Evolution Of COVID-19 Research Articles

Evolution of WHO COVID-19 mask guidelines amid intense demands for rapid advice.

During a health emergency, there is an urgent need to rapidly develop guidelines that meet minimum quality standards, as exemplified by the development of WHO guidelines on mask use in health care and community settings during the COVID-19 pandemic. Between January 2020 and October 2023, WHO developed 21 guideline updates on the use of masks as part of infection prevention and control (IPC) practices. Guideline developers had to deal with an ever-growing volume of evidence of variable quality. Initially, indirect evidence drawn from other severe respiratory illnesses and established minimum requirements for IPC were used. As direct evidence began to emerge, WHO commissioned a living systematic review on mask use in June 2020, which formed the basis of evidence-to-decision making. As more evidence became available, additional considerations were incorporated into the process of recommendation formulation, including harms, acceptability, feasibility and resource use. Target populations for the mask guidelines expanded to include the general public, including children. A broad range of disciplines supported guideline development, including IPC, epidemiology, infectious diseases, occupational health, engineering, pneumology, paediatrics, and water, sanitation and hygiene, as well as civil society representatives. Additional expertise was engaged in the areas of ventilation and aerobiology to expand the range of perspectives regarding modes of transmission. Despite challenges, the experience of rapidly and regularly updating advice on mask use during an emergency health response has shown that it is possible to apply the minimum standards for ensuring the guideline methodology is trustworthy and transparent, with increasing rigor over time as evidence improves. Overall, the experience of developing guidelines during the COVID-19 pandemic underscores the importance of adapting to evolving evidence, incorporating diverse perspectives, and maintaining transparency to ensure a rigorous methodology and in return guideline trustworthiness and effectiveness.

COVID-19 and protection measures adopted in rural amazon communities during the first months of the pandemic.

Motivation for the study. To document the evolution of COVID-19 in rural Amazonian populations, which are still little known. Main findings. COVID-19 spread rapidly through rural communities, initially spreading to mestizo hamlets and later affecting indigenous communities. Rural mortality varied by region and ethnicity. Social distancing was difficult, and travel to receive government vouchers contributed to contagion. Implications. Identifying the factors that contributed to contagion and the barriers to the adoption of protective measures in rural Amazonian populations will help to face future pandemics. To analyze the evolution of COVID-19 in rural populations of Loreto and Ucayali in the early stage of the pandemic. A community-level longitudinal observational study was conducted and based on two rounds of telephone surveys with local authorities of more than 400 indigenous and non-indigenous rural communities in Loreto and Ucayali, in July and August 2020. We collected information on cases and deaths by COVID-19 in their communities, protective measures adopted and if state assistance was received in the early stage of the pandemic. Descriptive statistics allowed us to evaluate the evolution of the pandemic after the initial outbreak and compare the trends of the two regions, as well as between indigenous and non-indigenous populations. In July 2020, COVID-19 had reached 91.5% of the communities, although deaths from COVID-19 were reported in 13.0% of the communities, with rural mortality being higher in Ucayali (0.111%) than in Loreto (0.047%) and in non-indigenous communities. By August, prevalence decreased from 44.0% to 32.0% of communities, but became more frequent in indigenous communities, and those in Ucayali. Traveling to the city to receive state bonuses and difficulties maintaining social distancing contributed to the spread. Our findings show the evolution of COVID-19 in rural communities and point to important areas of attention in future public policies, for the adoption of protective measures and reconsidering strategies for the distribution of assistance in the face of future pandemics. Motivation for the study. To document the evolution of COVID-19 in rural Amazonian populations, which are still little known. Main findings. COVID-19 spread rapidly through rural communities, initially spreading to mestizo hamlets and later affecting indigenous communities. Rural mortality varied by region and ethnicity. Social distancing was difficult, and travel to receive government vouchers contributed to contagion. Implications. Identifying the factors that contributed to contagion and the barriers to the adoption of protective measures in rural Amazonian populations will help to face future pandemics.

Mechanistic Insights into Targeting SARS-CoV-2 Papain-like Protease in the Evolution and Management of COVID-19

Mechanistic Insights into Targeting SARS-CoV-2 Papain-like Protease in the Evolution and Management of COVID-19

Evolution and impact of COVID-19: A comprehensive overview

This article focuses on COVID-19 during different times, from 2019 to 2024. Which include the background of COVID-19, structures, prevention spread ways and vaccines. Introducing these things about COVID-19 can help people know more about this various, although COVID-19 does not affect humans as much as it used to, and can help people know what got improved during the COVID-19 time, such as the vaccine. By reading this article, people can know more about the details of COVID-19 and can also know more ways to protect themselves. Also, readers can know different kinds of the variant with time goes then appears in the world, and development in the vaccine. This paper explores COVID-19's impact on humanity, the evolution of the virus, and advances in vaccines and treatments, providing insights into the pandemic's progression and response.

Disease Evolution-based Specificity Target Discovery (DESTD) by analyzing Jiawei-Maxing-Shigan Decoctions against COVID-19

IntroductionDiverse disease courses, each corresponding to a unique pathophysiological mechanism, characterize the coronavirus 2019 disease (COVID-19). Intensive research on discovering specificity targets for treating COVID-19 has significant implications for understanding pathogenesis mechanisms in disease evolution. Traditional Chinese medicine (TCM) has shown distinctive advantages in treating COVID-19 in different periods, which may provide new clues for target discovery. In order to accomplish the goal of providing a Disease Evolution-based Specificity Target Discovery (DESTD), the purpose of this paper was to conduct an analysis of the difference in target weighted value during each cycle of COVID-19. MethodsOur study employed a comprehensive methodology combining TCM pharmacology with modern biological techniques. Three Jiawei-Maxing-Shigan Decoctions (MS Decoction), were selected as samples, including Hanshi-Yufei decoction (HY Decoction), Shidu-Yufei decoction (SY Decoction), and Yidu-Bifei decoction (YB Decoction). The core components of these decoctions are the same, while the differences correspond to different periods of COVID-19. The DESTD approach consists of several parts: network pharmacology analysis to identify potential targets, molecular docking simulation to investigate TCM components acting on potential targets, and biological validation. This study employed several cell line models, including RAW264.7 (Mouse Mononuclear Macrophages Cells), 16HBE (Human Bronchial Epithelial Cells), and HUVECC (Human Umbilical Vein Endothelial Cells), to explore new approaches for key target discovery. ResultsDuring the mild phase of COVID-19, HY Decoction exhibited anti-inflammatory effects by specifically down-regulating the expression of heat shock protein HSP 90-alpha (HSP90AA1), transcription factor JUN (JUN) and other five genes in RAW264.7. In the common phrase, inflammation and fibrosis could be specifically alleviated by down-regulating the expression of interleukin-10 (IL10) and mitogen-activated protein kinase 14 (MAPK14) by SY Decoction respectively acting on RAW264.7 and 16HBE cell lines. In severe cases, YB Decoction specifically down-regulated the expression of prostaglandin endoperoxide synthase-2 (PTGS2), caspase-3 (CASP3), and three other genes to protect vascular endothelial cells from oxidative damage. Moreover, YB also played a role in anti-fibrosis by specifically down-regulating the expression of estrogen receptor beta (ESR2), vascular cell adhesion protein 1 (VCAM1), and other three genes, and up-regulating the expression of prostaglandin endoperoxide synthase-1 (PTGS1) in the severe period. These targeted actions align with a holistic treatment approach and provide tailored therapies for different stages of COVID-19. ConclusionThis research proposed a novel strategy for Disease Evolution-based Specificity Target Discovery (DESTD) to identify specific targets of different stages of disease and specific targets and mechanisms throughout the evolution of COVID-19.

The social evolution of COVID-19: pandemics as total social facts.

The COVID-19 pandemic was an unprecedented global event in recent history. Beginning with an initial outbreak in Wuhan, China, in December 2019, the virus spread rapidly across the globe, causing millions of deaths and triggering an unprecedented health, economic, and social crisis. The initial response to the outbreak in many countries was the implementation of social distancing measures, including the closure of schools and businesses, the cancellation of mass events, and the banning of travel. These measures were aimed at reducing the virus' spread and preventing health systems from being overwhelmed by the numerous severe COVID-19 cases. However, these measures also had a devastating economic impact, especially on precarious workers and freelancers, as well as those who were unable to work from home. As the pandemic (also considered a syndemic or synergistic epidemic) dragged on, countries adopted more flexible approaches to dealing with the virus, adopting mitigation measures rather than social distancing measures. These included the use of masks, testing and contact tracing, and the opening of businesses and schools with the implementation of additional safety measures. This paper highlights the social consequences of the pandemic, ultimately arguing that it is a total social fact (from the French fait social total), based on Marcel Mauss' categorization, since it encompassed and impacted all facets of human life.

A Metric of Societal Burden Based on Virus Succession to Determine Economic Losses and Health Benefits of China's Lockdown Policies: Model Development and Validation.

The COVID-19 pandemic had a profound impact on the global health system and economic structure. Although the implementation of lockdown measures achieved notable success in curbing the spread of the pandemic, it concurrently incurred substantial socioeconomic costs. The objective of this study was to delineate an equilibrium between the economic losses and health benefits of lockdown measures, with the aim of identifying the optimal boundary conditions for implementing these measures at various pandemic phases. This study used a model to estimate the half-lives of the observed case fatality rates of different strains. It was based on global infection and death data collected by the World Health Organization and strain sequence time series data provided by Nextstrain. The connection between the health benefits and economic losses brought by lockdown measures was established through the calculation of disability-adjusted life years. Taking China's city lockdowns as an example, this study determined the cost-benefit boundary of various lockdown measures during the evolution of COVID-19. The study reveals a direct proportionality between economic losses due to lockdowns and the observed case fatality rates of virus strains, a relationship that holds true irrespective of population size or per capita economic output. As SARS-CoV-2 strains evolve and population immunity shifts, there has been a notable decrease in the observed case fatality rate over time, exhibiting a half-life of roughly 8 months. This decline in fatality rates may offset the health benefits of maintaining unchanged lockdown measures, given that the resultant economic losses might exceed the health benefits. The initial enforcement of lockdown in Wuhan led to significant health benefits. However, with the decline in the observed case fatality rate of the virus strains, the economic losses increasingly outweighed the health benefits. Consequently, it is essential to consistently refine and enhance lockdown strategies in accordance with the evolving fatality and infection rates of different virus strains, thereby optimizing outcomes in anticipation of future pandemics.

Outpatient glucocorticoid use and COVID-19 outcomes: a population-based study

IntroductionOwing to controversy information surrounds effect of glucocorticoids on the evolution of COVID-19, we evaluate the effects of outpatient glucocorticoid use on the severity and progression of COVID-19 and risk of infection and analyse the effect of window of exposure and dose.MethodsWe conducted a population-based case − control study, involving 4 substudies: (i) Hospitalisation; (ii) Mortality, using subjects hospitalised with a PCR + as cases and subjects without a PCR + as controls; (iii) Progression, including subjects with a PCR + (hospitalised versus non-hospitalised); and (iv) Susceptibility, with all subjects with a PCR + and subjects without a PCR + . Adjusted odds ratios (ORa) and their 95% confidence intervals (95% CI) were calculated.ResultsThe outpatient glucocorticoid use was associated with an increased risk of hospitalisation (aOR 1.79; 95% CI 1.56–2.05), mortality (aOR 2.30; 95% CI 1.68–3.15), progression (aOR 1.69; 95% CI 1.43–2.00) and susceptibility (aOR 1.29, 95% CI 1.19–1.41). Furthermore, the effects was observed to be greater at higher doses and the closer that drug use approached the outcome date, with an almost fourfold increase in mortality among users in the previous month (aOR 3.85; 95% CI 2.63–5.62).ConclusionsAccording to the results of this real-world data study, outpatient glucocorticoid use should be considered in making decisions about intrahospital treatment.

Analyzing the COVID-19 Transmission Dynamics in Acre, Brazil: An Ecological Study.

The north region of Brazil is characterized by significant vulnerabilities, notably surpassing national poverty indicators. These disparities exacerbated the impact of respiratory illnesses on the healthcare system during the COVID-19 pandemic, particularly in areas with limited healthcare resources, inadequate infrastructure, and barriers to healthcare access. The crisis was further influenced by multiple lineages that emerged as significant virus variants associated with increased transmissibility. Within this context, our ecological study focused on analyzing the epidemiological evolution of COVID-19 in the state of Acre. We constructed time-series trends in incidence, lethality, and mortality from March 2020 to December 2022 using the Prais-Winsten regression model. Our findings revealed that in 2020, there was an increasing trend in incidence, while mortality and lethality continued to decrease (p < 0.05). In the following year, both incidence and mortality decreased, while lethality increased at a rate of 1.02% per day. By the end of 2022, trends remained stationary across all rates. These results underscore the importance of ongoing surveillance and adaptive public health measures to bolster the resilience of healthcare systems in remote and vulnerable regions. Indeed, continuous monitoring of the most predominant SARS-CoV-2 lineages and their dynamics is imperative. Such proactive actions are essential for addressing emerging challenges and ensuring effective responses to adverse situations.

Optimal vaccination and treatment policies with constrained inequalities to study limited vaccination resources for a multistrain reaction–diffusion [formula omitted] model of COVID-19

The availability of an effective vaccine is a major challenge in COVID-19 vaccination campaigns. In this article, we propose a comprehensive strategy incorporating distributed controls via vaccination and treatment while considering the limitations of available resources expressed as inequalities constrained. Our approach utilizes a spatiotemporal model that includes multiple strains of the virus and considers spatial heterogeneity, providing a more accurate representation of disease transmission. By using a generalized two-strain ODE model, we effectively capture the evolution of COVID-19, including various strains and variants. To validate our model, we establish the positivity and boundedness of the system’s solution. Through the exploration of vaccination and treatment controls. Our study aims to reduce the spread of the disease between strains and identify optimal values that minimize the number of infected individuals and associated costs. This work provides useful insights into effective strategies for controlling the COVID-19 epidemic, even with limited vaccination resources. Furthermore, we demonstrate the existence of controls and describe their characterizations in terms of state and adjoint functions. The penalty method is used to treat the inequalities constrained. More importantly, our numerical simulation shows that adequate vaccination resources and simultaneous treatment can effectively reduce and even eliminate the spread of COVID-19.

Epidemiological evolution of COVID-19: a three-year perspective on coping with the pandemic in the States of Rio Grande do Sul and Paraná, Southern Region of Brazil

Introduction: The novel Coronavirus disease (COVID-19) is caused by SARS-CoV-2 and represents the agent responsible for a respiratory and metabolic disease with a high fatality rate, remaining a public health problem to this day. There has been an exacerbation of social inequalities experienced in Brazil and worldwide. Notably, the absence of a consolidated and universal healthcare system has contributed to the increased inequalities and survival opportunities for those affected by SARS-CoV-2 infection and manifestation of COVID-19. The southern region has become the second region with the highest number of cases in Brazil. Objective: To avaluate the epidemiological outcomes of incidence, lethality, and mortality among the states of Rio Grande do Sul and Paraná in the southern region of Brazil. Methods: An ecological time-series study was conducted using official secondary data on COVID-19 cases and deaths publicly disclosed by the health departments of the states of Rio Grande do Sul and Paraná. Time series were constructed applying the Prais-Winsten regression model. Statistical analyses were performed using STATA 14.0 software. Results: It was observed that for the state of Rio Grande do Sul, the trends regarding incidence were increasing in 2020 and decreasing in 2021 (p&lt;0.05). Regarding mortality, it increased in 2020 and decreased in 2021 and 2022 (p&lt;0.05). When assessing lethality, a decreasing trend was observed for the entire period (p&lt;0.05). In relation to Paraná, incidence increased in 2020 and over the entire period, while it decreased in 2021 and 2022 (p&lt;0.05). Mortality was stationary in 2020 and decreased in 2021 and 2022 (p&lt;0.05). Conclusion: The COVID-19 pandemic is a serious public health problem in the states of Paraná and Rio Grande do Sul. Paraná presented a more severe epidemiological outcome compared to Rio Grande do Sul.

Long-Term Prediction of Large-Scale and Sporadic COVID-19 Epidemics Induced by the Original Strain in China Based on the Improved Nonautonomous Delayed Susceptible-Infected-Recovered-Dead and Susceptible-Infected-Removed Models

Abstract The COVID-19 virus emerged abruptly in early 2020 and disseminated swiftly, resulting in a substantial impact on public health. This paper aims to forecast the evolution of large-scale and sporadic COVID-19 outbreaks, stemming from the original strain, within the context of stringent quarantine measures in China. In order to accomplish our objective, we introduce a time-delay factor into the conventional susceptible-infected-removed/susceptible-infected-recovered-dead (SIR/SIRD) model. In the nonautonomous delayed SIRD model, the finite difference method is employed to determine that the transmission rate in a large-scale epidemic area exhibits an approximately exponential decay, the cure rate demonstrates a linear increase, and the death rate is approximately piecewise constant with a downward trend. We employ an improved delayed SIR model for sporadic epidemic regions characterized by extremely low or nearly zero mortality rates. In these regions, the transmission rate is estimated through a two-stage exponential decay function with variable coefficients, while the rate of removal aligns with the recovery rate in the previously mentioned SIRD model. The results of this study demonstrate a high level of concordance with the actual evolution of COVID-19, and the predictive precision can be consistently maintained within a margin of 3%. From the perspective of our model parameters, it is observed that under strict isolation policies, the transmission rate of COVID-19 in China is relatively low and has been significantly reduced. This suggests that government intervention has had a positive effect on epidemic prevention in the country. Moreover, our model has been successfully utilized to forecast the outbreaks caused by the SARS virus in 2003 and the COVID-19 outbreak induced by the Omicron virus in 2022, showcasing its broad applicability and efficacy. This study enables the prompt implementation of measures and allocation of medical resources in different regions, ultimately contributing to the mitigation of economic and social losses.

Epidemiology of COVID-19.

This chapter provides a detailed exploration of the epidemiology of COVID-19, focusing on several key aspects that offer valuable insights into the disease progression. A comprehensive comparison is made between the three related coronaviruses: SARS-CoV, MERS-CoV, and SARS-CoV-2, elucidating their similarities and differences in terms of transmission dynamics, clinical presentation, laboratory and radiological findings, infection mechanisms, and mortality rates. The concept of herd immunity is then discussed, exploring its relevance and potential implications for controlling the spread of COVID-19. Next, the chapter delves into the changing epidemiology of the disease, examining how various factors such as human behavior, public health interventions, and viral mutations have influenced its transmission patterns and severity over time. Finally, the timelines and evolution of COVID-19 are outlined, tracing the origins of the virus, its rapid global spread, and the emergence of new variants.

The response to COVID-19 in Argentina, Brazil, and Mexico: challenges to national coordination of health policies.

The article analyzes the fight against COVID-19 in three Latin American countries: Argentina, Brazil, and Mexico. A multiple case study was carried out in a comparative perspective, based on a bibliographic review, documentary analysis, and secondary data, considering characteristics of the countries and the health system, evolution of COVID-19, national governance, containment and mitigation measures, health systems response, constraints, positive aspects and limits of responses. The three countries had distinct health systems but were marked by insufficient funding and inequalities when hit by the pandemic and recorded high-COVID-19 mortality. Structural, institutional, and political factors influenced national responses. In Argentina, national leadership and intergovernmental political agreements favored the initial adoption of centralized control measures, which were not sustained. In Brazil, there were limits in national coordination and leadership related to the President's denialism and federative, political, and expert conflicts, despite a universal health system with intergovernmental commissions and participatory councils, which were little used during the pandemic. In Mexico, structural difficulties were associated with the Federal Government's initial reluctance to adopt restrictive measures, limits on testing, and relative slowness in immunization. In conclusion, facing health emergencies requires strengthening public health systems associated with federative, intersectoral, and civil society coordination mechanisms and effective global solidarity mechanisms.

Leveraging Human Mobility Data for Efficient Parameter Estimation in Epidemic Models of COVID-19

Effectively predicting the evolution of COVID-19 is of great significance to contain the pandemic. Extensive previous studies proposed a great number of SIR variants, which are efficient to capture the transmission characteristics of COVID-19. However, the parameter estimation methods in previous studies are based on data from epidemiological investigations, which inevitably have caused a large delay. The popularity of digital trajectory data world-wide makes it possible to understand epidemic spreading from human mobility perspective. The major advantage of digital trajectory data lies in that the co-location level of a population is reflected at every moment, making it possible to forecast the evolution in advance. We showed that the mobility data contributed by mobile phone users could be exploited to estimate the contact probability between individuals, thus revealing the dynamic transmission of COVID-19. Specifically, we developed an estimation method to obtain human co-location levels and quantified the variations of human mobility during the epidemic. Then, we extended the infection rate with a real-time co-location level to further forecast the transmission of an epidemic, predicting the epidemic size much more accurately than conventional methods. Finally, the proposed method was applied to evaluate the quantitative effect of different non-pharmacological interventions by predicting the epidemic situations with various mobility characteristics. The empirical results and simulations corroborated our theoretical analysis, providing effective guidance to contain the pandemic.

Melatonin and Covid-19: An opened Pandora's box and the hope for the time being

The SARS-CoV-2 pandemic is a global health concern still ongoing. No single therapeutic intervention with high efficacy exists and virus mutations continue to improve immune evasion decreasing vaccine efficacy. Therefore, a therapy which instead targets severe symptoms of COVID-19 should be contemplated. Hyperinflammation, cytokine storm and oxidation are relevant in the evolution of COVID-19. Considering the anti-inflammatory, anti-oxidative and cytoprotective effects of melatonin (MEL) on viral infections, its potential links with COVID-19 should be researched. In addition, evidence suggests a viricidal action of MEL and an increase in the efficacy of SARS-CoV-2 vaccines. One of the neglected and excellent options for therapy is MEL. We strongly support and encourage the use of MEL as a therapy for COVID-19.

COVID-19 Symptoms Time Trend: Comparison Between 2020, 2021, 2022 and 2023 Years in a General Medicine Office in Toledo, Spain

Background: It is not well known if there have been changes in the clinical-epidemiological characteristics during the evolution of COVID-19 from 2020 to 2023. Objective: To know the variations the clinical features of cases of COVID-19 from 2020 to 2023. Methodology: Descriptive analysis and comparison of secondary data of cases of COVID-19 from previous studies in 2020, 2021, 2022 and 2023 years, all of them carried out in the same population of patients treated in a general medicine office in Toledo, Spain. Results: 100 COVID-19 cases were included in 2020, 42 in 2021, 46 in 2022, and 76 in 2023. The statistically significant differences between the symptoms were: 1) ENT symptoms (anosmia / ageusia, odynophagia, dysphonia, rhinorrhea, sneezing, nasal congestion, pharyngeal dryness-mucus, ear pain, epixtasis) were significantly more frequent in 2021, 2022 and 2023 versus 2020. And 2) Digestive symptoms (anorexia, nausea / vomiting, diarrhea, abdominal pain) were significantly less frequent in 2022 and 2023 versus 2020 and 2021. Conclusion: In the general practice setting in Toledo (Spain), from 2020 to 2023, a variation in the clinical presentation of COVID-19 is observed, with a tendency to predominate ENT symptoms and to present fewer digestive symptoms from 2021. This trend clinical presentation carries the risk that it can make one forget prevention measures, the careful evaluation of symptoms, and epidemiological control.

Incidence, Risk Factors and Clinical Impact of Sars-Cov-2 Prolonged Viral Shedding in Patients Diagnosed with Hematologic Malignancies

Patients with hematologic malignancies (HM) infected by SARS-CoV-2 present a higher risk of prolonged viral shedding, severe infection and mortality. However, data regarding clinical outcomes and viral evolution of prolonged COVID-19 in HM are still scarce. Our aim was to describe the incidence, risk factors, clinical outcomes and viral evolution of the protracted SARS-CoV-2 infection in patients with HM. We collected clinical data and respiratory samples of patients with HM who consulted at Vall d´Hebron Universitary Hospital and were diagnosed with a laboratory-confirmed SARS-CoV-2 infection between March 2020 and March 2023. Prolonged COVID-19 was defined as a positive real-time reverse transcription polymerase chain reaction (rRT-PCR) with a cycle threshold (Ct) &amp;lt;35 beyond 30 days since microbiological diagnosis with the same genotyped viral variant. Whole-genome sequencing of the virus was performed following the ARTIC, v4.1 protocol on a MiSeq platform. We considered an exacerbation of persistent COVID-19 the reappearance of symptoms and/or a positive SARS-CoV-2 rRT-PCR after initial clinical improvement or a previous negative rRT-PCR. Results: Among a total of 400 patients with HM developed COVID-19, 269 patients (67.25%) were eligible for prolonged COVID-19 assessment, whereas 52 patients (13%) died due to COVID-19 before day 30 and 79 patients (19.5%) did not have follow-up rRT-PCR. Prolonged COVID-19 was documented in 126 of 269 evaluable patients (46.8%) with a median time of rRT-PCR positivity of 60 days (IQR 43-95). Median age of patients with prolonged COVID-19 was 68 years and 46.8% were female. The main HM was lymphoma (58.7%), the majority of patients were under active treatment (78.6%), and the most frequent active therapy was anti-CD20 monoclonal antibodies+/- chemotherapy (25.4%). In the univariate analysis, active treatment with bispecific T-cell engagers antibodies (BiTEs) (OR 9.2; p=0.048) and anti-CD38 monoclonal antibodies (mAb) (OR 4.9; p=0.02), heavily pretreated patients (&amp;gt; 2 previous lines of therapy) (OR 3.1; p=0.02), and severe/critical acute COVID-19 (OR 7.4; p=&amp;lt;0.01) were significantly associated with prolonged viral shedding. Vaccination (OR 0.2; p=&amp;lt;0.01) and the development of an optimal humoral vaccine-induced immunogenicity (anti-spike IgG &amp;gt;260 BAU/mL) (OR 0.1; p=&amp;lt;0.01) were significantly associated with an inferior risk of developing prolonged COVID-19. In the multivariate analysis, active treatment with anti-CD20 mAb, BiTEs, and BTK inhibitors; lack of vaccination; an impaired humoral vaccine-induced immunogenicity and severe acute COVID-19 remained significantly associated with prolonged COVID-19 (Figure 1). CAR T-cell therapy also showed a trend to develop prolonged COVID-19 but did not reach statistical significance (OR 14.4; p=0.08). Prolonged COVID-19 clinical spectrum in HM ranged from a chronic asymptomatic infection in 29.4% of cases (37/126) to severe infection and COVID-19 related death in 16.7% of cases (21/126). During prolonged COVID-19, 51.6% of patients (65/126) experienced an increase in the severity of the infection, 54% (68/126) patients suffered a delay/interruption of their hematologic treatment, and 22.2% (28/126) had to suspend it indefinitely. A 55.6% of patients (70/126) experienced an exacerbation of persistent COVID-19 with a median of 1 episode (IQR 1-2) per patient. Through those exacerbations, 84.3% (59/70) required hospital readmission with a median of 17 days of length of stay (IQR 9-27). To treat prolonged COVID-19, 55.6% of patients (70/126) required a median of 1 course of antiviral treatment and 31% (39/126) received convalescent plasma. Of note, 37.3% of prolonged COVID-19 patients (47/126) developed a pneumopathy being organizing pneumonia the most frequent one in 28.6% (36/126) of cases. Finally, data of the kinetics of viral load and intra-host viral mutations in prolonged COVID-19 patients with a positive rRT-PCR beyond 90 days will be available for the congress. In conclusion, prolonged COVID-19 is frequent in patients with HM and clinical repercussion are severe. Patients with risk factors for prolonged viral replication, particularly those with a suboptimal humoral vaccine-induced immunogenicity and acute severe disease, require a personalized diagnostic, therapeutic and follow-up.

Network-based uncertainty quantification for mathematical models in epidemiology

After the new Coronavirus disease (COVID-19) emerged in the end of January 2020 in Germany, a large number of individuals suffered from severe symptoms and eventually needed intensive care in hospitals. Due to the rapid spread of the disease, the number of deceased individuals increased as well, which is a motivation to prevent as many new infections as possible. Therefore, the knowledge about the current evolution of the virus spread is crucial to predict its future behavior and to react with suitable interventions. In this paper, the evolution of the COVID-19 pandemic in Germany is forecasted by a network-based inference method, in which the interactions of individuals are taken into account using a contact matrix. Then the results are compared to the predictions without considering a contact matrix as well as to the logistic regression, which shows the advantage of incorporating the contact matrix. Furthermore, the basic reproduction number of the pandemic in Germany using a neural network approach is estimated and used for further predictions of the evolution of COVID-19 in Germany. In order to mathematically model the different compartments of the population in the considered regions, the classical SIR model is employed. In this work, we deploy the LASSO (Least Absolute Shrinkage and Selection Operator) for the unknown parameter estimation. Furthermore, we calculate and illustrate the MAPE (Mean Absolute Percentage Error) of the estimations to show the accuracy of the predictions. The results include model parameter estimation and model validation, as well as the outbreak forecasting using network-informed algorithms. Our findings show that the network-inference based approach outperforms the logistic regression as well as the neural network approach and the SIR model calibration without a contact network. Furthermore according to the results, the network-inference based approach is particularly suitable for short- to mid-term predictions, even when there is not much information about the new disease. Moreover, the predictions based on the estimation of the reproduction number in Germany can yield more reliable results with increasing the availability of data, but could not outperform the network-inference based algorithm.

A deterministic compartmental model for the transition between variants in the spread of Covid-19 in Italy.

We propose a deterministic epidemic model to describe the transition between two variants of the same virus, through the combination of a series of realistic mechanisms such as partial cross immunity, waning immunity for vaccinated individuals and a novel data-based algorithm to describe the average immunological status of the population. The model is validated on the evolution of Covid-19 in Italy, during the period in which the transition between Delta and Omicron variant occurred, with very satisfactory agreement with the experimental data. According to our model, if the vaccine efficacy had been equal against Delta and Omicron variant infections, the transition would have been smoothed and the epidemic would have gone extinct. This circumstance confirms the fundamental role of vaccines in combating the epidemic, and the importance of identifying vaccines capable of intercepting new variants.