Estimating the burden of SARS-CoV-2 in France

SARS-CoV-2 epidemiology, kinetics, and evolution: A narrative review

Since winter 2019, SARS-CoV-2 has emerged, spread, and evolved all around the globe. We explore 4 y of evolutionary epidemiology of this virus, ranging from the applied public health challenges to the more conceptual evolutionary biology perspectives. Through this review, we first present the spread and lethality of the infections it causes, starting from its emergence in Wuhan (China) from the initial epidemics all around the world, compare the virus to other betacoronaviruses, focus on its airborne transmission, compare containment strategies ("zero-COVID" vs. "herd immunity"), explain its phylogeographical tracking, underline the importance of natural selection on the epidemics, mention its within-host population dynamics. Finally, we discuss how the pandemic has transformed (or should transform) the surveillance and prevention of viral respiratory infections and identify perspectives for the research on epidemiology of COVID-19.

The interaction between population age structure and policy interventions on the spread of COVID-19

COVID-19 has triggered an unprecedented public health crisis and a global economic shock. As countries and cities have transitioned away from strict pandemic restrictions, the most effective reopening strategies may vary significantly based on their demographic characteristics and social contact patterns. In this study, we employed an extended age-specific compartment model that incorporates population mobility to investigate the interaction between population age structure and various containment interventions in New York, Los Angeles, Daegu, and Nairobi - four cities with distinct age distributions that served as local epicenters of the epidemic from January 2020 to March 2021. Our results demonstrated that individual social distancing or quarantine strategies alone cannot effectively curb the spread of infection over a one-year period. However, a combined strategy, including school closure, 50 % working from home, 50 % reduction in other mobility, 10 % quarantine rate, and city lockdown interventions, can effectively suppress the infection. Furthermore, our findings revealed that social-distancing policies exhibit strong age-specific effects, and age-targeted interventions can yield significant spillover benefits. Specifically, reducing contact rates among the population under 20 can prevent 14 %, 18 %, 56 %, and 99 % of infections across all age groups in New York, Los Angeles, Daegu, and Nairobi, respectively, surpassing the effectiveness of policies exclusively targeting adults over 60 years old. In particular, to protect the elderly, it is essential to reduce contacts between the younger population and people of all age groups, especially those over 60 years old. While an older population structure may escalate fatality risk, it might also decrease infection risk. Moreover, a higher basic reproduction number amplifies the impact of an older population structure on the fatality risk of the elderly. The considerable variations in susceptibility, severity, and mobility across age groups underscore the need for targeted interventions to effectively control the spread of COVID-19 and mitigate risks in future pandemics.

COVID-19 case fatality rate and infection fatality rate from 2020 to 2023: Nationwide analysis in Austria

BACKGROUND

Comprehensive analyses of COVID-19 case fatality rates (CFRs) and infection fatality rates (IFRs) that span the entire pandemic are not yet available but critical to retrospectively evaluate the COVID-19 disease burden and its related public health policies. We used nationwide individual participant data from Austria, the continental country with the highest SARS-CoV-2 testing rate per capita, to calculate COVID-19 CFR and estimate IFR covering the entire pandemic.

METHODS

This retrospective observational study included all Austrian residents and covered the time from February 2020 to May 2023, examining CFRs overall, monthly, and during dominant SARS-CoV-2 variant periods. CFRs were calculated for the whole population and stratified according to immunization status (presence of previous vaccination and/or infection), age, gender and nursing home residency. We additionally estimated the IFRs based on estimations of undocumented infections using a test positivity model.

RESULTS

The overall CFR of 30-day COVID-19 mortality was 0.31 % but varied depending on month, with the highest being 5.9 % in April 2020 and the lowest 0.07 % in January 2022. The variant periods reflected this trend of decreasing CFR, with the highest for Wuhan-Hu-1 (2.05 %) and the lowest for BA.1 (0.08 %). Overall CFRs were particularly high in the group without any previous immunizing event (0.67 %), the elderly (85 + year group: 7.88 %) and in nursing home residents (7.92 %). Nursing home residents accounted for 30.82 % of all COVID-19 deaths while representing only 1.22 % of diagnosed infections. Total SARS-CoV-2 infections were estimated to be almost double than confirmed cases with a corresponding overall IFR of 0.16 %.

CONCLUSION

This estimation of nationwide CFR and IFR across the entirety of the SARS-CoV-2 pandemic gives crucial insights into the period-dependent variability of the severity of diagnosed COVID-19 cases and its risk factors. Our findings further underline the disproportionate severity of COVID-19 among the elderly and especially nursing home residents.

Protective Effect of Daily Physical Activity Against COVID-19 in a Young Adult Population on Reunion Island

The global fight against pandemics is a major public health issue. Epidemiological studies showed a reduced risk of the coronavirus disease 2019 (COVID-19) severity with the practice of regular physical activity (PA) in clinical populations. Here, we investigated the effect of PA against COVID-19 in a young general population. Methods: Two hundred ninety volunteers over 18 years old from Reunion Island responded to an online survey concerning sociodemographic, lifestyle and clinical information. Daily PA was studied using the International Physical Activity Questionnaire short version (IPAQ) and classified by overall score and intensities of PA. Results: Among 290 responders [179 women, median age = 27.5 years (interquartile range = 21.3 years)], 141 (48.6%) reported COVID-19 infection. Multivariate logistic analysis adjusted for age, sex, body mass index, chronic disease and alcohol consumption showed that the number of days per week of regular intense PA was independently associated with a low risk of COVID-19 infection [odds ratio (OR) 0.86; 95% confidence interval (CI) 0.24 to 0.99; p = 0.030], while regular moderate PA was not [OR 1.10; 95%CI 0.97 to 1.23; p = 0.137]. Conclusions: In a population of young adults, regular intense PA could offer a protective effect against COVID-19. Additional research is required to confirm this association in various viral infections and elucidate the fundamental mechanisms involved.

Shifts in Respiratory Virus Epidemiology on Reunion Island From 2017 to 2023: Impact of COVID-19 Pandemic and Non-Pharmaceutical Interventions

Background

The COVID‐19 pandemic has reshaped the landscape of respiratory viral infections globally. This study examines these changes on Reunion Island, a French department in the southeastern Indian Ocean.

Methods

Retrospective data from 2017 to 2023, from over 24,000 samples collected across the hospital system, partner laboratories, and a network of sentinel physicians, were analyzed and correlated with the number of consultations at the hospital emergency department and with sentinel physicians for symptoms of acute respiratory infections (ARIs). The epidemiology of respiratory viruses was analyzed by comparing the pre‐ and post‐COVID‐19 periods to assess disruptions in seasonal patterns, changes in virus prevalence, and the affected age groups.

Results

Our database effectively captured the epidemiology of respiratory infections across the island, as demonstrated by its strong correlation with the number of consultations for ARI. Post‐COVID‐19, the influenza virus exhibited multiple epidemic waves within a single year, deviating from its traditional single annual peak and showing a significant decline in circulation from 2020 to 2023. The circulation of respiratory syncytial virus was also impacted post‐COVID‐19, with epidemics starting earlier and lasting longer compared with pre‐COVID‐19 years. Human rhinovirus circulated more prominently in the post‐COVID period, accounting for up to one‐third of positive cases, becoming the most prevalent respiratory virus (excluding SARS‐CoV‐2).

Conclusions

These findings suggest a notable impact of the COVID‐19 pandemic and associated NPIs on respiratory virus circulation on Reunion Island since mid‐2020. They underscore the complex interplay between viral interference, public health interventions, behavioral changes, and youth immunity, emphasizing the need for adaptable strategies in managing respiratory virus outbreaks in the post‐COVID‐19 era.

Pathogenic and periodontal bacteria may contribute to the fatal outcome of critically ill elderly COVID-19 patients

Some studies suggest that the respiratory microbiome of COVID-19 patients differs from that of healthy individuals, infected patients may have reduced diversity and increased levels of opportunistic bacteria, however, the role of the microbiome in fatal SARS-CoV-2 infection remains poorly understood. Our study aimed to determine whether there are differences in the respiratory microbiome between patients who recovered from COVID-19 and those who died, by characterizing the bacterial communities of both groups. A total of 24 patients who recovered from COVID-19 and 24 who died were included in the study, patient data were analyzed for signs, symptoms and clinical variables. Airway samples were collected and the 16 S rRNA variable regions V3-V4 were amplified and sequenced using the Illumina MiSeq platform. Elevated levels of blood urea nitrogen, creatinine and lactate dehydrogenase, and higher frequencies of cardiovascular disease, diabetes mellitus and renal disease were observed in patients with a fatal outcome. Compared to patients who recovered from COVID-19, patients who died exhibited a microbiome enriched in periodontal and pathogenic bacteria such as Klebsiella pneumoniae. Our results highlighted a dual relationship between SARS CoV-2 infection and an exacerbated periodontopathogen-induced immune response.

Critical mobility in policy making for epidemic containment

When considering airborne epidemic spreading in social systems, a natural connection arises between mobility and epidemic contacts. As individuals travel, possibilities to encounter new people either at the final destination or during the transportation process appear. Such contacts can lead to new contagion events. In fact, mobility has been a crucial target for early non-pharmaceutical containment measures against the recent COVID-19 pandemic, with a degree of intensity ranging from public transportation line closures to regional, city or even home confinements. Nonetheless, quantitative knowledge on the relationship between mobility-contagions and, consequently, on the efficiency of containment measures remains elusive. Here we introduce an agent-based model with a simple interaction between mobility and contacts. Despite its simplicity, our model shows the emergence of a critical mobility level, inducing major outbreaks when surpassed. We explore the interplay between mobility restrictions and the infection in recent intervention policies seen across many countries, and how interventions in the form of closures triggered by incidence rates can guide the epidemic into an oscillatory regime with recurrent waves. We consider how the different interventions impact societal well-being, the economy and the population. Finally, we propose a mitigation framework based on the critical nature of mobility in an epidemic, able to suppress incidence and oscillations at will, preventing extreme incidence peaks with potential to saturate health care resources.

Molecular characteristics of organic matters in PM2.5 associated with upregulation of respiratory virus infection in vitro

The extent to which organic matters (OM) in PM2.5 affect virus infections and the key organic molecules involved in this process remain unclear. Herein, this study utilized ultra-high resolution mass spectrometry coupled with in vitro experiments to identify the organic molecules associated with respiratory virus infection for the first time. Water-soluble organic matters (WSOM) and water-insoluble organic matters (WIOM) were separated from PM2.5 samples collected at the urban area of Guangzhou, China. Their molecular compositions were analyzed using Fourier transform ion cyclotron resonance mass spectrometry. Subsequently, in vitro experiments were conducted to explore the impact of WSOM and WIOM exposure on the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pseudo-virus infection in A549 cells. Results revealed that WSOM and WIOM respectively promoted 1.7 to 2.1-fold and 1.9 to 3.5-fold upregulation of SARS-CoV-2 pseudo-virus infection in a concentration-dependent manner (at 25 to 100 μg mL-1) compared to the virus-only control group. Partial least squares model analysis indicated that the increased virus infection was likely related to phthalate ester and nitro-aromatic molecules in WSOM, as well as LipidC molecules with aliphatic and olefinic structures in WIOM. Interestingly, the molecules responsible for upregulating SARS-CoV-2 receptor angiotensin-converting enzyme 2 (ACE2) expression and virus infection differed. Thus, it was concluded that ACE2 upregulation alone may not fully elucidate the mechanisms underlying increased susceptibility to virus infection. The findings highlight the critical importance of aromatic and lipid molecules found in OM in relation to respiratory virus infection.

Association Between Severities of Obstructive Sleep Apnea and COVID-19 Outcomes

Introduction Obstructive sleep apnea (OSA) is characterized by repetitive upper airway collapse resulting in episodes of apnea and hypopnea. Studies have shown worsened coronavirus disease 2019 (COVID-19) severity due to coexisting respiratory conditions and suggest increased severity of COVID-19 in patients with or at high risk of OSA. However, the extent of this correlation is unclear. This retrospective study aimed to evaluate the association between OSA severity and COVID-19 severity and assess the impact of continuous positive airway pressure (CPAP) compliance. Methods This single-center retrospective study was conducted at King Abdulaziz University Hospital (KAUH), a tertiary care center in Jeddah, Saudi Arabia. Data were collected from 62 adult patients with OSA who were diagnosed via polysomnography (PSG) and had a positive documented severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) polymerase chain reaction (PCR) test result. COVID-19 severity was categorized into mild, moderate, and severe. Results There was no significant correlation between OSA severity as measured by the apnea-hypopnea index (AHI), low oxyhemoglobin desaturation (LSAT), arousal index (AI), respiratory disturbance index (RDI), or the type of treatment used, including adherence to CPAP, and the outcomes of COVID-19. However, higher arousal with respiratory index (ARI) and a lower percentage of time with SpO2 < 90% (T90) values were linked to moderate COVID-19 severity with significant p-values of 0.046 and 0.007, respectively. Conclusion There was no significant correlation between the severity or types of OSA treatment and the severity of COVID-19. Further research including multicenter studies with bigger populations and extensive sleep study data is warranted. Understanding the OSA-COVID-19 link may improve risk stratification and patient management.

Patterns and drivers of excess mortality during the COVID-19 pandemic in 13 Western European countries

BACKGROUND

Important differences in excess mortality between European countries during the COVID-19 pandemic have been reported. Understanding the drivers of these differences is essential to pandemic preparedness.

METHODS

We examined patterns in age- and sex-standardized cumulative excess mortality in 13 Western European countries during the first 30 months of the COVID-19 pandemic and the correlation of country-level characteristics of interest with excess mortality.

RESULTS

In a timeline analysis, we identified notable differences in seeding events, particularly in early 2020 and when the Alpha variant emerged, likely contributing to notable differences in excess mortality between countries (lowest in Denmark during that period). These differences were more limited from July 2021 onwards. Lower excess mortality was associated with implementing stringent non-pharmaceutical interventions (NPIs) when hospital admissions were still low in 2020 (correlation coefficient rho = 0.65, p = 0.03) and rapid rollout of vaccines in the elderly in early 2021 (rho =  - 0.76, p = 0.002). Countries which implemented NPIs while hospital admissions were low tended to experience lower gross domestic product (GDP) losses in 2020 (rho =  - 0.55, p = 0.08). Structural factors, such as high trust in the national government (rho =  - 0.77, p = 0.002) and low ratio of population at risk of poverty (rho = 0.55, p = 0.05), were also associated with lower excess mortality.

CONCLUSIONS

These results suggest the benefit of early implementation of NPIs and swift rollout of vaccines to the most vulnerable. Further analyses are required at a more granular level to better understand how these factors impacted excess mortality and help guide pandemic preparedness plans.

Mean-field-game approach to nonpharmaceutical interventions in a social-structure model of epidemics

The design of coherent and efficient policies to address infectious diseases and their consequences requires modeling not only epidemics dynamics but also individual behaviors, as the latter has a strong influence on the former. In our work, we provide a theoretical model for this problem, taking into account the social structure of a population. This model is based on a mean-field-game version of a SIR compartmental model, in which individuals are grouped by their age class and interact together in different settings. This social heterogeneity allows us to reproduce realistic situations while remaining usable in practice. In our game theoretical approach, individuals can choose to limit their contacts by making a trade-off between the risks incurred by infection and the cost of being confined. The aggregation of all these individual choices and optimizations forms a Nash equilibrium through a system of coupled equations that we derive and solve numerically. The global cost born by the population within this scenario is then compared to its societal optimum counterpart (i.e., the cost associated with the optimal set of strategies from the point of view of the society as a whole), and we investigate how the gap between these two costs can be partially bridged within a constrained Nash equilibrium for which a governmental institution would, under specific conditions, impose "partial lockdowns" such as the ones that were imposed during the COVID-19 pandemic. Finally, we consider the consequences of the finiteness of the population size N_{tot}, or of a time T at which an external event (e.g., a vaccine) would end the epidemic, and show that the variation of these parameters could lead to first-order phase transitions in the choice of optimal strategies. In this paper, all the strategies considered to mitigate epidemics correspond to nonpharmaceutical interventions, and we provide here a theoretical framework within which guidelines for public policies depending on the characteristics of an epidemic and on the cost of restrictions on the society could be assessed.

Modeling the interplay between disease spread, behaviors, and disease perception with a data-driven approach

Individuals' perceptions of disease influence their adherence to preventive measures, shaping the dynamics of disease spread. Despite extensive research on the interaction between disease spread, human behaviors, and interventions, few models have incorporated real-world behavioral data on disease perception, limiting their applicability. In this study, we propose an approach to integrate survey data on contact patterns and disease perception into a data-driven compartmental model, by hypothesizing that perceived severity is a determinant of behavioral change. We explore scenarios involving a competition between a COVID-19 wave and a vaccination campaign, where individuals' behaviors vary based on their perceived severity of the disease. Results indicate that behavioral heterogeneities influenced by perceived severity affect epidemic dynamics, in a way depending on the interplay between two contrasting effects. On the one hand, longer adherence to protective measures by groups with high perceived severity provides greater protection to vulnerable individuals, while premature relaxation of behaviors by low perceived severity groups facilitates virus spread. Differences in behavior across different population groups may impact strongly the epidemiological curves, with a transition from a scenario with two successive epidemic peaks to one with only one (higher) peak and overall more numerous severe outcomes and deaths. The specific modeling choices for how perceived severity modulates behavior parameters do not strongly impact the model's outcomes. Moreover, the study of several simplified models indicate that the observed phenomenology depends on the combination of data describing age-stratified contact patterns and of the feedback loop between disease perception and behavior, while it is robust with respect to the lack of precise information on the distribution of perceived severity in the population. Sensitivity analyses confirm the robustness of our findings, emphasizing the consistent impact of behavioral heterogeneities across various scenarios. Our study underscores the importance of integrating risk perception into infectious disease transmission models and gives hints on the type of data that further extensive data collection should target to enhance model accuracy and relevance.

SARS-CoV-2 infection and the risk of depressive symptoms: a retrospective longitudinal study from the population-based CONSTANCES cohort

BACKGROUND

Should COVID-19 have a direct impact on the risk of depression, it would suggest specific pathways for prevention and treatment. In this retrospective population-based study, we aimed to examine the association of prior SARS-CoV-2 infection with depressive symptoms, distinguishing self-reported v. biologically confirmed COVID-19.

METHODS

32 007 participants from the SAPRIS survey nested in the French CONSTANCES cohort were included. COVID-19 was measured as followed: ad hoc serologic testing, self-reported PCR or serology positive test results, and self-reported COVID-19. Depressive symptoms were measured with the Center of Epidemiologic Studies-Depression Scale (CES-D). Outcomes were depressive symptoms (total CES-D score, its four dimensions, and clinically significant depressive symptoms) and exposure was prior COVID-19 (no COVID-19/self-reported unconfirmed COVID-19/biologically confirmed COVID-19).

RESULTS

In comparison to participants without COVID-19, participants with self-reported unconfirmed COVID-19 and biologically confirmed COVID-19 had higher CES-D scores (β for one interquartile range increase [95% CI]: 0.15 [0.08-0.22] and 0.09 [0.05-0.13], respectively) and somatic complaints dimension scores (0.15 [0.09-0.21] and 0.10 [0.07-0.13]). Only those with self-reported but unconfirmed COVID-19 had higher depressed affect dimension scores (0.08 [0.01-0.14]). Accounting for ad hoc serologic testing only, the CES-D score and the somatic complaints dimension were only associated with the combination of self-reported COVID-19 and negative serology test results.

CONCLUSIONS

The association between COVID-19 and depressive symptoms was merely driven by somatic symptoms of depression and did not follow a gradient consistent with the hypothesis of a direct impact of SARS-CoV-2 infection on the risk of depression.

Impact of pharmaceutical and non-pharmaceutical interventions on COVID-19 in Tunisia

BACKGROUND

In COVID-19 management, a variety of pharmaceutical interventions (PI) and non- pharmaceutical interventions (NPI) were adopted to limit the spread of the disease and its associated deaths. We aimed to evaluate the impact of PI and NPI on risks of COVID-19 transmission and deaths.

METHOD

We collected aggregate data from March 2nd, 2020, to December 1, 2022 from the Tunisian Ministry of Health's website and OurWorldInData.org site. NPI Periods (NPIP: March 2020 to March 2021) and PI Periods (PIP) were distributed to NPIP1, 2, 3 and 4 and to PIP1, 2, 3 and 4, respectively. We calculated the Relative Risks (RR) and 95% Confidence Intervals (CI) by comparing the subsequent period with previous one.

RESULTS

The risk of SARS-CoV-2 transmission increased progressively from the zero cases period (NPIP2) to the mitigate strategy period (NPIP3) (RR = 14.0; 95% CI: 12.4-15.8) and to the stop-and-go epidemic control period (NPIP4) (RR = 23.1 (95% CI: 22.4-23.9). It was stabilized in the targeted vaccination period (PIP1) (RR = 1.08, 95% CI: 1.07-1.08) and reduced during the mass vaccination period (PIP2) (RR: 0.50, 95% CI: 0.50-0.51). SARS-CoV-2 transmission, increased during PIP3 concomitant with the Omicron wave (RR = 2.65, 95% CI: 2.64-2.67). It remained at a low level in PIP4 (RR = 0.18; 95% CI: 0.18-0.18). Compared to NPIP2, NPIP3 and NPIP4 were associated with a higher risk of COVID-19 mortality (RR = 3.337; 95% CI: 1.797-6.195) and (RR = 72.63 (95% CI: 54.01-97.68), respectively. Since the start of the immunization program, the risk of COVID-19 death has consistently decreased. In comparison to each previous period, the risk transitioned in PIP1 to RR = 0.91; 95% CI: 0.88-0.93, then to RR = 0.85; 95% CI: 0.83-0.88 in PIP2, to RR = 0.47; 95% CI: 0.45-0.50 in PIP3, and to RR = 0.19; 95% CI: 0.18-0.24 during PIP4.

CONCLUSION

In terms of lowering the risk of transmission and mortality, the NP strategy at the beginning of the epidemic outperformed the IP strategy during the outbreak.

Do COVID-19 Infectious Disease Models Incorporate the Social Determinants of Health? A Systematic Review

Objectives

To identify COVID-19 infectious disease models that accounted for social determinants of health (SDH).

Methods

We searched MEDLINE, EMBASE, Cochrane Library, medRxiv, and the Web of Science from December 2019 to August 2020. We included mathematical modelling studies focused on humans investigating COVID-19 impact and including at least one SDH. We abstracted study characteristics (e.g., country, model type, social determinants of health) and appraised study quality using best practices guidelines.

Results

83 studies were included. Most pertained to multiple countries (n = 15), the United States (n = 12), or China (n = 7). Most models were compartmental (n = 45) and agent-based (n = 7). Age was the most incorporated SDH (n = 74), followed by gender (n = 15), race/ethnicity (n = 7) and remote/rural location (n = 6). Most models reflected the dynamic nature of infectious disease spread (n = 51, 61%) but few reported on internal (n = 10, 12%) or external (n = 31, 37%) model validation.

Conclusion

Few models published early in the pandemic accounted for SDH other than age. Neglect of SDH in mathematical models of disease spread may result in foregone opportunities to understand differential impacts of the pandemic and to assess targeted interventions.

Systematic Review Registration

[https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020207706], PROSPERO, CRD42020207706.

Changes in adolescent mental health during the Covid pandemic

BACKGROUND

Previous pandemics around the world have shown that negative emotions are intensified in individuals when restrictions are imposed on human daily life activities. This study aims to draw attention to the pandemic-specific factors that might be associated with the severity of depression, anxiety, and COVID-19 phobia of high school students.

METHODS

A total of 1431 high school students aged 14 to 18 years were invited to participate in this study using online survey forms. They were asked to fill out a questionnaire about themselves and the changes in their lives during the pandemic. They completed the COVID-19 Phobia Scale (C19P-S) and the Revised Children's Anxiety and Depression Scale (RCADS).

RESULTS

Findings showed that being a girl is an increased risk factor for anxiety, depression, and COVID-19 phobia. In addition, following the official daily COVID-19 data and having a healthcare professional in the building of residence are significant risk factors for COVID-19 phobia. Having a psychiatric disorder, having a chronic disease, losing anyone due to COVID-19 infection, undergoing a COVID-19 diagnostic test, and meeting friends in person are increased risk factors for anxiety or depression during the pandemic.

CONCLUSIONS

Changes in adolescents' lives caused by the COVID-19 pandemic are negatively affecting their mental health. Studies are needed to maintain the mental well-being of adolescents under the conditions of this pandemic.

Analytical solution of susceptible-infected-recovered models on homogeneous networks

The ability to actually implement epidemic models is a crucial stake for public institutions, as they may be overtaken by the increasing complexity of current models and sometimes tend to revert to less elaborate models such as the susceptible-infected-recovered (SIR) model. In our work, we study a simple epidemic propagation model, called SIR-k, which is based on a homogeneous network of degree k, where each individual has the same number k of neighbors. This model represents a refined version of the basic SIR which assumes a completely homogeneous population. We show that nevertheless, analytical expressions, simpler and richer than the ones existing for the SIR model, can be derived for this SIR-k model. In particular, we obtain an exact implicit analytical solution for any k, from which quantities such as the epidemic threshold or the total number of agents infected during the epidemic can be obtained. We furthermore obtain simple exact explicit solutions for small ks, and in the large k limit we find a new formulation of the analytical solution of the basic SIR model, which comes with new insights.

A potential allosteric inhibitor of SARS-CoV-2 main protease (Mpro) identified through metastable state analysis

Anti-COVID19 drugs, such as nirmatrelvir, have been developed targeting the SARS-CoV-2 main protease, Mpro, based on the critical requirement of its proteolytic processing of the viral polyproteins into functional proteins essential for viral replication. However, the emergence of SARS-CoV-2 variants with Mpro mutations has raised the possibility of developing resistance against these drugs, likely due to therapeutic targeting of the Mpro catalytic site. An alternative to these drugs is the development of drugs that target an allosteric site distant from the catalytic site in the protein that may reduce the chance of the emergence of resistant mutants. Here, we combine computational analysis with in vitro assay and report the discovery of a potential allosteric site and an allosteric inhibitor of SARS-CoV-2 Mpro. Specifically, we identified an Mpro metastable state with a deformed catalytic site harboring potential allosteric sites, raising the possibility that stabilization of this metastable state through ligand binding can lead to the inhibition of Mpro activity. We then performed a computational screening of a library (∼4.2 million) of drug-like compounds from the ZINC database and identified several candidate molecules with high predicted binding affinity. MD simulations showed stable binding of the three top-ranking compounds to the putative allosteric sites in the protein. Finally, we tested the three compounds in vitro using a BRET-based Mpro biosensor and found that one of the compounds (ZINC4497834) inhibited the Mpro activity. We envisage that the identification of a potential allosteric inhibitor of Mpro will aid in developing improved anti-COVID-19 therapy.

Expanding the use of mathematical modeling in healthcare epidemiology and infection prevention and control

During the coronavirus disease 2019 pandemic, mathematical modeling has been widely used to understand epidemiological burden, trends, and transmission dynamics, to facilitate policy decisions, and, to a lesser extent, to evaluate infection prevention and control (IPC) measures. This review highlights the added value of using conventional epidemiology and modeling approaches to address the complexity of healthcare-associated infections (HAI) and antimicrobial resistance. It demonstrates how epidemiological surveillance data and modeling can be used to infer transmission dynamics in healthcare settings and to forecast healthcare impact, how modeling can be used to improve the validity of interpretation of epidemiological surveillance data, how modeling can be used to estimate the impact of IPC interventions, and how modeling can be used to guide IPC and antimicrobial treatment and stewardship decision-making. There are several priority areas for expanding the use of modeling in healthcare epidemiology and IPC. Importantly, modeling should be viewed as complementary to conventional healthcare epidemiological approaches, and this requires collaboration and active coordination between IPC, healthcare epidemiology, and mathematical modeling groups.

COVID-19 in Pediatric Populations

The COVID-19 pandemic reshaped the landscape of respiratory viral illnesses, causing common viruses to fade as SARS-CoV-2 took precedence. By 2023, more than 96% of the children in the United States were estimated to have been infected with SARS-CoV-2, with certain genetic predispositions and underlying health conditions posing risk factors for severe disease in children. Children, in general though, exhibit immunity advantages, protecting against aspects of the SARS-CoV-2 infection known to drive increased severity in older adults. Post-COVID-19 complications such as multisystem inflammatory syndrome in children and long COVID have emerged, underscoring the importance of vaccination. Here, we highlight the risks of severe pediatric COVID-19, age-specific immunoprotection, comparisons of SARS-CoV-2 with other respiratory viruses, and factors contributing to post-COVID-19 complications in children.

Inference of epidemic dynamics in the COVID-19 era and beyond

The COVID-19 pandemic demonstrated the key role that epidemiology and modelling play in analysing infectious threats and supporting decision making in real-time. Motivated by the unprecedented volume and breadth of data generated during the pandemic, we review modern opportunities for analysis to address questions that emerge during a major modern epidemic. Following the broad chronology of insights required - from understanding initial dynamics to retrospective evaluation of interventions, we describe the theoretical foundations of each approach and the underlying intuition. Through a series of case studies, we illustrate real life applications, and discuss implications for future work.

Dietary habits, traveling and the living situation potentially influence the susceptibility to SARS-CoV-2 infection: results from healthcare workers participating in the RisCoin Study

PURPOSE

To explore occupational and non-occupational risk and protective factors for the coronavirus disease 2019 (COVID-19) in healthcare workers (HCWs).

METHODS

Serum specimens and questionnaire data were obtained between October 7 and December 16, 2021 from COVID-19-vaccinated HCWs at a quaternary care hospital in Munich, Germany, and were analyzed in the RisCoin Study.

RESULTS

Of 3,696 participants evaluated, 6.6% have had COVID-19 at least once. Multivariate logistic regression analysis identified working in patient care occupations (7.3% had COVID-19, 95% CI 6.4-8.3, Pr = 0.0002), especially as nurses, to be a potential occupation-related COVID-19 risk factor. Non-occupational factors significantly associated with high rates of the disease were contacts to COVID-19 cases in the community (12.8% had COVID-19, 95% CI 10.3-15.8, Pr < 0.0001), being obese (9.9% had COVID-19, 95% CI 7.1-13.5, Pr = 0.0014), and frequent traveling abroad (9.4% had COVID-19, 95% CI 7.1-12.3, Pr = 0.0088). On the contrary, receiving the basic COVID-19 immunization early during the pandemic (5.9% had COVID-19, 95% CI 5.1-6.8, Pr < 0.0001), regular smoking (3.6% had COVID-19, 95% CI 2.1-6.0, Pr = 0.0088), living with the elderly (3.0% had COVID-19, 95% CI 1.0-8.0, Pr = 0.0475), and frequent consumption of ready-to-eat meals (2.6% had COVID-19, 95% CI 1.1-5.4, Pr = 0.0045) were non-occupational factors potentially protecting study participants against COVID-19.

CONCLUSION

The newly discovered associations between the living situation, traveling as well as dietary habits and altered COVID-19 risk can potentially help refine containment measures and, furthermore, contribute to new mechanistic insights that may aid the protection of risk groups and vulnerable individuals.

Reviving health mediation during the COVID-19 crisis and beyond: an implementation study in deprived neighbourhoods of Marseille, France

Introduction

In 2020, during France's COVID-19 response, healthcare professionals from a hospital and an association initiated health mediation interventions in Marseille's vulnerable neighbourhoods, funded by the regional health authorities. This mixed method research evaluates the CORHESAN program that lasted until June 2022.

Methods

We examined CORHESAN documents and reports, conducted interviews, and analysed activity data, comparing it to the COVID-19 hotspots identified on a weekly basis at the neighbourhood level, using generalised linear mixed models (GLMMs).

Results

CORHESAN was implemented by a team of up to nine health mediators, six private nurses hired on an ad hoc basis, supervised by a general coordinator and two part-time medical and nursing coordinators. Multiple partnerships were established with shelters, associations, social-housing landlords and local institutions. The team accompanied 6,253 people affected by COVID-19 or contact in the practical implementation of their isolation and contact tracing. Of the 5,180 nasopharyngeal samples for RT-PCR and 1,875 for antigenic testing: 12% were taken at home and 27% in partner facilities in the targeted neighbourhoods; 32% were taken from symptomatic patients and 30% in the context of contact tracing; and 40% were positive. Multiple awareness sessions on prevention methods and distributions of personal protection kits and self-diagnostic tests were conducted in the streets, in shelters, in associations or at home. A total of 5,929 doses of COVID-19 vaccine were administered in a walk-in vaccination centre, at temporary street vaccination posts, during operations at partner facilities, or during home-visits to patients with limited autonomy. GLMMs showed that the intervention significantly targeted its testing interventions in neighbourhoods with socioeconomic disadvantage and/or past under-testing (adjusted odds ratio (aOR), 2.75 [1.50-5.00]) and those with high hotspot level (aOR for level-3 versus level-0, 1.83 [1.24-2.71]).

Discussion

The pandemic emphasised the potential of health mediation interventions to address health disparities. Building on this, a new program began in July 2022, aiming at enhancing cancer screening and vaccinations in deprived areas of Marseille. Evaluations are ongoing to assess its activities and impact, and provide evidence to future implementation initiatives.

A multiscale modeling framework for Scenario Modeling: Characterizing the heterogeneity of the COVID-19 epidemic in the US

The Scenario Modeling Hub (SMH) initiative provides projections of potential epidemic scenarios in the United States (US) by using a multi-model approach. Our contribution to the SMH is generated by a multiscale model that combines the global epidemic metapopulation modeling approach (GLEAM) with a local epidemic and mobility model of the US (LEAM-US), first introduced here. The LEAM-US model consists of 3142 subpopulations each representing a single county across the 50 US states and the District of Columbia, enabling us to project state and national trajectories of COVID-19 cases, hospitalizations, and deaths under different epidemic scenarios. The model is age-structured, and multi-strain. It integrates data on vaccine administration, human mobility, and non-pharmaceutical interventions. The model contributed to all 17 rounds of the SMH, and allows for the mechanistic characterization of the spatio-temporal heterogeneities observed during the COVID-19 pandemic. Here we describe the mathematical and computational structure of our model, and present the results concerning the emergence of the SARS-CoV-2 Alpha variant (lineage designation B.1.1.7) as a case study. Our findings show considerable spatial and temporal heterogeneity in the introduction and diffusion of the Alpha variant, both at the level of individual states and combined statistical areas, as it competes against the ancestral lineage. We discuss the key factors driving the time required for the Alpha variant to rise to dominance within a population, and quantify the impact that the emergence of the Alpha variant had on the effective reproduction number at the state level. Overall, we show that our multiscale modeling approach is able to capture the complexity and heterogeneity of the COVID-19 pandemic response in the US.

Inference under superspreading: Determinants of SARS-CoV-2 transmission in Germany

Superspreading, under-reporting, reporting delay, and confounding complicate statistical inference on determinants of disease transmission. A model that accounts for these factors within a Bayesian framework is estimated using German Covid-19 surveillance data. Compartments based on date of symptom onset, location, and age group allow to identify age-specific changes in transmission, adjusting for weather, reported prevalence, and testing and tracing. Several factors were associated with a reduction in transmission: public awareness rising, information on local prevalence, testing and tracing, high temperature, stay-at-home orders, and restaurant closures. However, substantial uncertainty remains for other interventions including school closures and mandatory face coverings. The challenge of disentangling the effects of different determinants is discussed and examined through a simulation study. On a broader perspective, the study illustrates the potential of surveillance data with demographic information and date of symptom onset to improve inference in the presence of under-reporting and reporting delay.

Age at death during the Covid-19 lockdown in French metropolitan regions: a non parametric quantile regression approach

BACKGROUND

Lockdowns have been implemented to limit the number of hospitalisations and deaths during the first wave of 2019 coronavirus disease. These measures may have affected differently death characteristics, such age and sex. France was one of the hardest hit countries in Europe with a decreasing east-west gradient in excess mortality. This study aimed at describing the evolution of age at death quantiles during the lockdown in spring 2020 (17 March-11 May 2020) in the French metropolitan regions focusing on 3 representatives of the epidemic variations in the country: Bretagne, Ile-de-France (IDF) and Bourgogne-Franche-Comté (BFC).

METHODS

Data were extracted from the French public mortality database from 1 January 2011 to 31 August 2020. The age distribution of mortality observed during the lockdown period (based on each decile, plus quantiles 1, 5, 95 and 99) was compared with the expected one using Bayesian non-parametric quantile regression.

RESULTS

During the lockdown, 5457, 5917 and 22 346 deaths were reported in Bretagne, BFC and IDF, respectively. An excess mortality from + 3% in Bretagne to + 102% in IDF was observed during lockdown compared to the 3 previous years. Lockdown led to an important increase in the first quantiles of age at death, irrespective of the region, while the increase was more gradual for older age groups. It corresponded to fewer young people, mainly males, dying during the lockdown, with an increase in the age at death in the first quantile of about 7 years across regions. In females, a less significant shift in the first quantiles and a greater heterogeneity between regions were shown. A greater shift was observed in eastern region and IDF, which may also represent excess mortality among the elderly.

CONCLUSIONS

This study focused on the innovative outcome of the age distribution at death. It shows the first quantiles of age at death increased differentially according to sex during the lockdown period, overall shift seems to depend on prior epidemic intensity before lockdown and complements studies on excess mortality during lockdowns.

Using a population-based Kalman estimator to model the COVID-19 epidemic in France: estimating associations between disease transmission and non-pharmaceutical interventions

In response to the COVID-19 pandemic caused by SARS-CoV-2, governments have adopted a wide range of non-pharmaceutical interventions (NPI). These include stringent measures such as strict lockdowns, closing schools, bars and restaurants, curfews, and barrier gestures such as mask-wearing and social distancing. Deciphering the effectiveness of each NPI is critical to responding to future waves and outbreaks. To this end, we first develop a dynamic model of the French COVID-19 epidemics over a one-year period. We rely on a global extended Susceptible-Infectious-Recovered (SIR) mechanistic model of infection that includes a dynamic transmission rate over time. Multilevel data across French regions are integrated using random effects on the parameters of the mechanistic model, boosting statistical power by multiplying integrated observation series. We estimate the parameters using a new population-based statistical approach based on a Kalman filter, used for the first time in analysing real-world data. We then fit the estimated time-varying transmission rate using a regression model that depends on the NPIs while accounting for vaccination coverage, the occurrence of variants of concern (VoC), and seasonal weather conditions. We show that all NPIs considered have an independent significant association with transmission rates. In addition, we show a strong association between weather conditions that reduces transmission in summer, and we also estimate increased transmissibility of VoC.

Spatial spread of COVID-19 during the early pandemic phase in Italy

Quantifying the potential spatial spread of an infectious pathogen is key to defining effective containment and control strategies. The aim of this study is to estimate the risk of SARS-CoV-2 transmission at different distances in Italy before the first regional lockdown was imposed, identifying important sources of national spreading. To do this, we leverage on a probabilistic model applied to daily symptomatic cases retrospectively ascertained in each Italian municipality with symptom onset between January 28 and March 7, 2020. Results are validated using a multi-patch dynamic transmission model reproducing the spatiotemporal distribution of identified cases. Our results show that the contribution of short-distance ( ≤ 10 k m ) transmission increased from less than 40% in the last week of January to more than 80% in the first week of March 2020. On March 7, 2020, that is the day before the first regional lockdown was imposed, more than 200 local transmission foci were contributing to the spread of SARS-CoV-2 in Italy. At the time, isolation measures imposed only on municipalities with at least ten ascertained cases would have left uncontrolled more than 75% of spillover transmission from the already affected municipalities. In early March, national-wide restrictions were required to curb short-distance transmission of SARS-CoV-2 in Italy.

Estimating SARS-CoV-2 infection probabilities with serological data and a Bayesian mixture model

The individual results of SARS-CoV-2 serological tests measured after the first pandemic wave of 2020 cannot be directly interpreted as a probability of having been infected. Plus, these results are usually returned as a binary or ternary variable, relying on predefined cut-offs. We propose a Bayesian mixture model to estimate individual infection probabilities, based on 81,797 continuous anti-spike IgG tests from Euroimmun collected in France after the first wave. This approach used serological results as a continuous variable, and was therefore not based on diagnostic cut-offs. Cumulative incidence, which is necessary to compute infection probabilities, was estimated according to age and administrative region. In France, we found that a "negative" or a "positive" test, as classified by the manufacturer, could correspond to a probability of infection as high as 61.8% or as low as 67.7%, respectively. "Indeterminate" tests encompassed probabilities of infection ranging from 10.8 to 96.6%. Our model estimated tailored individual probabilities of SARS-CoV-2 infection based on age, region, and serological result. It can be applied in other contexts, if estimates of cumulative incidence are available.

Progranulin promotes regulatory T cells plasticity by mitochondrial metabolism through AMPK/PGC-1α pathway in ARDS

As the aging population increases, the focus on elderly patients with acute respiratory distress syndrome (ARDS) is also increasing. In this article, we found progranulin (PGRN) differential expression in ARDS patients and healthy controls, even in young and old ARDS patients. Its expression strongly correlates with several cytokines in both young and elderly ARDS patients. PGRN has comparable therapeutic effects in young and elderly mice with lipopolysaccharide-induced acute lung injury, manifesting as lung injury, apoptosis, inflammation, and regulatory T cells (Tregs) differentiation. Considering that Tregs differentiation relies on metabolic reprogramming, we discovered that Tregs differentiation was mediated by mitochondrial function, especially in the aged population. Furthermore, we demonstrated that PGRN alleviated the mitochondrial damage during Tregs differentiation through the AMPK/PGC-1α pathway in T cells. Collectively, PGRN may regulate mitochondria function to promote Tregs differentiation through the AMPK/PGC-1α pathway to improve ARDS.

Exploring factors shaping antibiotic resistance patterns in Streptococcus pneumoniae during the 2020 COVID-19 pandemic

Non-pharmaceutical interventions implemented to block SARS-CoV-2 transmission in early 2020 led to global reductions in the incidence of invasive pneumococcal disease (IPD). By contrast, most European countries reported an increase in antibiotic resistance among invasive Streptococcus pneumoniae isolates from 2019 to 2020, while an increasing number of studies reported stable pneumococcal carriage prevalence over the same period. To disentangle the impacts of the COVID-19 pandemic on pneumococcal epidemiology in the community setting, we propose a mathematical model formalizing simultaneous transmission of SARS-CoV-2 and antibiotic-sensitive and -resistant strains of S. pneumoniae. To test hypotheses underlying these trends five mechanisms were built into the model and examined: (1) a population-wide reduction of antibiotic prescriptions in the community, (2) lockdown effect on pneumococcal transmission, (3) a reduced risk of developing an IPD due to the absence of common respiratory viruses, (4) community azithromycin use in COVID-19 infected individuals, (5) and a longer carriage duration of antibiotic-resistant pneumococcal strains. Among 31 possible pandemic scenarios involving mechanisms individually or in combination, model simulations surprisingly identified only two scenarios that reproduced the reported trends in the general population. They included factors (1), (3), and (4). These scenarios replicated a nearly 50% reduction in annual IPD, and an increase in antibiotic resistance from 20% to 22%, all while maintaining a relatively stable pneumococcal carriage. Exploring further, higher SARS-CoV-2 R0 values and synergistic within-host virus-bacteria interaction mechanisms could have additionally contributed to the observed antibiotic resistance increase. Our work demonstrates the utility of the mathematical modeling approach in unraveling the complex effects of the COVID-19 pandemic responses on AMR dynamics.

Estimating the population effectiveness of interventions against COVID-19 in France: A modelling study

BACKGROUND

Non-pharmaceutical interventions (NPIs) and vaccines have been widely used to manage the COVID-19 pandemic. However, uncertainty persists regarding the effectiveness of these interventions due to data quality issues, methodological challenges, and differing contextual factors. Accurate estimation of their effects is crucial for future epidemic preparedness.

METHODS

To address this, we developed a population-based mechanistic model that includes the impact of NPIs and vaccines on SARS-CoV-2 transmission and hospitalization rates. Our statistical approach estimated all parameters in one step, accurately propagating uncertainty. We fitted the model to comprehensive epidemiological data in France from March 2020 to October 2021. With the same model, we simulated scenarios of vaccine rollout.

RESULTS

The first lockdown was the most effective, reducing transmission by 84 % (95 % confidence interval (CI) 83-85). Subsequent lockdowns had diminished effectiveness (reduction of 74 % (69-77) and 11 % (9-18), respectively). A 6 pm curfew was more effective than one at 8 pm (68 % (66-69) vs. 48 % (45-49) reduction), while school closures reduced transmission by 15 % (12-18). In a scenario without vaccines before November 2021, we predicted 159,000 or 168 % (95 % prediction interval (PI) 70-315) more deaths and 1,488,000 or 300 % (133-492) more hospitalizations. If a vaccine had been available after 100 days, over 71,000 deaths (16,507-204,249) and 384,000 (88,579-1,020,386) hospitalizations could have been averted.

CONCLUSION

Our results highlight the substantial impact of NPIs, including lockdowns and curfews, in controlling the COVID-19 pandemic. We also demonstrate the value of the 100 days objective of the Coalition for Epidemic Preparedness Innovations (CEPI) initiative for vaccine availability.

Modeling NO2 air pollution variation during and after COVID-19-regulation using principal component analysis of satellite imagery

By implementing Principal Component Analysis (PCA) of multitemporal satellite data, this paper presents modeling solutions for air pollutant variation in three scenarios related to COVID-19 lockdown: pre, during, and after lockdown. Tropospheric NO2 satellite data from Sentinel-5P was used. Two novel PCA-models were developed: Weighted Principal Component Analysis (WPCA) and Rescaled Principal Component Analysis (RPCA). Model results were tested for goodness-of-fit to empirical NO2 data. The models were used to predict actual near-surface NO2 concentrations. Model-predicted NO2 concentrations were validated with NO2 data acquired at ground monitoring stations. Besides, meteorological bias affecting NO2 was assessed. It was found that the weather component had substantial impact on NO2 built-ups, propitiating air pollutant decrease during lockdown and increase after. WPCA and RPCA models well fitted to observed NO2. Both models accurately estimated near-surface NO2 concentrations. Modeled NO2 variation results evidenced the prolongated effect of the total lockdown (up to half a year). Model-predicted NO2 concentrations were found to highly correlate with monitoring station NO2 data collected on the ground. It is concluded that PCA is reliable in identifying and predicting air pollution variation patterns. The implementation of PCA is recommended when analyzing other pollutant gases.

Unlocking the importance of perceived governance: The impact on COVID-19 in NUTS-2 European regions

In the immediate aftermath of the pandemic, governments implemented non-pharmaceutical interventions (NPIs). Previous literature suggests that NPI effectiveness is influenced by governance quality. The acceptance and perceived necessity of these measures by the public are crucial to their success, as NPIs cannot be easily enforced without public support. Does regional governance also play a role? This study examines the correlation between the quality of governance in European NUTS-2 regions and the spread of COVID-19. The findings indicate that overall perceived governance, and its perceived quality and corruption pillars, significantly impact the effectiveness of these interventions. This effect was pronounced during the first wave and then diminished in importance, disappearing before vaccines were available, suggesting that regional governance matters especially in the immediate aftermath of an exogenous shock.

Smoking changes adaptive immunity with persistent effects

Individuals differ widely in their immune responses, with age, sex and genetic factors having major roles in this inherent variability1-6. However, the variables that drive such differences in cytokine secretion-a crucial component of the host response to immune challenges-remain poorly defined. Here we investigated 136 variables and identified smoking, cytomegalovirus latent infection and body mass index as major contributors to variability in cytokine response, with effects of comparable magnitudes with age, sex and genetics. We find that smoking influences both innate and adaptive immune responses. Notably, its effect on innate responses is quickly lost after smoking cessation and is specifically associated with plasma levels of CEACAM6, whereas its effect on adaptive responses persists long after individuals quit smoking and is associated with epigenetic memory. This is supported by the association of the past smoking effect on cytokine responses with DNA methylation at specific signal trans-activators and regulators of metabolism. Our findings identify three novel variables associated with cytokine secretion variability and reveal roles for smoking in the short- and long-term regulation of immune responses. These results have potential clinical implications for the risk of developing infections, cancers or autoimmune diseases.

The impact of spatial connectivity on NPIs effectiveness

BACKGROUND

France implemented a combination of non-pharmaceutical interventions (NPIs) to manage the COVID-19 pandemic between September 2020 and June 2021. These included a lockdown in the fall 2020 - the second since the start of the pandemic - to counteract the second wave, followed by a long period of nighttime curfew, and by a third lockdown in the spring 2021 against the Alpha wave. Interventions have so far been evaluated in isolation, neglecting the spatial connectivity between regions through mobility that may impact NPI effectiveness.

METHODS

Focusing on September 2020-June 2021, we developed a regionally-based epidemic metapopulation model informed by observed mobility fluxes from daily mobile phone data and fitted the model to regional hospital admissions. The model integrated data on vaccination and variants spread. Scenarios were designed to assess the impact of the Alpha variant, characterized by increased transmissibility and risk of hospitalization, of the vaccination campaign and alternative policy decisions.

RESULTS

The spatial model better captured the heterogeneity observed in the regional dynamics, compared to models neglecting inter-regional mobility. The third lockdown was similarly effective to the second lockdown after discounting for immunity, Alpha, and seasonality (51% vs 52% median regional reduction in the reproductive number R0, respectively). The 6pm nighttime curfew with bars and restaurants closed, implemented in January 2021, substantially reduced COVID-19 transmission. It initially led to 49% median regional reduction of R0, decreasing to 43% reduction by March 2021. In absence of vaccination, implemented interventions would have been insufficient against the Alpha wave. Counterfactual scenarios proposing a sequence of lockdowns in a stop-and-go fashion would have reduced hospitalizations and restriction days for low enough thresholds triggering and lifting restrictions.

CONCLUSIONS

Spatial connectivity induced by mobility impacted the effectiveness of interventions especially in regions with higher mobility rates. Early evening curfew with gastronomy sector closed allowed authorities to delay the third wave. Stop-and-go lockdowns could have substantially lowered both healthcare and societal burdens if implemented early enough, compared to the observed application of lockdown-curfew-lockdown, but likely at the expense of several labor sectors. These findings contribute to characterize the effectiveness of implemented strategies and improve pandemic preparedness.

Influencing factors of hospitalization in maintenance haemodialysis outpatients after a diagnosis of COVID-19

BACKGROUND

The clinical manifestations of maintenance haemodialysis (MHD) outpatients diagnosed with coronavirus disease 2019 (COVID-19) are highly heterogeneous. They are prone to progress to severe conditions, and they often require hospitalization. To better guide the management of MHD outpatients, this retrospective observational study assessed risk factors for hospitalization of MHD patients after a diagnosis of COVID-19.

METHODS

The demographic data, comorbidities, laboratory indicators and imaging data of 128 MHD outpatients at our haemodialysis centre with confirmed COVID-19 infection from December 2022 to January 2023 were collected. The relationships between these factors and hospitalization of patients were analyzed.

RESULTS

Among the 128 patients, 25 (19.53%) were hospitalized. One of the 25 inpatients was mechanically ventilated, and two of them died. Multivariate logistic regression analysis showed that the hospitalization rate was correlated with age, comorbid diabetes and peripheral blood lymphocyte count.

CONCLUSION

Older age, comorbid diabetes and lower lymphocyte count are important risk factors for hospitalization of MHD outpatients after a diagnosis of COVID-19. Focusing on these factors may help in early identification of patients who may need to be admitted due to potential disease progression.

Bayesian monitoring of COVID-19 in Sweden

In an effort to provide regional decision support for the public healthcare, we design a data-driven compartment-based model of COVID-19 in Sweden. From national hospital statistics we derive parameter priors, and we develop linear filtering techniques to drive the simulations given data in the form of daily healthcare demands. We additionally propose a posterior marginal estimator which provides for an improved temporal resolution of the reproduction number estimate as well as supports robustness checks via a parametric bootstrap procedure. From our computational approach we obtain a Bayesian model of predictive value which provides important insight into the progression of the disease, including estimates of the effective reproduction number, the infection fatality rate, and the regional-level immunity. We successfully validate our posterior model against several different sources, including outputs from extensive screening programs. Since our required data in comparison is easy and non-sensitive to collect, we argue that our approach is particularly promising as a tool to support monitoring and decisions within public health. Significance: Using public data from Swedish patient registries we develop a national-scale computational model of COVID-19. The parametrized model produces valuable weekly predictions of healthcare demands at the regional level and validates well against several different sources. We also obtain critical epidemiological insights into the disease progression, including, e.g., reproduction number, immunity and disease fatality estimates. The success of the model hinges on our novel use of filtering techniques which allows us to design an accurate data-driven procedure using data exclusively from healthcare demands, i.e., our approach does not rely on public testing and is therefore very cost-effective.

Application of Machine Learning Prediction of Individual SARS-CoV-2 Vaccination and Infection Status to the French Serosurveillance Survey From March 2020 to 2022: Cross-Sectional Study

BACKGROUND

The seroprevalence of SARS-CoV-2 infection in the French population was estimated with a representative, repeated cross-sectional survey based on residual sera from routine blood testing. These data contained no information on infection or vaccination status, thus limiting the ability to detail changes observed in the immunity level of the population over time.

OBJECTIVE

Our aim is to predict the infected or vaccinated status of individuals in the French serosurveillance survey based only on the results of serological assays. Reference data on longitudinal serological profiles of seronegative, infected, and vaccinated individuals from another French cohort were used to build the predictive model.

METHODS

A model of individual vaccination or infection status with respect to SARS-CoV-2 obtained from a machine learning procedure was proposed based on 3 complementary serological assays. This model was applied to the French nationwide serosurveillance survey from March 2020 to March 2022 to estimate the proportions of the population that were negative, infected, vaccinated, or infected and vaccinated.

RESULTS

From February 2021 to March 2022, the estimated percentage of infected and unvaccinated individuals in France increased from 7.5% to 16.8%. During this period, the estimated percentage increased from 3.6% to 45.2% for vaccinated and uninfected individuals and from 2.1% to 29.1% for vaccinated and infected individuals. The decrease in the seronegative population can be largely attributed to vaccination.

CONCLUSIONS

Combining results from the serosurveillance survey with more complete data from another longitudinal cohort completes the information retrieved from serosurveillance while keeping its protocol simple and easy to implement.

Impact of vaccinations, boosters and lockdowns on COVID-19 waves in French Polynesia

Estimating the impact of vaccination and non-pharmaceutical interventions on COVID-19 incidence is complicated by several factors, including successive emergence of SARS-CoV-2 variants of concern and changing population immunity from vaccination and infection. We develop an age-structured multi-strain COVID-19 transmission model and inference framework to estimate vaccination and non-pharmaceutical intervention impact accounting for these factors. We apply this framework to COVID-19 waves in French Polynesia and estimate that the vaccination programme averted 34.8% (95% credible interval: 34.5-35.2%) of 223,000 symptomatic cases, 49.6% (48.7-50.5%) of 5830 hospitalisations and 64.2% (63.1-65.3%) of 1540 hospital deaths that would have occurred in a scenario without vaccination up to May 2022. We estimate the booster campaign contributed 4.5%, 1.9%, and 0.4% to overall reductions in cases, hospitalisations, and deaths. Our results suggest that removing lockdowns during the first two waves would have had non-linear effects on incidence by altering accumulation of population immunity. Our estimates of vaccination and booster impact differ from those for other countries due to differences in age structure, previous exposure levels and timing of variant introduction relative to vaccination, emphasising the importance of detailed analysis that accounts for these factors.

Impact of vaccination against severe COVID-19 in the French population aged 50 years and above: a retrospective population-based study

BACKGROUND

Given the widespread implementation of COVID-19 vaccination to mitigate the pandemic from the end of 2020, it is important to retrospectively evaluate its impact, in particular by quantifying the number of severe outcomes prevented through vaccination.

METHODS

We estimated the number of hospitalizations, intensive care unit (ICU) admissions and deaths directly averted by vaccination in France, in people aged ≥ 50 years, from December 2020 to March 2022, based on (1) the number of observed events, (2) vaccination coverage, and (3) vaccine effectiveness. We accounted for the effect of primary vaccination and the first booster dose, the circulating variants, the age groups, and the waning of vaccine-induced protection over time.

RESULTS

An estimated 480,150 (95% CI: 260,072-582,516) hospitalizations, 132,156 (50,409-157,767) ICU admissions and 125,376 (53,792-152,037) deaths were directly averted by vaccination in people aged ≥ 50 years, which corresponds to a reduction of 63.2% (48.2-67.6), 68.7% (45.6-72.4) and 62.7% (41.9-67.1) respectively, compared to what would have been expected without vaccination over the study period. An estimated 5852 (2285-6853) deaths were directly averted among the 50-59 years old, 16,837 (6568-19,473) among the 60-69 years old, 32,136 (13,651-36,758) among the 70-79 years old and 70,551 (31,288-88,953) among the ≥ 80 years old.

CONCLUSIONS

The vaccination campaign in France considerably reduced COVID-19 morbidity and mortality, as well as stress on the healthcare system.

Risk Factors for Long COVID in Older Adults

As time has passed following the COVID-19 pandemic, individuals infected with SARS-CoV-2 have gradually exhibited a variety of symptoms associated with long COVID in the postacute phase of infection. Simultaneously, in many countries worldwide, the process of population aging has been accelerating. Within this context, the elderly population has not only become susceptible and high-risk during the acute phase of COVID-19 but also has considerable risks when confronting long COVID. Elderly individuals possess specific immunological backgrounds, and during the process of aging, their immune systems can enter a state known as "immunosenescence". This further exacerbates "inflammaging" and the development of various comorbidities in elderly individuals, rendering them more susceptible to long COVID. Additionally, long COVID can inflict both physical and mental harm upon elderly people, thereby reducing their overall quality of life. Consequently, the impact of long COVID on elderly people should not be underestimated. This review seeks to summarize the infection characteristics and intrinsic factors of older adults during the COVID-19 pandemic, with a focus on the physical and mental impact of long COVID. Additionally, it aims to explore potential strategies to mitigate the risk of long COVID or other emerging infectious diseases among older adults in the future.

Monitoring the reproductive number of COVID-19 in France: Comparative estimates from three datasets

BACKGROUND

The effective reproduction number (Rt) quantifies the average number of secondary cases caused by one person with an infectious disease. Near-real-time monitoring of Rt during an outbreak is a major indicator used to monitor changes in disease transmission and assess the effectiveness of interventions. The estimation of Rt usually requires the identification of infected cases in the population, which can prove challenging with the available data, especially when asymptomatic people or with mild symptoms are not usually screened. The purpose of this study was to perform sensitivity analysis of Rt estimates for COVID-19 surveillance in France based on three data sources with different sensitivities and specificities for identifying infected cases.

METHODS

We applied a statistical method developed by Cori et al. to estimate Rt using (1) confirmed cases identified from positive virological tests in the population, (2) suspected cases recorded by a national network of emergency departments, and (3) COVID-19 hospital admissions recorded by a national administrative system to manage hospital organization.

RESULTS

Rt estimates in France from May 27, 2020, to August 12, 2022, showed similar temporal trends regardless of the dataset. Estimates based on the daily number of confirmed cases provided an earlier signal than the two other sources, with an average lag of 3 and 6 days for estimates based on emergency department visits and hospital admissions, respectively.

CONCLUSION

The COVID-19 experience confirmed that monitoring temporal changes in Rt was a key indicator to help the public health authorities control the outbreak in real time. However, gaining access to data on all infected people in the population in order to estimate Rt is not straightforward in practice. As this analysis has shown, the opportunity to use more readily available data to estimate Rt trends, provided that it is highly correlated with the spread of infection, provides a practical solution for monitoring the COVID-19 pandemic and indeed any other epidemic.

Production-sharing of critical resources with dynamic demand under pandemic situation: The COVID-19 pandemic

The COVID-19 virus's high transmissibility has resulted in the virus's rapid spread throughout the world, which has brought several repercussions, ranging from a lack of sanitary and medical products to the collapse of medical systems. Hence, governments attempt to re-plan the production of medical products and reallocate limited health resources to combat the pandemic. This paper addresses a multi-period production-inventory-sharing problem (PISP) to overcome such a circumstance, considering two consumable and reusable products. We introduce a new formulation to decide on production, inventory, delivery, and sharing quantities. The sharing will depend on net supply balance, allowable demand overload, unmet demand, and the reuse cycle of reusable products. Undeniably, the dynamic demand for products during pandemic situations must be reflected effectively in addressing the multi-period PISP. A bespoke compartmental susceptible-exposed-infectious-hospitalized-recovered-susceptible (SEIHRS) epidemiological model with a control policy is proposed, which also accounts for the influence of people's behavioral response as a result of the knowledge of adequate precautions. An accelerated Benders decomposition-based algorithm with tailored valid inequalities is offered to solve the model. Finally, we consider a realistic case study - the COVID-19 pandemic in France - to examine the computational proficiency of the decomposition method. The computational results reveal that the proposed decomposition method coupled with effective valid inequalities can solve large-sized test problems in a reasonable computational time and 9.88 times faster than the commercial Gurobi solver. Moreover, the sharing mechanism reduces the total cost of the system and the unmet demand on the average up to 32.98% and 20.96%, respectively.

Gender disparities in symptomology of COVID-19 among adults in Arkansas

Only a few studies and reports assessing the natural history and symptomatology for COVID-19 by gender have been reported in literature to date. Thus, the objective of this study was to examine patterns in symptomology of COVID-19 by gender among a diverse adult population in Arkansas. Data on COVID-19 symptoms was collected at day of testing, 7th day and 14th day among participants at UAMS mobile testing units throughout the state of Arkansas. Diagnosis for SARS-CoV-2 infection was confirmed via nasopharyngeal swab and RT-PCR methods. Data analysis was conducted using Chi-square test and Poisson regression to assess the differences in characteristics by gender. A total of 60,648 community members and patients of Arkansas received RT-PCR testing. Among adults testing positive, we observed a statistically significant difference for fever (p < 0.001) and chills (p = 0.04). Males were more likely to report having a fever (22.6% vs. 17.1%; p < 0.001) and chills (14.9% vs. 12.6%; p = 0.04) compared to females. Among adults testing negative, females were more likely to report each symptom than males. To conclude, we observed a greater prevalence of certain symptoms such as fever and chills among men testing positive for COVID-19, compared to women during the time of testing. These differences elucidate the important issue of rapidly emerging health disparities during the COVID-19 pandemic.

Excess Mortality in Kidney and Kidney-Pancreas Transplant Recipients in the COVID-19 Pandemic in Portugal-A Cohort Study

The COVID-19 pandemic increased morbidity and mortality worldwide, particularly in the Kidney and Kidney-Pancreas Transplant Recipient (KTR/KPTR) population. Aiming at assessing the absolute and relative excess mortality (EM) in a Portuguese KTR/KPTR cohort, we conducted a retrospective observational study of two KTR/KPTRs cohorts: cohort 1 (P1; n = 2,179) between September/2012 and March/2020; cohort 2 (P2; n = 2067) between March/2020, and August/2022. A correlation between relative and absolute EM and age, sex, time from transplantation and cause of death was explored. A total of 145 and 84 deaths by all causes were observed in P1 and P2, respectively. The absolute EM in P2 versus P1 was 19.2 deaths (observed/expected mortality ratio 1.30, p = 0.006), and the relative EM was 1.47/1,000 person-months (95% CI 1.11-1.93, p = 0.006). Compared to the same period in the general population, the standardized mortality rate by age in P2 was 3.86 (95% CI 2.40-5.31), with a peak at 9.00 (95% CI 4.84-13.16) in P2C. The higher EM identified in this population was associated, mainly, with COVID-19 infection, with much higher values during the second seasonal COVID-19 peak when compared to the general population, despite generalized vaccination. These highlight the need for further preventive measures and improved therapies in these patients.

Measuring the efficacy of a vaccine during an epidemic

The urgency to develop vaccines during the COVID-19 pandemic has resulted in the acceleration of clinical trials. Specifically, a broad spectrum of efficacy levels has been reported for various vaccines based on phase III cohort studies. Our study demonstrates that conducting large cohort phase III clinical trials during the peak of an epidemic leads to a significant underestimation of vaccine efficacy, even in the absence of confounding factors. Furthermore, we find that this underestimation increases with the proportion of infectious individuals in the population during the experiment and the severity of the epidemic, as measured by its basic reproduction number.

Evaluating the strategies to control SARS-CoV-2 Delta variant spread in New Caledonia, a zero-COVID country until September 2021

Objectives

New Caledonia, a former zero-COVID country, was confronted with a SARS-CoV-2 Delta variant outbreak in September 2021. We evaluate the relative contribution of vaccination, lockdown, and timing of interventions on healthcare burden.

Methods

We developed an age-stratified mathematical model of SARS-CoV-2 transmission and vaccination calibrated for New Caledonia and evaluated three alternative scenarios.

Results

High virus transmission early on was estimated, with R0 equal to 6.6 (95% confidence interval [6.4-6.7]). Lockdown reduced R0 by 73% (95% confidence interval [70-76%]). Easing the lockdown increased transmission (39% reduction of the initial R0); but we did not observe an epidemic rebound. This contrasts with the rebound in hospital admissions (+116% total hospital admissions) that would have been expected in the absence of an intensified vaccination campaign (76,220 people or 34% of the eligible population were first-dose vaccinated during 1 month of lockdown). A 15-day earlier lockdown would have led to a significant reduction in the magnitude of the epidemic (-53% total hospital admissions).

Conclusion

The success of the response against the Delta variant epidemic in New Caledonia was due to an effective lockdown that provided additional time for people to vaccinate. Earlier lockdown would have greatly mitigated the magnitude of the epidemic.

SARS-CoV-2 Serology Across Scales: A Framework for Unbiased Estimation of Cumulative Incidence Incorporating Antibody Kinetics and Epidemic Recency

Serosurveys are a key resource for measuring severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) population exposure. A growing body of evidence suggests that asymptomatic and mild infections (together making up over 95% of all infections) are associated with lower antibody titers than severe infections. Antibody levels also peak a few weeks after infection and decay gradually. We developed a statistical approach to produce estimates of cumulative incidence from raw seroprevalence survey results that account for these sources of spectrum bias. We incorporate data on antibody responses on multiple assays from a postinfection longitudinal cohort, along with epidemic time series to account for the timing of a serosurvey relative to how recently individuals may have been infected. We applied this method to produce estimates of cumulative incidence from 5 large-scale SARS-CoV-2 serosurveys across different settings and study designs. We identified substantial differences between raw seroprevalence and cumulative incidence of over 2-fold in the results of some surveys, and we provide a tool for practitioners to generate cumulative incidence estimates with preset or custom parameter values. While unprecedented efforts have been launched to generate SARS-CoV-2 seroprevalence estimates over this past year, interpretation of results from these studies requires properly accounting for both population-level epidemiologic context and individual-level immune dynamics.

Clinical characteristics and predictors for in-hospital mortality in adult COVID-19 patients: A retrospective single center cohort study in Vilnius, Lithuania

BACKGROUND

The COVID-19 infection had spread worldwide causing many deaths. Mortality rates and patients' characteristics varied within and between countries, making it important to understand the peculiarities of different populations. The aim of this study was to identify the main predictors associated with in-hospital mortality due to COVID-19 in Vilnius, Lithuania.

MATERIALS AND METHODS

This was a retrospective observational cohort study conducted at Vilnius University Hospital Santaros Clinics, Lithuania. The study included SARS-CoV-2 positive patients aged over 18 years and hospitalized between March 2020 and May 2021. Depersonalized data were retrieved from electronic medical records. The predictive values of laboratory parameters were evaluated using ROC analysis. Multivariable binary logistic regression was performed to reveal predictors of in-hospital mortality due to COVID-19.

RESULTS

Among 2794 patients, 54.4% were male, the age median was 59 years (IQR 48-70), 47.4% had at least one comorbidity. The most common comorbidities were arterial hypertension (36.9%) and diabetes mellitus (13.7%). Overall, 12.7% of patients died. Multivariable regression revealed that age (OR 1.04, 95%CI 1.02-1.06), congestive heart failure (OR 3.06, 95%CI 1.96-4.77), obesity (OR 3.90, 95%CI 2.12-7.16), COPD (OR 2.92, 95%CI 1.12-7.60), previous stroke (OR 5.80, 95%CI 2.07-16.21), urea >7.01 mmol/l (OR 2.32, 95%CI 1.47-3.67), AST/ALT >1.49 (OR 1.54, 95%CI 1.08-2.21), LDH >452.5 U/l (OR 2.60, 95%CI 1.74-3.88), CRP >92.68 mg/l (OR 1.58, 95%CI 1.06-2.35), IL-6 >69.55 ng/l (OR 1.62, 95%CI 1.10-2.40), and troponin I >18.95 ng/l (OR 2.04, 95%CI 1.38-3.02), were associated with increased risk for in-hospital mortality in COVID-19 patients.

CONCLUSIONS

Age, congestive heart failure, obesity, COPD, prior stroke, and increased concentration of urea, LDH, CRP, IL-6, troponin I, ALT to AST ratio were identified to be the predictors for in-hospital mortality of COVID-19 patients.