Estimating the burden of SARS-CoV-2 in France

Endothelial FoxM1 reactivates aging-impaired endothelial regeneration for vascular repair and resolution of inflammatory lung injury

Aging is a major risk factor of high incidence and increased mortality of acute respiratory distress syndrome (ARDS). Here, we demonstrated that persistent lung injury and high mortality in aged mice after sepsis challenge were attributable to impaired endothelial regeneration and vascular repair. Genetic lineage tracing study showed that endothelial regeneration after sepsis-induced vascular injury was mediated by lung resident endothelial proliferation in young adult mice, whereas this intrinsic regenerative program was impaired in aged mice. Expression of forkhead box M1 (FoxM1), an important mediator of endothelial regeneration in young mice, was not induced in lungs of aged mice. Transgenic FOXM1 expression or in vivo endothelium-targeted nanoparticle delivery of the FOXM1 gene driven by an endothelial cell (EC)-specific promoter reactivated endothelial regeneration, normalized vascular repair and resolution of inflammation, and promoted survival in aged mice after sepsis challenge. In addition, treatment with the FDA-approved DNA demethylating agent decitabine was sufficient to reactivate FoxM1-dependent endothelial regeneration in aged mice, reverse aging-impaired resolution of inflammatory injury, and promote survival. Mechanistically, aging-induced Foxm1 promoter hypermethylation in mice, which could be inhibited by decitabine treatment, inhibited Foxm1 induction after sepsis challenge. In COVID-19 lung autopsy samples, FOXM1 was not induced in vascular ECs of elderly patients in their 80s, in contrast with middle-aged patients (aged 50 to 60 years). Thus, reactivation of FoxM1-mediated endothelial regeneration and vascular repair may represent a potential therapy for elderly patients with ARDS.

A data-driven Markov process for infectious disease transmission

The 2019 coronavirus pandemic exudes public health and socio-economic burden globally, raising an unprecedented concern for infectious diseases. Thus, describing the infectious disease transmission process to design effective intervention measures and restrict its spread is a critical scientific issue. We propose a level-dependent Markov model with infinite state space to characterize viral disorders like COVID-19. The levels and states in this model represent the stages of outbreak development and the possible number of infectious disease patients. The transfer of states between levels reflects the explosive transmission process of infectious disease. A simulation method with heterogeneous infection is proposed to solve the model rapidly. After that, simulation experiments were conducted using MATLAB according to the reported data on COVID-19 published by Johns Hopkins. Comparing the simulation results with the actual situation shows that our proposed model can well capture the transmission dynamics of infectious diseases with and without imposed interventions and evaluate the effectiveness of intervention strategies. Further, the influence of model parameters on transmission dynamics is analyzed, which helps to develop reasonable intervention strategies. The proposed approach extends the theoretical study of mathematical modeling of infectious diseases and contributes to developing models that can describe an infinite number of infected persons.

Estimating SARS-CoV-2 infections and associated changes in COVID-19 severity and fatality

Background

The difficulty in identifying SARS-CoV-2 infections has not only been the major obstacle to control the COVID-19 pandemic but also to quantify changes in the proportion of infections resulting in hospitalization, intensive care unit (ICU) admission, or death.

Methods

We developed a model of SARS-CoV-2 transmission and vaccination informed by official estimates of the time-varying reproduction number to estimate infections that occurred in Italy between February 2020 and 2022. Model outcomes were compared with the Italian National surveillance data to estimate changes in the SARS-CoV-2 infection ascertainment ratio (IAR), infection hospitalization ratio (IHR), infection ICU ratio (IIR), and infection fatality ratio (IFR) in five different sub-periods associated with the dominance of the ancestral lineages and Alpha, Delta, and Omicron BA.1 variants.

Results

We estimate that, over the first 2 years of pandemic, the IAR ranged between 15% and 40% (range of 95%CI: 11%-61%), with a peak value in the second half of 2020. The IHR, IIR, and IFR consistently decreased throughout the pandemic with 22-44-fold reductions between the initial phase and the Omicron period. At the end of the study period, we estimate an IHR of 0.24% (95%CI: 0.17-0.36), IIR of 0.015% (95%CI: 0.011-0.023), and IFR of 0.05% (95%CI: 0.04-0.08).

Conclusions

Since 2021, changes in the dominant SARS-CoV-2 variant, vaccination rollout, and the shift of infection to younger ages have reduced SARS-CoV-2 infection ascertainment. The same factors, combined with the improvement of patient management and care, contributed to a massive reduction in the severity and fatality of COVID-19.

Fear itself. Is fear a determinant of the efficacy oflockdowns?

Among non-pharmaceutical measures for fighting the COVID-19 pandemic, one of the most important is the implementation of lockdowns. The cost and effectiveness of this policy remains a much-debated topic in economics. In this study we investigate whether a 'fear effect' is at work in influencing the effectiveness of lockdowns. According to previous contributions on the topic, fear can increase protective habits, and for this reason we may imagine that a high number of COVID-19-caused deaths creates fear among the population, which may make people more likely to follow government prescriptions and observe lockdowns strictly. By means of a qualitative-quantitative analysis, we find that among the 46 countries that reported coronavirus-caused deaths before the implementation of a lockdown, the top quartile for per capita deaths has better results in terms of reducing new COVID-19 cases after a lockdown, compared to the worst quartile. This suggests that the number of reported deaths, as well as its communication to the population, are important determinants of the effectiveness of a lockdown.

Non-Pharmacologic Interventions in COVID-19 Pandemic Management; a Systematic Review

Introduction

Different countries throughout the world have adopted non-pharmacologic interventions to reduce and control SARS - CoV-2. In this systematic approach, the impact of non-pharmacologic interventions in management of COVID-19 pandemic was assessed.

Methods

Following Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, systematic search was carried out on the basis of a search strategy on PubMed, Web of Science, Scopus, and WHO databases on COVID-19. The impact of travel ban, personal protective equipment, distancing, contact tracing, school closure, and social distancing and the combined effect of interventions on COVID-19 were assessed.

Results

Of the 14,857 articles found, 44 were relevant. Studies in different countries have shown that various non-pharmacological interventions have been used during the COVID-19 pandemic. The travel ban, either locally or internationally in most of the countries, movement restriction, social distancing, lockdown, Personal Protective Equipment (PPE), quarantine, school closure, work place closure, and contact tracing had a significant impact on the reduction of mortality or morbidity of COVID-19.

Conclusion

Evidence shows that the implementation of non-pharmacologic interventions (NPIs), for this study suggests that the effectiveness of any NPI alone is probably limited, thus, a combination of various actions, for example, social distancing, isolation, and quarantine, distancing in the workplace and use of personal protective equipment, is more effective in reducing COVID-19.

Using real-time ascertainment rate estimate from infection and hospitalization dataset for modeling the spread of infectious disease: COVID-19 case study in the Czech Republic

We present a novel approach to estimate the time-varying ascertainment rate in almost real-time, based on the surveillance of positively tested infectious and hospital admission data. We also address the age dependence of the estimate. The ascertainment rate estimation is based on the Bayes theorem. It can be easily calculated and used (i) as part of a mechanistic model of the disease spread or (ii) to estimate the unreported infections or changes in their proportion in almost real-time as one of the early-warning signals in case of undetected outbreak emergence. The paper also contains a case study of the COVID-19 epidemic in the Czech Republic. The case study demonstrates the usage of the ascertainment rate estimate in retrospective analysis, epidemic monitoring, explanations of differences between waves, usage in the national Anti-epidemic system, and monitoring of the effectiveness of non-pharmaceutical interventions on Czech nationwide surveillance datasets. The Czech data reveal that the probability of hospitalization due to SARS-CoV-2 infection for the senior population was 12 times higher than for the non-senior population in the monitored period from the beginning of March 2020 to the end of May 2021. In a mechanistic model of COVID-19 spread in the Czech Republic, the ascertainment rate enables us to explain the links between all basic compartments, including new cases, hospitalizations, and deaths.

Psychological benefits of the COVID-19 vaccination: A Bangladeshi comparative study

Background and Aims

Despite evidence that COVID-19 vaccination can strengthen mental health, there is limited evidence about this in Bangladesh. Thus, this comparative study assessed the prevalence and factors associated with mental health problems between vaccine receivers and nonreceivers.

Methods

Using a snowball sampling technique, a web-based cross-sectional study was conducted among a total of 459 participants. The survey questionnaire included sociodemographic information, the Patient Health Questionnaire (PHQ-9), the Generalized Anxiety Disorder (GAD-7), and the Trauma Screening Questionnaire (TSQ-10).

Results

The study found that mental health problems were nonsignificantly prevalent in the vaccine nonreceivers than those who received it (i.e., 24.79% vs. 20.60% for depression, 21.20% vs. 16.60% for anxiety, and 15.30% vs. 12.60% for posttraumatic stress disorder). Female gender, chronic condition, smoking status, and alcohol consumption were the risk factors for mental health problems.

Conclusion

This study's findings suggest that the COVID-19 vaccination necessarily improves mental health outcomes. However, the study had limitations in terms of its design and sampling technique, and further research is needed to establish a cause-effect relationship between vaccination and mental health problems.

Estimating the economic impact of COVID-19 disruption on access to sexual and reproductive health and rights in Eastern and Southern Africa

Background

The Coronavirus disease 2019 (COVID-19) resulted in the disruption of Sexual and Reproductive Health Rights (SRHR) services in the Eastern and Southern Africa region. To date, studies estimating the impact of COVID-19 disruptions have mainly focused on SRHR services without estimating the economic implication.

Method

We used national service coverage data on the effectiveness of interventions from the lives saved tool (LiST), a mathematical modeling tool that estimates the effects of service coverage change in mortality. We computed years lost due to COVID-19 disruption on SRHR using life expectancy at birth, number of years of life lost due to child mortality, and life expectancy at average maternal death. We calculated the economic value of the lives saved, using the values of statistical life year for each of the countries, comparing 2019 (pre-COVID-19) to 2020 (COVID-19 era).

Findings

The total life-years lost were 1,335,663, with 1,056,174 life-years lost attributed to child mortality and 279,249 linked to maternal mortalities, with high case-fatality rates in the Democratic Republic of Congo, Burundi, and Tanzania. The findings show COVID-19 disruptions on SRHR services between 2019 and 2020 resulted in US$ 3.6 billion losses, with the highest losses in Angola (USD 777 million), South Africa (USD 539 million), and Democratic Republic of Congo (USD 361 million).

Conclusion

The monetized value of disability adjusted life years can be used as evidence for advocacy, increased investment, and appropriate mitigation strategies. Countries should strengthen their health systems functionality, incorporating and transforming lessons learned from shock events.

The power of clans: How social capital sheltered firms during the COVID-19 pandemic

Data from 3,555 Chinese listed firms show that firms in cities with greater clan strength faced smaller losses and swifter recovery following COVID-19. Clans were significantly related to individual values facilitating pandemic prevention; these ties guaranteed economic activities and sheltered firms from the shock. Our results frame social capital as a complementarity to formal institutions during crises.

Disproportionate impacts of COVID-19 in a large US city

COVID-19 has disproportionately impacted individuals depending on where they live and work, and based on their race, ethnicity, and socioeconomic status. Studies have documented catastrophic disparities at critical points throughout the pandemic, but have not yet systematically tracked their severity through time. Using anonymized hospitalization data from March 11, 2020 to June 1, 2021 and fine-grain infection hospitalization rates, we estimate the time-varying burden of COVID-19 by age group and ZIP code in Austin, Texas. During this 15-month period, we estimate an overall 23.7% (95% CrI: 22.5-24.8%) infection rate and 29.4% (95% CrI: 28.0-31.0%) case reporting rate. Individuals over 65 were less likely to be infected than younger age groups (11.2% [95% CrI: 10.3-12.0%] vs 25.1% [95% CrI: 23.7-26.4%]), but more likely to be hospitalized (1,965 per 100,000 vs 376 per 100,000) and have their infections reported (53% [95% CrI: 49-57%] vs 28% [95% CrI: 27-30%]). We used a mixed effect poisson regression model to estimate disparities in infection and reporting rates as a function of social vulnerability. We compared ZIP codes ranking in the 75th percentile of vulnerability to those in the 25th percentile, and found that the more vulnerable communities had 2.5 (95% CrI: 2.0-3.0) times the infection rate and only 70% (95% CrI: 60%-82%) the reporting rate compared to the less vulnerable communities. Inequality persisted but declined significantly over the 15-month study period. Our results suggest that further public health efforts are needed to mitigate local COVID-19 disparities and that the CDC's social vulnerability index may serve as a reliable predictor of risk on a local scale when surveillance data are limited.

Forecasting COVID-19 activity in Australia to support pandemic response: May to October 2020

As of January 2021, Australia had effectively controlled local transmission of COVID-19 despite a steady influx of imported cases and several local, but contained, outbreaks in 2020. Throughout 2020, state and territory public health responses were informed by weekly situational reports that included an ensemble forecast of daily COVID-19 cases for each jurisdiction. We present here an analysis of one forecasting model included in this ensemble across the variety of scenarios experienced by each jurisdiction from May to October 2020. We examine how successfully the forecasts characterised future case incidence, subject to variations in data timeliness and completeness, showcase how we adapted these forecasts to support decisions of public health priority in rapidly-evolving situations, evaluate the impact of key model features on forecast skill, and demonstrate how to assess forecast skill in real-time before the ground truth is known. Conditioning the model on the most recent, but incomplete, data improved the forecast skill, emphasising the importance of developing strong quantitative models of surveillance system characteristics, such as ascertainment delay distributions. Forecast skill was highest when there were at least 10 reported cases per day, the circumstances in which authorities were most in need of forecasts to aid in planning and response.

Epidemiological inference for emerging viruses using segregating sites

Epidemiological models are commonly fit to case and pathogen sequence data to estimate parameters and to infer unobserved disease dynamics. Here, we present an inference approach based on sequence data that is well suited for model fitting early on during the expansion of a viral lineage. Our approach relies on a trajectory of segregating sites to infer epidemiological parameters within a Sequential Monte Carlo framework. Using simulated data, we first show that our approach accurately recovers key epidemiological quantities under a single-introduction scenario. We then apply our approach to SARS-CoV-2 sequence data from France, estimating a basic reproduction number of approximately 2.3-2.7 under an epidemiological model that allows for multiple introductions. Our approach presented here indicates that inference approaches that rely on simple population genetic summary statistics can be informative of epidemiological parameters and can be used for reconstructing infectious disease dynamics during the early expansion of a viral lineage.

Big data technology in infectious diseases modeling, simulation, and prediction after the COVID-19 outbreak

After the outbreak of COVID-19, the interaction of infectious disease systems and social systems has challenged traditional infectious disease modeling methods. Starting from the research purpose and data, researchers improved the structure and data of the compartment model or used agents and artificial intelligence based models to solve epidemiological problems. In terms of modeling methods, the researchers use compartment subdivision, dynamic parameters, agent-based model methods, and artificial intelligence related methods. In terms of factors studied, the researchers studied 6 categories: human mobility, nonpharmaceutical interventions (NPIs), ages, medical resources, human response, and vaccine. The researchers completed the study of factors through modeling methods to quantitatively analyze the impact of social systems and put forward their suggestions for the future transmission status of infectious diseases and prevention and control strategies. This review started with a research structure of research purpose, factor, data, model, and conclusion. Focusing on the post-COVID-19 infectious disease prediction simulation research, this study summarized various improvement methods and analyzes matching improvements for various specific research purposes.

UK Research Ethics Committee's review of the global first SARS-CoV-2 human infection challenge studies

This paper describes the UK Research Ethics Committee's (REC) preparations and review of the global first SARS-CoV-2 human infection challenge studies. To frame our review, we used the WHO guidance and our UK Health Research Authority ethical review framework. The WHO criteria covered most issues we were concerned about, but we would recommend one further criterion directing RECs to consider alternative research designs. Could research questions be equally well answered by less intrusive studies? The committee met virtually, ensuring broad representation across the UK nations and also ensuring applicants could attend easily. We worked in collaboration with the applicants but while we recognise that such proximity might raise the accusation of 'collusion', we made every effort to maintain 'moral distance' and all decisions were made by the committee alone. Prior existing processes and policy facilitated training and review but even with this preparation, review took time and this could have hindered a rapid response to the emergency. Review for the various follow-on studies will now be speedier and once the pandemic has subsided, our group could be reconvened in future emergencies. In conclusion, we have tried to make decisions in good faith. We know there is controversy and disagreement and reasonable people may feel we have made the wrong decision. A more detailed analysis, built on the WHO guidance, is provided in online supplemental material.

Place of death in France: impact of the first wave (March-May 2020) of the Covid-19 epidemic

BACKGROUND

The Covid-19 epidemic entailed a major public health issue in France challenging the efficiency of the public health system. The distribution of deaths by place in France may have been affected by the epidemic and mitigation actions. This article presents mortality rate ratios by place of death in France during the first lockdown (17 March - 10 May, 2020) of the Covid-19 epidemic.

METHODS

We considered five places of death recorded in death certificates. Deaths in 2020 were compared to deaths from 2015 to 2019. We employed quasi-Poisson regressions in order to stablish mortality rate ratios (MRR) during the Covid-19 epidemic, for all-cause and non-Covid-19 deaths. Analysis was conducted in Metropolitan France, and for three groups of regions defined according to the intensity of the first COVID-19 epidemic wave.

RESULTS

A significant increase in all-cause and non-COVID-19 mortality at home was observed for all age groups. Also, an increase in mortality was observed in nursing homes, mostly due to Covid-19. Non-covid-19 mortality in public hospitals decreased significantly in all the country. These trends were mainly observed for cancers.

CONCLUSIONS

Overall mortality increased during the first wave of the Covid-19 epidemic. Most Covid-19 deaths took place in public hospitals and nursing homes at old ages. There was a displacement of non-Covid-19 mortality from public hospitals to home and nursing homes, particularly in the most highly exposed area. Among hypotheses to explain such a displacement, population avoidance of hospital care, or redeployment of hospital activity in this emergent context can be cited. Further analysis is needed to understand the reasons of the increase in non-Covid-19 mortality in nursing homes and at home.

Heparin, Low Molecular Weight Heparin, and Non-Anticoagulant Derivatives for the Treatment of Inflammatory Lung Disease

Unfractionated heparin has multiple pharmacological activities beyond anticoagulation. These anti-inflammatory, anti-microbial, and mucoactive activities are shared in part by low molecular weight and non-anticoagulant heparin derivatives. Anti-inflammatory activities include inhibition of chemokine activity and cytokine synthesis, inhibitory effects on the mechanisms of adhesion and diapedesis involved in neutrophil recruitment, inhibition of heparanase activity, inhibition of the proteases of the coagulation and complement cascades, inhibition of neutrophil elastase activity, neutralisation of toxic basic histones, and inhibition of HMGB1 activity. This review considers the potential for heparin and its derivatives to treat inflammatory lung disease, including COVID-19, ALI, ARDS, cystic fibrosis, asthma, and COPD via the inhaled route.

Sex, Age, and Comorbidities Are Associated with SARS-CoV-2 Infection, COVID-19 Severity, and Fatal Outcome in a Mexican Population: A Retrospective Multi-Hospital Study

People with comorbidities and the male sex are at a higher risk of developing severe COVID-19. In the present study, we aim to investigate the associated factors for infection, severity, and death due to COVID-19 in a population from Nuevo León, México. Epidemiological COVID-19 data were collected from 65 hospitals from December 2020 to May 2022. A total of 75,232 cases were compiled from which 25,722 cases were positive for SARS-CoV-2. Male sex, older age, diabetes, obesity, and hypertension were associated with infection. In addition to the above-mentioned factors, renal disease, cardiovascular disease, and immunosuppression were found to be associated with increased COVID-19 severity. These factors, as well as neurological diseases, are also associated with death due to COVID-19. When comparing the different variants of SARs-CoV-2, the variant B1.1.519 increased the probability of death by 2.23 times compared to the AY.20 variant. Male sex, older age, diabetes, obesity, and hypertension are associated with SARS-CoV-2 infection, severity, and death. Along with the aforementioned comorbidities, renal disease, cardiovascular disease, and immunosuppression are also associated with severity and death. Another factor associated with death is the presence of neurological disease. The SARS-CoV-2 B1.1.519 variant increases the odds of death compared to the SARS-CoV-2 AY.20 variant.

Estimating the Under-ascertainment of COVID-19 cases in Toronto, Ontario, March to May 2020

Background

Public health surveillance data do not always capture all cases, due in part to test availability and health care seeking behaviour. Our study aimed to estimate under-ascertainment multipliers for each step in the reporting chain for COVID-19 in Toronto, Canada.

Design and methods

We applied stochastic modeling to estimate these proportions for the period from March 2020 (the beginning of the pandemic) through to May 23, 2020, and for three distinct windows with different laboratory testing criteria within this period.

Results

For each laboratory-confirmed symptomatic case reported to Toronto Public Health during the entire period, the estimated number of COVID-19 infections in the community was 18 (5th and 95th percentile: 12, 29). The factor most associated with under-reporting was the proportion of those who sought care that received a test.

Conclusions

Public health officials should use improved estimates to better understand the burden of COVID-19 and other similar infections.

Retrospective Review of Outcomes of Multiple Myeloma (MM) Patients With COVID-19 Infection (Two-Center Study)

INTRODUCTION

COVID-19 has profound effects on patients with multiple myeloma (MM) mainly due to underlying immune dysfunction and associated therapies leading to increased susceptibility to infections. The overall risk of morbidity and mortality (M&M) in MM patients due to COVID-19 infection is unclear with various studies suggesting case fatality rate of 22% to 29%. Additionally, most of these studies did not stratify patients by their molecular risk profile.

METHODS

Here, we aim to investigate the effects of COVID-19 infection with associated risk factors in MM patients and the effectiveness of newly implemented screening and treatment protocols on outcomes. After obtaining institutional review board approvals from each participating institution, we collected data from MM patients diagnosed with SARS-CoV-2 infection from March 1, 2020, to October 30, 2020, at 2 myeloma centers (Levine Cancer Institute & University of Kansas medical center).

RESULTS

We identified a total of 162 MM patients who had COVID-19 infection. The majority of patients were males (57%) with a median age of 64 years. Most patients had an associated comorbid condition. Their myeloma disease status and prior autologous stem cell transplant at the time of infection had no impact on hospitalization or mortality. In univariate analysis, chronic kidney disease, hepatic dysfunction, diabetes, and hypertension were associated with an increased risk of hospitalization. In multivariate analysis regarding survival, increased age and lymphopenia were associated with increased COVID-19-related mortality.

CONCLUSION

Our study supports the use of infection mitigation measures in all MM patients, and adjustment of treatment pathways in MM patients diagnosed with COVID-19.

Impact of non-pharmaceutical interventions, weather, vaccination, and variants on COVID-19 transmission across departments in France

BACKGROUND

Multiple factors shape the temporal dynamics of the COVID-19 pandemic. Quantifying their relative contributions is key to guide future control strategies. Our objective was to disentangle the individual effects of non-pharmaceutical interventions (NPIs), weather, vaccination, and variants of concern (VOC) on local SARS-CoV-2 transmission.

METHODS

We developed a log-linear model for the weekly reproduction number (R) of hospital admissions in 92 French metropolitan departments. We leveraged (i) the homogeneity in data collection and NPI definitions across departments, (ii) the spatial heterogeneity in the timing of NPIs, and (iii) an extensive observation period (14 months) covering different weather conditions, VOC proportions, and vaccine coverage levels.

FINDINGS

Three lockdowns reduced R by 72.7% (95% CI 71.3-74.1), 70.4% (69.2-71.6) and 60.7% (56.4-64.5), respectively. Curfews implemented at 6/7 pm and 8/9 pm reduced R by 34.3% (27.9-40.2) and 18.9% (12.04-25.3), respectively. School closures reduced R by only 4.9% (2.0-7.8). We estimated that vaccination of the entire population would have reduced R by 71.7% (56.4-81.6), whereas the emergence of VOC (mainly Alpha during the study period) increased transmission by 44.6% (36.1-53.6) compared with the historical variant. Winter weather conditions (lower temperature and absolute humidity) increased R by 42.2% (37.3-47.3) compared to summer weather conditions. Additionally, we explored counterfactual scenarios (absence of VOC or vaccination) to assess their impact on hospital admissions.

INTERPRETATION

Our study demonstrates the strong effectiveness of NPIs and vaccination and quantifies the role of weather while adjusting for other confounders. It highlights the importance of retrospective evaluation of interventions to inform future decision-making.

Optimal timing of non-pharmaceutical interventions during an epidemic

In response to the recent outbreak of the SARS-CoV-2 virus governments have aimed to reduce the virus's spread through, inter alia, non-pharmaceutical intervention. We address the question when such measures should be implemented and, once implemented, when to remove them. These issues are viewed through a real-options lens and we develop an SIRD-like continuous-time Markov chain model to analyze a sequence of options: the option to intervene and introduce measures and, after intervention has started, the option to remove these. Measures can be imposed multiple times. We implement our model using estimates from empirical studies and, under fairly general assumptions, our main conclusions are that: (1) measures should be put in place not long after the first infections occur; (2) if the epidemic is discovered when there are many infected individuals already, then it is optimal never to introduce measures; (3) once the decision to introduce measures has been taken, these should stay in place until the number of susceptible or infected members of the population is close to zero; (4) it is never optimal to introduce a tier system to phase-in measures but it is optimal to use a tier system to phase-out measures; (5) a more infectious variant may reduce the duration of measures being in place; (6) the risk of infections being brought in by travelers should be curbed even when no other measures are in place. These results are robust to several variations of our base-case model.

Sick leave due to COVID-19 during the first pandemic wave in France, 2020

OBJECTIVES

To quantify the burden of COVID-19-related sick leave during the first pandemic wave in France, accounting for sick leaves due to symptomatic COVID-19 ('symptomatic sick leaves') and those due to close contact with COVID-19 cases ('contact sick leaves').

METHODS

We combined data from a national demographic database, an occupational health survey, a social behaviour survey and a dynamic SARS-CoV-2 transmission model. Sick leave incidence from 1 March 2020 to 31 May 2020 was estimated by summing daily probabilities of symptomatic and contact sick leaves, stratified by age and administrative region.

RESULTS

There were an estimated 1.70M COVID-19-related sick leaves among France's 40M working-age adults during the first pandemic wave, including 0.42M due to COVID-19 symptoms and 1.28M due to COVID-19 contacts. There was great geographical variation, with peak daily sick leave incidence ranging from 230 in Corse (Corsica) to 33 000 in Île-de-France (the greater Paris region), and greatest overall burden in regions of north-eastern France. Regional sick leave burden was generally proportional to local COVID-19 prevalence, but age-adjusted employment rates and contact behaviours also contributed. For instance, 37% of symptomatic infections occurred in Île-de-France, but 45% of sick leaves. Middle-aged workers bore disproportionately high sick leave burden, owing predominantly to greater incidence of contact sick leaves.

CONCLUSIONS

France was heavily impacted by sick leave during the first pandemic wave, with COVID-19 contacts accounting for approximately three-quarters of COVID-19-related sick leaves. In the absence of representative sick leave registry data, local demography, employment patterns, epidemiological trends and contact behaviours can be synthesised to quantify sick leave burden and, in turn, predict economic consequences of infectious disease epidemics.

Rapid Assessment of COVID-19 Mortality Risk with GASS Classifiers

Risk prediction models are fundamental to effectively triage incoming COVID-19 patients. However, current triaging methods often have poor predictive performance, are based on variables that are expensive to measure, and often lead to hard-to-interpret decisions. We introduce two new classification methods that can predict COVID-19 mortality risk from the automatic analysis of routine clinical variables with high accuracy and interpretability. SVM22-GASS and Clinical-GASS classifiers leverage machine learning methods and clinical expertise, respectively. Both were developed using a derivation cohort of 499 patients from the first wave of the pandemic and were validated with an independent validation cohort of 250 patients from the second pandemic phase. The Clinical-GASS classifier is a threshold-based classifier that leverages the General Assessment of SARS-CoV-2 Severity (GASS) score, a COVID-19-specific clinical score that recently showed its effectiveness in predicting the COVID-19 mortality risk. The SVM22-GASS model is a binary classifier that non-linearly processes clinical data using a Support Vector Machine (SVM). In this study, we show that SMV22-GASS was able to predict the mortality risk of the validation cohort with an AUC of 0.87 and an accuracy of 0.88, better than most scores previously developed. Similarly, the Clinical-GASS classifier predicted the mortality risk of the validation cohort with an AUC of 0.77 and an accuracy of 0.78, on par with other established and emerging machine-learning-based methods. Our results demonstrate the feasibility of accurate COVID-19 mortality risk prediction using only routine clinical variables, readily collected in the early stages of hospital admission.

Rapid Assessment of COVID-19 Mortality Risk with GASS Classifiers

Risk prediction models are fundamental to effectively triage incoming COVID-19 patients. However, current triaging methods often have poor predictive performance, are based on variables that are expensive to measure, and often lead to hard-to-interpret decisions. We introduce two new classification methods that can predict COVID-19 mortality risk from the automatic analysis of routine clinical variables with high accuracy and interpretability. SVM22-GASS and Clinical-GASS classifiers leverage machine learning methods and clinical expertise, respectively. Both were developed using a derivation cohort of 499 patients from the first wave of the pandemic and were validated with an independent validation cohort of 250 patients from the second pandemic phase. The Clinical-GASS classifier is a threshold-based classifier that leverages the General Assessment of SARS-CoV-2 Severity (GASS) score, a COVID-19-specific clinical score that recently showed its effectiveness in predicting the COVID-19 mortality risk. The SVM22-GASS model is a binary classifier that non-linearly processes clinical data using a Support Vector Machine (SVM). In this study, we show that SMV22-GASS was able to predict the mortality risk of the validation cohort with an AUC of 0.87 and an accuracy of 0.88, better than most scores previously developed. Similarly, the Clinical-GASS classifier predicted the mortality risk of the validation cohort with an AUC of 0.77 and an accuracy of 0.78, on par with other established and emerging machine-learning-based methods. Our results demonstrate the feasibility of accurate COVID-19 mortality risk prediction using only routine clinical variables, readily collected in the early stages of hospital admission.

An update on SARS-CoV-2 immunization and future directions

Millions of people have died as a result of SARS-CoV-2, which was first discovered in China and has since spread globally. Patients with SARS-CoV-2 infection may show a range of symptoms, including fever, coughing, and shortness of breath, or they may show no symptoms at all. To treat COVID-19 symptoms and avoid serious infections, many medications and vaccinations have been employed. However, to entirely eradicate COVID-19 from the world, next-generation vaccine research is required because of the devastating consequences it is having for humanity and every nation's economy. Scientists are working hard to eradicate this dangerous virus across the world. SARS-CoV-2 has also undergone significant mutation, leading to distinct viral types such as the alpha, beta, gamma, delta, and omicron variants. This has sparked discussion about the effectiveness of current vaccines for the newly formed variants. A proper comparison of these vaccinations is required to compare their efficacy as the number of people immunized against SARS-CoV-2 globally increases. Population-level statistics evaluating the capacity of these vaccines to reduce infection are therefore being developed. In this paper, we analyze the many vaccines on the market in terms of their production process, price, dosage needed, and efficacy. This article also discusses the challenges of achieving herd immunity, the likelihood of reinfection, and the importance of convalescent plasma therapy in reducing infection.

Assessing the impacts of COVID-19 vaccination programme's timing and speed on health benefits, cost-effectiveness, and relative affordability in 27 African countries

BACKGROUND

The COVID-19 vaccine supply shortage in 2021 constrained roll-out efforts in Africa while populations experienced waves of epidemics. As supply improves, a key question is whether vaccination remains an impactful and cost-effective strategy given changes in the timing of implementation.

METHODS

We assessed the impact of vaccination programme timing using an epidemiological and economic model. We fitted an age-specific dynamic transmission model to reported COVID-19 deaths in 27 African countries to approximate existing immunity resulting from infection before substantial vaccine roll-out. We then projected health outcomes (from symptomatic cases to overall disability-adjusted life years (DALYs) averted) for different programme start dates (01 January to 01 December 2021, n = 12) and roll-out rates (slow, medium, fast; 275, 826, and 2066 doses/million population-day, respectively) for viral vector and mRNA vaccines by the end of 2022. Roll-out rates used were derived from observed uptake trajectories in this region. Vaccination programmes were assumed to prioritise those above 60 years before other adults. We collected data on vaccine delivery costs, calculated incremental cost-effectiveness ratios (ICERs) compared to no vaccine use, and compared these ICERs to GDP per capita. We additionally calculated a relative affordability measure of vaccination programmes to assess potential nonmarginal budget impacts.

RESULTS

Vaccination programmes with early start dates yielded the most health benefits and lowest ICERs compared to those with late starts. While producing the most health benefits, fast vaccine roll-out did not always result in the lowest ICERs. The highest marginal effectiveness within vaccination programmes was found among older adults. High country income groups, high proportions of populations over 60 years or non-susceptible at the start of vaccination programmes are associated with low ICERs relative to GDP per capita. Most vaccination programmes with small ICERs relative to GDP per capita were also relatively affordable.

CONCLUSION

Although ICERs increased significantly as vaccination programmes were delayed, programmes starting late in 2021 may still generate low ICERs and manageable affordability measures. Looking forward, lower vaccine purchasing costs and vaccines with improved efficacies can help increase the economic value of COVID-19 vaccination programmes.

Estimating the burden of influenza-attributable severe acute respiratory infections on the hospital system in Metropolitan France, 2012-2018

BACKGROUND

To date, estimating the burden of seasonal influenza on the hospital system in France has been restricted to influenza diagnoses in patients (estimated hospitalization rate of 35/100,000 on average from 2012 to 2018). However, many hospitalizations for diagnosed respiratory infections (e.g. pneumonia, acute bronchitis) occur without concurrent screening for virological influenza, especially in the elderly. Specifically, we aimed to estimate the burden of influenza on the French hospital system by examining the proportion of severe acute respiratory infections (SARI) attributable to influenza.

METHODS

Using French national hospital discharge data from 1/7/2012 to 30/6/2018, we extracted SARI hospitalizations with ICD-10 codes J09-J11 (influenza codes) in main or associated diagnoses, and J12-J20 (pneumonia and bronchitis codes) in main diagnoses. We estimated influenza-attributable SARI hospitalizations during influenza epidemics, as the number of influenza-coded hospitalizations plus the influenza-attributable number of pneumonia- and acute bronchitis-coded hospitalizations using periodic regression and generalized linear models. Additional analyses stratified by age group, diagnostic category (pneumonia and bronchitis), and region of hospitalization were performed using the periodic regression model only.

RESULTS

The average estimated hospitalization rate of influenza-attributable SARI during the five annual influenza epidemics covered (2013-2014 to 2017-2018) was 60/100,000 with the periodic regression model, and 64/100,000 with the generalized linear model. Over the six epidemics (2012-2013 to 2017-2018), of the 533,456 SARI hospitalizations identified, an estimated 227,154 were influenza-attributable (43%). Fifty-six percent of cases were diagnosed with influenza, 33% pneumonia, and 11% bronchitis. Diagnoses varied between age groups: 11% of patients under 15 years old had pneumonia versus 41% of patients aged 65 and older.

CONCLUSION

Compared to influenza surveillance in France to date, analyzing excess SARI hospitalizations provided a much larger estimate of the burden of influenza on the hospital system. This approach was more representative and allowed the burden to be assessed according to age group and region. The emergence of SARS-Cov-2 has led to a change in the dynamics of winter respiratory epidemics. The co-circulation of the three current major respiratory viruses (influenza, SARS-Cov-2, and RSV) and the evolution of diagnostic confirmation practices must now be taken into account when analyzing SARI.

The optimal allocation of Covid-19 vaccines

We develop a simple model of vaccine prioritization for a potential pandemic. We illustrate how the model applies to the case of Covid-19, using an early 2020 primitive estimate of occupation-based exposure risks and age-based infection fatality rates. Even based on primitive estimates the vaccine distribution strongly emphasizes age-based mortality risk rather than occupation-based exposure risk. Among others, our result suggests that 50-year-old food-processing workers and 60-year-old financial advisors should have been equally prioritized. We also find that the priorities minimally change when certain populations' exposure risks are altered by targeted stay-at-home orders or call-up of essential workers.

Impact of random and targeted disruptions on information diffusion during outbreaks

Outbreaks are complex multi-scale processes that are impacted not only by cellular dynamics and the ability of pathogens to effectively reproduce and spread, but also by population-level dynamics and the effectiveness of mitigation measures. A timely exchange of information related to the spread of novel pathogens, stay-at-home orders, and other measures can be effective at containing an infectious disease, particularly during the early stages when testing infrastructure, vaccines, and other medical interventions may not be available at scale. Using a multiplex epidemic model that consists of an information layer (modeling information exchange between individuals) and a spatially embedded epidemic layer (representing a human contact network), we study how random and targeted disruptions in the information layer (e.g., errors and intentional attacks on communication infrastructure) impact the total proportion of infections, peak prevalence (i.e., the maximum proportion of infections), and the time to reach peak prevalence. We calibrate our model to the early outbreak stages of the SARS-CoV-2 pandemic in 2020. Mitigation campaigns can still be effective under random disruptions, such as failure of information channels between a few individuals. However, targeted disruptions or sabotage of hub nodes that exchange information with a large number of individuals can abruptly change outbreak characteristics, such as the time to reach the peak of infection. Our results emphasize the importance of the availability of a robust communication infrastructure during an outbreak that can withstand both random and targeted disruptions.

Immune Factors Drive Expression of SARS-CoV-2 Receptor Genes Amid Sexual Disparity

The emergence of COVID-19 has led to significant morbidity and mortality, with around seven million deaths worldwide as of February 2023. There are several risk factors such as age and sex that are associated with the development of severe symptoms due to COVID-19. There have been limited studies that have explored the role of sex differences in SARS-CoV-2 infection. As a result, there is an urgent need to identify molecular features associated with sex and COVID-19 pathogenesis to develop more effective interventions to combat the ongoing pandemic. To address this gap, we explored sex-specific molecular factors in both mouse and human datasets. The host immune targets such as TLR7, IRF7, IRF5, and IL6, which are involved in the immune response against viral infections, and the sex-specific targets such as AR and ESSR were taken to investigate any possible link with the SARS-CoV-2 host receptors ACE2 and TMPRSS2. For the mouse analysis, a single-cell RNA sequencing dataset was used, while bulk RNA-Seq datasets were used to analyze the human clinical data. Additional databases such as the Database of Transcription Start Sites (DBTS), STRING-DB, and the Swiss Regulon Portal were used for further analysis. We identified a 6-gene signature that showed differential expression in males and females. Additionally, this gene signature showed potential prognostic utility by differentiating ICU patients from non-ICU patients due to COVID-19. Our study highlights the importance of assessing sex differences in SARS-CoV-2 infection, which can assist in the optimal treatment and better vaccination strategies.

The dental triage method at Rothschild Hospital during the first lockdown due to the COVID-19 pandemic

OBJECTIVE

This study aims to (1) assess the efficacy of a face-to-face emergency protocol in children and adults and (2) measure the efficacies of prediagnosis at the triage level and clinical diagnosis at the emergency department level during the COVID-19 pandemic.

METHODS

A triage protocol was applied for patients at the entry of the Rothschild Hospital (AP-HP) between March 18th and May 11th, 2020. First, patients underwent a triage based on self-reported symptoms. If their condition was deemed urgent, they were oriented toward dental professionals, who performed an intraoral examination leading to a clinical diagnosis. Triage and diagnoses were categorized into four emergency groups: infectious, prosthetic, traumatic, and others. The agreement between triage and clinical diagnosis was tested (χ2 test). Positive predictive value (PPV), negative predictive value (NPV), sensitivity, and specificity for each diagnostic category were assessed to evaluate the performance and efficacy of the triage.

RESULTS

Out of 1562 dental visits, 1064 were included in this analysis. The most frequently reported symptoms by children at triage were pain (31.5%) and trauma (22%). Adults mainly complained of abscesses (45.1%) and pulpitis (20.5%). The most frequent clinical diagnoses were abscesses (29.2%) and pulpitis (20.5%) among children and adults, respectively. Tooth extraction was the most frequent treatment modality. Systemic antibiotics were prescribed for 49.2% of patients. Regardless of the age class, the PPV was high for groups 1 to 3, ranging from 78.9% to 100%. The NPV was high in all groups, ranging from 68.8% to 99.1%.

CONCLUSION

This study demonstrates that the triage implanted during the first COVID-19 lockdown was effective and is an appropriate tool for the referral of adults and children before clinical examination.

Mobility decisions, economic dynamics and epidemic

We propose a model, which nests a susceptible-infected-recovered-deceased (SIRD) epidemic model into a dynamic macroeconomic equilibrium framework with agents' mobility. The latter affect both their income and their probability of infecting and being infected. Strategic complementarities among individual mobility choices drive the evolution of aggregate economic activity, while infection externalities caused by individual mobility affect disease diffusion. The continuum of rational forward-looking agents coordinates on the Nash equilibrium of a discrete time, finite-state, infinite-horizon Mean Field Game. We prove the existence of an equilibrium and provide a recursive construction method for the search of an equilibrium(a), which also guides our numerical investigations. We calibrate the model by using Italian experience on COVID-19 epidemic and we discuss policy implications.

Managing sources of error during pandemics

The COVID-19 pandemic has highlighted important considerations for modeling future pandemics.

A randomised trial of anti-GM-CSF otilimab in severe COVID-19 pneumonia (OSCAR)

BACKGROUND

Granulocyte-macrophage colony-stimulating factor (GM-CSF) and dysregulated myeloid cell responses are implicated in the pathophysiology and severity of COVID-19.

METHODS

In this randomised, sequential, multicentre, placebo-controlled, double-blind study, adults aged 18-79 years (Part 1) or ≥70 years (Part 2) with severe COVID-19, respiratory failure and systemic inflammation (elevated C-reactive protein/ferritin) received a single intravenous infusion of otilimab 90 mg (human anti-GM-CSF monoclonal antibody) plus standard care (NCT04376684). The primary outcome was the proportion of patients alive and free of respiratory failure at Day 28.

RESULTS

In Part 1 (n=806 randomised 1:1 otilimab:placebo), 71% of otilimab-treated patients were alive and free of respiratory failure at Day 28 versus 67% who received placebo; the model-adjusted difference of 5.3% was not statistically significant (95% CI -0.8-11.4%, p=0.09). A nominally significant model-adjusted difference of 19.1% (95% CI 5.2-33.1%, p=0.009) was observed in the predefined 70-79 years subgroup, but this was not confirmed in Part 2 (n=350 randomised) where the model-adjusted difference was 0.9% (95% CI -9.3-11.2%, p=0.86). Compared with placebo, otilimab resulted in lower serum concentrations of key inflammatory markers, including the putative pharmacodynamic biomarker CC chemokine ligand 17, indicative of GM-CSF pathway blockade. Adverse events were comparable between groups and consistent with severe COVID-19.

CONCLUSIONS

There was no significant difference in the proportion of patients alive and free of respiratory failure at Day 28. However, despite the lack of clinical benefit, a reduction in inflammatory markers was observed with otilimab, in addition to an acceptable safety profile.

Modeling the impact of national and regional lockdowns on the 2020 spring wave of COVID-19 in France

Several countries have implemented lockdowns to control their COVID-19 epidemic. However, questions like "where" and "when" still require answers. We assessed the impact of national and regional lockdowns considering the French first epidemic wave of COVID-19 as a case study. In a regional lockdown scenario aimed at preventing intensive care units (ICU) saturation, almost all French regions would have had to implement a lockdown within 10 days and 96% of ICU capacities would have been used. For slowly growing epidemics, with a lower reproduction number, the expected delays between regional lockdowns increase. However, the public health costs associated with these delays tend to grow with time. In a quickly growing pandemic wave, defining the timing of lockdowns at a regional rather than national level delays by a few days the implementation of a nationwide lockdown but leads to substantially higher morbidity, mortality, and stress on the healthcare system.

Modeling the impact of national and regional lockdowns on the 2020 spring wave of COVID-19 in France

Several countries have implemented lockdowns to control their COVID-19 epidemic. However, questions like "where" and "when" still require answers. We assessed the impact of national and regional lockdowns considering the French first epidemic wave of COVID-19 as a case study. In a regional lockdown scenario aimed at preventing intensive care units (ICU) saturation, almost all French regions would have had to implement a lockdown within 10 days and 96% of ICU capacities would have been used. For slowly growing epidemics, with a lower reproduction number, the expected delays between regional lockdowns increase. However, the public health costs associated with these delays tend to grow with time. In a quickly growing pandemic wave, defining the timing of lockdowns at a regional rather than national level delays by a few days the implementation of a nationwide lockdown but leads to substantially higher morbidity, mortality, and stress on the healthcare system.

Data-driven multiscale dynamical framework to control a pandemic evolution with non-pharmaceutical interventions

Before the availability of vaccines, many countries have resorted multiple times to drastic social restrictions to prevent saturation of their health care system, and to regain control over an otherwise exponentially increasing COVID-19 pandemic. With the advent of data-sharing, computational approaches are key to efficiently control a pandemic with non-pharmaceutical interventions (NPIs). Here we develop a data-driven computational framework based on a time discrete and age-stratified compartmental model to control a pandemic evolution inside and outside hospitals in a constantly changing environment with NPIs. Besides the calendrical time, we introduce a second time-scale for the infection history, which allows for non-exponential transition probabilities. We develop inference methods and feedback procedures to successively recalibrate model parameters as new data becomes available. As a showcase, we calibrate the framework to study the pandemic evolution inside and outside hospitals in France until February 2021. We combine national hospitalization statistics from governmental websites with clinical data from a single hospital to calibrate hospitalization parameters. We infer changes in social contact matrices as a function of NPIs from positive testing and new hospitalization data. We use simulations to infer hidden pandemic properties such as the fraction of infected population, the hospitalisation probability, or the infection fatality ratio. We show how reproduction numbers and herd immunity levels depend on the underlying social dynamics.

Risk Factors of Severe COVID-19: A Review of Host, Viral and Environmental Factors

The clinical course and outcome of COVID-19 are highly variable, ranging from asymptomatic infections to severe disease and death. Understanding the risk factors of severe COVID-19 is relevant both in the clinical setting and at the epidemiological level. Here, we provide an overview of host, viral and environmental factors that have been shown or (in some cases) hypothesized to be associated with severe clinical outcomes. The factors considered in detail include the age and frailty, genetic polymorphisms, biological sex (and pregnancy), co- and superinfections, non-communicable comorbidities, immunological history, microbiota, and lifestyle of the patient; viral genetic variation and infecting dose; socioeconomic factors; and air pollution. For each category, we compile (sometimes conflicting) evidence for the association of the factor with COVID-19 outcomes (including the strength of the effect) and outline possible action mechanisms. We also discuss the complex interactions between the various risk factors.

Modelling the end of a Zero-COVID strategy using nirmatrelvir/ritonavir, vaccination and NPIs in Wallis and Futuna

Background

Ending Zero-COVID is challenging, particularly when vaccine coverage is low. Considering Wallis and Futuna, a French Zero-COVID territory affected by reluctance to vaccination, low immunity and high levels of comorbidities, we investigate how targeted use of nirmatrelvir/ritonavir (brand name Paxlovid) can complement vaccination and non-pharmaceutical interventions (NPIs), and mitigate the epidemic rebound expected when Zero-COVID ends.

Methods

We developed a discrete age-stratified compartmental model describing SARS-CoV-2 spread and healthcare impact once Wallis and Futuna reopens. It accounts for comorbidity risk groups (CRG), vaccine coverage (2 doses, 3 doses), the effectiveness of vaccines (recent or old injection), treatments and NPIs. In our baseline scenario, cases aged 65+ in intermediate/high CRG and 40+ in high CRG are eligible for treatment.

Findings

The epidemic is expected to start 13-20 days after reopening with a doubling time of 1.6-3.7 days. For medium transmission intensity (R0 = 5), 134 (115-156) hospital admissions are expected within 3 months, with no pharmaceutical measures. In our baseline scenario, admissions are reduced by 11%-21% if 50% of the target group receive treatment, with maximum impact when combined with NPIs and vaccination. The number of hospitalisations averted (HA) per patient treated (PT) is maximum when 65+ in high CRG are targeted (0.124 HA/PT), quickly followed by 65+ in intermediate/high CRG (0.097 HA/PT), and any 65+ (0.093 HA/PT). Expanding the target group increases both PT and HA, but marginal gains diminish.

Interpretation

Modelling suggests that test and treat may contribute to the mitigation of epidemic rebounds at the end of Zero-COVID, particularly in populations with low immunity and high levels of comorbidities.

Funding

RECOVER, VEO, AXA, Groupama, SpF, IBEID, INCEPTION, EMERGEN.

Optimal control analysis of a multigroup SEAIHRD model for COVID-19 epidemic

The COVID-19 pandemic has threatened public health and caused substantial economic loss to most countries worldwide. A multigroup susceptible-exposed-asymptomatic-infectious-hospitalized-recovered-dead (SEAIHRD) compartment model is first constructed to model the spread of the disease by dividing the population into three age groups: young (aged 0-19), prime (aged 20-64), and elderly (aged 65 and over). Then, we develop a free terminal time, partially fixed terminal state optimal control problem to minimize deaths and costs associated with hospitalization and the implementation of different control strategies. And the optimal strategies are derived under different assumptions about medical resources and vaccination. Specifically, we explore optimal control strategies for reaching herd immunity in the COVID-19 outbreak in a free terminal time situation to evaluate the effect of nonpharmaceutical interventions (NPIs) and vaccination as control measures. The transmission rate of SARS-CoV-2 is calibrated by using real data in the United States at the early stage of the epidemic. Through numerical simulation, we conclude that the outbreak of COVID-19 can be contained by implementing appropriate control of the prime age population and relatively strict control measures for young and elderly populations. Within a specific period, strict control measures should be implemented before the vaccine is marketed.

Age-specific SARS-CoV-2 infection fatality rates derived from serological data vary with income and income inequality

The ongoing COVID-19 pandemic has killed at least 1.1 million people in the United States and over 6.7 million globally. Accurately estimating the age-specific infection fatality rate (IFR) of SARS-CoV-2 for different populations is crucial for assessing and understanding the impact of COVID-19 and for appropriately allocating vaccines and treatments to at-risk groups. We estimated age-specific IFRs of wild-type SARS-CoV-2 using published seroprevalence, case, and death data from New York City (NYC) from March to May 2020, using a Bayesian framework that accounted for delays between key epidemiological events. IFRs increased 3-4-fold with every 20 years of age, from 0.06% in individuals between 18-45 years old to 4.7% in individuals over 75. We then compared IFRs in NYC to several city- and country-wide estimates including England, Switzerland (Geneva), Sweden (Stockholm), Belgium, Mexico, and Brazil, as well as a global estimate. IFRs in NYC were higher for individuals younger than 65 years old than most other populations, but similar for older individuals. IFRs for age groups less than 65 decreased with income and increased with income inequality measured using the Gini index. These results demonstrate that the age-specific fatality of COVID-19 differs among developed countries and raises questions about factors underlying these differences, including underlying health conditions and healthcare access.

Clinical features and outcomes of COVID-19 patients hospitalized for psychiatric disorders: a French multi-centered prospective observational study

BACKGROUND

Patients with psychiatric disorders are exposed to high risk of COVID-19 and increased mortality. In this study, we set out to assess the clinical features and outcomes of patients with current psychiatric disorders exposed to COVID-19.

METHODS

This multi-center prospective study was conducted in 22 psychiatric wards dedicated to COVID-19 inpatients between 28 February and 30 May 2020. The main outcomes were the number of patients transferred to somatic care units, the number of deaths, and the number of patients developing a confusional state. The risk factors of confusional state and transfer to somatic care units were assessed by a multivariate logistic model. The risk of death was analyzed by a univariate analysis.

RESULTS

In total, 350 patients were included in the study. Overall, 24 (7%) were transferred to medicine units, 7 (2%) died, and 51 (15%) patients presented a confusional state. Severe respiratory symptoms predicted the transfer to a medicine unit [odds ratio (OR) 17.1; confidence interval (CI) 4.9-59.3]. Older age, an organic mental disorder, a confusional state, and severe respiratory symptoms predicted mortality in univariate analysis. Age >55 (OR 4.9; CI 2.1-11.4), an affective disorder (OR 4.1; CI 1.6-10.9), and severe respiratory symptoms (OR 4.6; CI 2.2-9.7) predicted a higher risk, whereas smoking (OR 0.3; CI 0.1-0.9) predicted a lower risk of a confusional state.

CONCLUSION

COVID-19 patients with severe psychiatric disorders have multiple somatic comorbidities and have a risk of developing a confusional state. These data underline the need for extreme caution given the risks of COVID-19 in patients hospitalized for psychiatric disorders.

Network rule extraction under the network formal context based on three-way decision

Knowledge discovery combined with network structure is an emerging field of network data analysis and mining. Three-way concept analysis is a method that can fit the human mind in uncertain decisions and analysis. In reality, when three-way concept analysis is placed in the background of a network, not only the three-way rules need to be obtained, but also the network characteristic values of these rules should be obtained, which is of great significance for concept cognition in the network. This paper mainly combines complex network analysis with the formal context of three-way decision. Firstly, the network formal context of three-way decision (NFC3WD) is proposed to unify the two studies mentioned above into one data framework. Then, the network weaken-concepts of three-way decision (NWC3WD) and their corresponding sub-networks are studied. Therefore, we can not only find out the network weaken-concepts but also know the average influence of the sub-network, as well as the influence difference within the sub-network. Furthermore, the concept logic of network and the properties of its operators are put forward, which lays a foundation for designing the algorithm of rule extraction. Subsequently, the bidirectional rule extraction algorithm and reduction algorithm based on confidence degree are also explored. Meanwhile, these algorithms are applied to the diagnosis examples of COVID-19 from which we can not only get diagnostic rules, but also know the importance of the population corresponding to these diagnostic rules in the network through network eigenvalues. Finally, experimental analysis is made to show the superiority of the proposed method.

Genome Structure, Life Cycle, and Taxonomy of Coronaviruses and the Evolution of SARS-CoV-2

Coronaviruses have a broad host range and exhibit high zoonotic potential. In this chapter, we describe their genomic organization in terms of encoded proteins and provide an introduction to the peculiar discontinuous transcription mechanism. Further, we present evolutionary conserved genomic RNA secondary structure features, which are involved in the complex replication mechanism. With a focus on computational methods, we review the emergence of SARS-CoV-2 starting with the 2019 strains. In that context, we also discuss the debated hypothesis of whether SARS-CoV-2 was created in a laboratory. We focus on the molecular evolution and the epidemiological dynamics of this recently emerged pathogen and we explain how variants of concern are detected and characterised. COVID-19, the disease caused by SARS-CoV-2, can spread through different transmission routes and also depends on a number of risk factors. We describe how current computational models of viral epidemiology, or more specifically, phylodynamics, have facilitated and will continue to enable a better understanding of the epidemic dynamics of SARS-CoV-2.

Clinical outcomes of hospitalized COVID-19 patients treated with remdesivir: a retrospective analysis of a large tertiary care center in Germany

PURPOSE

The benefits of antiviral treatment with remdesivir in hospitalized patients with COVID-19 remain controversial. Clinical analyses are needed to demonstrate which patient populations are most likely to benefit.

METHODS

In a retrospective monocentric analysis, patients with COVID-19 treated between July 1, 2020 and June 30, 2021 at Hospital St. Georg, Leipzig, Germany were evaluated. The primary endpoint was time to clinical improvement, and the secondary endpoint was 28-day mortality. Propensity score matching was used for the endpoint analysis.

RESULTS

A total of 839 patients were fully evaluated, 68% of whom received specific COVID-19 drug therapy. Remdesivir was used in 31.3% of the patients, corticosteroids in 61.7%, and monoclonal antibodies in 2.3%. While dexamethasone administration was the most common therapeutic approach during the second pandemic wave, combination therapy with remdesivir and corticosteroids predominated during the third wave. Cox regression analysis revealed that combination therapy was not associated with faster clinical improvement (median: 13 days in both matched groups, HR 0.97 [95% CI 0.77-1.21], P = 0.762). By contrast, 28-day mortality was significantly lower in the corticosteroid-remdesivir group (14.8% versus 22.2% in the corticosteroid group, HR 0.60 [95% CI 0.39-0.95], P = 0.03) in the low-care setting. This effect was also demonstrated in a subgroup analysis of patients with remdesivir monotherapy (n = 44) versus standard of care (SOC).

CONCLUSION

In COVID-19 patients with only mild disease (low-flow oxygen therapy and treatment in a normal ward) who received corticosteroids and/or remdesivir in addition to SOC, early administration of remdesivir was associated with a measurable survival benefit.

Long-term cardiovascular health status and physical functioning of nonhospitalized patients with COVID-19 compared with non-COVID-19 controls

Coronavirus disease 2019 (COVID-19) is reported to have long-term effects on cardiovascular health and physical functioning, even in the nonhospitalized population. The physiological mechanisms underlying these long-term consequences are however less well described. We compared cardiovascular risk factors, arterial stiffness, and physical functioning in nonhospitalized patients with COVID-19, at a median of 6 mo postinfection, versus age- and sex-matched controls. Cardiovascular risk was assessed using blood pressure and biomarker concentrations (amino-terminal pro-B-type-natriuretic-peptide, high-sensitive cardiac troponin I, C-reactive protein), and arterial stiffness was assessed using carotid-femoral pulse wave velocity. Physical functioning was evaluated using accelerometry, handgrip strength, gait speed and questionnaires on fatigue, perceived general health status, and health-related quality of life (hrQoL). We included 101 former patients with COVID-19 (aged 59 [interquartile range, 55-65] yr, 58% male) and 101 controls. At 175 [126-235] days postinfection, 32% of the COVID-19 group reported residual symptoms, notably fatigue, and 7% required post-COVID-19 care. We found no differences in blood pressure, biomarker concentrations, or arterial stiffness between both groups. Former patients with COVID-19 showed a higher handgrip strength (43 [33-52] vs. 38 [30-48] kg, P = 0.004) and less sleeping time (8.8 [7.7-9.4] vs. 9.8 [8.9-10.3] h/day, P < 0.001) and reported fatigue more often than controls. Accelerometry-based habitual physical activity levels, gait speed, perception of general health status, and hrQoL were not different between groups. In conclusion, one in three nonhospitalized patients with COVID-19 reports residual symptoms at a median of 6 mo postinfection, but we were unable to relate these symptoms to increases in cardiovascular risk factors, arterial stiffness, or physical dysfunction.NEW & NOTEWORTHY We examined cardiovascular and physical functioning outcomes in nonhospitalized patients with COVID-19, at a median of 6 mo postinfection. When compared with matched controls, minor differences in physical functioning were found, but objective measures of cardiovascular risk and arterial stiffness did not differ between groups. However, one in three former patients with COVID-19 reported residual symptoms, notably fatigue. Follow-up studies should investigate the origins of residual symptoms and their long-term consequences in former, nonhospitalized patients with COVID-19.

Designing an optimal sequence of non-pharmaceutical interventions for controlling COVID-19

The COVID-19 pandemic has had an unprecedented impact on global health and the economy since its inception in December, 2019 in Wuhan, China. Non-pharmaceutical interventions (NPI) like lockdowns and curfews have been deployed by affected countries for controlling the spread of infections. In this paper, we develop a Mixed Integer Non-Linear Programming (MINLP) epidemic model for computing the optimal sequence of NPIs over a planning horizon, considering shortages in doctors and hospital beds, under three different lockdown scenarios. We analyse two strategies - centralised (homogeneous decisions at the national level) and decentralised (decisions differentiated across regions), for two objectives separately - minimization of infections and deaths, using actual pandemic data of France. We linearize the quadratic constraints and objective functions in the MINLP model and convert it to a Mixed Integer Linear Programming (MILP) model. A major result that we show analytically is that under the epidemic model used, the optimal sequence of NPIs always follows a decreasing severity pattern. Using this property, we further simplify the MILP model into an Integer Linear Programming (ILP) model, reducing computational time up to 99%. Our numerical results show that a decentralised strategy is more effective in controlling infections for a given severity budget, yielding up to 20% lesser infections, 15% lesser deaths and 60% lesser shortages in healthcare resources. These results hold without considering logistics aspects and for a given level of compliance of the population.

Structural and Managerial Risk Factors for COVID-19 Occurrence in French Nursing Homes

BACKGROUND

Nursing home (NH) residents accounted for half of the deaths during the 2020 spring wave of the coronavirus disease 2019 (COVID-19) epidemic in France. Our objective was to identify structural and managerial factors associated with COVID-19 outbreaks in NHs.

METHODS

We conducted in July 2020 a retrospective study by questionnaire addressed to NH directors in the Brittany region of France. The questions related to structural characteristics of the establishment, human resources, and crisis management decisions. The primary endpoint was the occurrence of at least one confirmed case of COVID-19 among residents between March 1, 2020 and May 31, 2020. The secondary endpoint was total mortality during this period. We used multivariate regressions to identify factors associated with these outcomes.

RESULTS

Responses were collected from 231 NHs hosting 20,881 residents, representing a participation rate of 47%. In 24 (10%) NHs, at least one resident presented confirmed COVID-19. NHs often implemented stringent protective measures, with 65% of them choosing to confine residents to their rooms. In multivariate analysis, factors associated with a reduced risk of case occurrence were in-room meal service, early ban of family visits, and daily access to an outdoor space. No association was found between mortality and the factors studied. Our results show an early and strict implementation of lockdown measures, with good epidemiological results in a context of shortage of personal protective equipment (PPE) and non-vaccination. Nevertheless, it raises ethical questions concerning respect of residents' wellbeing and rights.

CONCLUSION

Cessation of communal dining seems to be the main measure likely to be effective in preventive terms. It does not seem that room lockdown and cessation of group activities should be recommended, particularly if mask wearing is possible.

Bi-objective optimization of a stochastic resilient vaccine distribution network in the context of the COVID-19 pandemic

This paper develops an approach to optimize a vaccine distribution network design through a mixed-integer nonlinear programming model with two objectives: minimizing the total expected number of deaths among the population and minimizing the total distribution cost of the vaccination campaign. Additionally, we assume that a set of input parameters (e.g., death rate, social contacts, vaccine supply, etc.) is uncertain, and the distribution network is exposed to disruptions. We then investigate the resilience of the distribution network through a scenario-based robust-stochastic optimization approach. The proposed model is linearized and finally validated through a real case study of the COVID-19 vaccination campaign in France. We show that the current vaccination strategies are not optimal, and vaccination prioritization among the population and the equity of vaccine distribution depend on other factors than those conceived by health policymakers. Furthermore, we demonstrate that a vaccination strategy mixing the population prioritization and the quarantine restrictions leads to an 8.5% decrease in the total number of deaths.