Matthew C. Woodruff et al.

Chronic inflammation, neutrophil activity, and autoreactivity splits long COVID

https://www.nature.com/articles/s41467-023-40012-7

Abstract

While immunologic correlates of COVID-19 have been widely reported, their associations with post-acute sequelae of COVID-19 (PASC) remain less clear. Due to the wide array of PASC presentations, understanding if specific disease features associate with discrete immune processes and therapeutic opportunities is important. Here we profile patients in the recovery phase of COVID-19 via proteomics screening and machine learning to find signatures of ongoing antiviral B cell development, immune-mediated fibrosis, and markers of cell death in PASC patients but not in controls with uncomplicated recovery. Plasma and immune cell profiling further allow the stratification of PASC into inflammatory and non-inflammatory types. Inflammatory PASC, identifiable through a refined set of 12 blood markers, displays evidence of ongoing neutrophil activity, B cell memory alterations, and building autoreactivity more than a year post COVID-19. Our work thus helps refine PASC categorization to aid in both therapeutic targeting and epidemiological investigation of PASC.

John Harvey et al.

Epidemiological waves - Types, drivers and modulators in the COVID-19 pandemic

Cell, May 2023; doi.org/10.1016/j.heliyon.2023.e16015

Abstract

A discussion of ‘waves’ of the COVID-19 epidemic in different countries is a part of the national conversation for many, but there is no hard and fast means of delineating these waves in the available data and their connection to waves in the sense of mathematical epidemiology is only tenuous.

Methods

We present an algorithm which processes a general time series to identify substantial, significant and sustained periods of increase in the value of the time series, which could reasonably be described as ‘observed waves’. This provides an objective means of describing observed waves in time series. We use this method to synthesize evidence across different countries to study types, drivers and modulators of waves.

Results

The output of the algorithm as applied to epidemiological time series related to COVID-19 corresponds to visual intuition and expert opinion. Inspecting the results of individual countries shows how consecutive observed waves can differ greatly with respect to the case fatality ratio. Furthermore, in large countries, a more detailed analysis shows that consecutive observed waves have different geographical ranges. We also show how waves can be modulated by government interventions and find that early implementation of NPIs correlates with a reduced number of observed waves and reduced mortality burden in those waves.

Conclusion

It is possible to identify observed waves of disease by algorithmic methods and the results can be fruitfully used to analyse the progression of the epidemic.

JialiYu et al.

Artificial Intelligence-based HDX (AI-HDX) Prediction Reveals Fundamental Characteristics to Protein Dynamics: Mechanisms on SARS-CoV-2 Immune Escape

Cell, February 2023; doi.org/10.1016/j.isci.2023.106282

Abstract

Three-dimensional structure and dynamics are essential for protein function. Advancements in hydrogen-deuterium exchange (HDX) techniques enable probing protein dynamic information in physiologically relevant conditions. HDX-coupled mass spectrometry (HDX-MS) has been broadly applied in pharmaceutical industries. However, it is challenging to obtain dynamics information at the single amino acid resolution and time-consuming to perform the experiments and process the data. Here, we demonstrate the first deep-learning model, artificial intelligence-based HDX (AI-HDX), that predicts intrinsic protein dynamics based on the protein sequence. It uncovers the protein structural dynamics by combining deep learning, experimental HDX, sequence alignment, and protein structure prediction. AI-HDX can be broadly applied to drug discovery, protein engineering, and biomedical studies. As a demonstration, we elucidated receptor-binding domain structural dynamics as a potential mechanism of anti-SARS-CoV-2 antibody efficacy and immune escape. AI-HDX fundamentally differs from the current AI tools for protein analysis and may transform protein design for various applications.

Lue Ping Zhao et al.

Using Haplotype-Based Artificial Intelligence to Evaluate SARS-CoV-2 Novel Variants and Mutations

JAMA, February 2023; doi:10.1001/jamanetworkopen.2023.0191

Abstract

Importance  Earlier detection of emerging novel SARS-COV-2 variants is important for public health surveillance of potential viral threats and for earlier prevention research. Artificial intelligence may facilitate early detection of SARS-CoV2 emerging novel variants based on variant-specific mutation haplotypes and, in turn, be associated with enhanced implementation of risk-stratified public health prevention strategies.

Objective  To develop a haplotype-based artificial intelligence (HAI) model for identifying novel variants, including mixture variants (MVs) of known variants and new variants with novel mutations.

Design, Setting, and Participants  This cross-sectional study used serially observed viral genomic sequences globally (prior to March 14, 2022) to train and validate the HAI model and used it to identify variants arising from a prospective set of viruses from March 15 to May 18, 2022.

Main Outcomes and Measures  Viral sequences, collection dates, and locations were subjected to statistical learning analysis to estimate variant-specific core mutations and haplotype frequencies, which were then used to construct an HAI model to identify novel variants.

Conclusions and Relevance  In this cross-sectional study, an HAI model found SARS-COV-2 viruses with MV or novel mutations in the global population, which may require closer examination and monitoring. These results suggest that HAI may complement phylogenic variant assignment, providing additional insights into emerging novel variants in the population.

ElhamJamshidi et al.

Personalized predictions of adverse side effects of the COVID-19 vaccines

Cell, December 2022; doi.org/10.1016/j.heliyon.2022.e12753

Abstract

Background

Misconceptions about adverse side effects are thought to influence public acceptance of the Coronavirus disease 2019 (COVID-19) vaccines negatively. To address such perceived disadvantages of vaccines, a novel machine learning (ML) approach was designed to generate personalized predictions of the most common adverse side effects following injection of six different COVID-19 vaccines based on personal and health-related characteristics.

Methods

Prospective data of adverse side effects following COVID-19 vaccination in 19943 participants from Iran and Switzerland was utilized. Six vaccines were studied: The AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2, and the mRNA-1273 vaccine. The eight side effects were considered as the model output: fever, fatigue, headache, nausea, chills, joint pain, muscle pain, and injection site reactions. The total input parameters for the first and second dose predictions were 46 and 54 features, respectively, including age, gender, lifestyle variables, and medical history. The performances of multiple ML models were compared using Area Under the Receiver Operating Characteristic Curve (ROC-AUC).

Conclusions

Using a large cohort of recipients vaccinated with COVID-19 vaccines, a novel and personalized strategy was established to predict the occurrence of the most common adverse side effects with high accuracy. This technique can serve as a tool to inform COVID-19 vaccine selection and generate personalized factsheets to curb concerns about adverse side effects.

D.P. Maison et al.

Dynamic SARS-CoV-2 emergencealgorithm for rationally-designedlogicalnext-generation vaccines

Communications Biology, October 2022; doi: 10.1038/s42003-022-04030-3

Abstract

The authorsacquired SARS-CoV-2 positive clinical samples and compared the worldwideemerged spike mutations from Variants of Concern/Interest, and developed an algorithm for monitoring the evolution of SARS-CoV-2 in the context of vaccines and monoclonalantibodies.

B. Kandala et al.

Accelerating model-informed decisions for COVID-19 vaccine candidates using a model-based meta-analysis approach

eBioMedicine, October 2022;doi.org/10.1016/j.ebiom.2022.104264

Abstract

The COVID-19 pandemic has increased the need for innovative quantitative decision tools to support rapid development of safe and efficacious vaccines against SARS-CoV-2. To meet that need, the authors developed and applied a model-based meta-analysis (MBMA) approach integrating non-clinical and clinical immunogenicity and protection data.

K. Leung et al.

Mixing patterns and the spread of pandemics

Computational Social Science, September 2022; doi.org/10.1038/s43588-022-00312-2

Abstract

Integrating social mixing data into epidemic models can help policy makers better understand epidemic spread. However, empirical mixing data might not be immediately available in most populations. In a recent work, a network model methodology is proposed to construct micro-level social mixing structure when empirical data are not available.

C.R. Wells et al.

The global impact of disproportionate vaccination coverage on COVID-19 mortality

Lancet Infect Dis, September 2022; doi: 10.1016/S1473-3099(22)00417-0

Abstract

Over the course of the first year of COVID-19 vaccination, between Dec 8, 2020, and Dec 8, 2021, 8·33 billion doses were administered among 4·36 billion people globally.1 In this study, by fitting a mathematical model to excess mortality, it is estimated that in 185 countries and territories 31·4 million COVID-19- related deaths would have occurred during this timeframe in the absence of COVID-19 vaccination.

O.J. Watson et al.

Global impact of the first year of COVID-19 vaccination: a mathematical modelling study

Lancet Infect Dis, September 2022; doi: 10.1016/S1473-3099(22)00320-6

Abstract

The first COVID-19 vaccine outside a clinical trial setting was administered on Dec 8, 2020. To ensure global vaccine equity, vaccine targets were set by the COVID-19 Vaccines Global Access (COVAX) Facility and WHO. However, due to vaccine shortfalls, these targets were not achieved by the end of 2021. We aimed to quantify the global impact of the first year of COVID-19 vaccination programmes.

A mathematical model of COVID-19 transmission and vaccination was separately fit to reported COVID-19 mortality and all-cause excess mortality in 185 countries and territories. The impact of COVID-19 vaccination programmes was determined by estimating the additional lives lost if no vaccines had been distributed. We also estimated the additional deaths that would have been averted had the vaccination coverage targets of 20% set by COVAX and 40% set by WHO been achieved by the end of 2021.

COVID-19 vaccination has substantially altered the course of the pandemic, saving tens of millions of lives globally. However, inadequate access to vaccines in low-income countries has limited the impact in these settings, reinforcing the need for global vaccine equity and coverage.

V.N. Parikh et al.

Deconvoluting complex correlates of COVID-19 severity with a multi-omic pandemic tracking strategy

Nature Communications, August 2022; doi: 10.1038/s41467-022-32397-8

Abstract

The SARS-CoV-2 pandemic has differentially impacted populations across race and ethnicity. A multi-omic approach represents a powerful tool to examine risk across multi-ancestry genomes. We leverage a pandemic tracking strategy in which we sequence viral and host genomes and transcriptomes from nasopharyngeal swabs of 1049 individuals (736 SARS-CoV-2 positive and 313 SARS-CoV-2 negative) and integrate them with digital phenotypes from electronic health records from a diverse catchment area in Northern California. Genome-wide association disaggregated by admixture mapping reveals novel COVID-19-severity-associated regions containing previously reported markers of neurologic, pulmonary and viral disease susceptibility. Phylodynamic tracking of consensus viral genomes reveals no association with disease severity or inferred ancestry. Summary data from multiomic investigation reveals metagenomic and HLA associations with severe COVID-19. The wealth of data available from residual nasopharyngeal swabs in combination with clinical data abstracted automatically at scale highlights a powerful strategy for pandemic tracking, and reveals distinct epidemiologic, genetic, and biological associations for those at the highest risk.

J.M. Pogue et al.

Monoclonals for patients hospitalised with COVID-19

Lancet Respir Med., July 2022; doi: 10.1016/S2213-2600(22)00222-3

Abstract

This study represents the third trial in which intravenous monoclonal antibody treatment was associated with decreased mortality in some patients who are hospitalised. The RECOVERY trial compared casirivimab–imdevimab with standard care in 9785 patients hospitalised with COVID-19. Although treatment was not associated with a 28-day mortality benefit in the overall cohort (RR 0·94 [95% CI 0·86–1·03]), mortality was lower in patients who were seronegative at the time of enrolment (RR 0·80 [0·70–0·91]). In the company-sponsored trial of 1223 patients who were hospitalised, casirivimab–imdevimab treatment was associated with a significant reduction in 28-day mortality (relative risk reduction [RRR] 35·9% [95% CI 7·3–55·7]), most predominately observed in the seronegative subgroup (RRR 55·6% (24·2–74]).

K. J. Kramer et al.

Single-cell profiling of the antigen-specific response to BNT162b2 SARS-CoV-2 RNA vaccine

Nature communications, June 2022 ; doi.org/10.1038/s41467-022-31142-5

Abstract

RNA-based vaccines against SARS-CoV-2 have proven critical to limiting COVID-19 disease severity and spread. Cellular mechanisms driving antigen-specific responses to these vaccines, however, remain uncertain. Here we identify and characterize antigen-specific cells and antibody responses to the RNA vaccine BNT162b2 using multiple single-cell technologies for in depth analysis of longitudinal samples from a cohort of healthy participants. Mass cytometry and unbiased machine learning pinpoint an expanding, population of antigen-specific memory CD4⁺ and CD8⁺ T cells with characteristics of follicular or peripheral helper cells. B cell receptor sequencing suggest progression from IgM, with apparent cross-reactivity to endemic coronaviruses, to SARS-CoV-2-specific IgA and IgG memory B cells and plasmablasts. Responding lymphocyte populations correlate with eventual SARS-CoV-2 IgG, and a participant lacking these cell populations failed to sustain SARS-CoV-2-specific antibodies and experienced breakthrough infection. These integrated proteomic and genomic platforms identify an antigen-specific cellular basis of RNA vaccine-based immunity.

J.W. Cabore et al.  

COVID-19 in the 47 countries of the WHO African region: a modelling analysis of past trends and future patterns

The Lancet, June 2002; doi.org/10.1016/S2214-109X(22)00233-9

Abstract

Background COVID-19 has affected the African region in many ways. We aimed to generate robust information on the transmission dynamics of COVID-19 in this region since the beginning of the pandemic and throughout 2022.

MethodsFor each of the 47 countries of the WHO African region, we consolidated COVID-19 data from reported infections and deaths (from WHO statistics); published literature on socioecological, biophysical, and public health interventions; and immunity status and variants of concern, to build a dynamic and comprehensive picture of COVID-19 burden. The model is consolidated through a partially observed Markov decision process, with a Fourier series to produce observed patterns over time based on the SEIRD (denoting susceptible, exposed, infected, recovered, and dead) modelling framework. The model was set up to run weekly, by country, from the date the first infection was reported in each country until Dec 31, 2021. New variants were introduced into the model based on sequenced data reported by countries. The models were then extrapolated until the end of 2022 and included three scenarios based on possible new variants with varying transmissibility, severity, or immunogenicity.

Findings Between Jan 1, 2020, and Dec 31, 2021, our model estimates the number of SARS-CoV-2 infections in the African region to be 505·6 million (95% CI 476·0–536·2), inferring that only 1·4% (one in 71) of SARS-CoV-2 infections in the region were reported. Deaths are estimated at 439 500 (95% CI 344 374–574 785), with 35·3% (one in three) of these reported as COVID-19-related deaths. Although the number of infections were similar between 2020 and 2021, 81% of the deaths were in 2021. 52·3% (95% CI 43·5–95·2) of the region's population is estimated to have some SARS-CoV-2 immunity, given vaccination coverage of 14·7% as of Dec 31, 2021. By the end of 2022, we estimate that infections will remain high, at around 166·2 million (95% CI 157·5–174·9) infections, but deaths will substantially reduce to 22 563 (14 970–38 831).

InterpretationThe African region is estimated to have had a similar number of COVID-19 infections to that of the rest of the world, but with fewer deaths. Our model suggests that the current approach to SARS-CoV-2 testing is missing most infections. These results are consistent with findings from representative seroprevalence studies. There is, therefore, a need for surveillance of hospitalisations, comorbidities, and the emergence of new variants of concern, and scale-up of representative seroprevalence studies, as core response strategies.

L. Zhou et al.

An interpretable deep learning workflow for discovering subvisual abnormalities in CT scans of COVID-19 inpatients and survivors

Nature, May 2022; doi.org/10.1038/s42256-022-00483-7

Abstract

Tremendous efforts have been made to improve diagnosis and treatment of COVID-19, but knowledge on long-term complications is limited. In particular, a large portion of survivors has respiratory complications, but currently, experienced radiologists and state-of-the-art artificial intelligence systems are not able to detect many abnormalities from follow-up computerized tomography (CT) scans of COVID-19 survivors. Here we propose Deep-LungParenchyma-Enhancing (DLPE), a computer-aided detection (CAD) method for detecting and quantifying pulmonary parenchyma lesions on chest CT. Through proposing a number of deep-learning-based segmentation models and assembling them in an interpretable manner, DLPE removes irrelevant tissues from the perspective of pulmonary parenchyma, and calculates the scan-level optimal window, which considerably enhances parenchyma lesions relative to the lung window. Aided by DLPE, radiologists discovered novel and interpretable lesions from COVID-19 inpatients and survivors, which were previously invisible under the lung window. Based on DLPE, we removed the scan-level bias of CT scans, and then extracted precise radiomics from such novel lesions. We further demonstrated that these radiomics have strong predictive power for key COVID-19 clinical metrics on an inpatient cohort of 1,193 CT scans and for sequelae on a survivor cohort of 219 CT scans. Our work sheds light on the development of interpretable medical artificial intelligence and showcases how artificial intelligence can discover medical findings that are beyond sight.

Cai et al.

Modeling transmission of SARS-CoV-2 Omicron in China

Nature Medicine, May 2022; doi.org/10.1038/s41591-022-01855-7

Abstract

Having adopted a dynamic zero-COVID strategy to respond to SARS-CoV-2 variants with higher transmissibility since August 2021, China is now considering whether and for how long this policy can remain in place. The debate has thus shifted towards the identification of mitigation strategies for minimizing disruption to the healthcare system in the case of a nationwide epidemic. To this aim, we developed an age-structured stochastic compartmental susceptible-latent-infectious-removed-susceptible (SLIRS) model of SARS-CoV-2 transmission calibrated on the initial growth phase for the 2022 Omicron outbreak in Shanghai, to project COVID-19 burden (i.e., number of cases, patients requiring hospitalization and intensive care, and deaths) under hypothetical mitigation scenarios. The model also considers age-specific vaccine coverage data, vaccine efficacy against different clinical endpoints, waning of immunity, different antiviral therapies, and non-pharmaceutical interventions. We find that the level of immunity induced by the March 2022 vaccination campaign would be insufficient to prevent an Omicron wave that would result in exceeding critical care capacity with a projected intensive care unit peak demand of 15.6-times the existing capacity and causing approximately 1.55 million deaths. However, we also estimate that protecting vulnerable individuals by ensuring accessibility to vaccines and antiviral therapies, and maintaining implementation of non-pharmaceutical interventions could be sufficient to prevent overwhelming the healthcare system, suggesting that these factors should be points of emphasis in future mitigation policies.

M. Poletti

Mapping the epithelial–immune cell interactome upon infection in the gut and the upper airways

Systems biology and applications, May 2022; doi.org/10.1038/s41540-022-00224-x

Abstract

Increasing evidence points towards the key role of the epithelium in the systemic and over-activated immune response to viral infection, including SARS-CoV-2 infection. Yet, how viral infection alters epithelial–immune cell interactions regulating inflammatory responses, is not well known. Available experimental approaches are insufficient to properly analyse this complex system, and computational predictions and targeted data integration are needed as an alternative approach. In this work, we propose an integrated computational biology framework that models how infection alters intracellular signalling of epithelial cells and how this change impacts the systemic immune response through modified interactions between epithelial cells and local immune cell populations. As a proof-of-concept, we focused on the role of intestinal and upper-airway epithelial infection. To characterise the modified epithelial–immune interactome, we integrated intra- and intercellular networks with single-cell RNA-seq data from SARS-CoV-2 infected human ileal and colonic organoids as well as from infected airway ciliated epithelial cells. This integrated methodology has proven useful to point out specific epithelial–immune interactions driving inflammation during disease response, and propose relevant molecular targets to guide focused experimental analysis.

A. Cicchetti et al.

Analisi dei modelli di risposta al Covid-19 in Italia: Instant Report ALTEMS # 2020-2022. Una fotografia a due anni dal primo caso in Italia

Report Altems 2020-2022, Aprile 2022;

COVID-19 Excess Mortality Collaborators

Estimating excess mortality due to the COVID-19 pandemic: a systematic analysis of COVID-19-related mortality, 2020–21

Lancet, March 2022; doi.org/10.1016/ S0140-6736(21)02796-3

Abstract

Background

Mortality statistics are fundamental to public health decision making. Mortality varies by time and location, and its measurement is affected by well known biases that have been exacerbated during the COVID-19 pandemic. This paper aims to estimate excess mortality from the COVID-19 pandemic in 191 countries and territories, and 252 subnational units for selected countries, from Jan 1, 2020, to Dec 31, 2021.

Methods

All-cause mortality reports were collected for 74 countries and territories and 266 subnational locations (including 31 locations in low-income and middle-income countries) that had reported either weekly or monthly deaths from all causes during the pandemic in 2020 and 2021, and for up to 11 year previously. In addition, we obtained excess mortality data for 12 states in India. Excess mortality over time was calculated as observed mortality, after excluding data from periods affected by late registration and anomalies such as heat waves, minus expected mortality. Six models were used to estimate expected mortality; final estimates of expected mortality were based on an ensemble of these models. Ensemble weights were based on root mean squared errors derived from an out-of-sample predictive validity test. As mortality records are incomplete worldwide, we built a statistical model that predicted the excess mortality rate for locations and periods where all-cause mortality data were not available. We used least absolute shrinkage and selection operator (LASSO) regression as a variable selection mechanism and selected 15 covariates, including both covariates pertaining to the COVID-19 pandemic, such as seroprevalence, and to background population health metrics, such as the Healthcare Access and Quality Index, with direction of effects on excess mortality concordant with a meta-analysis by the US Centers for Disease Control and Prevention. With the selected best model, we ran a prediction process using 100 draws for each covariate and 100 draws of estimated coefficients and residuals, estimated from the regressions run at the draw level using draw-level input data on both excess mortality and covariates. Mean values and 95% uncertainty intervals were then generated at national, regional, and global levels. Out-of-sample predictive validity testing was done on the basis of our final model specification.

Findings

Although reported COVID-19 deaths between Jan 1, 2020, and Dec 31, 2021, totalled 5·94 million worldwide, we estimate that 18·2 million (95% uncertainty interval 17·1–19·6) people died worldwide because of the COVID-19 pandemic (as measured by excess mortality) over that period. The global all-age rate of excess mortality due to the COVID-19 pandemic was 120·3 deaths (113·1–129·3) per 100 000 of the population, and excess mortality rate exceeded 300 deaths per 100 000 of the population in 21 countries. The number of excess deaths due to COVID-19 was largest in the regions of south Asia, north Africa and the Middle East, and eastern Europe. At the country level, the highest numbers of cumulative excess deaths due to COVID-19 were estimated in India (4·07 million [3·71–4·36]), the USA (1·13 million [1·08–1·18]), Russia (1·07 million [1·06–1·08]), Mexico (798 000 [741 000–867 000]), Brazil (792 000 [730 000–847 000]), Indonesia (736 000 [594 000–955 000]), and Pakistan (664 000 [498 000–847 000]). Among these countries, the excess mortality rate was highest in Russia (374·6 deaths [369·7–378·4] per 100 000) and Mexico (325·1 [301·6–353·3] per 100 000), and was similar in Brazil (186·9 [172·2–199·8] per 100 000) and the USA (179·3 [170·7–187·5] per 100 000).

Interpretation

The full impact of the pandemic has been much greater than what is indicated by reported deaths due to COVID-19 alone. Strengthening death registration systems around the world, long understood to be crucial to global public health strategy, is necessary for improved monitoring of this pandemic and future pandemics. In addition, further research is warranted to help distinguish the proportion of excess mortality that was directly caused by SARS-CoV-2 infection and the changes in causes of death as an indirect consequence of the pandemic.

Koelle, K.; et al.

The changing epidemiology of SARS-CoV-2

Science, https://www.science.org/doi/epdf/10.1126/science.abm4915

CONTENUTO E COMMENTO: Questa review sull’evoluzione dell’epidemiologia della pandemia di COVID-19 e sulle sfide da essa poste si concentra sui ruoli chiave che i modelli matematici e le analisi quantitative dei dati empirici hanno giocato nel permetterci di affrontare i vari interrogativi che via via ci siamo posti e nel provare a controllare la pandemia. Questi interrogativi, succedutisi nel corso di questi due anni vengono messi in correlazione, in questo lavoro, con le fasi della pandemia, ad esempio all’inizio del 2020 ci siamo chiesti se  SARS-CoV-2 avesse il potenziale di causare una pandemia, oggi invece ci stiamo chiedendo :  SARS-CoV-2 continuerà ancora ad evolversi per sfuggire all’immunità ?? Le domande sul vaccino si sono invece allargate a quelle riguardanti la misura in cui la vaccinazione potrebbe ridurre la trasmissione.Altre questioni analizzate sono  le proporzioni delle diffusioni delle diverse varianti di SARS-CoV-2, o il rischio di reinfezione.

Bartsch S et al.

Maintaining face mask use before and after achieving different COVID-19 vaccination coverage levels: a modelling study

Lancet Publich Health, https://www.sciencedirect.com/science/article/pii/S2468266722000408

CONTENUTO E COMMENTO : Studio di modello matematico confrontante due scenari : cosa sarebbe successo se, in parallelo alla diffusione della campagna vaccinale, a) le mascherine non fossero state o b) fossero state usate.

Tale simulazione mette in evidenza come l’utilizzo delle mascherine fino al raggiungimento di una copertura vaccinale del 70-90% sia conveniente, dal punto di vista economico, in tutti gli scenari esplorati. In particolare, perché il beneficio sia più pronunciato se l’utilizzo delle mascherine viene rinforzato nei mesi invernali e prolungato fino a 2-10 settimane dal raggiungimento della soglia vaccinale. Ovviamente, la comparsa di varianti che riducono l’efficacia dei vaccini non fa che consolidare l’efficace e la convenienza economica delle mascherine.