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.)

Suicide in late life: A viewpoint.

Suicide in old age represents a sad public health concern. Despite the global decline in rates of suicide and the general amelioration of quality of life and access to health care for older adults, their rates of suicide remain the highest virtually in every part of the world. With the aging of the world population and the growing number of mononuclear families, the risk of an increase in isolation, loneliness and dependency does not appear ungrounded. The Covid-19 pandemic is claiming the life of many older persons and creating unprecedented conditions of distress, particularly for this segment of the population. This article briefly examines the main characteristics of suicidal behavior in late life, including observations deriving from the spread of the Sars-2 coronavirus and possible strategies for prevention.

Leveraging Serosurveillance and Postmortem Surveillance to Quantify the Impact of COVID-19 in Africa.

BACKGROUND: The COVID-19 pandemic has had a devastating impact on global health, the magnitude of which appears to differ intercontinentally: for example, reports suggest 271,900 per million people have been infected in Europe versus 8,800 per million people in Africa. While Africa is the second largest continent by population, its reported COVID-19 cases comprise <3% of global cases. Although social, environmental, and environmental explanations have been proposed to clarify this discrepancy, systematic infection underascertainment may be equally responsible. METHODS: We seek to quantify magnitudes of underascertainment in COVID-19's cumulative incidence in Africa. Using serosurveillance and postmortem surveillance, we constructed multiplicative factors estimating ratios of true infections to reported cases in Africa since March 2020. RESULTS: Multiplicative factors derived from serology data (subset of 12 nations) suggested a range of COVID-19 reporting rates, from 1 in 2 infections reported in Cape Verde (July 2020) to 1 in 3,795 infections reported in Malawi (June 2020). A similar set of multiplicative factors for all nations derived from postmortem data points toward the same conclusion: reported COVID-19 cases are unrepresentative of true infections, suggesting a key reason for low case burden in many African nations is significant underdetection and underreporting. CONCLUSIONS: While estimating COVID-19's exact burden is challenging, the multiplicative factors we present furnish incidence estimates reflecting likely-to-worst-case ranges of infection. Our results stress the need for expansive surveillance to allocate resources in areas experiencing discrepancies between reported cases, projected infections, and deaths.

Workplace Deceptions During the Pandemic: Differences in Conspiracy Beliefs, Psychological Functioning, and Covid-19 Experiences.

The global pandemic has disrupted virtually all countries on health, psychological functioning, and economies, to name a few. Accurate information has also fallen victim to the pandemic, which has been rife with misinformation and conspiracy theories. The current study investigated Covid-19 deceptions related to employment. With complete anonymity via MTurk, 389 participants from the United States rated their likelihood of deception regarding hypothetical four workplace scenarios. The first set of analyses examined differences between high and low risk of deceptions for each scenario based on participants' self-appraisals. The largest differences were found for general conspiracy beliefs and affective disorders, specifically major depression and generalized anxiety. The second set of analyses focused across the workplace scenarios on two operationalized groups with Likely-Deceptive (n = 189) vastly outnumbering Likely-Genuine (n = 55). Personal experiences with Covid-19 dramatically increased deceptions. Testing positive for Covid-19 increased the odds of being in the Likely-Deceptive by twelve-fold. Two discriminant models examined cognitive misbeliefs and psychological functioning. When both were combined, depression and Covid-19 misinformation produced the strongest structure coefficients followed closely by general conspiracy beliefs and generalized anxiety. The far-ranging implications of these findings are discussed.

Epidemiologic Profile of Severe Acute Respiratory Infection in Brazil During the COVID-19 Pandemic: An Epidemiological Study.

Background: The COVID-19 is a significant public health issue, and monitoring confirmed cases and deaths is an essential epidemiologic tool. We evaluated the features in Brazilian hospitalized patients due to severe acute respiratory infection (SARI) during the COVID-19 pandemic in Brazil. We grouped the patients into the following categories: Influenza virus infection (G1), other respiratory viruses' infection (G2), other known etiologic agents (G3), SARS-CoV-2 infection (patients with COVID-19, G4), and undefined etiological agent (G5). Methods: We performed an epidemiological study using data from DataSUS (https://opendatasus.saude.gov.br/) from December 2019 to October 2021. The dataset included Brazilian hospitalized patients due to SARI. We considered the clinical evolution of the patients with SARI during the COVID-19 pandemic according to the SARI patient groups as the outcome. We performed the multivariate statistical analysis using logistic regression, and we adopted an Alpha error of 0.05. Results: A total of 2,740,272 patients were hospitalized due to SARI in Brazil, being the São Paulo state responsible for most of the cases [802,367 (29.3%)]. Most of the patients were male (1,495,416; 54.6%), aged between 25 and 60 years (1,269,398; 46.3%), and were White (1,105,123; 49.8%). A total of 1,577,279 (68.3%) patients recovered from SARI, whereas 701,607 (30.4%) died due to SARI, and 30,551 (1.3%) did not have their deaths related to SARI. A major part of the patients was grouped in G4 (1,817,098; 66.3%) and G5 (896,207; 32.7%). The other groups account for <1% of our sample [G1: 3,474 (0.1%), G2: 16,627 (0.6%), and G3: 6,866 (0.3%)]. The deaths related to SARI were more frequent in G4 (574,887; 34.7%); however, the deaths not related to SARI were more frequent among the patients categorized into the G3 (1,339; 21.3%) and G5 (25,829; 4.1%). In the multivariate analysis, the main predictors to classify the patients in the G5 when compared with G4 or G1-G4 were female sex, younger age, Black race, low educational level, rural place of residence, and the use of antiviral to treat the clinical signs. Furthermore, several features predict the risk of death by SARI, such as older age, race (Black, Indigenous, and multiracial background), low educational level, residence in a flu outbreak region, need for intensive care unit, and need for mechanical ventilatory support. Conclusions: The possible COVID-19 underreporting (G5) might be associated with an enhanced mortality rate, more evident in distinct social groups. In addition, the patients' features are unequal between the patients' groups and can be used to determine the risk of possible COVID-19 underreporting in our population. Patients with a higher risk of death had a different epidemiological profile when compared with patients who recovered from SARI, like older age, Black, Indigenous, and multiracial background races, low educational level, residence in a flu outbreak region, need for intensive care unit and need for mechanical ventilatory support.

Infectious Disease Underreporting Is Predicted by Country-Level Preparedness, Politics, and Pathogen Severity.

Underreporting of infectious diseases is a pervasive challenge in public health that has emerged as a central issue in characterizing the dynamics of the COVID-19 pandemic. Infectious diseases are underreported for a range of reasons, including mild or asymptomatic infections, weak public health infrastructure, and government censorship. In this study, we investigated factors associated with cross-country and cross-pathogen variation in reporting. We performed a literature search to collect estimates of empirical reporting rates, calculated as the number of cases reported divided by the estimated number of true cases. This literature search yielded a dataset of reporting rates for 32 pathogens, representing 52 countries. We combined epidemiological and social science theory to identify factors specific to pathogens, country health systems, and politics that could influence empirical reporting rates. We performed generalized linear regression to test the relationship between the pathogen- and country-specific factors that we hypothesized could influence reporting rates, and the reporting rate estimates that we collected in our literature search. Pathogen- and country-specific factors were predictive of reporting rates. Deadlier pathogens and sexually transmitted diseases were more likely to be reported. Country epidemic preparedness was positively associated with reporting completeness, while countries with high levels of media bias in favor of incumbent governments were less likely to report infectious disease cases. Underreporting is a complex phenomenon that is driven by factors specific to pathogens, country health systems, and politics. In this study, we identified specific and measurable components of these broader factors that influence pathogen- and country-specific reporting rates and used model selection techniques to build a model that can guide efforts to diagnose, characterize, and reduce underreporting. Furthermore, this model can characterize uncertainty and correct for bias in reported infectious disease statistics, particularly when outbreak-specific empirical estimates of underreporting are unavailable. More precise estimates can inform control policies and improve the accuracy of infectious disease models.

COVID-19 Underreporting in Brazil among Patients with Severe Acute Respiratory Syndrome during the Pandemic: An Ecological Study.

Underreporting of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection is a global problem and might hamper Coronavirus Disease (COVID-19) epidemiological control. Taking this into consideration, we estimated possible SARS-CoV-2 infection underreporting in Brazil among patients with severe acute respiratory syndrome (SARS). An ecological study using a descriptive analysis of the SARS report was carried out based on data supplied by the Influenza Epidemiological Surveillance Information (SIVEP)-Flu (in Brazilian Portuguese, Sistema de Vigilância Epidemiológica da Gripe) in the period between January 2015 and March 2021. The number of SARS cases and related deaths after infection by SARS-CoV-2 or Influenzae was described. The estimation of underreporting was evaluated considering the relative increase in the number of cases with undefined etiological agent comparing 2020 to 2015-2019; and descriptive analysis was carried out including data from January-March/2021. In our data, SARS-CoV-2 infection and the presence of SARS with undefined etiological agent were associated with the higher number of cases and deaths from SARS in 2020/2021. SARS upsurge was six times over that expected in 2020, according to SARS seasonality in previous years (2015-2019). The lowest possible underdiagnosis rate was observed in the age group < 2 y.o. and individuals over 30 y.o., with ~50%; while in the age groups 10-19 and 20-29 y.o., the rates were 200-250% and 100%, respectively. For the remaining age groups (2-5 and 5-9 y.o.) underreporting was over 550%, except for female individuals in the age group 2-5 y.o., in which a ~500% rate was found. Our study described that the SARS-CoV-2 infection underreporting rate in Brazil in SARS patients is alarming and presents different indices, mainly associated with the patients' age groups. Our results, mainly the underreporting index according to sex and age, should be evaluated with caution.

Development, validation and evaluation of learning activities to support the reporting of adverse drug reactions during the COVID-19 pandemic

Background: The fast tracking of the production of COVID-19 vaccines gave rise to aspects of general concern regarding their safety. The vigilance aspect of adverse drug reaction (ADR) reporting is a means to build up the science behind the safety aspects. The aim was to develop, validate and apply learning activities for healthcare professionals (HCPs) to educate and support them on ADR reporting. Methods: Two educational webinars were developed, validated, applied and evaluated by pharmacists, medical doctors, dentists and nurses. Results: Evaluation forms about the webinars were completed by 103 out of 132 HCPs (first webinar), and 73 out of 90 HCPs (second webinar). Conclusion: HCPs agreed that the educational webinars made them more aware of the importance of ADR reporting and the webinars helped them overcome barriers to ADR reporting.

Results Reporting and Early Termination of Childhood Obesity Trials Registered on ClinicalTrials.gov.

Objective: Childhood obesity is one of the most severe challenges of public health in the twenty-first century and may increase the risk of various physical and psychological diseases in adulthood. The prevalence and predictors of unreported results and premature termination in pediatric obesity research are not clear. We aimed to characterize childhood obesity trials registered on ClinicalTrials.gov and identify features associated with early termination and lack of results reporting. Methods: Records were downloaded and screened for all childhood obesity trials from the inception of ClinicalTrials.gov to July 29, 2021. We performed descriptive analyses of characteristics, Cox regression for early termination, and logistic regression for lack of results reporting. Results: We identified 1,312 trials registered at ClinicalTrials.gov. Among clinicalTrials.gov registered childhood obesity-related intervention trials, trial unreported results were 88.5 and 4.3% of trials were prematurely terminated. Additionally, the factors that reduced the risk of unreported outcomes were US-registered clinical studies and drug intervention trials. Factors associated with a reduced risk of early termination are National Institutes of Health (NIH) or other federal agency funding and large trials. Conclusion: The problem of unreported results in clinical trials of childhood obesity is serious. Therefore, timely bulletin of the results and reasons for termination remain urgent aims for childhood obesity trials.

Save lives or save livelihoods? A cross-country analysis of COVID-19 pandemic and economic growth.

This paper studies whether containing COVID-19 pandemic by stringent strategies deteriorates or saves economic growth. Since there are country-specific factors that could affect both economic growth and deaths due to COVID-19, we first start with a cross-country analysis on identifying risk and protective factors on the COVID-19 deaths using large across-country variation. Using data on 100 countries from 3 January to 27 November 2020 and taking into account the possibility of underreporting, we find that for deaths per million population, GDP per capita, population density, and income inequality are the three most important risk factors; government effectiveness, temperature, and hospital beds are the three most important protective factors. Second, inspired by the stochastic frontier literature, we construct a measure of pandemic containment effectiveness (PCE) after controlling for country-specific factors and rank countries by their PCE scores for deaths. Finally, by linking the PCE score with GDP growth data in Quarters 2 and 3 of 2020, we find that PCE is positively associated with economic growth in major economies. Countries with average PCE scores, such as Malaysia, would gain more GDP growth by 3.47 percentage points if they could improve their PCE scores for deaths to South Korea's level in Q2 of 2020. Therefore, there is not a trade-off between lives and livelihood facing by governments. Instead, to save economy, it is important to contain the pandemic first. Our conclusion is also mainly valid for infections due to COVID-19.

A Typology of COVID-19 Data Gaps and Noise From Long-Term Care Facilities: Approximating the True Numbers.

Although there is agreement that COVID-19 has had devastating impacts in long-term care facilities (LTCFs), estimates of cases and deaths have varied widely with little attention to the causes of this variation. We developed a typology of data vulnerabilities and a strategy for approximating the true total of COVID-19 cases and deaths in LTCFs. Based on iterative qualitative consensus, we categorized LTCF reporting vulnerabilities and their potential impacts on accuracy. Concurrently, we compiled one dataset based on LTCF self-reports and one based on confirmatory matching with California's COVID-19 databases, including death certificates. Through March 2021, Alameda County LTCFs reported 6663 COVID-19 cases and 481 deaths. In contrast, our confirmatory matching file includes 5010 cases and 594 deaths, corresponding to 25% fewer cases but 23% more deaths. We argue that the higher (self-report) case total approximates the lower bound of true COVID-19 cases, and the higher (confirmed match) death total approximates the lower bound of true COVID-19 deaths, both of which are higher than state and federal counts. LTCFs other than nursing facilities accounted for 35% of cases and 29% of deaths. Improving the accuracy of COVID-19 figures, particularly across types of LTCFs, would better inform interventions for these vulnerable populations.

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.

COVID-19: Regional Differences in Austria.

In the turbulent year 2020, overshadowed by the global COVID-19 pandemic, Austria experienced multiple waves of increased case incidence. While governmental measures to curb the numbers were based on current knowledge of infection risk factors, a retrospective analysis of incidence and lethality at the district level revealed correlations of relative infection risk with socioeconomic, geographical, and behavioral population parameters. We identified unexpected correlations between political orientation and smoking behavior and COVID-19 infection risk and/or mortality. For example, a decrease in daily smokers by 2.3 percentage points would be associated with an increase in cumulative incidence by 10% in the adjusted model, and an increase in voters of the right-wing populist party by 1.6 percentage points with an increase in cumulative mortality by 10%. While these parameters are apparently only single elements of complex causal chains that finally lead to individual susceptibility and vulnerability levels, our findings might have identified ecological parameters that can be utilized to develop fine-tuned communications and measures in upcoming challenges of this and other pandemics.

High seroprevalence of SARS-CoV-2 but low infection fatality ratio eight months after introduction in Nairobi, Kenya.

BACKGROUND: The lower than expected COVID-19 morbidity and mortality in Africa has been attributed to multiple factors, including weak surveillance. This study estimated the burden of SARS-CoV-2 infections eight months into the epidemic in Nairobi, Kenya. METHODS: A population-based, cross-sectional survey was conducted using multi-stage random sampling to select households within Nairobi in November 2020. Sera from consenting household members were tested for antibodies to SARS-CoV-2. Seroprevalence was estimated after adjusting for population structure and test performance. Infection fatality ratios (IFRs) were calculated by comparing study estimates with reported cases and deaths. RESULTS: Among 1,164 individuals, the adjusted seroprevalence was 34.7% (95% CI 31.8-37.6). Half of the enrolled households had at least one positive participant. Seropositivity increased in more densely populated areas (spearman's r=0.63; p=0.009). Individuals aged 20-59 years had at least two-fold higher seropositivity than those aged 0-9 years. The IFR was 40 per 100,000 infections, with individuals ≥60 years old having higher IFRs. CONCLUSION: Over one-third of Nairobi residents had been exposed to SARS-CoV-2 by November 2020, indicating extensive transmission. However, the IFR was >10-fold lower than that reported in Europe and the USA, supporting the perceived lower morbidity and mortality in sub-Saharan Africa.

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.

Estimating the wave 1 and wave 2 infection fatality rates from SARS-CoV-2 in India.

OBJECTIVE: There has been much discussion and debate around the underreporting of COVID-19 infections and deaths in India. In this short report we first estimate the underreporting factor for infections from publicly available data released by the Indian Council of Medical Research on reported number of cases and national seroprevalence surveys. We then use a compartmental epidemiologic model to estimate the undetected number of infections and deaths, yielding estimates of the corresponding underreporting factors. We compare the serosurvey based ad hoc estimate of the infection fatality rate (IFR) with the model-based estimate. Since the first and second waves in India are intrinsically different in nature, we carry out this exercise in two periods: the first wave (April 1, 2020-January 31, 2021) and part of the second wave (February 1, 2021-May 15, 2021). The latest national seroprevalence estimate is from January 2021, and thus only relevant to our wave 1 calculations. RESULTS: Both wave 1 and wave 2 estimates qualitatively show that there is a large degree of "covert infections" in India, with model-based estimated underreporting factor for infections as 11.11 (95% credible interval (CrI) 10.71-11.47) and for deaths as 3.56 (95% CrI 3.48-3.64) for wave 1. For wave 2, underreporting factor for infections escalate to 26.77 (95% CrI 24.26-28.81) and to 5.77 (95% CrI 5.34-6.15) for deaths. If we rely on only reported deaths, the IFR estimate is 0.13% for wave 1 and 0.03% for part of wave 2. Taking underreporting of deaths into account, the IFR estimate is 0.46% for wave 1 and 0.18% for wave 2 (till May 15). Combining waves 1 and 2, as of May 15, while India reported a total of nearly 25 million cases and 270 thousand deaths, the estimated number of infections and deaths stand at 491 million (36% of the population) and 1.21 million respectively, yielding an estimated (combined) infection fatality rate of 0.25%. There is considerable variation in these estimates across Indian states. Up to date seroprevalence studies and mortality data are needed to validate these model-based estimates.

COVID-19 underreporting and its impact on vaccination strategies.

BACKGROUND: Underreporting cases of infectious diseases poses a major challenge in the analysis of their epidemiological characteristics and dynamical aspects. Without accurate numerical estimates it is difficult to precisely quantify the proportions of severe and critical cases, as well as the mortality rate. Such estimates can be provided for instance by testing the presence of the virus. However, during an ongoing epidemic, such tests' implementation is a daunting task. This work addresses this issue by presenting a methodology to estimate underreported infections based on approximations of the stable rates of hospitalization and death. METHODS: We present a novel methodology for the stable rate estimation of hospitalization and death related to the Corona Virus Disease 2019 (COVID-19) using publicly available reports from various distinct communities. These rates are then used to estimate underreported infections on the corresponding areas by making use of reported daily hospitalizations and deaths. The impact of underreporting infections on vaccination strategies is estimated under different disease-transmission scenarios using a Susceptible-Exposed-Infective-Removed-like (SEIR) epidemiological model. RESULTS: For the considered locations, during the period of study, the estimations suggest that the number of infected individuals could reach 30% of the population of these places, representing, in some cases, more than six times the observed numbers. These results are in close agreement with estimates from independent seroprevalence studies, thus providing a strong validation of the proposed methodology. Moreover, the presence of large numbers of underreported infections can reduce the perceived impact of vaccination strategies in reducing rates of mortality and hospitalization. CONCLUSIONS: pBy using the proposed methodology and employing a judiciously chosen data analysis implementation, we estimate COVID-19 underreporting from publicly available data. This leads to a powerful way of quantifying underreporting impact on the efficacy of vaccination strategies. As a byproduct, we evaluate the impact of underreporting in the designing of vaccination strategies.

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.