Potential Biases in Estimating Absolute and Relative Case-Fatality Risks during Outbreaks

Estimates of SARS-CoV-2 Hospitalization and Fatality Rates in the Prevaccination Period, United States

Few precise estimates of hospitalization and fatality rates from COVID-19 exist for naive populations, especially within demographic subgroups. We estimated rates among persons with SARS-CoV-2 infection in the United States during May 1-December 1, 2020, before vaccines became available. Both rates generally increased with age; fatality rates were highest for persons >85 years of age (24%) and lowest for children 1-14 years of age (0.01%). Age-adjusted case hospitalization rates were highest for African American or Black, not Hispanic persons (14%), and case-fatality rates were highest for Asian or Pacific Islander, not Hispanic persons (4.4%). Eighteen percent of hospitalized patients and 44.2% of those admitted to an intensive care unit died. Male patients had higher hospitalization (6.2% vs. 5.2%) and fatality rates (1.9% vs. 1.5%) than female patients. These findings highlight the importance of collecting surveillance data to devise appropriate control measures for persons in underserved racial/ethnic groups and older adults.

Gender/Sex Disparities in the COVID-19 Cascade From Testing to Mortality: An Intersectional Analysis of Swiss Surveillance Data

Objectives

This study investigates gender and sex disparities in COVID-19 epidemiology in the Canton of Vaud, Switzerland, focusing on the interplay with socioeconomic position (SEP) and age.

Methods

We analyzed COVID-19 surveillance data from March 2020 to June 2021, using an intersectional approach. Negative binomial regression models assessed disparities between women and men, across SEP quintiles and age groups, in testing, positivity, hospitalizations, ICU admissions, and mortality (Incidence Rate Ratios [IRR], with 95% Confidence Intervals [CI]).

Results

Women had higher testing and positivity rates than men, while men experienced more hospitalizations, ICU admissions, and deaths. The higher positivity in women under 50 was mitigated when accounting for their higher testing rates. Within SEP quintiles, gender/sex differences in testing and positivity were not significant. In the lowest quintile, women's mortality risk was 68% lower (Q1: IRR 0.32, CI 0.20-0.52), with decreasing disparities with increasing SEP quintiles (Q5: IRR 0.66, CI 0.41-1.06).

Conclusion

Our findings underscore the complex epidemiological patterns of COVID-19, shaped by the interactions of gender/sex, SEP, and age, highlighting the need for intersectional perspectives in both epidemiological research and public health strategy development.

Estimation of standardized real-time fatality rate for ongoing epidemics

BACKGROUND

The fatality rate is a crucial metric for guiding public health policies during an ongoing epidemic. For COVID-19, the age structure of the confirmed cases changes over time, bringing a substantial impact on the real-time estimation of fatality. A 'spurious decrease' in fatality rate can be caused by a shift in confirmed cases towards younger ages even if the fatalities remain unchanged across different ages.

METHODS

To address this issue, we propose a standardized real-time fatality rate estimator. A simulation study is conducted to evaluate the performance of the estimator. The proposed method is applied for real-time fatality rate estimation of COVID-19 in Germany from March 2020 to May 2022.

FINDINGS

The simulation results suggest that the proposed estimator can provide an accurate trend of disease fatality in all cases, while the existing estimator may convey a misleading signal of the actual situation when the changes in temporal age distribution take place. The application to Germany data shows that there was an increment in the fatality rate at the implementation of the 'live with COVID' strategy.

CONCLUSIONS

As many countries have chosen to coexist with the coronavirus, frequent examination of the fatality rate is of paramount importance.

Diagnostics for Public Health - Infectious Disease Surveillance and Control

AbstractAccurate diagnostics are critical in public health to ensure successful disease tracking, prevention, and control. Many of the same characteristics are desirable for diagnostic procedures in both medicine and public health: for example, low cost, high speed, low invasiveness, ease of use and interpretation, day-to-day consistency, and high accuracy. This review lays out five principles that are salient when the goal of diagnosis is to improve the overall health of a population rather than that of a particular patient, and it applies them in two important use cases: pandemic infectious disease and antimicrobial resistance.

Disease Burden of Meningitis Caused by Streptococcus pneumoniae Among Under-Fives in China: A Systematic Review and Meta-analysis

INTRODUCTION

Streptococcus pneumoniae is the leading cause of meningitis, with a case fatality of up to about 50%. Children younger than 5 years are at greater risk for pneumococcal meningitis compared with other populations. It is of significant importance to provide a comprehensive understanding of the burden of pneumococcal meningitis among under-fives in the low pneumococcal conjugate vaccine (PCV) coverage period in China.

METHODS

A systematic review was conducted. We searched both English (PubMed, Ovid-EMBASE, Biosis, Web of Science, and Cochrane) and Chinese (CNKI, Wanfang, and ViP) databases for studies on bacterial meningitis in China published between January 1980 and July 2022. Ineligible studies were excluded based on study design and data integrity. Heterogeneity was assessed with I2 and estimates of bacterial meningitis morbidity and mortality were pooled using random-effects models. Subgroup analysis was conducted to trace the source of the heterogeneity and summarize average estimates.

RESULTS

A total of 13,082 studies were identified in the literature, and 56 studies were finally included for data analysis. The estimated incidence of pneumococcal meningitis was 2.10 cases per 100,000 children younger than 5 years each year (95% CI: 0.59-7.46), with a pooled case fatality rate of 24.59% (95%CI: 19.35-30.28%) in China. It was estimated that 1617.16 (95% CI: 454.35-5744.78) pneumococcal meningitis cases and 548.86 (95% CI: 474.80-627.62) deaths occurred among under-fives in China in 2020. Streptococcus pneumoniae played an important role in the etiology of confirmed bacterial meningitis cases, with a pooled proportion of 22.05% (95% CI: 17.83-26.27%). The most prevalent serotypes were 6B, 14, 19F, 19A, and 23F, which were preventable with a vaccine.

CONCLUSIONS

Pneumococcal meningitis remains one of the most important health problems among children younger than 5 years in China. Immunization programs should be promoted to avoid preventable cases and deaths.

Some principles for using epidemiologic study results to parameterize transmission models

Background

Infectious disease models, including individual based models (IBMs), can be used to inform public health response. For these models to be effective, accurate estimates of key parameters describing the natural history of infection and disease are needed. However, obtaining these parameter estimates from epidemiological studies is not always straightforward. We aim to 1) outline challenges to parameter estimation that arise due to common biases found in epidemiologic studies and 2) describe the conditions under which careful consideration in the design and analysis of the study could allow us to obtain a causal estimate of the parameter of interest. In this discussion we do not focus on issues of generalizability and transportability.

Methods

Using examples from the COVID-19 pandemic, we first identify different ways of parameterizing IBMs and describe ideal study designs to estimate these parameters. Given real-world limitations, we describe challenges in parameter estimation due to confounding and conditioning on a post-exposure observation. We then describe ideal study designs that can lead to unbiased parameter estimates. We finally discuss additional challenges in estimating progression probabilities and the consequences of these challenges.

Results

Causal estimation can only occur if we are able to accurately measure and control for all confounding variables that create non-causal associations between the exposure and outcome of interest, which is sometimes challenging given the nature of the variables we need to measure. In the absence of perfect control, non-causal parameter estimates should still be used, as sometimes they are the best available information we have.

Conclusions

Identifying which estimates from epidemiologic studies correspond to the quantities needed to parameterize disease models, and determining whether these parameters have causal interpretations, can inform future study designs and improve inferences from infectious disease models. Understanding the way in which biases can arise in parameter estimation can inform sensitivity analyses or help with interpretation of results if the magnitude and direction of the bias is understood.

Undiagnosed Pulmonary Tuberculosis (TB) and Coronavirus Disease 2019 (COVID-19) in Adults Dying at Home in a High-TB-Burden Setting, Before and During Pandemic COVID-19: An Autopsy Study

BACKGROUND

Missing or undiagnosed patients with tuberculosis (TB) or coronavirus disease 2019 (COVID-19) are of concern. Identifying both infections in patients with no diagnosis prior to death contributes to understanding the burden of disease. To confirm reports of global reduction in TB incidence, a 2012 autopsy study of adults dying at home of natural causes in a high-TB-burden setting was repeated, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) assessments after the first COVID-19 surge in South Africa.

METHODS

Adult decedents who died at home with insufficient information to determine cause of death, no recent hospitalization, and no current antemortem TB or COVID-19 diagnosis were identified between March 2019 and October 2020 with a 4-month halt during lockdown. A standardized verbal autopsy followed by minimally invasive needle autopsy (MIA) was performed. Biopsies were taken for histopathology from liver, bilateral brain and lung; bronchoalveolar lavage fluid was collected for Xpert (MTB/RIF) and mycobacterial culture, and blood for human immunodeficiency virus (HIV) polymerase chain reaction (PCR) testing. After the start of the COVID-19 pandemic, a nasopharyngeal swab and lung tissue were subjected to SARS-CoV-2 PCR testing.

RESULTS

Sixty-six MIAs were completed in 25 men and 41 women (median age, 60 years); 68.2% had antemortem respiratory symptoms and 30.3% were people with HIV. Overall, TB was diagnosed in 11 of 66 (16.7%) decedents, and 14 of 41 (34.1%) in the COVID-19 pandemic were SARS-CoV-2 positive.

CONCLUSIONS

Undiagnosed TB in adults dying at home has decreased but remains unacceptably high. Forty percent of decedents had undiagnosed COVID-19, suggesting that estimates of excess deaths may underestimate the impact of SARS-CoV-2 on mortality.

An Epidemic Model for Multi-Intervention Outbreaks

Modeling is an important tool to utilize at the beginning of an infectious disease outbreak, as it allows estimation of parameters - such as the basic reproduction number, R 0 -that can be used to postulate how the outbreak may continue to spread. However, there exist many challenges that need to be accounted for, such as an unknown first case date, retrospective reporting of 'probable' cases, changing dynamics between case count and death count trends, and the implementation of multiple control efforts and their delayed or diminished effects. Using the near-daily data provided from the recent outbreak of Sudan ebolavirus in Uganda as a case study, we create a model and present a framework aimed at overcoming these aforementioned challenges. The impact of each challenge is examined by comparing model estimates and fits throughout our framework. Indeed, we found that allowing for multiple fatality rates over the course of an outbreak generally resulted in better fitting models. On the other hand, not knowing the start date of an outbreak appeared to have large and non-uniform effects on parameter estimates, particularly at the beginning stages of an outbreak. While models that did not account for the decaying effect of interventions on transmission underestimated R 0 , all decay models run on the full dataset yielded precise R 0 estimates, demonstrating the robustness of R 0 as a measure of disease spread when examining data from the entire outbreak.

A new method for the joint estimation of instantaneous reproductive number and serial interval during epidemics

Although some methods for estimating the instantaneous reproductive number during epidemics have been developed, the existing frameworks usually require information on the distribution of the serial interval and/or additional contact tracing data. However, in the case of outbreaks of emerging infectious diseases with an unknown natural history or undetermined characteristics, the serial interval and/or contact tracing data are often not available, resulting in inaccurate estimates for this quantity. In the present study, a new framework was specifically designed for joint estimates of the instantaneous reproductive number and serial interval. Concretely, a likelihood function for the two quantities was first introduced. Then, the instantaneous reproductive number and the serial interval were modeled parametrically as a function of time using the interpolation method and a known traditional distribution, respectively. Using the Bayesian information criterion and the Markov Chain Monte Carlo method, we ultimately obtained their estimates and distribution. The simulation study revealed that our estimates of the two quantities were consistent with the ground truth. Seven data sets of historical epidemics were considered and further verified the robust performance of our method. Therefore, to some extent, even if we know only the daily incidence, our method can accurately estimate the instantaneous reproductive number and serial interval to provide crucial information for policymakers to design appropriate prevention and control interventions during epidemics.

Strengthening a One Health approach to emerging zoonoses

Given the enormous global impact of the COVID-19 pandemic, outbreaks of highly pathogenic avian influenza in Canada, and manifold other zoonotic pathogen activity, there is a pressing need for a deeper understanding of the human-animal-environment interface and the intersecting biological, ecological, and societal factors contributing to the emergence, spread, and impact of zoonotic diseases. We aim to apply a One Health approach to pressing issues related to emerging zoonoses, and propose a functional framework of interconnected but distinct groups of recommendations around strategy and governance, technical leadership (operations), equity, education and research for a One Health approach and Action Plan for Canada. Change is desperately needed, beginning by reorienting our approach to health and recalibrating our perspectives to restore balance with the natural world in a rapid and sustainable fashion. In Canada, a major paradigm shift in how we think about health is required. All of society must recognize the intrinsic value of all living species and the importance of the health of humans, other animals, and ecosystems to health for all.

Evolution of the lethality due to SARS-CoV-2 in Spain according to age group and sex

The emergence of SARS-CoV-2 in China in December 2019 has posed a major challenge to health systems in all countries around the world. One of the most relevant epidemiological measures to consider during the course of a pandemic is the proportion of cases that eventually die from the disease (case fatality ratio, CFR). Monitoring the evolution of this indicator is of paramount importance because it allows for the assessment of both variations in the lethality of the virus and the effectiveness of the control measures implemented by health authorities. One of the problems with estimating the CFR in practice is that the available data only show daily or weekly counts of new cases and deaths; there is no information on when each deceased patient was infected and therefore it is not possible to know exactly how many cases there were at the time the patient became infected. Various approaches have been proposed for calculating the CFR by correcting for the time lag between infection and death. In this paper, we present a novel methodology to perform a non-parametric estimation of the evolution of the CFR by initially identifying an optimal time lag between infections and deaths. The goodness of this procedure is assessed by means of a simulation study and the method is applied to the estimation of the CFR in Spain in the period from July 2020 to March 2022.

Time-dependent risk of COVID-19 death with overwhelmed health-care capacity in Japan, 2020-2022

BACKGROUND

It has been descriptively argued that the case fatality risk (CFR) of coronavirus disease (COVID-19) is elevated when medical services are overwhelmed. The relationship between CFR and pressure on health-care services should thus be epidemiologically explored to account for potential epidemiological biases. The purpose of the present study was to estimate the age-dependent CFR in Tokyo and Osaka over time, investigating the impact of caseload demand on the risk of death.

METHODS

We estimated the time-dependent CFR, accounting for time delay from diagnosis to death. To this end, we first determined the time distribution from diagnosis to death, allowing variations in the delay over time. We then assessed the age-dependent CFR in Tokyo and Osaka. In Osaka, the risk of intensive care unit (ICU) admission was also estimated.

RESULTS

The CFR was highest among individuals aged 80 years and older and during the first epidemic wave from February to June 2020, estimated as 25.4% (95% confidence interval [CI] 21.1 to 29.6) and 27.9% (95% CI 20.6 to 36.1) in Tokyo and Osaka, respectively. During the fourth wave of infection (caused by the Alpha variant) in Osaka the CFR among the 70s and ≥ 80s age groups was, respectively, 2.3 and 1.5 times greater than in Tokyo. Conversely, despite the surge in hospitalizations, the risk of ICU admission among those aged 80 and older in Osaka decreased. Such time-dependent variation in the CFR was not seen among younger patients < 70 years old. With the Omicron variant, the CFR among the 80s and older in Tokyo and Osaka was 3.2% (95% CI 3.0 to 3.5) and 2.9% (95% CI 2.7 to 3.1), respectively.

CONCLUSION

We found that without substantial control, the CFR can increase when a surge in cases occurs with an identifiable elevation in risk-especially among older people. Because active treatment options including admission to ICU cannot be offered to the elderly with an overwhelmed medical service, the CFR value can potentially double compared with that in other areas of health care under less pressure.

A novel method to monitor COVID-19 fatality rate in real-time, a key metric to guide public health policy

An accurate estimator of the real-time fatality rate is warranted to monitor the progress of ongoing epidemics, hence facilitating the policy-making process. However, most of the existing estimators fail to capture the time-varying nature of the fatality rate and are often biased in practice. A simple real-time fatality rate estimator with adjustment for reporting delays is proposed in this paper using the fused lasso technique. This approach is easy to use and can be broadly applied to public health practice as only basic epidemiological data are required. A large-scale simulation study suggests that the proposed estimator is a reliable benchmark for formulating public health policies during an epidemic with high accuracy and sensitivity in capturing the changes in the fatality rate over time, while the other two commonly-used case fatality rate estimators may convey delayed or even misleading signals of the true situation. The application to the COVID-19 data in Germany between January 2020 and January 2022 demonstrates the importance of the social restrictions in the early phase of the pandemic when vaccines were not available, and the beneficial effects of vaccination in suppressing the fatality rate to a low level since August 2021 irrespective of the rebound in infections driven by the more infectious Delta and Omicron variants during the fourth wave.

Quantitatively evaluate the impact of domestic aviation control measures on the spread of COVID-19 in China

To quantitatively evaluate the impact of domestic aviation control measures on the spread of COVID-19 in China. The number of international flights from March to September 2019 simulated the number of flights from March to September 2020 without implementing aviation control measures. In addition, the proportion of asymptomatic persons and the delay in case reporting were adjusted to estimate the prevalence of each country during the same period and calculate the estimated imported cases. The estimated imported cases were assigned each day with weight, and the estimated daily reported cases were obtained based on the actual daily number of domestic cases in China. Effective Reproduction Number ([Formula: see text]) was calculated based on delayed distribution, Basic Reproductive Number ([Formula: see text]) distribution, and generation time distribution were reported in previous studies. Gaussian Process was used to estimate the effect of time-varying on [Formula: see text], and the estimated [Formula: see text] was compared with the actual [Formula: see text]. The estimated imported cases increased significantly compared with the actual number of imported cases. The estimated imported cases were mainly concentrated in North America and Europe from March to April and gradually increased in many East Asian countries from May to September. The difference between predicted [Formula: see text] and actual [Formula: see text] was statistically significant. The estimated imported cases and the estimated [Formula: see text] have increased compared to the actual situation. This paper quantitatively proves that Chinese aviation control measures significantly suppress the COVID-19 epidemic, which is conducive to promoting and applying this measure.

Novel methods for estimating the instantaneous and overall COVID-19 case fatality risk among care home residents in England

The COVID-19 pandemic has had high mortality rates in the elderly and frail worldwide, particularly in care homes. This is driven by the difficulty of isolating care homes from the wider community, the large population sizes within care facilities (relative to typical households), and the age/frailty of the residents. To quantify the mortality risk posed by disease, the case fatality risk (CFR) is an important tool. This quantifies the proportion of cases that result in death. Throughout the pandemic, CFR amongst care home residents in England has been monitored closely. To estimate CFR, we apply both novel and existing methods to data on deaths in care homes, collected by Public Health England and the Care Quality Commission. We compare these different methods, evaluating their relative strengths and weaknesses. Using these methods, we estimate temporal trends in the instantaneous CFR (at both daily and weekly resolutions) and the overall CFR across the whole of England, and dis-aggregated at regional level. We also investigate how the CFR varies based on age and on the type of care required, dis-aggregating by whether care homes include nursing staff and by age of residents. This work has contributed to the summary of measures used for monitoring the UK epidemic.

During an epidemic, the case fatality risk (CFR), i.e. the probability that an individual dies after testing positive for a disease, is a key parameter informing the public health response. However, calculating the CFR is not trivial, since there are cases who may die in the future but have not died yet. Therefore, statistical methods are required to correct for the distribution of times between testing positive and dying. In this paper, we derive multiple methods, some existing and some novel, within a consistent methodological framework. This allows us to understand how these different approaches are related and their relative strengths and weaknesses. During the COVID-19 pandemic, care homes have been particularly affected, due to the high risk of COVID-19-associated mortality in the frail and elderly. We apply our CFR methods to data from English care homes to analyse changes in the care home CFR throughout the pandemic.

The impact of face-mask mandates on all-cause mortality in Switzerland: a quasi-experimental study

Background

Whereas there is strong evidence that wearing a face mask is effective in reducing the spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), evidence on the impact of mandating the wearing of face masks on deaths from coronavirus disease 2019 (COVID-19) and all-cause mortality is more sparse and likely to vary by context. Focusing on a quasi-experimental setting in Switzerland, we aimed to determine (i) the effect of face-mask mandates for indoor public spaces on all-cause mortality; and (ii) how the effect has varied over time, and by age and sex.

Methods

Our analysis exploited the fact that between July and October 2020, nine cantons in Switzerland extended a face-mask mandate at different time points from being restricted to public transportation only to applying to all public indoor places. We used both a Difference-in-Differences approach with fixed-effects for canton and week and an event-study approach.

Results

In our main Difference-in-Differences model, the face-mask mandate was associated with a 0.3% reduction in all-cause mortality [95% confidence interval (CI): −3.4% to 2.7%; P = 0.818]. This null effect was confirmed in the event-study approach and a variety of robustness checks. Combining the face-mask mandate with social distancing rules led to an estimated 5.1% (95% CI: −7.9% to −2.4%; P = 0.001) reduction in all-cause mortality.

Conclusions

Mandating face-mask use in public indoor spaces in Switzerland in mid-to-late 2020 does not appear to have resulted in large reductions in all-cause mortality in the short term. There is some suggestion that combining face-mask mandates with social distancing rules reduced all-cause mortality.

Comparing COVID-19 fatality across countries: a synthetic demographic indicator

Background

The case fatality rate (CFR) is one of the most important measures for monitoring disease progression and evaluating appropriate policy health measures over the course of the COVID-19 pandemic. To remove biases arising from the age structure of COVID-19 cases in international comparisons of the CFR, existing studies have relied mainly on direct standardisation.

Objective

We propose and validate a synthetic indicator of COVID-19 fatality (SCFR) that improves its comparability across countries by adjusting for the age and sex structure of COVID-19 cases without relying on the arbitrary choice of a standard population.

Results

Contrary to what comparisons of the crude CFR suggest, differences in COVID-19 fatality across countries according to the proposed SCFR are not very stark. Importantly, once we adjust for the age structure of COVID-19 cases, the higher case fatality among men emerges as the main driver of international differences in COVID-19 CFR.

Conclusions

The SCFR is a simple indicator that is useful for monitoring the fatality of SARS-CoV-2 mutations and the efficacy of health policy measures for COVID-19, including vaccination.

Contributions

(1) A simple synthetic indicator of COVID-19 fatality that improves its comparability across countries by adjusting for the age and sex structure of COVID-19 cases; (2) Evidence that sex differences in COVID-19 fatality drive international differences in the overall CFR.

How reported outbreak data can shape individual behavior in a social world

Agencies reporting on disease outbreaks face many choices about what to report and the scale of its dissemination. Reporting impacts an epidemic by influencing individual decisions directly, and the social network in which they are made. We simulated a dynamic multiplex network model—with coupled infection and communication layers—to examine behavioral impacts from the nature and scale of epidemiological information reporting. We explored how adherence to protective behaviors (social distancing) can be facilitated through epidemiological reporting, social construction of perceived risk, and local monitoring of direct connections, but eroded via social reassurance. We varied reported information (total active cases, daily new cases, hospitalizations, hospital capacity exceeded, or deaths) at one of two scales (population level or community level). Total active and new case reporting at the population level were the most effective approaches, relative to the other reporting approaches. Case reporting, which synergizes with test-trace-and-isolate and vaccination policies, should remain a priority throughout an epidemic.

Pregnancy and the Risk of In-Hospital Coronavirus Disease 2019 (COVID-19) Mortality

OBJECTIVE

To evaluate whether pregnancy is an independent risk factor for in-hospital mortality among patients of reproductive age hospitalized with coronavirus disease 2019 (COVID-19) viral pneumonia.

METHODS

We conducted a retrospective cohort study (April 2020-May 2021) of 23,574 female inpatients aged 15-45 years with an International Classification of Diseases, Tenth Revision, Clinical Modification diagnosis code for COVID-19 discharged from 749 U.S. hospitals in the Premier Healthcare Database. We used a viral pneumonia diagnosis to select for patients with symptomatic COVID-19. The associations between pregnancy and in-hospital mortality, intensive care unit (ICU) admission, and mechanical ventilation were analyzed using propensity score-matched conditional logistic regression. Models were matched for age, marital status, race and ethnicity, Elixhauser comorbidity score, payer, hospital number of beds, season of discharge, hospital region, obesity, hypertension, diabetes mellitus, chronic pulmonary disease, deficiency anemias, depression, hypothyroidism, and liver disease.

RESULTS

In-hospital mortality occurred in 1.1% of pregnant patients and 3.5% of nonpregnant patients hospitalized with COVID-19 and viral pneumonia (propensity score-matched odds ratio [OR] 0.39, 95% CI 0.25-0.63). The frequency of ICU admission for pregnant and nonpregnant patients was 22.0% and 17.7%, respectively (OR 1.34, 95% CI 1.15-1.55). Mechanical ventilation was used in 8.7% of both pregnant and nonpregnant patients (OR 1.05, 95% CI 0.86-1.29). Among patients who were admitted to an ICU, mortality was lower for pregnant compared with nonpregnant patients (OR 0.33, 95% CI 0.20-0.57), though mechanical ventilation rates were similar (35.7% vs 38.3%, OR 0.90, 95% CI 0.70-1.16). Among patients with mechanical ventilation, pregnant patients had a reduced risk of in-hospital mortality compared with nonpregnant patients (0.26, 95% CI 0.15-0.46).

CONCLUSION

Despite a higher frequency of ICU admission, in-hospital mortality was lower among pregnant patients compared with nonpregnant patients with COVID-19 viral pneumonia, and these findings persisted after propensity score matching.

Near real-time surveillance of the SARS-CoV-2 epidemic with incomplete data

When responding to infectious disease outbreaks, rapid and accurate estimation of the epidemic trajectory is critical. However, two common data collection problems affect the reliability of the epidemiological data in real time: missing information on the time of first symptoms, and retrospective revision of historical information, including right censoring. Here, we propose an approach to construct epidemic curves in near real time that addresses these two challenges by 1) imputation of dates of symptom onset for reported cases using a dynamically-estimated "backward" reporting delay conditional distribution, and 2) adjustment for right censoring using the NobBS software package to nowcast cases by date of symptom onset. This process allows us to obtain an approximation of the time-varying reproduction number (Rt) in real time. We apply this approach to characterize the early SARS-CoV-2 outbreak in two Spanish regions between March and April 2020. We evaluate how these real-time estimates compare with more complete epidemiological data that became available later. We explore the impact of the different assumptions on the estimates, and compare our estimates with those obtained from commonly used surveillance approaches. Our framework can help improve accuracy, quantify uncertainty, and evaluate frequently unstated assumptions when recovering the epidemic curves from limited data obtained from public health systems in other locations.

From null to midline: changes in head posture do not predictably change head tremor in cervical dystonia

Introduction

A common view is that head tremor (HT) in cervical dystonia (CD) decreases when the head assumes an unopposed dystonic posture and increases when the head is held at midline. However, this has not been examined with objective measures in a large, multicenter cohort.

Methods

For 80 participants with CD and HT, we analyzed videos from examination segments in which participants were instructed to 1) let their head drift to its most comfortable position (null point) and then 2) hold their head straight at midline. We used our previously developed Computational Motor Objective Rater (CMOR) to quantify changes in severity, amplitude, and frequency between the two postures.

Results

Although up to 9% of participants had exacerbated HT in midline, across the whole cohort, paired t-tests reveal no significant changes in overall severity (t = -0.23, p = 0.81), amplitude (t = -0.80, p = 0.43), and frequency (t = 1.48, p = 0.14) between the two postures.

Conclusions

When instructed to first let their head drift to its null point and then to hold their head straight at midline, most patient's changes in HT were below the thresholds one would expect from the sensitivity of clinical rating scales. Counter to common clinical impression, CMOR objectively showed that HT does not consistently increase at midline posture in comparison to the null posture.

Global monitoring of the impact of the COVID-19 pandemic through online surveys sampled from the Facebook user base

Simultaneously tracking the global impact of COVID-19 is challenging because of regional variation in resources and reporting. Leveraging self-reported survey outcomes via an existing international social media network has the potential to provide standardized data streams to support monitoring and decision-making worldwide, in real time, and with limited local resources. The University of Maryland Global COVID-19 Trends and Impact Survey (UMD-CTIS), in partnership with Facebook, has invited daily cross-sectional samples from the social media platform's active users to participate in the survey since its launch on April 23, 2020. We analyzed UMD-CTIS survey data through December 20, 2020, from 31,142,582 responses representing 114 countries/territories weighted for nonresponse and adjusted to basic demographics. We show consistent respondent demographics over time for many countries/territories. Machine Learning models trained on national and pooled global data verified known symptom indicators. COVID-like illness (CLI) signals were correlated with government benchmark data. Importantly, the best benchmarked UMD-CTIS signal uses a single survey item whereby respondents report on CLI in their local community. In regions with strained health infrastructure but active social media users, we show it is possible to define COVID-19 impact trajectories using a remote platform independent of local government resources. This syndromic surveillance public health tool is the largest global health survey to date and, with brief participant engagement, can provide meaningful, timely insights into the global COVID-19 pandemic at a local scale.

Bayesian workflow for disease transmission modeling in Stan

This tutorial shows how to build, fit, and criticize disease transmission models in Stan, and should be useful to researchers interested in modeling the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic and other infectious diseases in a Bayesian framework. Bayesian modeling provides a principled way to quantify uncertainty and incorporate both data and prior knowledge into the model estimates. Stan is an expressive probabilistic programming language that abstracts the inference and allows users to focus on the modeling. As a result, Stan code is readable and easily extensible, which makes the modeler's work more transparent. Furthermore, Stan's main inference engine, Hamiltonian Monte Carlo sampling, is amiable to diagnostics, which means the user can verify whether the obtained inference is reliable. In this tutorial, we demonstrate how to formulate, fit, and diagnose a compartmental transmission model in Stan, first with a simple susceptible-infected-recovered model, then with a more elaborate transmission model used during the SARS-CoV-2 pandemic. We also cover advanced topics which can further help practitioners fit sophisticated models; notably, how to use simulations to probe the model and priors, and computational techniques to scale-up models based on ordinary differential equations.

COVID-19 mortality in women and men in sub-Saharan Africa: a cross-sectional study

INTRODUCTION

Since sex-based biological and gender factors influence COVID-19 mortality, we wanted to investigate the difference in mortality rates between women and men in sub-Saharan Africa (SSA).

METHOD

We included 69 580 cases of COVID-19, stratified by sex (men: n=43 071; women: n=26 509) and age (0-39 years: n=41 682; 40-59 years: n=20 757; 60+ years: n=7141), from 20 member nations of the WHO African region until 1 September 2020. We computed the SSA-specific and country-specific case fatality rates (CFRs) and sex-specific CFR differences across various age groups, using a Bayesian approach.

RESULTS

A total of 1656 deaths (2.4% of total cases reported) were reported, with men accounting for 70.5% of total deaths. In SSA, women had a lower CFR than men (mean [Formula: see text] = -0.9%; 95% credible intervals (CIs) -1.1% to -0.6%). The mean CFR estimates increased with age, with the sex-specific CFR differences being significant among those aged 40 years or more (40-59 age group: mean [Formula: see text] = -0.7%; 95% CI -1.1% to -0.2%; 60+ years age group: mean [Formula: see text] = -3.9%; 95% CI -5.3% to -2.4%). At the country level, 7 of the 20 SSA countries reported significantly lower CFRs among women than men overall. Moreover, corresponding to the age-specific datasets, significantly lower CFRs in women than men were observed in the 60+ years age group in seven countries and 40-59 years age group in one country.

CONCLUSIONS

Sex and age are important predictors of COVID-19 mortality globally. Countries should prioritise the collection and use of sex-disaggregated data so as to design public health interventions and ensure that policies promote a gender-sensitive public health response.

COVID-19 Infection in Kidney Transplant Recipients From a Single Center in Iran

OBJECTIVES

COVID-19 has been spreading rapidly throughout the world, with nearly every country thus far documenting this infection. In this study, our aim was to evaluate the risk factors for increased mortality in deceased donor kidney transplant recipients with COVID-19 at a single center in Iran.

MATERIALS AND METHODS

This was a retrospective study in a single center. During the 17-month ongoing COVID19 pandemic in Iran, there were 153 deceased donor kidney recipients at our center with suspected COVID19 symptoms. Of these patients, 138 had positive COVID-19 tests, and thus a therapeutic regimen was commenced for these patients. Data were analyzed with SPSS version 16 software.

RESULTS

The patients were predominantly male (83, 60.1%) with a median age of 47.09 ± 13.75 years and a median time since transplant of 51 months (IQR, 1-276 months). Among these patients, 84 (60.8%) had hypertension and 43 (31.2%) had diabetes mellitus. We observed a significant relationship between disease severity and mortality (P < .001). After risk adjustments for age, presence of diabetes mellitus and hypertension and blood group type were factors that showed a significantly higher risk of death.

CONCLUSIONS

Deceased donor kidney transplant recipients with confirmed COVID-19 experienced less fever as an initial symptom. However, recipients with COVID-19 and an underlying disease had a higher rate of mortality, severity of infection, and progression of disease. Appropriate management of renal complications and vaccinations in deceased donor kidney transplant recipients may help lead to more favorable outcomes.

Aubin M, Zhou G, Gluck MJ, Berger S, Rhee J, Petersen E, Mormann B, Loesche M, Hu Y, Chen Z, Yu J, Gebre M, Atyeo C, Gorman MJ, Zhu AL, Burke J, Slein M, Hasdianda MA, Jambaulikar G, Boyer EW, Sabeti PC, Barouch DH, Julg B, Kucharski AJ, Musk ER, Lauffenburger DA, Alter G, Menon AS. Epidemiological and Immunological Features of Obesity and SARS-CoV-2

Obesity is a key correlate of severe SARS-CoV-2 outcomes while the role of obesity on risk of SARS-CoV-2 infection, symptom phenotype, and immune response remain poorly defined. We examined data from a prospective SARS-CoV-2 cohort study to address these questions. Serostatus, body mass index, demographics, comorbidities, and prior COVID-19 compatible symptoms were assessed at baseline and serostatus and symptoms monthly thereafter. SARS-CoV-2 immunoassays included an IgG ELISA targeting the spike RBD, multiarray Luminex targeting 20 viral antigens, pseudovirus neutralization, and T cell ELISPOT assays. Our results from a large prospective SARS-CoV-2 cohort study indicate symptom phenotype is strongly influenced by obesity among younger but not older age groups; we did not identify evidence to suggest obese individuals are at higher risk of SARS-CoV-2 infection; and remarkably homogenous immune activity across BMI categories suggests immune protection across these groups may be similar.

Assessing relative COVID-19 mortality during the second wave: a prospective Swiss population-based study

OBJECTIVE

During the first COVID-19 wave in Switzerland, relative mortality was at least eight times higher compared with the uninfected general population. We aimed to assess sex-specific and age-specific relative mortality associated with a SARS-CoV-2 diagnosis during the second wave.

DESIGN

Prospective population-based study.

SETTING

Individuals testing positive for SARS-CoV-2 after the start of the second wave on 1 October 2020 were followed up until death or administrative censoring on 31 December 2020.

PARTICIPANTS

5 179 740 inhabitants of Switzerland in fall 2018 aged 35-95 years (without COVID-19) and 257 288 persons tested positive for SARS-CoV-2 by PCR or antigen testing during the second wave.

PRIMARY AND SECONDARY OUTCOME MEASURES

The planned outcome measure was time to death from any cause, measured from the date of a SARS-CoV-2 diagnosis or 1 October in the general population. Information on confirmed SARS-CoV-2 diagnoses and deaths was matched by calendar time with the all-cause mortality of the general Swiss population of 2018. Proportional hazards models were used to estimate sex-specific and age-specific mortality rates and probabilities of death within 60 days.

RESULTS

The risk of death for individuals tested positive for SARS-CoV-2 in the second wave in Switzerland increased at least sixfold compared with the general population. HRs, reflecting the risk attributable to a SARS-CoV-2 infection, were higher for men (1.40, 95% CI 1.29 to 1.52) and increased for each additional year of age (1.01, 95% CI 1.01 to 1.02). COVID-19 mortality was reduced by at least 20% compared with the first wave in spring 2020.

CONCLUSION

General mortality patterns, increased for men and older persons, were similar in spring and in fall. Absolute and relative COVID-19 mortality was smaller in fall.

TRIAL REGISTRATION

The protocol for this study was registered on 3 December 2020 at https://osf.io/gbd6r.

SARS-CoV-2 infections in 165 countries over time

BACKGROUND

Understanding the dynamics of the COVID-19 pandemic and evaluating the efficacy of control measures requires knowledge of the number of infections over time. This number, however, often differs from the number of confirmed cases because of a large fraction of asymptomatic infections and different testing strategies.

METHODS

This study uses death count statistics, age-dependent infection fatality risks, and stochastic modeling to estimate the prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections among adults (aged 20 years or older) in 165 countries over time, from early 2020 until June 25, 2021. The accuracy of the approach is confirmed through comparison with previous nationwide seroprevalence surveys.

RESULTS

The estimates presented reveal that the fraction of infections that are detected vary widely over time and between countries, and hence confirmed cases alone often yield a false picture of the pandemic. As of June 25, 2021, the nationwide cumulative fraction of SARS-CoV-2 infections (cumulative infections relative to population size) was estimated as 98% (95% confidence interval [CI] 93-100%) for Peru, 83% (95% CI 61-94%) for Brazil, and 36% (95% CI 23-61%) for the United States.

CONCLUSIONS

The time-resolved estimates presented expand the possibilities to study the factors that influenced and still influence the pandemic's progression in 165 countries.

A Bayesian estimate of the early COVID-19 infection fatality ratio in Brazil based on a random seroprevalence survey

BACKGROUND

A number of estimates of the infection fatality ratio (IFR) of SARS-CoV-2 in different countries have been published. In Brazil, the fragile political situation, together with socioeconomic and ethnic diversity, could result in substantially different IFR estimates.

METHODS

We infer the IFR in Brazil in 2020 by combining three datasets. We compute the prevalence via the population-based seroprevalence survey, EPICOVID19-BR. For the fatalities we obtain the absolute number using the public Painel Coronavírus dataset and the age-relative number using the public SIVEP-Gripe dataset. The time delay between the development of antibodies and subsequent fatality is estimated via the SIVEP-Gripe dataset. We obtain the IFR for each survey stage and 27 federal states. We include the effect of fading IgG antibody levels by marginalizing over the test detectability time window.

RESULTS

We infer a country-wide average IFR (maximum posterior and 95% CI) of 1.03% (0.88-1.22%) and age-specific IFRs of 0.032% (0.023-0.041%) [< 30 years], 0.22% (0.18-0.27%) [30-49 years], 1.2% (1.0-1.5%) [50-69 years], and 3.0% (2.4-3.9%) [≥ 70 years]. We find that the fatality ratio in the country increased significantly at the end of June 2020, likely due to the increased strain on the health system.

CONCLUSIONS

Our IFR estimate is based on data and does not rely on extrapolating models. This estimate sets a baseline value with which future medications and treatment protocols may be confronted.

Predictors of severity and development of critical illness of Egyptian COVID-19 patients: A multicenter study

Objectives

We conducted the present multicenter, retrospective study to assess the epidemiological, clinical, laboratory, and radiological characteristics associated with critical illness among patients with COVID-19 from Egypt.

Methods

The present study was a multicenter, retrospective study that retrieved the data of all Egyptian cases with confirmed COVID-19 admitted to hospitals affiliated to the General Organization for Teaching Hospitals and Institutes (GOTHI) through the period from March to July 2020. The diagnosis of COVID-19 was based on a positive reverse transcription-polymerase chain reaction (RT-PCR) laboratory test.

Results

This retrospective study included 2724 COVID-19 patients, of whom 423 (15.52%) were critically ill. Approximately 45.86% of the critical group aged above 60 years, compared to 39.59% in the non-critical group (p = 0.016). Multivariate analysis showed that many factors were predictors of critically illness, including age >60 years (OR = 1.30, 95% CI [1.05, 1.61], p = 0.014), low oxygen saturation (OR = 0.93, 95% CI [0.91, 0.95], p<0.001), low Glasgow coma scale (OR = 0.75, 95% CI [0.67, 0.84], p<0.001), diabetes (OR = 1.62, 95% CI [1.26, 2.08], p<0.001), cancer (OR = 2.47, 95% CI [1.41, 4.35], p = 0.002), and serum ferritin (OR = 1.004, 95% CI [1.0003, 1.008], p = 0.031).

Conclusion

In the present report, we demonstrated that many factors are associated with COVID-19 critical illness, including older age groups, fatigue, elevated temperature, increased pulse, lower oxygen saturation, the preexistence of diabetes, malignancies, cardiovascular disease, renal diseases, and pulmonary disease. Moreover, elevated serum levels of ALT, AST, and ferritin are associated with worse outcomes. Further studies are required to identify independent predictors of mortality for patients with COVID-19.

Anosmia, ageusia, and other COVID-19-like symptoms in association with a positive SARS-CoV-2 test, across six national digital surveillance platforms: an observational study

BACKGROUND

Multiple voluntary surveillance platforms were developed across the world in response to the COVID-19 pandemic, providing a real-time understanding of population-based COVID-19 epidemiology. During this time, testing criteria broadened and health-care policies matured. We aimed to test whether there were consistent associations of symptoms with SARS-CoV-2 test status across three surveillance platforms in three countries (two platforms per country), during periods of testing and policy changes.

METHODS

For this observational study, we used data of observations from three volunteer COVID-19 digital surveillance platforms (Carnegie Mellon University and University of Maryland Facebook COVID-19 Symptom Survey, ZOE COVID Symptom Study app, and the Corona Israel study) targeting communities in three countries (Israel, the UK, and the USA; two platforms per country). The study population included adult respondents (age 18-100 years at baseline) who were not health-care workers. We did logistic regression of self-reported symptoms on self-reported SARS-CoV-2 test status (positive or negative), adjusted for age and sex, in each of the study cohorts. We compared odds ratios (ORs) across platforms and countries, and we did meta-analyses assuming a random effects model. We also evaluated testing policy changes, COVID-19 incidence, and time scales of duration of symptoms and symptom-to-test time.

FINDINGS

Between April 1 and July 31, 2020, 514 459 tests from over 10 million respondents were recorded in the six surveillance platform datasets. Anosmia-ageusia was the strongest, most consistent symptom associated with a positive COVID-19 test (robust aggregated rank one, meta-analysed random effects OR 16·96, 95% CI 13·13-21·92). Fever (rank two, 6·45, 4·25-9·81), shortness of breath (rank three, 4·69, 3·14-7·01), and cough (rank four, 4·29, 3·13-5·88) were also highly associated with test positivity. The association of symptoms with test status varied by duration of illness, timing of the test, and broader test criteria, as well as over time, by country, and by platform.

INTERPRETATION

The strong association of anosmia-ageusia with self-reported positive SARS-CoV-2 test was consistently observed, supporting its validity as a reliable COVID-19 signal, regardless of the participatory surveillance platform, country, phase of illness, or testing policy. These findings show that associations between COVID-19 symptoms and test positivity ranked similarly in a wide range of scenarios. Anosmia, fever, and respiratory symptoms consistently had the strongest effect estimates and were the most appropriate empirical signals for symptom-based public health surveillance in areas with insufficient testing or benchmarking capacity. Collaborative syndromic surveillance could enhance real-time epidemiological investigations and public health utility globally.

FUNDING

National Institutes of Health, National Institute for Health Research, Alzheimer's Society, Wellcome Trust, and Massachusetts Consortium on Pathogen Readiness.

[Impact of COVID-19 on mortality in the autonomous community of Castilla y León (Spain)]

OBJECTIVE

To estimate the increase in mortality associated with the SARS-CoV-2 coronavirus pandemic in the autonomous community of Castilla y León (Spain).

METHOD

Ecological study based on population and death data for the months of March 2016 to 2020 in Castilla y León. The general and provincial standardized rates, the relative risks of the year 2020 with respect to previous years and the risks adjusted by sex, periods and province, using Poisson regression, were calculated. Trend analysis was performed using joinpoint linear regression.

RESULTS

An increase in mortality was observed in March 2020 with respect to previous years, with an increase of 39% for men (relative risk [RR]: 1.39; 95% confidence interval [95%CI]: 1.32-1.47) and 28% for women (RR: 1.28; 95%CI: 1.21-1.35). The model predicts excess mortality of 775 deaths. In the trend analysis there is a significant turning point in 2019 in men, globally and for almost all provinces. The increase in mortality is general, although heterogeneous by sex, age group and province.

CONCLUSIONS

Although the observed increase in mortality cannot be totally attributed to the disease, it is the best estimate we have of the real impact on deaths directly or indirectly related to it. The number of declared deaths only reaches two thirds of the increase in mortality observed.

US Ventilator Allocation and Patient Triage Policies in Anticipation of the COVID-19 Surge

Before the predicted March 2020 surge of COVID-19, US healthcare organizations were charged with developing resource allocation policies. We assessed policy preparedness and substantive triage criteria within existing policies using a cross-sectional survey distributed to public health personnel and healthcare providers between March 23 and April 23, 2020. Personnel and providers from 68 organizations from 34 US states responded. While half of the organizations did not yet have formal allocation policies, all but 4 were in the process of developing policies. Using manual abstraction and natural language processing, we summarize the origins and features of the policies. Most policies included objective triage criteria, specified inapplicable criteria, separated triage and clinical decision making, detailed reassessment plans, offered an appeals process, and addressed palliative care. All but 1 policy referenced a sequential organ failure assessment score as a triage criterion, and 10 policies categorically excluded patients. Six policies were almost identical, tracing their origins to influenza planning. This sample of policies reflects organizational strategies of exemplar-based policy development and the use of objective criteria in triage decisions, either before or instead of clinical judgment, to support ethical distribution of resources. Future guidance is warranted on how to adapt policies across disease type, choose objective criteria, and specify processes that rely on clinical judgments.

COVID-19 and the flu: data simulations and computational modelling to guide public health strategies

BACKGROUND

Pandemics threaten lives and economies. This article addresses the global threat of the anticipated overlap of COVID-19 with seasonal-influenza.

OBJECTIVES

Scientific evidence based on simulation methodology is presented to reveal the impact of a dual outbreak, with scenarios intended for propagation analysis. This article aims at researchers, clinicians of family medicine, general practice and policy-makers worldwide. The implications for the clinical practice of primary health care are discussed. Current research is an effort to explore new directions in epidemiology and health services delivery.

METHODS

Projections consisted of machine learning, dynamic modelling algorithms and whole simulations. Input data consisted of global indicators of infectious diseases. Four simulations were run for '20% versus 60% flu-vaccinated populations' and '10 versus 20 personal contacts'. Outputs consisted of numerical values and mathematical graphs. Outputs consisted of numbers for 'never infected', 'vaccinated', 'infected/recovered', 'symptomatic/asymptomatic' and 'deceased' individuals. Peaks, percentages, R0, durations are reported.

RESULTS

The best-case scenario was one with a higher flu-vaccination rate and fewer contacts. The reverse generated the worst outcomes, likely to disrupt the provision of vital community services. Both measures were proven effective; however, results demonstrated that 'increasing flu-vaccination rates' is a more powerful strategy than 'limiting social contacts'.

CONCLUSIONS

Results support two affordable preventive measures: (i) to globally increase influenza-vaccination rates, (ii) to limit the number of personal contacts during outbreaks. The authors endorse changing practices and research incentives towards multidisciplinary collaborations. The urgency of the situation is a call for international health policy to promote interdisciplinary modern technologies in public health engineering.

Adjusting Reported COVID-19 Deaths for the Prevailing Routine Death Surveillance in India

In India, the "low mortality" narrative based on the reported COVID-19 deaths may be causing more harm than benefit. The extent to which COVID-19 deaths get reported depends on the coverage of routine death surveillance [death registration along with medical certification of cause of death (MCCD)] and the errors in MCCD. In India, the coverage of routine death surveillance is 18.1%. This is compounded by the fact that COVID-19 death reporting is focused among reported cases and the case detection ratio is low. To adjust for the coverage of routine death surveillance and errors in MCCD, we calculated a correction (multiplication) factor at national and state level to produce an estimated number of COVID-19 deaths. As on July 31, 2020, we calculated the infection fatality ratio (IFR) for India (0.58:100-1.16:100) using these estimated COVID-19 deaths; this is comparable with the IFR range in countries with near perfect routine death surveillance. We recommend the release of excess deaths data during COVID-19 (at least in states with high death registration) and post-mortem COVID-19 testing as a surveillance activity for a better understanding of under-reporting. In its absence, we should adjust reported COVID-19 deaths for the coverage of routine death surveillance and errors in MCCD. This way we will have a clear idea of the true burden of deaths and our public health response will never be inadequate. We recommend that "reported" or "estimated" is added before the COVID-19 death data and related indicators for better clarity and interpretation.

Estimating case fatality risk of severe Yellow Fever cases: systematic literature review and meta-analysis

BACKGROUND

Case fatality risk (CFR), commonly referred to as a case fatality ratio or rate, represents the probability of a disease case being fatal. It is often estimated for various diseases through analysis of surveillance data, case reports, or record examinations. Reported CFR values for Yellow Fever vary, offering wide ranges. Estimates have not been found through systematic literature review, which has been used to estimate CFR of other diseases. This study aims to estimate the case fatality risk of severe Yellow Fever cases through a systematic literature review and meta-analysis.

METHODS

A search strategy was implemented in PubMed and Ovid Medline in June 2019 and updated in March 2021, seeking reported severe case counts, defined by fever and either jaundice or hemorrhaging, and the number of those that were fatal. The searches yielded 1,133 studies, and title/abstract review followed by full text review produced 14 articles reporting 32 proportions of fatal cases, 26 of which were suitable for meta-analysis. Four studies with one proportion each were added to include clinical case data from the recent outbreak in Brazil. Data were analyzed through an intercept-only logistic meta-regression with random effects for study. Values of the I2 statistic measured heterogeneity across studies.

RESULTS

The estimated CFR was 39 % (95 % CI: 31 %, 47 %). Stratifying by continent showed that South America observed a higher CFR than Africa, though fewer studies reported estimates for South America. No difference was seen between studies reporting surveillance data and studies investigating outbreaks, and no difference was seen among different symptom definitions. High heterogeneity was observed across studies.

CONCLUSIONS

Approximately 39 % of severe Yellow Fever cases are estimated to be fatal. This study provides the first systematic literature review to estimate the CFR of Yellow Fever, which can provide insight into outbreak preparedness and estimating underreporting.

Assessing excess mortality in times of pandemics based on principal component analysis of weekly mortality data-the case of COVID-19

The COVID-19 outbreak has called for renewed attention to the need for sound statistical analyses to monitor mortality patterns and trends over time. Excess mortality has been suggested as the most appropriate indicator to measure the overall burden of the pandemic in terms of mortality. As such, excess mortality has received considerable interest since the outbreak of COVID-19 began. Previous approaches to estimate excess mortality are somewhat limited, as they do not include sufficiently long-term trends, correlations among different demographic and geographic groups, or autocorrelations in the mortality time series. This might lead to biased estimates of excess mortality, as random mortality fluctuations may be misinterpreted as excess mortality. We propose a novel approach that overcomes the named limitations and draws a more realistic picture of excess mortality. Our approach is based on an established forecasting model that is used in demography, namely, the Lee-Carter model. We illustrate our approach by using the weekly age- and sex-specific mortality data for 19 countries and the current COVID-19 pandemic as a case study. Our findings show evidence of considerable excess mortality during 2020 in Europe, which affects different countries, age, and sex groups heterogeneously. Our proposed model can be applied to future pandemics as well as to monitor excess mortality from specific causes of death.

Benchmarking the Covid-19 pandemic across countries and states in the USA under heterogeneous testing

Scientists and policymakers need to compare the incidence of Covid-19 across territories or periods with various levels of testing. Benchmarking based on the increase in total cases or case fatality rates is one way of comparing the evolution of the pandemic across countries or territories and could inform policy decisions about strategies to control coronavirus transmission. However, comparing cases and fatality rates across regions is challenging due to heterogeneity in testing and health systems. We show two complementary ways of benchmarking across territories and in time. First, we used multivariate regressions to estimate the test-elasticity of Covid-19 case incidence. Cases grow less than proportionally with testing when assessing weekly changes or looking across states in the USA. They tend to be proportional or even more than proportional when comparing the month-to-month evolution of an average country in the pandemic. Our results were robust to various model specifications. Second, we decomposed the growth in cases into test growth and positive test ratio growth to intuitively visualize the components of case growth. We hope these results can help support evidence-based decisions by public officials and help the public discussion when comparing across territories and in time.

Risk Factors for Illness Severity Among Pregnant Women With Confirmed Severe Acute Respiratory Syndrome Coronavirus 2 Infection-Surveillance for Emerging Threats to Mothers and Babies Network, 22 State, Local, and Territorial Health Departments, 29 March 2020-5 March 2021

Background

Pregnant women with coronavirus disease 2019 (COVID-19) are at increased risk for severe illness compared with nonpregnant women. Data to assess risk factors for illness severity among pregnant women with COVID-19 are limited. This study aimed to determine risk factors associated with COVID-19 illness severity among pregnant women with SARS-CoV-2 infection.

Methods

Pregnant women with SARS-CoV-2 infection confirmed by molecular testing were reported during March 29, 2020–March 5, 2021 through the Surveillance for Emerging Threats to Mothers and Babies Network (SET-NET). Criteria for illness severity (asymptomatic, mild, moderate-to-severe, or critical) were adapted from National Institutes of Health and World Health Organization criteria. Crude and adjusted risk ratios for moderate-to-severe or critical COVID-19 illness were calculated for selected demographic and clinical characteristics.

Results

Among 7,950 pregnant women with SARS-CoV-2 infection, moderate-to-severe or critical COVID-19 illness was associated with age 25 years and older, healthcare occupation, pre-pregnancy obesity, chronic lung disease, chronic hypertension, and pregestational diabetes mellitus. Risk of moderate-to-severe or critical illness increased with the number of underlying medical or pregnancy-related conditions.

Conclusions

Older age and having underlying medical conditions were associated with increased risk of moderate-to-severe or critical COVID-19 illness among pregnant women. This information might help pregnant women understand their risk for moderate-to-severe or critical COVID-19 illness and inform targeted public health messaging.

Epidemiology of Coronavirus Disease 2019 in Japan: Descriptive Findings and Lessons Learned through Surveillance during the First Three Waves

Introduction

Coronavirus disease 2019 (COVID-19) has caused unprecedented global morbidity and mortality. Japan has faced three epidemic "waves" of COVID-19 from early 2020 through early 2021. Here we narratively review the three waves in Japan, describe the key epidemiologic features of COVID-19, and discuss lessons learned.

Methods

We assessed publicly available surveillance data, routine surveillance reports, and other relevant sources-multiple indicators were monitored to improve interpretation of surveillance data. Weekly trends for each wave were described based on the number of case notifications; number of tests performed; proportion of those tests that were positive for the novel coronavirus; the prevalent number of COVID-19 hospitalizations (total hospitalizations and those categorized as severe); and number of COVID-19 deaths. For each indicator and wave, we recorded the first calendar week to show an increase over two consecutive previous weeks, along with the peak week.

Results

The spring wave was characterized by detection of cases imported from China, followed by notifications of sporadic cases without travel history, clusters, and mild/asymptomatic cases. The summer wave saw a large increase in notifications and a younger age distribution, but in the context of increased testing with lower test positivity. The winter wave brought considerable morbidity and mortality, surpassing the cumulative case counts and fatalities from the earlier waves, with high peak values. Overall, relative to the first wave, the burden of severe outcomes was lower in the second and higher in the third wave, but varied by prefecture. In all three waves, severe outcomes peaked after notification counts and test positivity peaked; severe outcomes were also consistently skewed toward the elderly.

Conclusions

Important lessons were learned from each wave and across waves-some aspects remained constant, while others changed over time. In order to rapidly detect an increase in incidence, continuous, timely, and sensitive surveillance-using multiple information sources with careful interpretations-will be key in COVID-19 control.

[Case fatality rate of COVID-19: absence of epidemiological pattern]

Objective

Analyze a set of indicators to understand the variability of the evolution and impact of the COVID-19 epidemic in a set of selected countries.

Method

Ecological study of a group of countries with more than 200 reported cases. Demographic variables, health expenditure variables, and variables about characteristics of health services were included as explanatory variables. and incidence, mortality and fatality rates have been analyzed as response variables. In addition, a relative fatality index has been created. Data are from international organizations. Spearman's correlation coefficient was used to estimate the magnitude of the associations.

Results

Number of tests and of medical professionals are associated with a higher incidence rate. Mortality and case fatality rate are not associated with demographic, health expenditure, or health services variables.

Conclusion

Differences suggest a general underestimation of the magnitude of the epidemic. Improvement of case identification and effectiveness of epidemiological surveillance systems is necessary.

The Spatiotemporal Interaction Effect of COVID-19 Transmission in the United States

(1) Background: Human mobility between geographic units is an important way in which COVID-19 is spread across regions. Due to the pressure of epidemic control and economic recovery, states in the United States have adopted different policies for mobility limitations. Assessing the impact of these policies on the spatiotemporal interaction of COVID-19 transmission among counties in each state is critical to formulating epidemic policies. (2) Methods: We utilized Moran’s I index and K-means clustering to investigate the time-varying spatial autocorrelation effect of 49 states (excluding the District of Colombia) with daily new cases at the county level from 22 January 2020 to 20 August 2020. Based on the dynamic spatial lag model (SLM) and the SIR model with unreported infection rate (SIRu), the integrated SLM-SIRu model was constructed to estimate the inter-county spatiotemporal interaction coefficient of daily new cases in each state, which was further explored by Pearson correlation test and stepwise OLS regression with socioeconomic factors. (3) Results: The K-means clustering divided the time-varying spatial autocorrelation curves of the 49 states into four types: continuous increasing, fluctuating increasing, weak positive, and weak negative. The Pearson correlation analysis showed that the spatiotemporal interaction coefficients in each state estimated by SLM-SIRu were significantly positively correlated with the variables of median age, population density, and proportions of international immigrants and highly educated population, but negatively correlated with the birth rate. Further stepwise OLS regression retained only three positive correlated variables: poverty rate, population density, and highly educated population proportion. (4) Conclusions: This result suggests that various state policies in the U.S. have imposed different impacts on COVID-19 transmission among counties. All states should provide more protection and support for the low-income population; high-density populated states need to strengthen regional mobility restrictions; and the highly educated population should reduce unnecessary regional movement and strengthen self-protection.

Significantly Lower Case-fatality Ratio of Coronavirus Disease 2019 (COVID-19) than Severe Acute Respiratory Syndrome (SARS) in Hong Kong-A Territory-Wide Cohort Study

BACKGROUND

The case-fatality ratios (CFR) of coronavirus disease 2019 (COVID-19) and severe acute respiratory syndrome (SARS) appeared to differ substantially. We aimed to compare the CFR and its predictors of COVID-19 and SARS patients using a territory-wide cohort in Hong Kong.

METHODS

This was a territory-wide retrospective cohort study using data captured from all public hospitals in Hong Kong. Laboratory-confirmed COVID-19 and SARS patients were identified. The primary endpoint was a composite endpoint of intensive care unit admission, use of mechanical ventilation, and/or death.

RESULTS

We identified 1013 COVID-19 patients (mean age, 38.4 years; 53.9% male) diagnosed from 23 January to 14 April 2020 and 1670 SARS patients (mean age, 44.4 years; 44.0% male) from March to June 2003. Fifty-five (5.4%) COVID-19 patients and 432 (25.9%) SARS patients had reached the primary endpoint in 30 days. By 30 June 2003, 286 SARS patients had died (CFR, 17.1%). By 7 June 2020, 4 COVID-19 patients had died (CFR, 0.4%). After adjusting for demographic and clinical parameters, COVID-19 was associated with a 71% lower risk of primary endpoint compared with SARS (adjusted hazard ratio, 0.29; 95% confidence interval, .21-.40; P < .0001). Age, diabetes mellitus, and laboratory parameters (high lactate dehydrogenase, high C-reactive protein, and low platelet count) were independent predictors of the primary endpoint in COVID-19 patients, whereas use of antiviral treatments was not associated with primary endpoint.

CONCLUSIONS

The CFR of COVID-19 was 0.4%. Age and diabetes were associated with worse outcomes, whereas antiviral treatments were not.

Preliminary Evidence from a Cross-sectional Study on Epidemiology and Early Transmission Dynamics of COVID-19 in Karnataka State of India

Introduction Coronavirus disease 2019 (COVID-19) is an emerging infection and quickly disseminated around the world. This article studies the epidemiology and early transmission dynamics of COVID-19 in Karnataka, which would be useful for effective epidemic management and policy formulation.

Materials and Methods All COVID-19 cases reported in the state of Karnataka, India, till June 12, 2020, are included in the study. The epidemiology and transmission dynamics of COVID-19 in Karnataka is studied through descriptive statistical analysis.

Results The findings illustrate a gender-, age-, and region-based disparity in the susceptibility and fatality. There appears to be a male preponderance in the susceptibility, but a female preponderance in fatality. It is also found that the adults are more susceptible to the infection, while the elderly have the risk of high fatality. Further, infected individuals in the region with urbanization have a higher risk of fatality than other regions. The study shows that the chances of recovery for females are lower than males, and further, the chances of recovery are positively related to the age of the infected person. The chances of recovery are higher if the infected individual is younger and they diminish if the individual is older. The study also explores that the chances of recovery are affected by the patient’s geographical location. It is also noted that individuals who returned from foreign travel have better chances of recovery than the locally transmitted individuals.

Conclusion Though the risk of susceptibility to COVID-19 infection is equal to all, the burden of getting infected and the burden of fatality is unequally distributed among different demographic categories. To manage the contagious spread of epidemic, to reduce fatality, and to increase the chances of recovery, targeted policy actions are suggested to benefit the vulnerable demographic categories.

Asymptomatic Transmission and the Infection Fatality Risk for COVID-19: Implications for School Reopening

Asymptomatic infection occurs for numerous respiratory viral diseases, including influenza and coronavirus disease 2019 (COVID-19). We seek to clarify confusion in 3 areas: age-specific risks of transmission and/or disease; various definitions for the COVID-19 "mortality rate," each useful for specific purposes; and implications for student return strategies from preschool through university settings.

Modelling COVID-19 contagion: risk assessment and targeted mitigation policies

We use a spatial epidemic model with demographic and geographical heterogeneity to study the regional dynamics of COVID-19 across 133 regions in England. Our model emphasizes the role of variability of regional outcomes and heterogeneity across age groups and geographical locations, and provides a framework for assessing the impact of policies targeted towards subpopulations or regions. We define a concept of efficiency for comparative analysis of epidemic control policies and show targeted mitigation policies based on local monitoring to be more efficient than country-level or non-targeted measures. In particular, our results emphasize the importance of shielding vulnerable subpopulations and show that targeted policies based on local monitoring can considerably lower fatality forecasts and, in many cases, prevent the emergence of second waves which may occur under centralized policies.