death was 18.1 days (CI: 16.9 - 19.3) and showed no evidence of varying by age (heterogeneity P = 0.97). The estimated 90% percentile of time to death was 33.3 days (CI: 30.4 - 36.3; heterogeneity P = 0.85). The final age-specific model provided a good fit to the observed age-stratified mortality patterns. CONCLUSIONS: Deconvolution was demonstrated to be a powerful analysis method that could be applied to extensive data sources worldwide. Such analyses can inform transmission dynamics models and CFR assessment in emerging outbreaks. Based on these Australian data it is concluded that death from COVID-19 occurs within three weeks of diagnosis on average but takes five weeks in 10% of fatal cases. Fatality risk is negligible in the young but rises above 40% in the elderly, while time to death does not seem to vary by age.

Japan's voluntary lockdown: further evidence based on age-specific mobile location data.

Changes in people's behavior during the COVID-19 pandemic can be regarded as the result of two types of effects: the "intervention effect" (changes resulting from government orders for people to change their behavior) and the "information effect" (voluntary changes in people's behavior based on information about the pandemic). Using age-specific mobile location data, we examine how the intervention and information effects differ across age groups. Our main findings are as follows. First, the age profile of the intervention effect shows that the degree to which people refrained from going out was smaller for older age groups, who are at a higher risk of serious illness and death, than for younger age groups. Second, the age profile of the information effect shows that the degree to which people stayed at home tended to increase with age for weekends and holidays. Thus, while Acemoglu et al. (2020) proposed targeted lockdowns requiring stricter lockdown policies for the oldest group in order to protect those at a high risk of serious illness and death, our findings suggest that Japan's government intervention had a very different effect in that it primarily reduced outings by the young, and what led to the quarantining of older groups at higher risk instead was people's voluntary response to information about the pandemic. Third, the information effect has been on a downward trend since the summer of 2020. It is relatively more pronounced among the young, so that the age profile of the information effect remains upward sloping.

Delay-Adjusted Age-Specific COVID-19 Case Fatality Rates in a High Testing Setting: South Korea, February 2020 to February 2021.

In South Korea, a country with a high coronavirus disease 19 (COVID-19) testing rate, a total of 87,324 COVID-19 cases, including 1562 deaths, have been recorded as of 23 February 2021. This study assessed the delay-adjusted COVID-19 case fatality risk (CFR), including data from the second and third waves. A statistical method was applied to the data from 20 February 2021 through 23 February 2021 to minimize bias in the crude CFR, accounting for the survival interval as the lag time between disease onset and death. The resulting overall delay-adjusted CFR was 1.97% (95% credible interval: 1.94-2.00%). The delay-adjusted CFR was highest among adults aged ≥80 years and 70-79 years (22.88% and 7.09%, respectively). The cumulative incidence rate was highest among individuals aged ≥80 years and 60-69 years. The cumulative mortality rate was highest among individuals aged ≥80 years and 70-79 years (47 and 12 per million, respectively). In South Korea, older adults are being disproportionately affected by COVID-19 with a high death rate, although the incidence rate among younger individuals is relatively high. Interventions to prevent COVID-19 should target older adults to minimize the number of deaths.