Population-Based Model of the Fraction of Incidental COVID-19 Hospitalizations during the Omicron BA.1 Wave in the United States

1. Background: Some reports have suggested that as many as one-half of all hospital inpatients identified as COVID-19-positive during the Omicron BA.1 variant-driven wave were incidental cases admitted primarily for reasons other than their viral infections. To date, however, there are no prospective longitudinal studies of a representative panel of hospitals based on pre-established criteria for determining whether a patient was, in fact, admitted as a result of the disease. 2. Materials and Methods: To fill this gap, we developed a formula to estimate the fraction of incidental COVID-19 hospitalizations that relies on measurable, population-based parameters. We applied our approach to a longitudinal panel of 164 counties throughout the United States, covering a 4-week interval ending in the first week of January 2022. 3. Results: Within this panel, we estimated that COVID-19 incidence was rising exponentially at a rate of 9.34% per day (95% CI, 8.93–9.87). Assuming that only one-quarter of all Omicron BA.1 infections had been reported by public authorities, we further estimated the aggregate prevalence of active SARS-CoV-2 infection during the first week of January to be 3.45%. During the same week, among 250 high-COVID-volume hospitals within our 164-county panel, an estimated one in four inpatients was COVID-positive. Based upon these estimates, we computed that 10.6% of such COVID-19-positive hospitalized patients were incidental infections. Across individual counties, the median fraction of incidental COVID-19 hospitalizations was 9.5%, with an interquartile range of 6.7 to 12.7%. 4. Conclusion: Incidental COVID-19 infections appear to have been a nontrivial fraction of all COVID-19-positive hospitalized patients during the Omicron BA.1 wave. In the aggregate, however, the burden of patients admitted for complications of their viral infections was far greater. [ FROM AUTHOR] Copyright of COVID is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

A Bayesian Hierarchical Spatial Model to Correct for Misreporting in Count Data: Application to State-Level COVID-19 Data in the United States.

The COVID-19 pandemic that began at the end of 2019 has caused hundreds of millions of infections and millions of deaths worldwide. COVID-19 posed a threat to human health and profoundly impacted the global economy and people's lifestyles. The United States is one of the countries severely affected by the disease. Evidence shows that the spread of COVID-19 was significantly underestimated in the early stages, which prevented governments from adopting effective interventions promptly to curb the spread of the disease. This paper adopts a Bayesian hierarchical model to study the under-reporting of COVID-19 at the state level in the United States as of the end of April 2020. The model examines the effects of different covariates on the under-reporting and accurate incidence rates and considers spatial dependency. In addition to under-reporting (false negatives), we also explore the impact of over-reporting (false positives). Adjusting for misclassification requires adding additional parameters that are not directly identified by the observed data. Informative priors are required. We discuss prior elicitation and include R functions that convert expert information into the appropriate prior distribution.

Exploring the impact of under-reported cases on the COVID-19 spatiotemporal distributions using healthcare workers infection data.

A timely understanding of the spatiotemporal pattern and development trend of COVID-19 is critical for timely prevention and control. However, the under-reporting of casesis widespread in fields associated with public health. It is also possible to draw biased inferences and formulate inappropriate prevention and control policies if the phenomenon of under-reporting is not taken into account. Therefore, in this paper, a novel framework was proposed to explore the impact of under-reporting on COVID-19 spatiotemporal distributions, and empirical analysis was carried out using infection data of healthcare workers in Wuhan and Hubei (excluding Wuhan). The results show that (1) the lognormal distribution was the most suitable to describe the evolution of epidemic with time; (2) the estimated peak infection time of the reported cases lagged the peak infection time of the healthcare worker cases, and the estimated infection time interval of the reported cases was smaller than that of the healthcare worker cases. (3) The impact of under-reporting cases on the early stages of the pandemic was greater than that on its later stages, and the impact on the early onset area was greater than that on the late onset area. (4) Although the number of reported cases was lower than the actual number of cases, a high spatial correlation existed between the cumulatively reported cases and healthcare worker cases. The proposed framework of this study is highly extensible, and relevant researchers can use data sources from other counties to carry out similar research.

Under-reporting of COVID-19 in the Northern Health Authority region of British Columbia.

Asymptomatic and pauci-symptomatic presentations of COVID-19 along with restrictive testing protocols result in undetected COVID-19 cases. Estimating undetected cases is crucial to understanding the true severity of the outbreak. We introduce a new hierarchical disease dynamics model based on the N-mixtures hidden population framework. The new models make use of three sets of disease count data per region: reported cases, recoveries and deaths. Treating the first two as under-counted through binomial thinning, we model the true population state at each time point by partitioning the diseased population into the active, recovered and died categories. Both domestic spread and imported cases are considered. These models are applied to estimate the level of under-reporting of COVID-19 in the Northern Health Authority region of British Columbia, Canada, during 30 weeks of the provincial recovery plan. Parameter covariates are easily implemented and used to improve model estimates. We compare two distinct methods of model-fitting for this case study: (1) maximum likelihood estimation, and (2) Bayesian Markov chain Monte Carlo. The two methods agreed exactly in their estimates of under-reporting rate. When accounting for changes in weekly testing volumes, we found under-reporting rates varying from 60.2% to 84.2%.

Le recours à des protocoles de tests restrictifs et l'existence de formes asymptomatiques et paucisymptomatiques de la COVID­19 contribuent à la non détection de cas COVID­19. Pour comprendre la véritable gravité de l'épidémie, il est primordial d'estimer correctement le nombre de cas non détectés. A cette fin, les auteurs de ce travail proposent un nouveau modèle hiérarchique des dynamiques de la maladie basé sur l'approche de N­mélanges de population cachée. Ces modèles utilisent trois types de données régionales, à savoir, les nombres de cas déclarés, guéris et décédés. En faisant appel à l'amincissement binomial (binomial thinning) et en traitant les nombres de cas déclarés et guéris comme étant sous­évalués, les auteurs proposent une modélisation de l'état réel de l'épidémie basée sur une partition de la population malade en trois catégories : cas actifs, cas guéris et cas décédés. Cette partition tient compte des cas de propagation intérieure et des cas importés. Les auteurs ont utilisé les données recueillies durant les trente semaines du plan de rétablissement provincial de la région de l'Autorité sanitaire du Nord de la Colombie­Britannique, Canada pour illustrer leur approche et estimer le niveau de sous­déclaration COVID­19 associé. Des covariables peuvent être facilement incorporées au modèle proposé et améliorer la qualité des estimations. Deux méthodes d'ajustement sont retenues: (1) l'estimation par maximum de vraisemblance, et (2) la méthode de Monte Carlo par chaînes de Markov. Les estimations du taux de sous­déclaration obtenues par ces deux méthodes concordent exactement et varient entre 60,2% et 84,2% après ajustement des variations des volumes de tests hebdomadaires.

Lessons for preparedness and reasons for concern from the early COVID-19 epidemic in Iran.

INTRODUCTION: Many countries with an early outbreak of SARS-CoV-2 struggled to gauge the size and start date of the epidemic mainly due to limited testing capacities and a large proportion of undetected asymptomatic and mild infections. Iran was among the first countries with a major outbreak outside China. METHODS: We constructed a globally representative sample of 802 genomes, including 46 samples from patients inside or with a travel history to Iran. We then performed a phylogenetic analysis to identify clades related to samples from Iran and estimated the start of the epidemic and early doubling times in cases. We leveraged air travel data from 36 exported cases of COVID-19 to estimate the point-prevalence and the basic reproductive number across the country. We also analysed the province-level all-cause mortality data during winter and spring 2020 to estimate under-reporting of COVID-19-related deaths. Finally, we use this information in an SEIR model to reconstruct the early outbreak dynamics and assess the effectiveness of intervention measures in Iran. RESULTS: By identifying the most basal clade that contained genomes from Iran, our phylogenetic analysis showed that the age of the root is placed on 2019-12-21 (95 % HPD: 2019-09-07 - 2020-02-14). This date coincides with our estimated epidemic start date on 2019-12-25 (95 %CI: 2019-12-11 - 2020-02-24) based air travel data from exported cases with an early doubling time of 4.0 (95 %CI: 1.4-6.7) days in cases. Our analysis of all-cause mortality showed 21.9 (95 % CI: 16.7-27.2) thousand excess deaths by the end of summer. Our model forecasted the second epidemic peak and suggested that by 2020-08-31 a total of 15.0 (95 %CI: 4.9-25.0) million individuals recovered from the disease across the country. CONCLUSION: These findings have profound implications for assessing the stage of the epidemic in Iran despite significant levels of under-reporting. Moreover, the results shed light on the dynamics of SARS-CoV-2 transmissions in Iran and central Asia. They also suggest that in the absence of border screening, there is a high risk of introduction from travellers from areas with active outbreaks. Finally, they show both that well-informed epidemic models are able to forecast episodes of resurgence following a relaxation of interventions, and that NPIs are key to controlling ongoing epidemics.

Under-Reporting of COVID-19 Cases Among Indigenous Peoples in Brazil: A New Expression of Old Inequalities.

Objective: To estimate the incidence, mortality and lethality rates of COVID-19 among Indigenous Peoples in the Brazilian Amazon. Additionally, to analyze how external threats can contribute to spread the disease in Indigenous Lands (IL). Methods: The Brazilian Amazon is home to nearly half a million Indigenous persons, representing more than 170 ethnic groups. As a pioneer in heading Indigenous community-based surveillance (I-CBS) in Brazil, the Coordination of the Indigenous Organizations of the Brazilian Amazon (COIAB) started to monitor Indigenous COVID-19 cases in March of 2020. Brazil's Ministry of Health (MOH) was the main source of data regarding non-Indigenous cases and deaths; to contrast the government's tally, we used the information collected by I-CBS covering 25 Special Indigenous Sanitary Districts (DSEI) in the Brazilian Amazon. The incidence and mortality rates of COVID-19 were calculated using the total number of new cases and deaths accumulated between the 9th and 40th epidemiological weeks. We studied (a) the availability of health care facilities to attend to Indigenous Peoples; (b) illegal mines, land grabbing, and deforestation to perform a geospatial analysis to assess how external threats affect Indigenous incidence and mortality rates. We used the Generalized Linear Model (GLM) with Poisson regression to show the results. Results: MOH registered 22,127 cases and 330 deaths, while COIAB's survey recorded 25,356 confirmed cases and 670 deaths, indicating an under-reporting of 14 and 103%, respectively. Likewise, the incidence and mortality rates were 136 and 110% higher among Indigenous when compared with the national average. In terms of mortality, the most critical DSEIs were Alto Rio Solimões, Cuiabá, Xavante, Vilhena and Kaiapó do Pará. The GLM model reveals a direct correlation between deforestation, land grabbing and mining, and the incidence of cases among the Indigenous. Conclusion: Through this investigation it was possible to verify that not only the incidence and mortality rates due to COVID-19 among Indigenous Peoples are higher than those observed in the general population, but also that the data presented by the federal government are underreported. Additionally, it was evident that the presence of illegal economic activities increased the risk of spreading COVID-19 in ILs.

Estimation of COVID-19 Under-Reporting in the Brazilian States Through SARI.

Due to its impact, COVID-19 has been stressing the academy to search for curing, mitigating, or controlling it. It is believed that under-reporting is a relevant factor in determining the actual mortality rate and, if not considered, can cause significant misinformation. Therefore, this work aims to estimate the under-reporting of cases and deaths of COVID-19 in Brazilian states using data from the InfoGripe. InfoGripe targets notifications of Severe Acute Respiratory Infection (SARI). The methodology is based on the combination of data analytics (event detection methods) and time series modeling (inertia and novelty concepts) over hospitalized SARI cases. The estimate of real cases of the disease, called novelty, is calculated by comparing the difference in SARI cases in 2020 (after COVID-19) with the total expected cases in recent years (2016-2019). The expected cases are derived from a seasonal exponential moving average. The results show that under-reporting rates vary significantly between states and that there are no general patterns for states in the same region in Brazil. The states of Minas Gerais and Mato Grosso have the highest rates of under-reporting of cases. The rate of under-reporting of deaths is high in the Rio Grande do Sul and the Minas Gerais. This work can be highlighted for the combination of data analytics and time series modeling. Our calculation of under-reporting rates based on SARI is conservative and better characterized by deaths than for cases.

Reconstructing the early global dynamics of under-ascertained COVID-19 cases and infections.

BACKGROUND: Asymptomatic or subclinical SARS-CoV-2 infections are often unreported, which means that confirmed case counts may not accurately reflect underlying epidemic dynamics. Understanding the level of ascertainment (the ratio of confirmed symptomatic cases to the true number of symptomatic individuals) and undetected epidemic progression is crucial to informing COVID-19 response planning, including the introduction and relaxation of control measures. Estimating case ascertainment over time allows for accurate estimates of specific outcomes such as seroprevalence, which is essential for planning control measures. METHODS: Using reported data on COVID-19 cases and fatalities globally, we estimated the proportion of symptomatic cases (i.e. any person with any of fever ≥ 37.5 °C, cough, shortness of breath, sudden onset of anosmia, ageusia or dysgeusia illness) that were reported in 210 countries and territories, given those countries had experienced more than ten deaths. We used published estimates of the baseline case fatality ratio (CFR), which was adjusted for delays and under-ascertainment, then calculated the ratio of this baseline CFR to an estimated local delay-adjusted CFR to estimate the level of under-ascertainment in a particular location. We then fit a Bayesian Gaussian process model to estimate the temporal pattern of under-ascertainment. RESULTS: Based on reported cases and deaths, we estimated that, during March 2020, the median percentage of symptomatic cases detected across the 84 countries which experienced more than ten deaths ranged from 2.4% (Bangladesh) to 100% (Chile). Across the ten countries with the highest number of total confirmed cases as of 6 July 2020, we estimated that the peak number of symptomatic cases ranged from 1.4 times (Chile) to 18 times (France) larger than reported. Comparing our model with national and regional seroprevalence data where available, we find that our estimates are consistent with observed values. Finally, we estimated seroprevalence for each country. As of 7 June, our seroprevalence estimates range from 0% (many countries) to 13% (95% CrI 5.6-24%) (Belgium). CONCLUSIONS: We found substantial under-ascertainment of symptomatic cases, particularly at the peak of the first wave of the SARS-CoV-2 pandemic, in many countries. Reported case counts will therefore likely underestimate the rate of outbreak growth initially and underestimate the decline in the later stages of an epidemic. Although there was considerable under-reporting in many locations, our estimates were consistent with emerging serological data, suggesting that the proportion of each country's population infected with SARS-CoV-2 worldwide is generally low.

Bayesian modeling of COVID-19 cases with a correction to account for under-reported cases.

The novel of COVID-19 disease started in late 2019 making the worldwide governments came across a high number of critical and death cases, beyond constant fear of the collapse in their health systems. Since the beginning of the pandemic, researchers and authorities are mainly concerned with carrying out quantitative studies (modeling and predictions) overcoming the scarcity of tests that lead us to under-reporting cases. To address these issues, we introduce a Bayesian approach to the SIR model with correction for under-reporting in the analysis of COVID-19 cases in Brazil. The proposed model was enforced to obtain estimates of important quantities such as the reproductive rate and the average infection period, along with the more likely date when the pandemic peak may occur. Several under-reporting scenarios were considered in the simulation study, showing how impacting is the lack of information in the modeling.

Real-time naive case fatality rates can reflect timeliness of case confirmation during pandemics.

An objective law was observed that naive case fatality rates (CFRs) of a disease will decrease early and then gradually increase infinitely near the true CFR as time went on during an outbreak. The normal growth of naive CFR was an inherent character rather than indicating the disease was becoming more severe. According to the law, by monitoring real-time naive CFRs, it can help outbreak-controllers know if there were many cases left unconfirmed or undiscovered in the outbreak. We reflected on the use of the naive CFR in the context of COVID-19 outbreaks. The results showed that Hubei Province of China, France and South Korea had cases that were not confirmed in a timely manner during the initial stages of the outbreak. Delayed case confirmations existed for long periods of time in France, Italy, the United Kingdom, the Netherlands and Spain. Monitoring of real-time naive CFRs could be helpful for decision-makers to identify under-reporting of cases during pandemics.

Under-reporting of COVID-19 cases in Turkey.

Having an accurate account of the number of national COVID-19 cases is essential for understanding the national and global burden of the disease and managing COVID-19 prevention and control efforts. There is also substantial under-reporting of COVID-19 cases and deaths in many countries. In this article, the COVID-19 under-reporting problem in Turkey is addressed, and examples and reasons for the under-reporting are discussed.

Economic stress, emotional contagion and safety outcomes: A cross-country study.

BACKGROUND: Economic instability produced by financial crises can increase employment-related (i.e., job insecurity) and income-related (i.e., financial stress) economic stress. While the detrimental impact of job insecurity on safety outcomes has been extensively investigated, no study has examined the concurrent role of financial stress let alone their emotion-related predictors. OBJECTIVE: The present cross-country research sought to identify the simultaneous effects of affective job insecurity and financial stress in predicting employee safety injuries and accidents under-reporting, and to examine the extent to which emotional contagion of positive/negative emotions at work contribute to the level of experienced economic stress. METHODS: We performed multi-group measurement and structural invariance analyses. RESULTS: Data from employees in the US (N = 498) and Italy (N = 366) suggest that financial stress is the primary mediator between emotional contagion and poor safety outcomes. Moreover, greater anger-contagion predicted higher levels of financial strain and job insecurity whereas greater joy-contagion predicted reduced economic stress. CONCLUSIONS: Our findings support the relevance of considering the concurrent role of income-and employment-related stressors as predictors of safety-related outcomes. Theoretical and practical implications for safety are discussed in light of the globally increasing emotional pressure and concerns of income- and employment-related economic stress in today's workplace, particularly given the recent pandemic spread of the coronavirus disease (COVID-19).