Estimating the burden of SARS-CoV-2 in France

Application of recommended preventive measures against COVID-19 could help mitigate the risk of SARS-CoV-2 infection during dental practice: Results from a follow-up survey of French dentists

Background

During the first-wave of the COVID-19 pandemic, dentists were considered at high-risk of infection. In France, to stop the spread of SARS-CoV-2, a nationwide lockdown was enforced, during which dentists suspended their routine clinical activities, working solely on dental emergencies. This measure has had an indisputable mitigating effect on the pandemic. To continue protecting dentists after suspension of nationwide lockdown, implementation of preventive measures was recommended, including adequate personal protective equipment (PPE) and room aeration between patients. No study has explored whether implementation of such preventive measures since the end of the first-wave has had an impact on the contamination of dentists.

Methods

An online survey was conducted within a French dentist population between July and September 2020. To explore risk factors associated with COVID-19, univariate and multivariate logistic regression analyses were performed.

Results

The results showed that COVID-19 prevalence among the 3497 respondents was 3.6%. Wearing surgical masks during non-aerosol generating procedures was a risk factor of COVID-19, whereas reducing the number of patients was a protective factor.

Conclusions

Considering the similar COVID-19 prevalence between dentists and the general population, such data suggest that dentists are not overexposed in their work environment when adequate preventive measures are applied.

Impact

Dentists should wear specific PPE (FFP2, FFP3 or (K)N95 masks) including during non-aerosol generating procedures and reduce the number of patients to allow proper implementation of disinfection and aeration procedures. Considering the similarities between COVID-19 and other viral respiratory infections, such preventive measures may also be of interest to limit emerging variants spread as well as seasonal viral outbreaks.

Predicting COVID-19 confirmed cases in New York and DKI Jakarta by nonlinear fitting of a Bose-Einstein energy distribution and its implications on social restrictions

Objective

Global society pays huge economic toll and live loss due to COVID-19 (Coronavirus Disease 2019) pandemic. In order to have a better management of this pandemic, many institutions develop their own models to predict number of COVID-19 cases, hospitalizations and mortalities. These models, however, are shown to be unreliable and need to be revised on a daily basis.

Methods

Here, we develop a Bose–Einstein (BE)-based statistical model to predict daily COVID-19 cases up to 14 days in advance. This fat-tailed model is chosen based on three reasons. First, it contains a peak and decaying phase. Second, it also has both accelerated and decelerated phases which are similarly observed in an epidemic curve. Third, the shape of both the BE energy distribution and the epidemic curve is controlled by a set of parameters. The BE model daily predictions are then verified against simulated data and confirmed COVID-19 daily cases from two epidemic centres, i.e. New York and DKI Jakarta.

Result

Over- predictions occur at the earlier stage of the epidemic for all data sets. Models parameters for both simulated and New York data converge to a certain value only at the latest stage of the epidemic progress. At this stage, model's skill is high for both simulated and New York data, i.e. the predictability is greater than 80% with decreasing RMSE. On the other hand, at that stage, the DKI's model's predictability is still fluctuating with increasing RMSE.

Conclusion

This implies that New York could leave the stay-at-home order, but DKI Jakarta should continue its large-scale social restriction order. There remains a great challenge in predicting the full course of an epidemic using small data collected during the earlier phase of the epidemic.

Estimating a time-to-event distribution from right-truncated data in an epidemic: A review of methods

Time-to-event data are right-truncated if only individuals who have experienced the event by a certain time can be included in the sample. For example, we may be interested in estimating the distribution of time from onset of disease symptoms to death and only have data on individuals who have died. This may be the case, for example, at the beginning of an epidemic. Right truncation causes the distribution of times to event in the sample to be biased towards shorter times compared to the population distribution, and appropriate statistical methods should be used to account for this bias. This article is a review of such methods, particularly in the context of an infectious disease epidemic, like COVID-19. We consider methods for estimating the marginal time-to-event distribution, and compare their efficiencies. (Non-)identifiability of the distribution is an important issue with right-truncated data, particularly at the beginning of an epidemic, and this is discussed in detail. We also review methods for estimating the effects of covariates on the time to event. An illustration of the application of many of these methods is provided, using data on individuals who had died with coronavirus disease by 5 April 2020.

Quarantine and testing strategies to ameliorate transmission due to travel during the COVID-19 pandemic: a modelling study

BACKGROUND

Numerous countries imposed strict travel restrictions, contributing to the large socioeconomic burden during the COVID-19 pandemic. The long quarantines that apply to contacts of cases may be excessive for travel policy.

METHODS

We developed an approach to evaluate imminent countrywide COVID-19 infections after 0-14-day quarantine and testing. We identified the minimum travel quarantine duration such that the infection rate within the destination country did not increase compared to a travel ban, defining this minimum quarantine as "sufficient."

FINDINGS

We present a generalised analytical framework and a specific case study of the epidemic situation on November 21, 2021, for application to 26 European countries. For most origin-destination country pairs, a three-day or shorter quarantine with RT-PCR or antigen testing on exit suffices. Adaptation to the European Union traffic-light risk stratification provided a simplified policy tool. Our analytical approach provides guidance for travel policy during all phases of pandemic diseases.

INTERPRETATION

For nearly half of origin-destination country pairs analysed, travel can be permitted in the absence of quarantine and testing. For the majority of pairs requiring controls, a short quarantine with testing could be as effective as a complete travel ban. The estimated travel quarantine durations are substantially shorter than those specified for traced contacts.

FUNDING

EasyJet (JPT and APG), the Elihu endowment (JPT), the Burnett and Stender families' endowment (APG), the Notsew Orm Sands Foundation (JPT and APG), the National Institutes of Health (MCF), Canadian Institutes of Health Research (SMM) and Natural Sciences and Engineering Research Council of Canada EIDM-MfPH (SMM).

Synchronization in epidemic growth and the impossibility of selective containment

Containment, aiming to prevent the epidemic stage of community-spreading altogether, and mitigation, aiming to merely ‘flatten the curve’ of a wide-ranged outbreak, constitute two qualitatively different approaches to combating an epidemic through non-pharmaceutical interventions. Here, we study a simple model of epidemic dynamics separating the population into two groups, namely a low-risk group and a high-risk group, for which different strategies are pursued. Due to synchronization effects, we find that maintaining a slower epidemic growth behaviour for the high-risk group is unstable against any finite coupling between the two groups. More precisely, the density of infected individuals in the two groups qualitatively evolves very similarly, apart from a small time delay and an overall scaling factor quantifying the coupling between the groups. Hence, selective containment of the epidemic in a targeted (high-risk) group is practically impossible whenever the surrounding society implements a mitigated community-spreading. We relate our general findings to the ongoing COVID-19 pandemic.

Optimal vaccine allocation for COVID-19 in the Netherlands: A data-driven prioritization

For the control of COVID-19, vaccination programmes provide a long-term solution. The amount of available vaccines is often limited, and thus it is crucial to determine the allocation strategy. While mathematical modelling approaches have been used to find an optimal distribution of vaccines, there is an excessively large number of possible allocation schemes to be simulated. Here, we propose an algorithm to find a near-optimal allocation scheme given an intervention objective such as minimization of new infections, hospitalizations, or deaths, where multiple vaccines are available. The proposed principle for allocating vaccines is to target subgroups with the largest reduction in the outcome of interest. We use an approximation method to reconstruct the age-specific transmission intensity (the next generation matrix), and express the expected impact of vaccinating each subgroup in terms of the observed incidence of infection and force of infection. The proposed approach is firstly evaluated with a simulated epidemic and then applied to the epidemiological data on COVID-19 in the Netherlands. Our results reveal how the optimal allocation depends on the objective of infection control. In the case of COVID-19, if we wish to minimize deaths, the optimal allocation strategy is not efficient for minimizing other outcomes, such as infections. In simulated epidemics, an allocation strategy optimized for an outcome outperforms other strategies such as the allocation from young to old, from old to young, and at random. Our simulations clarify that the current policy in the Netherlands (i.e., allocation from old to young) was concordant with the allocation scheme that minimizes deaths. The proposed method provides an optimal allocation scheme, given routine surveillance data that reflect ongoing transmissions. This approach to allocation is useful for providing plausible simulation scenarios for complex models, which give a more robust basis to determine intervention strategies.

Effects of the Covid-19 pandemic on head lice and scabies infestation dynamics: a population based study in France

BACKGROUND

Lockdowns and physical distancing have dramatically limited the circulation of SARS-CoV-2 and other common communicable infections. However, little is known about their impact on head lice and scabies.

OBJECTIVES

To assess the impact of the 2020 French National lockdowns (March 17^th -May 11^th , and Oct 30^th -Dec 15^th ) and physical distancing recommendations (from February 2020) on the head lice and scabies infestation dynamics.

METHODS

The weekly sales of topical head lice treatments, topical scabies treatments, and oral ivermectin were extracted from the IQVIA database (60% of all French retail pharmacies) and analysed over a 5-year period (March 2016 - December 2020). A periodic regression model was fit to drug sales before the COVID-19 period (i.e. 2016-2019) and extrapolated afterwards in order to compare the sales observed in 2020 to the expected sales.

RESULTS

A decrease of the sales of tracer topical treatments for head lice and scabies was observed from March 2020, synchronously with the first French national lockdown. For the period March 2020-December 2020, the mean reduction in observed versus expected sales were 44% and 14% for head lice and scabies topical treatments, respectively. On the other hand, the observed decrease of oral ivermectin sales after March 2020 was much lower (4%), probably because of studies reporting the potential positive effects of this drug on the Covid-19 infection.

CONCLUSION

COVID-19 lockdown and physical distancing reduce circulation of head lice and scabies in France. Further studies are needed to assess long term impact of these social behaviour changes.

Disease transmission and control modelling at the science-policy interface

The coronavirus disease 2019 (COVID-19) pandemic has disrupted the lives of billions across the world. Mathematical modelling has been a key tool deployed throughout the pandemic to explore the potential public health impact of an unmitigated epidemic. The results of such studies have informed governments' decisions to implement non-pharmaceutical interventions to control the spread of the virus. In this article, we explore the complex relationships between models, decision-making, the media and the public during the COVID-19 pandemic in the United Kingdom of Great Britain and Northern Ireland (UK). Doing so not only provides an important historical context of COVID-19 modelling and how it has shaped the UK response, but as the pandemic continues and looking towards future pandemic preparedness, understanding these relationships and how they might be improved is critical. As such, we have synthesized information gathered via three methods: a survey to publicly list attendees of the Scientific Advisory Group for Emergencies, the Scientific Pandemic Influenza Group on Modelling and other comparable advisory bodies, interviews with science communication experts and former scientific advisors, and reviewing some of the key COVID-19 modelling literature from 2020. Our research highlights the desire for increased bidirectional communication between modellers, decision-makers and the public, as well as the need to convey uncertainty inherent in transmission models in a clear manner. These aspects should be considered carefully ahead of the next emergency response.

Adherence and sustainability of interventions informing optimal control against the COVID-19 pandemic

Background

After one year of stop-and-go COVID-19 mitigation, in the spring of 2021 European countries still experienced sustained viral circulation due to the Alpha variant. As the prospect of entering a new pandemic phase through vaccination was drawing closer, a key challenge remained on how to balance the efficacy of long-lasting interventions and their impact on the quality of life.

Methods

Focusing on the third wave in France during spring 2021, we simulate intervention scenarios of varying intensity and duration, with potential waning of adherence over time, based on past mobility data and modeling estimates. We identify optimal strategies by balancing efficacy of interventions with a data-driven "distress" index, integrating intensity and duration of social distancing.

Results

We show that moderate interventions would require a much longer time to achieve the same result as high intensity lockdowns, with the additional risk of deteriorating control as adherence wanes. Shorter strict lockdowns are largely more effective than longer moderate lockdowns, for similar intermediate distress and infringement on individual freedom.

Conclusions

Our study shows that favoring milder interventions over more stringent short approaches on the basis of perceived acceptability could be detrimental in the long term, especially with waning adherence.

Estimating the impact of influenza on the epidemiological dynamics of SARS-CoV-2

As in past pandemics, co-circulating pathogens may play a role in the epidemiology of coronavirus disease 2019 (COVID-19), caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In particular, experimental evidence indicates that influenza infection can up-regulate the expression of ACE2-the receptor of SARS-CoV-2 in human cells-and facilitate SARS-CoV-2 infection. Here we hypothesized that influenza impacted the epidemiology of SARS-CoV-2 during the early 2020 epidemic of COVID-19 in Europe. To test this hypothesis, we developed a population-based model of SARS-CoV-2 transmission and of COVID-19 mortality, which simultaneously incorporated the impact of non-pharmaceutical control measures and of influenza on the epidemiological dynamics of SARS-CoV-2. Using statistical inference methods based on iterated filtering, we confronted this model with mortality incidence data in four European countries (Belgium, Italy, Norway, and Spain) to systematically test a range of assumptions about the impact of influenza. We found consistent evidence for a 1.8-3.4-fold (uncertainty range across countries: 1.1 to 5.0) average population-level increase in SARS-CoV-2 transmission associated with influenza during the period of co-circulation. These estimates remained robust to a variety of alternative assumptions regarding the epidemiological traits of SARS-CoV-2 and the modeled impact of control measures. Although further confirmatory evidence is required, our results suggest that influenza could facilitate the spread and hamper effective control of SARS-CoV-2. More generally, they highlight the possible role of co-circulating pathogens in the epidemiology of COVID-19.

A three-phase population based sero-epidemiological study: Assessing the trend in prevalence of SARS-CoV-2 during COVID-19 pandemic in Jordan

The evolution of the COVID-19 pandemic in Jordan during the first 10 months of the epidemic was peculiar and can be easily categorized in three different phases: a first period featuring a very low number of reported cases, a second period with exponential growth from August with up to 8000 cases on the 18th November 2020, and a third phase with steady and progressive decline of the epidemiological curve. With the aim of better determine the entity of the population exposed to SARS-CoV-2, the Jordan Ministry of Health with the support of the WHO launched three rounds of the nationwide sero-prevalence survey. Using population proportionate to size (PPS) methodology, around 5000 individuals were selected from all Jordan governorates. Blood samples were collected from all participants and ELISA assays for total IgM, IgG antibodies to COVID-19 were used for testing at the National Public Health Laboratory. Results revealed that seroprevalence dramatically increased over time, with only a tiny fraction of seropositive individuals in August (0.3%), to increase up to more than 20-fold in October (7.0%) and to reach one-third of the overall population exposed by the end of 2020 (34.2%). While non age-specific trends were detected in infection rates across different age categories, in all three rounds of the seroprevalence study two out of three positive participants did not report any sign and/or symptom compatible with COVID-19. The serial cross-sectional surveys experience in Jordan allowed to gain additional insights of the epidemic over time in combination with context-specific aspects like adherence to public health and social measures (PHSM). On the other hand, such findings would be helpful for planning of public health mitigation measures like vaccinations and tailored restriction policies.

Identification of Workers at Increased Risk of Infection During a COVID-19 Outbreak in a Meat Processing Plant, France, May 2020

On 13 May 2020, a COVID-19 cluster was detected in a French processing plant. Infected workers were described. The associations between the SARS-CoV-2 infection and the socio-demographic and occupational characteristics were assessed in order to implement risk management measures targeting workers at increased risk of contamination. Workers were tested by RT-PCR from samples taken during screening campaigns. Workers who tested positive were isolated and their contacts were quarantined. Workers were described and associations with the SARS-CoV-2 infection were assessed through risk ratios using multivariable Poisson regression. Of the 1347 workers, 87.5% were tested: 140 cases were identified; 4 were hospitalised, including 2 admitted to intensive care. In the company, the cluster remained limited to deboning and cutting activities. The attack rate was 11.9% in the company, reaching 16.6% in the cutting department. Being an employee of a subcontractor significantly increased the risk of infection by 2.98 [1.81-4.99]. In the cutting department, an association with virus infection was found for a group of non-French speaking workers from the same Eastern European country (RR = 2.67 [1.76-4.05]). They shared accommodation or carpooled more frequently than the other cases. The outbreak investigation revealed a significantly increased risk of SARS-CoV-2 infection for workers of subcontractors and some foreign-born workers. There are many such populations in meat processing plants; the observed associations and the ways in which these workers are contaminated need to be confirmed by further work. Prevention campaigns should now target these workers. Environmental risk factors in the workplace setting remain to be clarified.

History of COVID-19 Symptoms and Seroprevalence of SARS-CoV-2 Antibodies in HIV-Infected Patients in Northern France after the First Wave of the Pandemic

To assess the prevalence of COVID-19 in people living with HIV (PLWHIV), we performed an epidemiological survey from 1 April through 1 August 2020 in an HIV reference center in Northern France. PLWHIV completed a questionnaire about risk exposures and symptoms consistent with COVID-19 and performed a SARS-CoV-2 serology. Among the 600 PLWHIV included, 16 have been infected with SARS-CoV-2. Symptoms consistent with COVID-19 were frequent both in SARS-CoV-2 positive and negative patients (67% vs. 32%, p = 0.02). Among SARS-CoV-2 infected patients, one (6%) has been hospitalized and five (31%) have been asymptomatic. Close contact with a confirmed COVID-19 case was the only factor associated with COVID-19 acquisition (40% vs. 13%, p = 0.01). The prevalence of COVID-19 in PLWHIV was 2.5%, half of the overall population estimate after the first wave of the pandemic in France. In conclusion, proportion of asymptomatic COVID-19 was high in PLWHIV. The prevalence of COVID-19 in PLWHIV was two times lower than in the general population.

Prevalence and transmission of COVID-19 in community and household levels of Bangladesh: Longini and Koopman epidemic modelling approach

AIM

To estimate the prevalence of COVID-19 pandemic and its transmission rates among people in both community and household levels of Bangladesh.

METHODS

We use the cross-sectional online survey data of 2080 individuals, collected from 442 households during June to September 2020 in Bangladesh. The Longini and Koopman stochastic epidemic modelling approach was adapted for analysing the data. To validate the results, a simulation study was conducted using the Markov Chain Monte Carlo (MCMC) method via the Metropolis-Hastings algorithm in the context of the Bayesian framework.

RESULTS

Overall, the prevalence of COVID-19 pandemic was 15.1% (315 out of 2080) among people in Bangladesh. This proportion was higher in smaller households (size one: 40.0%, two: 35.7% and three: 25.9%) than larger (four: 15.8%, five: 13.3%, six: 14.1%, seven: 12.5% eight: 8.7%, nine: 14.8% and ten or eleven: 5.7%). The transmission rate of COVID-19 in community people was higher (12.0%, 95% CI: 10.0% to 13.0%) than household members (9.0%, 95% CI: 6.0% to 11.0%).

CONCLUSION

The susceptible individuals have a higher risk of community infection than the household and the community transmission is more responsible than the household for COVID-19 pandemic in Bangladesh.

Lockdown impact on age-specific contact patterns and behaviours, France, April 2020

BackgroundMany countries implemented national lockdowns to contain the rapid spread of SARS-CoV-2 and avoid overburdening healthcare capacity.AimWe aimed to quantify how the French lockdown impacted population mixing, contact patterns and behaviours.MethodsWe conducted an online survey using convenience sampling and collected information from participants aged 18 years and older between 10 April and 28 April 2020.ResultAmong the 42,036 survey participants, 72% normally worked outside their home, and of these, 68% changed to telework during lockdown and 17% reported being unemployed during lockdown. A decrease in public transport use was reported from 37% to 2%. Participants reported increased frequency of hand washing and changes in greeting behaviour. Wearing masks in public was generally limited. A total of 138,934 contacts were reported, with an average of 3.3 contacts per individual per day; 1.7 in the participants aged 65 years and older compared with 3.6 for younger age groups. This represented a 70% reduction compared with previous surveys, consistent with SARS-CoV2 transmission reduction measured during the lockdown. For those who maintained a professional activity outside home, the frequency of contacts at work dropped by 79%.ConclusionThe lockdown affected the population's behaviour, work, risk perception and contact patterns. The frequency and heterogeneity of contacts, both of which are critical factors in determining how viruses spread, were affected. Such surveys are essential to evaluate the impact of lockdowns more accurately and anticipate epidemic dynamics in these conditions.

Cryptic transmission of SARS-CoV-2 and the first COVID-19 wave

Considerable uncertainty surrounds the timeline of introductions and onsets of local transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) globally1-7. Although a limited number of SARS-CoV-2 introductions were reported in January and February 2020 (refs.8,9), the narrowness of the initial testing criteria, combined with a slow growth in testing capacity and porous travel screening10, left many countries vulnerable to unmitigated, cryptic transmission. Here we use a global metapopulation epidemic model to provide a mechanistic understanding of the early dispersal of infections and the temporal windows of the introduction of SARS-CoV-2 and onset of local transmission in Europe and the USA. We find that community transmission of SARS-CoV-2 was likely to have been present in several areas of Europe and the USA by January 2020, and estimate that by early March, only 1 to 4 in 100 SARS-CoV-2 infections were detected by surveillance systems. The modelling results highlight international travel as the key driver of the introduction of SARS-CoV-2, with possible introductions and transmission events as early as December 2019 to January 2020. We find a heterogeneous geographic distribution of cumulative infection attack rates by 4 July 2020, ranging from 0.78% to 15.2% across US states and 0.19% to 13.2% in European countries. Our approach complements phylogenetic analyses and other surveillance approaches and provides insights that can be used to design innovative, model-driven surveillance systems that guide enhanced testing and response strategies.

The emergence, genomic diversity and global spread of SARS-CoV-2

Since the first cases of COVID-19 were documented in Wuhan, China in 2019, the world has witnessed a devastating global pandemic, with more than 238 million cases, nearly 5 million fatalities and the daily number of people infected increasing rapidly. Here we describe the currently available data on the emergence of the SARS-CoV-2 virus, the causative agent of COVID-19, outline the early viral spread in Wuhan and its transmission patterns in China and across the rest of the world, and highlight how genomic surveillance, together with other data such as those on human mobility, has helped to trace the spread and genetic variation of the virus and has also comprised a key element for the control of the pandemic. We pay particular attention to characterizing and describing the international spread of the major variants of concern of SARS-CoV-2 that were first identified in late 2020 and demonstrate that virus evolution has entered a new phase. More broadly, we highlight our currently limited understanding of coronavirus diversity in nature, the rapid spread of the virus and its variants in such an increasingly connected world, the reduced protection of vaccines, and the urgent need for coordinated global surveillance using genomic techniques. In summary, we provide important information for the prevention and control of both the ongoing COVID-19 pandemic and any new diseases that will inevitably emerge in the human population in future generations.

A quantitative assessment of epidemiological parameters required to investigate COVID-19 burden

Solid estimates describing the clinical course of SARS-CoV-2 infections are still lacking due to under-ascertainment of asymptomatic and mild-disease cases. In this work, we quantify age-specific probabilities of transitions between stages defining the natural history of SARS-CoV-2 infection from 1965 SARS-CoV-2 positive individuals identified in Italy between March and April 2020 among contacts of confirmed cases. Infected contacts of cases were confirmed via RT-PCR tests as part of contact tracing activities or retrospectively via IgG serological tests and followed-up for symptoms and clinical outcomes. In addition, we provide estimates of time intervals between key events defining the clinical progression of cases as obtained from a larger sample, consisting of 95,371 infections ascertained between February and July 2020. We found that being older than 60 years of age was associated with a 39.9% (95%CI: 36.2-43.6%) likelihood of developing respiratory symptoms or fever ≥ 37.5 °C after SARS-CoV-2 infection; the 22.3% (95%CI: 19.3-25.6%) of the infections in this age group required hospital care and the 1% (95%CI: 0.4-2.1%) were admitted to an intensive care unit (ICU). The corresponding proportions in individuals younger than 60 years were estimated at 27.9% (95%CI: 25.4-30.4%), 8.8% (95%CI: 7.3-10.5%) and 0.4% (95%CI: 0.1-0.9%), respectively. The infection fatality ratio (IFR) ranged from 0.2% (95%CI: 0.0-0.6%) in individuals younger than 60 years to 12.3% (95%CI: 6.9-19.7%) for those aged 80 years or more; the case fatality ratio (CFR) in these two age classes was 0.6% (95%CI: 0.1-2%) and 19.2% (95%CI: 10.9-30.1%), respectively. The median length of stay in hospital was 10 (IQR: 3-21) days; the length of stay in ICU was 11 (IQR: 6-19) days. The obtained estimates provide insights into the epidemiology of COVID-19 and could be instrumental to refine mathematical modeling work supporting public health decisions.

Communicating uncertainty in epidemic models

While mathematical models of disease transmission are widely used to inform public health decision-makers globally, the uncertainty inherent in results are often poorly communicated. We outline some potential sources of uncertainty in epidemic models, present traditional methods used to illustrate uncertainty and discuss alternative presentation formats used by modelling groups throughout the COVID-19 pandemic. Then, by drawing on the experience of our own recent modelling, we seek to contribute to the ongoing discussion of how to improve upon traditional methods used to visualise uncertainty by providing a suggestion of how this can be presented in a clear and simple manner.

Ultrametric diffusion equation on energy landscape to model disease spread in hierarchic socially clustered population

We present a new mathematical model of disease spread reflecting some specialties of the COVID-19 epidemic by elevating the role of hierarchic social clustering of population. The model can be used to explain slower approaching herd immunity, e.g., in Sweden, than it was predicted by a variety of other mathematical models and was expected by epidemiologists; see graphs Fig. 1, 2. The hierarchic structure of social clusters is mathematically modeled with ultrametric spaces having treelike geometry. To simplify mathematics, we consider trees with the constant number p > 1 of branches leaving each vertex. Such trees are endowed with an algebraic structure, these are p -adic number fields. We apply theory of the p -adic diffusion equation to describe a virus spread in hierarchically clustered population. This equation has applications to statistical physics and microbiology for modeling dynamics on energy landscapes. To move from one social cluster (valley) to another, a virus (its carrier) should cross a social barrier between them. The magnitude of a barrier depends on the number of social hierarchy's levels composing this barrier. We consider linearly increasing barriers. A virus spreads rather easily inside a social cluster (say working collective), but jumps to other clusters are constrained by social barriers. This behavior matches with the COVID-19 epidemic, with its cluster spreading structure. Our model differs crucially from the standard mathematical models of spread of disease, such as the SIR-model; in particular, by notion of the probability to be infected (at time t in a social cluster C ). We present socio-medical specialties of the COVID-19 epidemic supporting our model.

COVID-19 chez les patients atteints de rhumatismes inflammatoires chroniques en France : caractéristiques cliniques, facteurs de risque et maintien thérapeutique

Objectif

Évaluer comment les patients présentant un rhumatisme inflammatoire chronique ont fait face à l’épidémie de COVID-19 concernant leur maladie et identifier des facteurs de risque d’infection à SARS-CoV-2 chez ces patients.

Méthodes

Les patients suivis dans un service de rhumatologie français ou inscrits sur la plateforme sécurisée d’e-médecine Spondy+ ont été invités à remplir un questionnaire portant sur la présence de symptômes du COVID-19, sur les résultats des tests diagnostiques et les modifications de traitement durant la période de confinement. Les réponses au questionnaire ont été rapportées à l’aide de statistiques descriptives. Les facteurs associés au risque de COVID-19 et à un arrêt de traitement à visée rhumatologique ont été évalués à l’aide d’une régression logistique.

Résultats

Sur les 2081 questionnaires envoyés, nous avons obtenu 655 réponses provenant de 474 patients atteints de spondyloarthrite (SpA), 129 de polyarthrite rhumatoïde (PR) et 52 de rhumatisme psoriasique (RP). La moyenne d’âge était de 51 ans ± 13,4 ans avec une prédominance féminine (61,8 %). L’incidence de COVID-19 était de 6,9 % (IC 95 % : 5,1–9,2 %), avec 12 cas confirmés par PCR et 33 fortes suspicions. Cinq patients ont nécessité une hospitalisation dont un en unité de soins intensifs et aucun décès n’a été constaté. Les facteurs indépendamment associés à un risque d’infection étaient une notion de contage au SARS-CoV-2, un jeune âge, et l’absence d’intoxication tabagique. Plus de 30 % des patients rapportaient avoir suspendu ou arrêté au moins un traitement de leur rhumatisme inflammatoire durant la période de confinement, la plupart par peur d’une contamination (79,3 %). Parmi ceux-ci, 63,4 % ont rapporté une majoration de l’activité de leur maladie.

Conclusion

Notre étude ne montre pas d’augmentation de l’incidence ou de la sévérité de COVID-19 chez les patients présentant un rhumatisme inflammatoire chronique. Elle apporte des arguments en faveur de la sécurité des traitements anti-rhumatismaux en période épidémique.

Bayesian workflow for disease transmission modeling in Stan

This tutorial shows how to build, fit, and criticize disease transmission models in Stan, and should be useful to researchers interested in modeling the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic and other infectious diseases in a Bayesian framework. Bayesian modeling provides a principled way to quantify uncertainty and incorporate both data and prior knowledge into the model estimates. Stan is an expressive probabilistic programming language that abstracts the inference and allows users to focus on the modeling. As a result, Stan code is readable and easily extensible, which makes the modeler's work more transparent. Furthermore, Stan's main inference engine, Hamiltonian Monte Carlo sampling, is amiable to diagnostics, which means the user can verify whether the obtained inference is reliable. In this tutorial, we demonstrate how to formulate, fit, and diagnose a compartmental transmission model in Stan, first with a simple susceptible-infected-recovered model, then with a more elaborate transmission model used during the SARS-CoV-2 pandemic. We also cover advanced topics which can further help practitioners fit sophisticated models; notably, how to use simulations to probe the model and priors, and computational techniques to scale-up models based on ordinary differential equations.

An ethical analysis of vaccinating children against COVID-19: benefits, risks, and issues of global health equity

COVID-19 vaccination of children has begun in various high-income countries with regulatory approval and general public support, but largely without careful ethical consideration. This trend is expected to extend to other COVID-19 vaccines and lower ages as clinical trials progress. This paper provides an ethical analysis of COVID-19 vaccination of healthy children. Specifically, we argue that it is currently unclear whether routine COVID-19 vaccination of healthy children is ethically justified in most contexts, given the minimal direct benefit that COVID-19 vaccination provides to children, the potential for rare risks to outweigh these benefits and undermine vaccine confidence, and substantial evidence that COVID-19 vaccination confers adequate protection to risk groups, such as older adults, without the need to vaccinate healthy children. We conclude that child COVID-19 vaccination in wealthy communities before adults in poor communities worldwide is ethically unacceptable and consider how policy deliberations might evolve in light of future developments.

Bayesian inference across multiple models suggests a strong increase in lethality of COVID-19 in late 2020 in the UK

We apply Bayesian inference methods to a suite of distinct compartmental models of generalised SEIR type, in which diagnosis and quarantine are included via extra compartments. We investigate the evidence for a change in lethality of COVID-19 in late autumn 2020 in the UK, using age-structured, weekly national aggregate data for cases and mortalities. Models that allow a (step-like or graded) change in infection fatality rate (IFR) have consistently higher model evidence than those without. Moreover, they all infer a close to two-fold increase in IFR. This value lies well above most previously available estimates. However, the same models consistently infer that, most probably, the increase in IFR preceded the time window during which variant B.1.1.7 (alpha) became the dominant strain in the UK. Therefore, according to our models, the caseload and mortality data do not offer unequivocal evidence for higher lethality of a new variant. We compare these results for the UK with similar models for Germany and France, which also show increases in inferred IFR during the same period, despite the even later arrival of new variants in those countries. We argue that while the new variant(s) may be one contributing cause of a large increase in IFR in the UK in autumn 2020, other factors, such as seasonality, or pressure on health services, are likely to also have contributed.

SARS-CoV-2 transmission across age groups in France and implications for control

The shielding of older individuals has been proposed to limit COVID-19 hospitalizations while relaxing general social distancing in the absence of vaccines. Evaluating such approaches requires a deep understanding of transmission dynamics across ages. Here, we use detailed age-specific case and hospitalization data to model the rebound in the French epidemic in summer 2020, characterize age-specific transmission dynamics and critically evaluate different age-targeted intervention measures in the absence of vaccines. We find that while the rebound started in young adults, it reached individuals aged ≥80 y.o. after 4 weeks, despite substantial contact reductions, indicating substantial transmission flows across ages. We derive the contribution of each age group to transmission. While shielding older individuals reduces mortality, it is insufficient to allow major relaxations of social distancing. When the epidemic remains manageable (R close to 1), targeting those most contributing to transmission is better than shielding at-risk individuals. Pandemic control requires an effort from all age groups.

COVID-19 within families amplifies the prosociality gap between adolescents of high and low socioeconomic status

COVID-19 has had worse health, education, and labor market effects on groups with low socioeconomic status (SES) than on those with high SES. Little is known, however, about whether COVID-19 has also had differential effects on noncognitive skills that are important for life outcomes. Using panel data from before and during the pandemic, we show that COVID-19 affects one key noncognitive skill, that is, prosociality. While prosociality is already lower for low-SES students prior to the pandemic, we show that COVID-19 infections within families amplify the prosociality gap between French high school students of high and low SES by almost tripling its size in comparison to pre-COVID-19 levels.

Kinetics of the Severe Acute Respiratory Syndrome Coronavirus 2 Antibody Response and Serological Estimation of Time Since Infection

BACKGROUND

Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) induces a complex antibody response that varies by orders of magnitude between individuals and over time.

METHODS

We developed a multiplex serological test for measuring antibodies to 5 SARS-CoV-2 antigens and the spike proteins of seasonal coronaviruses. We measured antibody responses in cohorts of hospitalized patients and healthcare workers followed for up to 11 months after symptoms. A mathematical model of antibody kinetics was used to quantify the duration of antibody responses. Antibody response data were used to train algorithms for estimating time since infection.

RESULTS

One year after symptoms, we estimate that 36% (95% range, 11%-94%) of anti-Spike immunoglobulin G (IgG) remains, 31% (95% range, 9%-89%) anti-RBD IgG remains, and 7% (1%-31%) of anti-nucleocapsid IgG remains. The multiplex assay classified previous infections into time intervals of 0-3 months, 3-6 months, and 6-12 months. This method was validated using data from a seroprevalence survey in France, demonstrating that historical SARS-CoV-2 transmission can be reconstructed using samples from a single survey.

CONCLUSIONS

In addition to diagnosing previous SARS-CoV-2 infection, multiplex serological assays can estimate the time since infection, which can be used to reconstruct past epidemics.

Model-based estimation of transmissibility and reinfection of SARS-CoV-2 P.1 variant

Background

The SARS-CoV-2 variant of concern (VOC) P.1 (Gamma variant) emerged in the Amazonas State, Brazil, in November 2020. The epidemiological consequences of its mutations have not been widely studied, despite detection of P.1 in 36 countries, with local transmission in at least 5 countries. A range of mutations are seen in P.1, ten of them in the spike protein. It shares mutations with VOCs previously detected in the United Kingdom (B.1.1.7, Alpha variant) and South Africa (B.1.351, Beta variant).

Methods

We estimated the transmissibility and reinfection of P.1 using a model-based approach, fitting data from the national health surveillance of hospitalized individuals and frequency of the P.1 variant in Manaus from December-2020 to February-2021.

Results

Here we estimate that the new variant is about 2.6 times more transmissible (95% Confidence Interval: 2.4-2.8) than previous circulating variant(s). Manaus already had a high prevalence of individuals previously affected by the SARS-CoV-2 virus and our fitted model attributed 28% of Manaus cases in the period to reinfections by P.1, confirming the importance of reinfection by this variant. This value is in line with estimates from blood donors samples in Manaus city.

Conclusions

Our estimates rank P.1 as one of the most transmissible among the SARS-CoV-2 VOCs currently identified, and potentially as transmissible as the posteriorly detected VOC B.1.617.2 (Delta variant), posing a serious threat and requiring measures to control its global spread.

Optimal lockdowns for COVID-19 pandemics: Analyzing the efficiency of sanitary policies in Europe

Two main nonpharmaceutical policy strategies have been used in Europe in response to the COVID-19 epidemic: one aimed at natural herd immunity and the other at avoiding saturation of hospital capacity by crushing the curve. The two strategies lead to different results in terms of the number of lives saved on the one hand and production loss on the other hand. Using a susceptible-infected-recovered-dead model, we investigate and compare these two strategies. As the results are sensitive to the initial reproduction number, we estimate the latter for 10 European countries for each wave from January 2020 till March 2021 using a double sigmoid statistical model and the Oxford COVID-19 Government Response Tracker data set. Our results show that Denmark, which opted for crushing the curve, managed to minimize both economic and human losses. Natural herd immunity, sought by Sweden and the Netherlands does not appear to have been a particularly effective strategy, especially for Sweden, both in economic terms and in terms of lives saved. The results are more mixed for other countries, but with no evident trade-off between deaths and production losses.

Antibody status and cumulative incidence of SARS-CoV-2 infection among adults in three regions of France following the first lockdown and associated risk factors: a multicohort study

BACKGROUND

We aimed to estimate the seropositivity to anti-SARS-CoV-2 antibodies in May-June 2020 after the first lockdown period in adults living in three regions in France and to identify the associated risk factors.

METHODS

Between 4 May 2020 and 23 June 2020, 16 000 participants in a survey on COVID-19 from an existing consortium of three general adult population cohorts living in the Ile-de-France (IDF) or Grand Est (GE) (two regions with high rate of COVID-19) or in the Nouvelle-Aquitaine (NA) (with a low rate) were randomly selected to take a dried-blood spot for anti-SARS-CoV-2 antibodies assessment with three different serological methods (ClinicalTrial Identifier #NCT04392388). The primary outcome was a positive anti-SARS-CoV-2 ELISA IgG result against the spike protein of the virus (ELISA-S). Estimates were adjusted using sampling weights and post-stratification methods. Multiple imputation was used to infer the cumulative incidence of SARS-CoV-2 infection with adjustments for imperfect tests accuracies.

RESULTS

The analysis included 14 628 participants, 983 with a positive ELISA-S. The weighted estimates of seropositivity and cumulative incidence were 10.0% [95% confidence interval (CI): 9.1%, 10.9%] and 11.4% (95% CI: 10.1%, 12.8%) in IDF, 9.0% (95% CI: 7.7%, 10.2%) and 9.8% (95% CI: 8.1%, 11.8%) in GE and 3.1% (95% CI: 2.4%, 3.7%) and 2.9% (95% CI: 2.1%, 3.8%) in NA, respectively. Seropositivity was higher in younger participants [odds ratio (OR) = 1.84 (95% CI: 1.79, 6.09) in <40 vs 50-60 years old and OR = 0.56 (95% CI: 0.42, 0.74) in ≥70 vs 50-60 years old)] and when at least one child or adolescent lived in the same household [OR = 1.30 (95% CI: 1.11, 1.53)] and was lower in smokers compared with non-smokers [OR = 0.71 (95% CI: 0.57, 0.89)].

CONCLUSIONS

Seropositivity to anti-SARS-CoV-2 antibodies in the French adult population was ≤10% after the first wave. Modifiable and non-modifiable risk factors were identified.

Quantification of the effects of climatic conditions on French hospital admissions and deaths induced by SARS-CoV-2

An estimation of the impact of climatic conditions-measured with an index that combines temperature and humidity, the IPTCC-on the hospitalizations and deaths attributed to SARS-CoV-2 is proposed. The present paper uses weekly data from 54 French administrative regions between March 23, 2020 and January 10, 2021. Firstly, a Granger causal analysis is developed and reveals that past values of the IPTCC contain information that allow for a better prediction of hospitalizations or deaths than that obtained without the IPTCC. Finally, a vector autoregressive model is estimated to evaluate the dynamic response of hospitalizations and deaths after an increase in the IPTCC. It is estimated that a 10-point increase in the IPTCC causes hospitalizations to rise by 2.9% (90% CI 0.7-5.0) one week after the increase, and by 4.1% (90% CI 2.1-6.4) and 4.4% (90% CI 2.5-6.3) in the two following weeks. Over ten weeks, the cumulative effect is estimated to reach 20.1%. Two weeks after the increase in the IPTCC, deaths are estimated to rise by 3.7% (90% CI 1.6-5.8). The cumulative effect from the second to the tenth weeks reaches 15.8%. The results are robust to the inclusion of air pollution indicators.

SARS-CoV-2 epidemic after social and economic reopening in three US states reveals shifts in age structure and clinical characteristics

In the United States, state-level re-openings in spring 2020 presented an opportunity for the resurgence of SARS-CoV-2 transmission. One important question during this time was whether human contact and mixing patterns could increase gradually without increasing viral transmission, the rationale being that new mixing patterns would likely be associated with improved distancing, masking, and hygiene practices. A second key question to follow during this time was whether clinical characteristics of the epidemic would improve after the initial surge of cases. Here, we analyze age-structured case, hospitalization, and death time series from three states - Rhode Island, Massachusetts, and Pennsylvania - that had successful re-openings in May 2020 without summer waves of infection. Using a Bayesian inference framework on eleven daily data streams and flexible daily population contact parameters, we show that population-average mixing rates dropped by >50% during the lockdown period in March/April, and that the correlation between overall population mobility and transmission-capable mobility was broken in May as these states partially re-opened. We estimate the reporting rates (fraction of symptomatic cases reporting to health system) at 96.0% (RI), 72.1% (MA), and 75.5% (PA); in Rhode Island, when accounting for cases caught through general-population screening programs, the reporting rate estimate is 94.5%. We show that elderly individuals were less able to reduce contacts during the lockdown period when compared to younger individuals. Attack rate estimates through August 31 2020 are 6.4% (95% CI: 5.8% ‒ 7.3%) of the total population infected for Rhode Island, 5.7% (95% CI: 5.0% ‒ 6.8%) in Massachusetts, and 3.7% (95% CI: 3.1% ‒ 4.5%) in Pennsylvania, with some validation available through published seroprevalence studies. Infection fatality rates (IFR) estimates for the spring epidemic are higher in our analysis (>2%) than previously reported values, likely resulting from the epidemics in these three states affecting the most vulnerable sub-populations, especially the most vulnerable of the ≥80 age group.

The Online Education System: COVID-19 Demands, Trends, Implications, Challenges, Lessons, Insights, Opportunities, Outlooks, and Directions in the Work from Home

The aim of this exploratory research is to identify how working from home and the consequent social isolation interfered in teachers’ work and students’ learning and to identify the challenges, difficulties, advantages, opportunities, demands, trends, implications, outlooks, lessons, directions, and feelings of students and teachers in the teaching processes during the COVID-19 pandemic period. To reach its aim, the authors of this paper developed searches and scientific databases and they also sent an email questionnaire to Rio de Janeiro city schools. The descriptive analyses were made by descriptive statistics (proportions, rates, minimum, maximum, mean, median, standard deviation, coefficient of variation—CV). The results show that working from home and the consequent social isolation interfered in the students’ and teachers’ feelings and sensations and highlight the words “frustration”, “hope”, and “strangeness”. From the sample, 96.4% of the teachers affirmed that working from home and the social isolation interfered in their work and 97.4% of the teachers affirmed that working from home and the consequent social isolation interfered in the students’ learning. This research is the starting point to boost discussions on the subjects of COVID-19, working from home, social isolation, and education. This paper will support researchers in the development of future studies related to the subjects.

Understanding the Prevalence and Geographic Heterogeneity of SARS-CoV-2 Infection: Findings of the First Serosurvey in Uttar Pradesh, India

Population-based serological antibody test for SARS-CoV-2 infection helps in estimating the exposure in the community. We present the findings of the first district representative seroepidemiological survey conducted between 4 and 10 September 2020 among the population aged 5 years and above in the state of Uttar Pradesh, India. Multi-stage cluster sampling was used to select participants from 495 primary sampling units (villages in rural areas and wards in urban areas) across 11 selected districts to provide district-level seroprevalence disaggregated by place of residence (rural/urban), age (5–17 years/aged 18 +) and gender. A venous blood sample was collected to determine seroprevalence. Of 16,012 individuals enrolled in the study, 22.2% [95% CI 21.5–22.9] equating to about 10.4 million population in 11 districts were already exposed to SARS-CoV-2 infection by mid-September 2020. The overall seroprevalence was significantly higher in urban areas (30.6%, 95% CI 29.4–31.7) compared to rural areas (14.7%, 95% CI 13.9–15.6), and among aged 18 + years (23.2%, 95% CI 22.4–24.0) compared to aged 5–17 years (18.4%, 95% CI 17.0–19.9). No differences were observed by gender. Individuals exposed to a COVID confirmed case or residing in a COVID containment zone had higher seroprevalence (34.5% and 26.0%, respectively). There was also a wide variation (10.7–33.0%) in seropositivity across 11 districts indicating that population exposed to COVID was not uniform at the time of the study. Since about 78% of the population (36.5 million) in these districts were still susceptible to infection, public health measures remain essential to reduce further spread.

Estimating the impact of virus testing strategies on the COVID-19 case fatality rate using fixed-effects models

The SARS-CoV2 has now spread worldwide causing over four million deaths. Testing strategies are highly variable between countries and their impact on mortality is a major issue. Retrospective multicenter study with a prospective database on all inpatients throughout mainland France. Using fixed effects models, we exploit policy discontinuities at region borders in France to estimate the effect of testing on the case fatality rate. In France, testing policies are determined at a regional level, generating exogenous variation in testing rates between departments on each side of a region border. We compared all contiguous department pairs located on the opposite sides of a region border. The increase of one percentage point in the test rate is associated with a decrease of 0.0015 percentage point in the death rate, that is, for each additional 2000 tests, we could observe three fewer deaths. Our study suggests that COVID-19 population testing could have a significant impact on the mortality rate which should be considered in decision-making. As concern grows over the current second wave of COVID-19, our findings support the implementation of large-scale screening strategies in such epidemic contexts.

Biometric covariates and outcome in COVID-19 patients: are we looking close enough?

BACKGROUND

The impact of biometric covariates on risk for adverse outcomes of COVID-19 disease was assessed by numerous observational studies on unstratified cohorts, which show great heterogeneity. However, multilevel evaluations to find possible complex, e.g. non-monotonic multi-variate patterns reflecting mutual interference of parameters are missing. We used a more detailed, computational analysis to investigate the influence of biometric differences on mortality and disease evolution among severely ill COVID-19 patients.

METHODS

We analyzed a group of COVID-19 patients requiring Intensive care unit (ICU) treatment. For further analysis, the study group was segmented into six subgroups according to Body mass index (BMI) and age. To link the BMI/age derived subgroups with risk factors, we performed an enrichment analysis of diagnostic parameters and comorbidities. To suppress spurious patterns, multiple segmentations were analyzed and integrated into a consensus score for each analysis step.

RESULTS

We analyzed 81 COVID-19 patients, of whom 67 required mechanical ventilation (MV). Mean mortality was 35.8%. We found a complex, non-monotonic interaction between age, BMI and mortality. A subcohort of patients with younger age and intermediate BMI exhibited a strongly reduced mortality risk (p < 0.001), while differences in all other groups were not significant. Univariate impacts of BMI or age on mortality were missing. Comparing MV with non-MV patients, we found an enrichment of baseline CRP, PCT and D-Dimers within the MV group, but not when comparing survivors vs. non-survivors within the MV patient group.

CONCLUSIONS

The aim of this study was to get a more detailed insight into the influence of biometric covariates on the outcome of COVID-19 patients with high degree of severity. We found that survival in MV is affected by complex interactions of covariates differing to the reported covariates, which are hidden in generic, non-stratified studies on risk factors. Hence, our study suggests that a detailed, multivariate pattern analysis on larger patient cohorts reflecting the specific disease stages might reveal more specific patterns of risk factors supporting individually adapted treatment strategies.

COVID-19: Estimation of the transmission dynamics in Spain using a stochastic simulator and black-box optimization techniques

BACKGROUND AND OBJECTIVES

Epidemiological models of epidemic spread are an essential tool for optimizing decision-making. The current literature is very extensive and covers a wide variety of deterministic and stochastic models. However, with the increase in computing resources, new, more general, and flexible procedures based on simulation models can assess the effectiveness of measures and quantify the current state of the epidemic. This paper illustrates the potential of this approach to build a new dynamic probabilistic model to estimate the prevalence of SARS-CoV-2 infections in different compartments.

METHODS

We propose a new probabilistic model in which, for the first time in the epidemic literature, parameter learning is carried out using gradient-free stochastic black-box optimization techniques simulating multiple trajectories of the infection dynamics in a general way, solving an inverse problem that is defined employing the daily information from mortality records.

RESULTS

After the application of the new proposal in Spain in the first and successive waves, the result of the model confirms the accuracy to estimate the seroprevalence and allows us to know the real dynamics of the pandemic a posteriori to assess the impact of epidemiological measures by the Spanish government and to plan more efficiently the subsequent decisions with the prior knowledge obtained.

CONCLUSIONS

The model results allow us to estimate the daily patterns of COVID-19 infections in Spain retrospectively and examine the population's exposure to the virus dynamically in contrast to seroprevalence surveys. Furthermore, given the flexibility of our simulation framework, we can model situations -even using non-parametric distributions between the different compartments in the model- that other models in the existing literature cannot. Our general optimization strategy remains valid in these cases, and we can easily create other non-standard simulation epidemic models that incorporate more complex and dynamic structures.

Molecular hydrogen as an adjuvant therapy may be associated with increased oxygen saturation and improved exercise tolerance in a COVID-19 patient

Administration of molecular hydrogen dissolved in water to patient with COVID-19-like symptoms may improve oxygen levels and exercise capacity.

Estimates of global SARS-CoV-2 infection exposure, infection morbidity, and infection mortality rates in 2020

We aimed to estimate, albeit crudely and provisionally, national, regional, and global proportions of respective populations that have been infected with SARS-CoV-2 in the first year after the introduction of this virus into human circulation, and to assess infection morbidity and mortality rates, factoring both documented and undocumented infections. The estimates were generated by applying mathematical models to 159 countries and territories. The percentage of the world's population that has been infected as of 31 December 2020 was estimated at 12.56% (95% CI: 11.17-14.05%). It was lowest in the Western Pacific Region at 0.66% (95% CI: 0.59-0.75%) and highest in the Americas at 41.92% (95% CI: 37.95-46.09%). The global infection fatality rate was 10.73 (95% CI: 10.21-11.29) per 10,000 infections. Globally per 1000 infections, the infection acute-care bed hospitalization rate was 19.22 (95% CI: 18.73-19.51), the infection ICU bed hospitalization rate was 4.14 (95% CI: 4.10-4.18). If left unchecked with no vaccination and no other public health interventions, and assuming circulation of only wild-type variants and no variants of concern, the pandemic would eventually cause 8.18 million deaths (95% CI: 7.30-9.18), 163.67 million acute-care hospitalizations (95% CI: 148.12-179.51), and 33.01 million ICU hospitalizations (95% CI: 30.52-35.70), by the time the herd immunity threshold is reached at 60-70% infection exposure. The global population remained far below the herd immunity threshold by end of 2020. Global epidemiology reveals immense regional variation in infection exposure and morbidity and mortality rates.

Investigating the first stage of the COVID-19 pandemic in Ukraine using epidemiological and genomic data

The novel coronavirus SARS-CoV-2 was first detected in China in December 2019 and has rapidly spread around the globe. The World Health Organization declared COVID-19 a pandemic in March 2020 just three months after the introduction of the virus. Individual nations have implemented and enforced a variety of social distancing interventions to slow the virus spread, that had different degrees of success. Understanding the role of non-pharmaceutical interventions (NPIs) on COVID-19 transmission in different settings is highly important. While most such studies have focused on China, neighboring Asian counties, Western Europe, and North America, there is a scarcity of studies for Eastern Europe. The aim of this epidemiological study is to fill this gap by analyzing the characteristics of the first months of the epidemic in Ukraine using agent-based modelling and phylodynamics. Specifically, first we studied the dynamics of COVID-19 incidence and mortality and explored the impact of epidemic NPIs. Our stochastic model suggests, that even a small delay of weeks could have increased the number of cases by up to 50%, with the potential to overwhelm hospital systems. Second, the genomic data analysis suggests that there have been multiple introductions of SARS-CoV-2 into Ukraine during the early stages of the epidemic. Our findings support the conclusion that the implemented travel restrictions may have had limited impact on the epidemic spread. Third, the basic reproduction number for the epidemic that has been estimated independently from case counts data and from genomic data suggest sustained intra-country transmissions.

The stochastic dynamics of early epidemics: probability of establishment, initial growth rate, and infection cluster size at first detection

Emerging epidemics and local infection clusters are initially prone to stochastic effects that can substantially impact the early epidemic trajectory. While numerous studies are devoted to the deterministic regime of an established epidemic, mathematical descriptions of the initial phase of epidemic growth are comparatively rarer. Here, we review existing mathematical results on the size of the epidemic over time, and derive new results to elucidate the early dynamics of an infection cluster started by a single infected individual. We show that the initial growth of epidemics that eventually take off is accelerated by stochasticity. As an application, we compute the distribution of the first detection time of an infected individual in an infection cluster depending on testing effort, and estimate that the SARS-CoV-2 variant of concern Alpha detected in September 2020 first appeared in the UK early August 2020. We also compute a minimal testing frequency to detect clusters before they exceed a given threshold size. These results improve our theoretical understanding of early epidemics and will be useful for the study and control of local infectious disease clusters.

COVID-19 in Early Life: Infants and Children Are Affected Too

Compared with adults, children are less likely infected with SARS-CoV-2 and are often asymptomatic when infected. However, infection in children can lead to severe disease. The pandemic affects the lives of all children, especially those with lower socioeconomic status. This review highlights the physiological impacts of COVID-19 in early life.

Outcomes of Patients with COPD Hospitalized for Coronavirus Disease 2019

RATIONALE

There is controversy concerning the association of chronic obstructive pulmonary disease (COPD) as an independent risk factor for mortality in patients hospitalized with Coronavirus Disease 2019 (COVID-19). We hypothesize that patients with COPD hospitalized for COVID-19 have increased mortality risk.

OBJECTIVE

To assess whether COPD increased the risk of mortality among patients hospitalized for COVID-19.

METHODS

We conducted a retrospective cohort analysis of patients with COVID-19 between February 10, 2020, and November 10, 2020, and hospitalized within 14 days of diagnosis. Electronic health records from U.S. facilities (Optum COVID-19 data) were used.

RESULTS

In our cohort of 31,526 patients, 3030 (9.6%) died during hospitalization. Mortality in patients with COPD was higher than that of patients without COPD, 14.02% and 8.8%, respectively. Univariate (odds ratio [OR] 1.68; 95% confidence interval [CI] 1.54 to 1.84) and multivariate (OR 1.33; 95% CI 1.18 to 1.50) analysis showed that patients with COPD had greater odds of death due to COVID-19 than patients without COPD. We found significant interactions between COPD and sex and COPD and age. Specifically, the increased mortality risk associated with COPD was observed among female (OR 1.62; 95% CI 1.36 to 1.95) but not male patients (OR 1.14; 95% CI 0.97 to 1.34); and in patients aged 40 to 64 (OR 1.42; 95% CI 1.07 to 1.90) and 65 to 79 (OR 1.48; 95% CI 1.23 to 1.78) years.

CONCLUSIONS

COPD is an independent risk factor for death in adults aged 40 to 79 years hospitalized with COVID-19 infection.

Germinal center-induced immunity is correlated with protection against SARS-CoV-2 reinfection but not lung damage

Germinal centers (GCs) elicit protective humoral immunity through a combination of antibody-secreting cells and memory B cells, following pathogen invasion or vaccination. However, the possibility of a GC response inducing protective immunity against reinfection following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection remains unknown. We found GC activity was consistent with seroconversion observed in recovered macaques and humans. Rechallenge with a different clade of virus resulted in significant reduction in replicating virus titers in respiratory tracts in macaques with high GC activity. However, diffuse alveolar damage and increased fibrotic tissue were observed in lungs of reinfected macaques. Our study highlights the importance of GCs developed during natural SARS-CoV-2 infection in managing viral loads in subsequent infections. However, their ability to alleviate lung damage remains to be determined. These results may improve understanding of SARS-CoV-2–induced immune responses, resulting in better coronavirus disease 2019 (COVID-19) diagnosis, treatment, and vaccine development.

COVID-19: risk factors for severe cases of the Delta variant

BACKGROUND

Since April 2021, the SARS-CoV-2 (B.1.167) Delta variant has been rampant worldwide. Recently, this variant has spread in Guangzhou, China. Our objective was to characterize the clinical features and risk factors of severe cases of the Delta variant in Guangzhou.

METHODS

A total of 144 patients with the Delta variant were enrolled, and the data between the severe and non-severe groups were compared. Logistic regression methods and Cox multivariate regression analysis were used to investigate the risk factors of severe cases.

RESULTS

The severity of the Delta variant was 11.1%. Each 1-year increase in age (OR, 1.089; 95% CI, 1.035-1.147; P = 0.001) and each 1-μmol/L increase in total bilirubin (OR, 1.198; 95% CI, 1.021-1.406; P = 0.039) were risk factors for severe cases. Moreover, the risk of progression to severe cases increased 13.444-fold and 3.922-fold when the age was greater than 58.5 years (HR, 13.444; 95% CI, 2.989-60.480; P = 0.001) or the total bilirubin level was greater than 7.23 μmol/L (HR, 3.922; 95% CI, 1.260-12.207; P = 0.018), respectively.

CONCLUSION

Older age and elevated total bilirubin were independent risk factors for severe cases of the Delta variant in Guangzhou, especially if the age was greater than 58.5 years or the total bilirubin level was greater than 7.23 μmol/L.

No SARS-CoV-2 reinfection among staff health-care workers: Prospective hospital-wide screening during the first and second waves in Paris

Objectives

Risk of reinfection with SARS-CoV-2 among health-care workers (HCWs) is unknown. We assessed the incidence rate of SARS-CoV-2 reinfection in the real-life setting of a longitudinal observational cohort of HCWs from the Hôpital Européen Georges Pompidou, Assistance Publique-Hôpitaux de Paris, France, during the first and second waves of COVID-19 epidemic.

Methods

From March to December 2020, HCWs were subjected to molecular and serology testing of SARS-CoV-2. Reinfection was defined as a positive test result during the first wave, either by serology or PCR, followed by a positive PCR during the second wave. Evolution of COVID-19 status of HWCs was assessed by a Sankey diagram.

Results

A total of 7765 tests (4579 PCR and 3186 serology) were carried out and 4168 HCWs had at least one test result during the follow-up period with a positivity rate of 15.9%. No case of reinfection during the second wave could be observed among 102 positive HCWs of the first wave, nor among 175 HCWs found positive by PCR during the second wave who were negative during the first wave.

Conclusions

SARS-CoV-2 reinfection was not observed among HCWs, suggesting a protective immunity against reinfection that lasts at least 8 months post infection.

Differential impact of physical distancing strategies on social contacts relevant for the spread of SARS-CoV-2: evidence from a cross-national online survey, March-April 2020

OBJECTIVES

We investigate changes in social contact patterns following the gradual introduction of non-pharmaceutical interventions and their implications for infection transmission in the early phase of the pandemic.

DESIGN, SETTING AND PARTICIPANTS

We conducted an online survey based on targeted Facebook advertising campaigns across eight countries (Belgium, France, Germany, Italy, the Netherlands, Spain, UK and USA), achieving a sample of 51 233 questionnaires in the period 13 March-12 April 2020. Poststratification weights based on census information were produced to correct for selection bias.

OUTCOME MEASURES

Participants provided data on social contact numbers, adoption of protective behaviours and perceived level of threat. These data were combined to derive a weekly index of infection transmission, the net reproduction number [Formula: see text] .

RESULTS

Evidence from the USA and UK showed that the number of daily contacts mainly decreased after governments issued the first physical distancing guidelines. In mid-April, daily social contact numbers had decreased between 61% in Germany and 87% in Italy with respect to pre-COVID-19 levels, mostly due to a contraction in contacts outside the home. Such reductions, which were uniform across age groups, were compatible with an [Formula: see text] equal or smaller than one in all countries, except Germany. This indicates lower levels of infection transmission, especially in a period of gradual increase in the adoption rate of the face mask outside the home.

CONCLUSIONS

We provided a comparable set of statistics on social contact patterns during the COVID-19 pandemic for eight high-income countries, disaggregated by week and other demographic factors, which could be leveraged by the scientific community for developing more realistic epidemic models of COVID-19.