Excess death estimates compared with state-reported and observed COVID-19 deaths, New Jersey and the United States, 2020-2022.

Deaths associated with COVID-19 in the United States are currently estimated to be over 1.2 million, but the true burden of mortality due to the SARS-CoV-2 virus is unknown. Methods for identifying and reporting deaths related to COVID-19 differ between jurisdictions, and concerns about overreporting and underreporting exist. Excess death estimates for the pandemic period, based on data from the National Center for Health Statistics, may be used to approximate the number of COVID-19-associated deaths. In this analysis, we first describe the process by which the New Jersey Department of Health identified, classified, and reported COVID-19-associated deaths from January 2020 through December 2022. The National Center for Health Statistics' excess deaths estimates are first compared with New Jersey's reported COVID-19-associated deaths, and then with the observed COVID-19-associated deaths in the entire United States, by month, from January 2020 through December 2022. New Jersey's reported COVID-19-associated deaths (n = 35,555) accounted for (and slightly exceeded) the state's excess deaths estimated by the National Center for Health Statistics for 2020-2022 (n = 30,365). However, the overall number of United States observed COVID-19 deaths for 2020-2022 (n = 1,094,230) for the study period did not account for all estimated excess deaths in the nation for the same period (n = 1,233,366). The general congruence of New Jersey's reported COVID-19 deaths and the National Center for Health Statistics' excess death estimates may be due in part to New Jersey's early detailed classification system for identifying and reporting deaths associated with COVID-19, leading to more accurate COVID-19 death reporting by the state.

Characterization of hospitalized patients with acute kidney injury associated with COVID-19 in Spain: renal replacement therapy and mortality. FRA-COVID SEN Registry Data.

BACKGROUND: Acute kidney injury (AKI) is common among hospitalized patients with COVID-19 and associated with worse prognosis. The Spanish Society of Nephrology created the AKI- COVID Registry to characterize the population admitted for COVID-19 that developed AKI in Spanish hospitals. The need of renal replacement therapy (RRT) therapeutic modalities, and mortality in these patients were assessed MATERIAL AND METHOD: In a retrospective study, we analyzed data from the AKI-COVID Registry, which included patients hospitalized in 30 Spanish hospitals from May 2020 to November 2021. Clinical and demographic variables, factors related to the severity of COVID-19 and AKI, and survival data were recorded. A multivariate regression analysis was performed to study factors related to RRT and mortality. RESULTS: Data from 730 patients were recorded. A total of 71.9% were men, with a mean age of 70 years (60-78), 70.1% were hypertensive, 32.9% diabetic, 33.3% with cardiovascular disease and 23.9% had some degree of chronic kidney disease (CKD). Pneumonia was diagnosed in 94.6%, requiring ventilatory support in 54.2% and admission to the ICU in 44.1% of cases. The median time from the onset of COVID-19 symptoms to the appearance of AKI (37.1% KDIGO I, 18.3% KDIGO II, 44.6% KDIGO III) was 6 days (4-10). A total of 235 (33.9%) patients required RRT: 155 patients with continuous renal replacement therapy, 89 alternate-day dialysis, 36 daily dialysis, 24 extended hemodialysis and 17 patients with hemodiafiltration. Smoking habit (OR 3.41), ventilatory support (OR 20.2), maximum creatinine value (OR 2.41), and time to AKI onset (OR 1.13) were predictors of the need for RRT; age was a protective factor (0.95). The group without RRT was characterized by older age, less severe AKI, and shorter kidney injury onset and recovery time (p < 0.05). 38.6% of patients died during hospitalization; serious AKI and RRT were more frequent in the death group. In the multivariate analysis, age (OR 1.03), previous chronic kidney disease (OR 2.21), development of pneumonia (OR 2.89), ventilatory support (OR 3.34) and RRT (OR 2.28) were predictors of mortality while chronic treatment with ARBs was identified as a protective factor (OR 0.55). CONCLUSIONS: Patients with AKI during hospitalization for COVID-19 had a high mean age, comorbidities and severe infection. We defined two different clinical patterns: an AKI of early onset, in older patients that resolves in a few days without the need for RRT; and another more severe pattern, with greater need for RRT, and late onset, which was related to greater severity of the infectious disease. The severity of the infection, age and the presence of CKD prior to admission were identified as a risk factors for mortality in these patients. In addition chronic treatment with ARBs was identified as a protective factor for mortality.

Leading Causes of Death in the US, 2019-2023.

This Viewpoint from the National Center for Health Statistics reports the leading causes of death in the US from 2019 to 2023, including the emergence of COVID-19 and shifts in other top causes as pandemic deaths decreased.

A Modeling Study on the Effect of Interstate Mobility Restrictions on the SARS-CoV-2 Pandemic.

Mobility is a crucial element in comprehending the possible expansion of the transmission chain in an epidemic. In the initial phases, strategies for containing cases can be directly linked to population mobility restrictions, especially when only non-pharmaceutical measures are available. During the pandemic of COVID-19 in Brazil, mobility limitation measures were strongly opposed by a large portion of the population. Hypothetically, if the population had supported such measures, the sharp rise in the number of cases could have been suppressed. In this context, computational modeling offers systematic methods for analyzing scenarios about the development of the epidemiological situation taking into account specific conditions. In this study, we examine the impacts of interstate mobility in Brazil. To do so, we develop a metapopulational model that considers both intra and intercompartmental dynamics, utilizing graph theory. We use a parameter estimation technique that allows us to infer the effective reproduction number in each state and estimate the time-varying transmission rate. This makes it possible to investigate scenarios related to mobility and quantify the effect of people moving between states and how certain measures to limit movement might reduce the impact of the pandemic. Our results demonstrate a clear association between the number of cases and mobility, which is heightened when states are closer to each other. This serves as a proof of concept and shows how reducing mobility in more heavily trafficked areas can be more effective.

COVID-19 and Mental Health: A "Pandemic Within a Pandemic".

The COVID-19 pandemic has brought significant changes in daily life for humanity and has had a profound impact on mental health. As widely acknowledged, the pandemic has led to notable increases in rates of anxiety, depression, distress, and other mental health-related issues, affecting both infected patients and non-infected individuals. COVID-19 patients and survivors face heightened risks for various neurological and psychiatric disorders and complications. Vulnerable populations, including those with pre-existing mental health conditions and individuals living in poverty or frailty, may encounter additional challenges. Tragically, suicide rates have also risen, particularly among young people, due to factors such as unemployment, financial crises, domestic violence, substance abuse, and social isolation. Efforts are underway to address these mental health issues, with healthcare professionals urged to regularly screen both COVID-19 and post-COVID-19 patients and survivors for psychological distress, ensuring rapid and appropriate interventions. Ongoing periodic follow-up and multidimensional, interdisciplinary approaches are essential for individuals experiencing long-term psychiatric sequelae. Preventive strategies must be developed to mitigate mental health problems during both the acute and recovery phases of COVID-19 infection. Vaccination efforts continue to prioritize vulnerable populations, including those with mental health conditions, to prevent future complications. Given the profound implications of mental health problems, including shorter life expectancy, diminished quality of life, heightened distress among caregivers, and substantial economic burden, it is imperative that political and health authorities prioritize the mental well-being of all individuals affected by COVID-19, including infected individuals, non-infected individuals, survivors, and caregivers.

Global trends in COVID-19 incidence and case fatality rates (2019-2023): a retrospective analysis.

Objectives: Analyzing and comparing COVID-19 infection and case-fatality rates across different regions can help improve our response to future pandemics. Methods: We used public data from the WHO to calculate and compare the COVID-19 infection and case-fatality rates in different continents and income levels from 2019 to 2023. Results: The Global prevalence of COVID-19 increased from 0.011 to 0.098, while case fatality rates declined from 0.024 to 0.009. Europe reported the highest cumulative infection rate (0.326), with Africa showing the lowest (0.011). Conversely, Africa experienced the highest cumulative case fatality rates (0.020), with Oceania the lowest (0.002). Infection rates in Asia showed a steady increase in contrast to other continents which observed initial rises followed by decreases. A correlation between economic status and infection rates was identified; high-income countries had the highest cumulative infection rate (0.353) and lowest case fatality rate (0.006). Low-income countries showed low cumulative infection rates (0.006) but the highest case fatality rate (0.016). Initially, high and upper-middle-income countries experienced elevated initial infection and case fatality rates, which subsequently underwent significant reductions. Conclusions: COVID-19 rates varied significantly by continent and income level. Europe and the Americas faced surges in infections and low case fatality rates. In contrast, Africa experienced low infection rates and higher case fatality rates, with lower- and middle-income nations exceeding case fatality rates in high-income countries over time.

High-income ZIP codes in New York City demonstrate higher case rates during off-peak COVID-19 waves.

Introduction: Our study explores how New York City (NYC) communities of various socioeconomic strata were uniquely impacted by the COVID-19 pandemic. Methods: New York City ZIP codes were stratified into three bins by median income: high-income, middle-income, and low-income. Case, hospitalization, and death rates obtained from NYCHealth were compared for the period between March 2020 and April 2022. Results: COVID-19 transmission rates among high-income populations during off-peak waves were higher than transmission rates among low-income populations. Hospitalization rates among low-income populations were higher during off-peak waves despite a lower transmission rate. Death rates during both off-peak and peak waves were higher for low-income ZIP codes. Discussion: This study presents evidence that while high-income areas had higher transmission rates during off-peak periods, low-income areas suffered greater adverse outcomes in terms of hospitalization and death rates. The importance of this study is that it focuses on the social inequalities that were amplified by the pandemic.

The influence of cross-border mobility on the COVID-19 epidemic in Nordic countries.

Restrictions of cross-border mobility are typically used to prevent an emerging disease from entering a country in order to slow down its spread. However, such interventions can come with a significant societal cost and should thus be based on careful analysis and quantitative understanding on their effects. To this end, we model the influence of cross-border mobility on the spread of COVID-19 during 2020 in the neighbouring Nordic countries of Denmark, Finland, Norway and Sweden. We investigate the immediate impact of cross-border travel on disease spread and employ counterfactual scenarios to explore the cumulative effects of introducing additional infected individuals into a population during the ongoing epidemic. Our results indicate that the effect of inter-country mobility on epidemic growth is non-negligible essentially when there is sizeable mobility from a high prevalence country or countries to a low prevalence one. Our findings underscore the critical importance of accurate data and models on both epidemic progression and travel patterns in informing decisions related to inter-country mobility restrictions.

Prevalence and genotype distribution of HPV infection from Hangzhou of Zhejiang Province pre- and during COVID-19 pandemic.

Limited data exist on HPV prevalence and genotyping during the COVID-19 pandemic. A total of 130,243 samples from 129, 652 women and 591 men who visited the First People's Hospital of Linping District between 2016 and 2022 were recruited. HPV genotypes were detected by polymerase chain reaction (PCR) amplification and nucleic acid molecular hybridization. Then the prevalence characteristics of HPV genotypes and trends in HPV infection rates from 2016 to 2022 were analyzed. Results showed that among the study population, the overall prevalence of HPV infection was 15.29%, with 11.25% having single HPV infections and 4.04% having multiple HPV infections, consistent with previous findings. HPV genotypes exhibited similar distribution patterns in both male and female groups, with HPV16, HPV52, HPV58, HPV18, and HPV39 being the most prevalent. Age-related analysis unveiled a bimodal pattern in HPV prevalence, with peaks in infection rates observed in individuals below 20 and those aged 61-65 years. Comparing the pre- and during COVID-19 periods revealed significant disparities in HPV infections, with variations in specific HPV genotypes, including 16, 18, 35, 45, 52, 58, 59, and 68. This study provides valuable insights into the prevalence, distribution, and epidemiological characteristics of HPV infections in a large population. It also highlights the potential impact of the COVID-19 pandemic on HPV trends.

Modeling correlated uncertainties in stochastic compartmental models.

We consider compartmental models of communicable disease with uncertain contact rates. Stochastic fluctuations are often added to the contact rate to account for uncertainties. White noise, which is the typical choice for the fluctuations, leads to significant underestimation of the disease severity. Here, starting from reasonable assumptions on the social behavior of individuals, we model the contacts as a Markov process which takes into account the temporal correlations present in human social activities. Consequently, we show that the mean-reverting Ornstein-Uhlenbeck (OU) process is the correct model for the stochastic contact rate. We demonstrate the implication of our model on two examples: a Susceptibles-Infected-Susceptibles (SIS) model and a Susceptibles-Exposed-Infected-Removed (SEIR) model of the COVID-19 pandemic and compare the results to the available US data from the Johns Hopkins University database. In particular, we observe that both compartmental models with white noise uncertainties undergo transitions that lead to the systematic underestimation of the spread of the disease. In contrast, modeling the contact rate with the OU process significantly hinders such unrealistic noise-induced transitions. For the SIS model, we derive its stationary probability density analytically, for both white and correlated noise. This allows us to give a complete description of the model's asymptotic behavior as a function of its bifurcation parameters, i.e., the basic reproduction number, noise intensity, and correlation time. For the SEIR model, where the probability density is not available in closed form, we study the transitions using Monte Carlo simulations. Our modeling approach can be used to quantify uncertain parameters in a broad range of biological systems.

Mathematical Assessment of the Role of Human Behavior Changes on SARS-CoV-2 Transmission Dynamics in the United States.

The COVID-19 pandemic has not only presented a major global public health and socio-economic crisis, but has also significantly impacted human behavior towards adherence (or lack thereof) to public health intervention and mitigation measures implemented in communities worldwide. This study is based on the use of mathematical modeling approaches to assess the extent to which SARS-CoV-2 transmission dynamics is impacted by population-level changes of human behavior due to factors such as (a) the severity of transmission (such as disease-induced mortality and level of symptomatic transmission), (b) fatigue due to the implementation of mitigation interventions measures (e.g., lockdowns) over a long (extended) period of time, (c) social peer-pressure, among others. A novel behavior-epidemiology model, which takes the form of a deterministic system of nonlinear differential equations, is developed and fitted using observed cumulative SARS-CoV-2 mortality data during the first wave in the United States. The model fits the observed data, as well as makes a more accurate prediction of the observed daily SARS-CoV-2 mortality during the first wave (March 2020-June 2020), in comparison to the equivalent model which does not explicitly account for changes in human behavior. This study suggests that, as more newly-infected individuals become asymptomatically-infectious, the overall level of positive behavior change can be expected to significantly decrease (while new cases may rise, particularly if asymptomatic individuals have higher contact rate, in comparison to symptomatic individuals).

Mortalidad en pacientes hospitalizados por COVID-19 durante la primera ola en una institución de tercer nivel de Neuquén / Mortality in Patients Hospitalized for COVID-19 during the First Wave in a Tertiary Care Institution in Neuquén

Introducción: En diciembre de 2019, se detectó un brote de enfermedad por un nuevo coronavirus que evolucionó en pandemia con severa morbilidad respiratoria y mortali- dad. Los sistemas sanitarios debieron enfrentar una cantidad inesperada de pacientes con insuficiencia respiratoria. En Argentina, las medidas de cuarentena y control sani - tario retrasaron el primer pico de la pandemia y ofrecieron tiempo para preparar el sis- tema de salud con infraestructura, personal y protocolos basados en la mejor evidencia disponible en el momento. En una institución de tercer nivel de Neuquén, Argentina, se desarrolló un protocolo de atención para enfrentar la pandemia adaptado con la evo- lución de la mejor evidencia y evaluaciones periódicas de la mortalidad hospitalaria. Métodos: Estudio de cohorte observacional para evaluar la evolución de pacientes con COVID-19 con los protocolos asistenciales por la mortalidad hospitalaria global y al día 28 en la Clínica Pasteur de Neuquén en 2020. Resultados: Este informe describe los 501 pacientes diagnosticados hasta el 31 de di- ciembre de 2020. La mortalidad general fue del 16,6% (83/501) y del 12,2% (61/501) al día 28 de admisión. En los 139 (27,7%) pacientes con ventilación mecánica, la mortali- dad general y a los 28 días fue de 37,4% (52/139) y 28,1% (38/139) fallecieron, respec- tivamente. Los factores de riesgo identificados fueron edad, comorbilidades y altos re- querimientos de oxígeno al ingreso. Conclusión: La mortalidad observada en los pacientes hospitalizados en nuestra insti- tución en la primera ola de la pandemia COVID-19 fue similar a los informes internacio- nales y menor que la publicada en Argentina para el mismo período.

Introduction: In December 2019, an outbreak of disease due to a new coronavirus was detected that evolved into a pandemic with severe respiratory morbidity and mortality. Health systems had to face an unexpected number of patients with respiratory failure. In Argentina, quarantine and health control measures delayed the first peak of the pan - demic and offered time to prepare the health system with infrastructure, personnel and protocols based on the best evidence available at the time. In a third level institution of Neuquén, Argentina, a care protocol was developed to confront the pandemic adapted by evolving best evidence and periodic evaluations of hospital mortality. Methods: Observational cohort study to evaluate the evolution of patients hospitalized for COVID-19 with care protocols in terms of overall hospital mortality and at day 28 at the Pasteur Clinic in Neuquén in 2020. Results: This report describes the 501 patients diagnosed until December 31, 2020. Mortality was 16.6% (83/501) and 12.2% (61/501) on day 28 of admission. Among the 139 (27.7%) patients with mechanical ventilation, overall mortality and at 28 days it was 37.4% (52/139) and 28.1% (38/139), respectively. The risk factors identified were age, comorbidities and high oxygen requirements on admission. Conclusion: The mortality observed in patients hospitalized in our institution during the first wave of COVID-19 pandemic was similar to international reports and lower than other publications in Argentina for the same period.

The impact of health inequity on spatial variation of COVID-19 transmission in England.

Considerable spatial heterogeneity has been observed in COVID-19 transmission across administrative areas of England throughout the pandemic. This study investigates what drives these differences. We constructed a probabilistic case count model for 306 administrative areas of England across 95 weeks, fit using a Bayesian evidence synthesis framework. We incorporate the impact of acquired immunity, of spatial exportation of cases, and 16 spatially-varying socio-economic, socio-demographic, health, and mobility variables. Model comparison assesses the relative contributions of these respective mechanisms. We find that spatially-varying and time-varying differences in week-to-week transmission were definitively associated with differences in: time spent at home, variant-of-concern proportion, and adult social care funding. However, model comparison demonstrates that the impact of these terms is negligible compared to the role of spatial exportation between administrative areas. While these results confirm the impact of some, but not all, static measures of spatially-varying inequity in England, our work corroborates the finding that observed differences in disease transmission during the pandemic were predominantly driven by underlying epidemiological factors rather than aggregated metrics of demography and health inequity between areas. Further work is required to assess how health inequity more broadly contributes to these epidemiological factors.

Real-time forecasting of COVID-19-related hospital strain in France using a non-Markovian mechanistic model.

Projects such as the European Covid-19 Forecast Hub publish forecasts on the national level for new deaths, new cases, and hospital admissions, but not direct measurements of hospital strain like critical care bed occupancy at the sub-national level, which is of particular interest to health professionals for planning purposes. We present a sub-national French framework for forecasting hospital strain based on a non-Markovian compartmental model, its associated online visualisation tool and a retrospective evaluation of the real-time forecasts it provided from January to December 2021 by comparing to three baselines derived from standard statistical forecasting methods (a naive model, auto-regression, and an ensemble of exponential smoothing and ARIMA). In terms of median absolute error for forecasting critical care unit occupancy at the two-week horizon, our model only outperformed the naive baseline for 4 out of 14 geographical units and underperformed compared to the ensemble baseline for 5 of them at the 90% confidence level (n = 38). However, for the same level at the 4 week horizon, our model was never statistically outperformed for any unit despite outperforming the baselines 10 times spanning 7 out of 14 geographical units. This implies modest forecasting utility for longer horizons which may justify the application of non-Markovian compartmental models in the context of hospital-strain surveillance for future pandemics.

Challenges of COVID-19 Case Forecasting in the US, 2020-2021.

During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1-4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naïve baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making.

Markovian Approach for Exploring Competitive Diseases with Heterogeneity-Evidence from COVID-19 and Influenza in China.

Due to the complex interactions between multiple infectious diseases, the spreading of diseases in human bodies can vary when people are exposed to multiple sources of infection at the same time. Typically, there is heterogeneity in individuals' responses to diseases, and the transmission routes of different diseases also vary. Therefore, this paper proposes an SIS disease spreading model with individual heterogeneity and transmission route heterogeneity under the simultaneous action of two competitive infectious diseases. We derive the theoretical epidemic spreading threshold using quenched mean-field theory and perform numerical analysis under the Markovian method. Numerical results confirm the reliability of the theoretical threshold and show the inhibitory effect of the proportion of fully competitive individuals on epidemic spreading. The results also show that the diversity of disease transmission routes promotes disease spreading, and this effect gradually weakens when the epidemic spreading rate is high enough. Finally, we find a negative correlation between the theoretical spreading threshold and the average degree of the network. We demonstrate the practical application of the model by comparing simulation outputs to temporal trends of two competitive infectious diseases, COVID-19 and seasonal influenza in China.

Investigation of epidemiological and clinical characteristics of people infected with SARS-CoV-2 during the second pandemic of COVID-19 in Chengdu, China.

Objective: This study investigated the epidemiological and clinical characteristics of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infected patients during the second pandemic of COVID-19 (coronavirus disease of 2019) in Chengdu, China. Furthermore, the differences between first infection and re-infection cases were also compared and analyzed to provide evidence for better prevention and control of SARS-CoV-2 re-infection. Methods: An anonymous questionnaire survey was conducted using an online platform (wjx.cn) between May 20, 2023 to September 12, 2023. Results: This investigation included 62.94% females and 32.97% of them were 18-30 years old. Furthermore, 7.19-17.18% of the participants either did not receive vaccination at all or only received full vaccination, respectively. Moreover, 577 (57.64%) participants were exposed to cluster infection. The clinical manifestations of these patients were mainly mild to moderate; 78.18% of participants had a fever for 1-3 days, while 37.84% indicated a full course of disease for 4-6 days. In addition, 40.66% of the participants had re-infection and 72.97% indicated their first infection approximately five months before. The clinical symptoms of the first SARS-CoV-2 infection were moderate to severe, while re-infection indicated mild to moderate symptoms (the severity of symptoms other than diarrhea and conjunctival congestion had statistically significant differences) (p < 0.05). Moreover, 70.53 and 59.21% of first and re-infection cases had fever durations of 3-5 and 0-2 days, respectively. Whereas 47.91 and 46.40% of first and re-infection cases had a disease course of 7-9 and 4-6 days. Conclusion: The SARS-CoV-2 infected individuals in Chengdu, China, during the second pandemic of COVID-19 had mild clinical symptoms and a short course of disease. Furthermore, compared with the first infection, re-infection cases had mild symptoms, low incidences of complications, short fever duration, and course of disease.