Evolution Of COVID-19 Research Articles

Analysis of COVID-19 evolution based on testing closeness of sequential data

A practical algorithm has been developed for closeness analysis of sequential data that combines closeness testing with algorithms based on the Markov chain tester. It was applied to reported sequential data for COVID-19 to analyze the evolution of COVID-19 during a certain time period (week, month, etc.).

The evolution and global impact of Covid-19 and Omicron: the way forward?

Coronaviruses are a family of pathogenic viruses that including MERS, SARS, and SARS-Cov-2, and are known to cause respiratory and other illnesses in man and animals. The SARS-CoV-2 virus, responsible for the COVID-19 virus is a monopleiotropic clade of the coronavirus family first reported in Wuhan, China in December 2019 where it was found to cause a previously unreported form of viral pneumonia. The virus readily spreads by airborne microdroplet infection, and within a month of its report to the WHO in December 2019 was found in the USA and other countries and was declared a pandemic by the WHO within the first few months of its discovery and emergence. Numerous mutations of the virus with variations in infectivity and pathogenicity began to appear within the first year, including the current Delta and Omicron, both of which are more contagious than their SARS-CoV-2 progenitor strain. Omicron, while approximately five-fold more transmissible than Delta, may infect both vaccinated and unvaccinated people, and is now responsible for the majority of currently infected people but to date has resulted in only mild and non-life-threatening outcomes. Due to the greater infectivity and the mild illness attributed to the Omicron, it may be an important development in terminating the continued spread of the pandemic.

Analysis of the second wave of COVID-19 in India based on SEIR model.

India was under a grave threat from the second wave of the COVID-19 pandemic particularly in the beginning of May 2021. The situation appeared rather gloomy as the number of infected individuals/active cases had increased alarmingly during the months of May and June 2021 compared to the first wave peak. Indian government/state governments have been implementing various control measures such as lockdowns, setting up new hospitals, and putting travel restrictions at various stages to lighten the virus spread from the initial outbreak of the pandemic. Recently, we have studied the susceptible-exposed-infectious-removed (SEIR) dynamic modeling of the epidemic evolution of COVID-19 in India with the help of appropriate parameters quantifying the various governmental actions and the intensity of individual reactions. Our analysis had predicted the scenario of the first wave quite well. In this present article, we extend our analysis to estimate and analyze the number of infected individuals during the second wave of COVID-19 in India with the help of the above SEIR model. Our findings show that the people’s individual effort along with governmental actions such as implementations of curfews and accelerated vaccine strategy are the most important factors to control the pandemic in the present situation and in the future.

Spatiotemporal Associations between Local Safety Level Index and COVID-19 Infection Risks across Capital Regions in South Korea.

This study aims to provide an improved understanding of the local-level spatiotemporal evolution of COVID-19 spread across capital regions of South Korea during the second and third waves of the pandemic (August 2020~June 2021). To explain transmission, we rely upon the local safety level indices along with latent influences from the spatial alignment of municipalities and their serial (temporal) correlation. Utilizing a flexible hierarchical Bayesian model as an analytic operational framework, we exploit the modified BYM (BYM2) model with the Penalized Complexity (PC) priors to account for latent effects (unobserved heterogeneity). The outcome reveals that a municipality with higher population density is likely to have an elevated infection risk, whereas one with good preparedness for infectious disease tends to have a reduction in risk. Furthermore, we identify that including spatial and temporal correlations into the modeling framework significantly improves the performance and explanatory power, justifying our adoption of latent effects. Based on these findings, we present the dynamic evolution of COVID-19 across the Seoul Capital Area (SCA), which helps us verify unique patterns of disease spread as well as regions of elevated risk for further policy intervention and for supporting informed decision making for responding to infectious diseases.

Air transportation as a puzzle piece of COVID-19 in Africa?

COVID-19 has hit our society hard, with more than 242 million cases reported worldwide and more than 4.9 million directly related fatalities. The role of Africa throughout the pandemic has been puzzling, since the African continent seems to have gone through the pandemic better than other continents; clearly better than predicted by the public during the emergence of COVID-19 one year ago. While several factors have been proposed in the literature to explain the unexpected role of Africa, including a relatively young population, more historical-driven preparedness to other types of coronavirus and diseases, and a limited amount of testing, the puzzle is not considered to be solved. In this study, we aim to answer the question whether air transportation indicators can support us in explaining the evolution of COVID-19 in Africa? Using flight data for the year 2020, we explore how changes in the air transportation system correlate with evolution of epidemiological indicators. Our results suggest that air transportation could indeed play a critical role for the spread of COVID-19 in Africa as well. Overall, we hope that our analysis contributes towards a better understanding of COVID-19 and the role air transportation plays in an under-researched region of the world.

A bioinformatic analysis of the spike glycoprotein and evolution of COVID-19

A bioinformatic analysis of the spike glycoprotein and evolution of COVID-19

Mathematical modeling of SARS-nCoV-2 virus in Tamil Nadu, South India.

The purpose of this paper is to build a mathematical model for the study of the roles of lock-down, social distancing, vaccination, detection efficiency, and health care capacity planning of the COVID-19 pandemic taking into account the demographic topology of the State of Tamil Nadu, India. Two mathematical models are proposed for the evolution of the first and second wave of COVID-19 pandemic. The model for the first wave considers lock-down orders, social distancing measures, and detection efficiency. The model for the second wave considers more sub-populations and incorporates two more elements, vaccination and health care capacity. Daily reported data on the evolution of the COVID-19 pandemic are used to determine the parameter values. The dynamics produced by the mathematical model closely follow the evolution of COVID-19 in the State of Tamil Nadu. Numerical simulation shows that the lock-down effect is limited. Social distancing implementation and detection of positive cases are relatively ineffective compared with other big cities. Shortage of health care resources is one of the factors responsible for rapidly spreading in the second wave in Tamil Nadu.

COVID-19 spatiotemporal SIR model: Regional optimal control approach

In the present work, we consider a spatio-temporal model to describe the evolution of covid19 in an area Ω (Ω can be a city, a country,..). Taking into account the financial means of the considered country, we suppose that the number of available vaccines is destined to a region ω 1 ⊆Ω (ω 1 can be an industrial city, a university city...) and we suppose that the available treatments are dedicated to a region ω 2 ⊆Ω (ω 2 can be a military city,..), it is not excluded that ω 1 =ω 2 . To minimize the number of infection with minimal cost, we apply an optimal regional control strategy to stop the death of infected individuals in the considered area. Much of this work has been devoted to mathematical study, where the existence of the optimal controls and the solutions of the state system are proven, an optimal control characterization in terms of state and adjoint functions are provided, and the optimality system is solved numerically using a forward-backward sweep method. Our numerical results suggest that when vaccination and treatment procedures are used together, the control approach becomes more effective in protecting a specific region from epidemic transmission from neighboring regions.

SIR model for propagation of COVID-19 in the Paraíba's State (Brazil)

This work aims to apply the SIR-type compartmental model (Susceptible - Infected - Removed) in the evolution of Covid-19 in Paraíba's State and Campina Grande City. For that, the parameters of the model were considered to be variable during time evolution, within an appropriate range. The system of differential equations was solved numerically using the Euler method. The parameters were obtained by adjusting the model to the infected data provided by the Paraíba Health Department. According to the results obtained, the model describes the infected population well. There was a reduction in the effective reproduction number in Paraíba and the town of Campina Grande. It is noteworthy that understanding the dynamics of infection transmission and evaluating the effectiveness of control measures is crucial to assess the potential for sustained transmission to occur in new areas. The model can also be applied to describe epidemic dynamics in other regions and countries.

Host genetic markers associated with severe COVID-19: A systematic review

Background: Severity of COVID-19 has been linked to several factors. As any other polygenic-multifactorial phenotype, genotype is not determinant in this prediction but may add actionable information. There is no consensus yet as to which genetic markers are useful, but several studies have been published that postulate different hypotheses acknowledging the relevance of including host genetics among the variables that predict the risk for severe forms of the disease. Objective: The objective of this study is to perform a systematic review that summarizes the projects, studies and postulated markers in order to establish if their application in clinical practice is currently feasible. Materials and methods: A comprehensive search was conducted in Pubmed. The inclusion criterion was studies of patients with COVID-19 who had germinal genetic markers of interest sequenced. The selected studies had to include at least a group of patients with the severe form of the disease. Results: 7 studies that met the criteria were included, which involved 6347 individuals. Markers for 19 genes have been postulated as relevant. Conclusion: The performed analysis indicates that multiple markers may be correlated with worse evolution of COVID-19; however, great heterogeneity has been found among the studies, which still precludes their translation into clinical practice.

Application of principal component analysis on temporal evolution of COVID-19.

The COVID-19 is one of the worst pandemics in modern history. We applied principal component analysis (PCA) to the daily time series of the COVID-19 death cases and confirmed cases for the top 25 countries from April of 2020 to February of 2021. We calculated the eigenvalues and eigenvectors of the cross-correlation matrix of the changes in daily accumulated data over monthly time windows. The largest eigenvalue describes the overall evolution dynamics of the COVID-19 and indicates that evolution was faster in April of 2020 than in any other period. By using the first two PC coefficients, we can identify the group dynamics of the COVID-19 evolution. We observed groups under critical states in the loading plot and found that American and European countries are represented by strong clusters in the loading plot. The first PC plays an important role and the correlations (C1) between the normalized logarithmic changes in deaths or confirmed cases and the first PCs may be used as indicators of different phases of the COVID-19. By varying C1 over time, we identified different phases of the COVID-19 in the analyzed countries over the target time period.

Evolution of COVID-19 during the epidemiological week 16 to 53 of 2020 in the state of Acre Western Amazonia, Brazil

Introduction: coronavirus is part of a group of RNA viruses belonging to the Coronaviridae family, widely distributed in humans and other mammals. Currently, it has been seriously affecting the whole world, without a definitive cure yet. Objective: to analyse the association between the HDI and confirmed cumulative cases of COVID-19 that occurred during epidemiological week 16 to 53 of 2020, in the State of Acre. Methods: this is an ecological study of descriptive time series, evaluating the State of Acre and its 22 municipalities affected by COVID-19, in the period corresponding to the epidemiological weeks 16 to 53 of 2020. The State of Acre and its municipalities are aggregated by five regions with a total of approximately 881 thousand inhabitants, with an HDI of 0.663. Rio Branco is the state capital with 407,000 inhabitants. The 22 municipalities were analyzed, relating the HDI variables, confirmed cases per day and number of inhabitants to each other. Results: it was observed that the population evaluated, affected by COVID-19 during SE 16 to 53 of 2020, in the State of Acre, had as predominant general characteristics brown skin color, male sex, and the evolution to death from the disease was related with older age and comorbidity. Acre had a mortality rate (deaths per 100,000 inhabitants) of 90.9 and a lethality rate of 1.9%, with the highest mortality rate observed in the municipality of Rio Branco (121.3/100,000 inhabitants) and lethality in Rodrigues Alves (2.9%). The incidence of COVID-19 in Acre was 4,759.9 cases per 100,000 inhabitants, the municipalities of Assis Brasil and Xapuri had the highest incidences in the state with 10273.7 and 9330.8 new cases per 100,000 inhabitants, respectively. Conclusion: although the accumulated numbers of cases are different for the same day, the behavior is very similar, that is, the curves vary in the same way over time, regardless of the municipality observed.

Data based model for predicting COVID-19 morbidity and mortality in metropolis

There is an ongoing need for scientific analysis to help governments and public health authorities make decisions regarding the COVID-19 pandemic. This article presents a methodology based on data mining that can offer support for coping with epidemic diseases. The methodological approach was applied in São Paulo, Rio de Janeiro and Manaus, the cities in Brazil with the most COVID-19 deaths until the first half of 2021. We aimed to predict the evolution of COVID-19 in metropolises and identify air quality and meteorological variables correlated with confirmed cases and deaths. The statistical analyses indicated the most important explanatory environmental variables, while the cluster analyses showed the potential best input variables for the forecasting models. The forecast models were built by two different algorithms and their results have been compared. The relationship between epidemiological and environmental variables was particular to each of the three cities studied. Low solar radiation periods predicted in Manaus can guide managers to likely increase deaths due to COVID-19. In São Paulo, an increase in the mortality rate can be indicated by drought periods. The developed models can predict new cases and deaths by COVID-19 in studied cities. Furthermore, the methodological approach can be applied in other cities and for other epidemic diseases.

Using GAM functions and Markov-Switching models in an evaluation framework to assess countries\u2019 performance in controlling the COVID-19 pandemic

BackgroundThe COVID-19 pandemic has initiated several initiatives to better understand its behavior, and some projects are monitoring its evolution across countries, which naturally leads to comparisons made by those using the data. However, most “at a glance” comparisons may be misleading because the curve that should explain the evolution of COVID-19 is different across countries, as a result of the underlying geopolitical or socio-economic characteristics. Therefore, this paper contributes to the scientific endeavour by creating a new evaluation framework to help stakeholders adequately monitor and assess the evolution of COVID-19 in countries, considering the occurrence of spikes, "secondary waves" and structural breaks in the time series.MethodsGeneralized Additive Models were used to model cumulative and daily curves for confirmed cases and deaths. The Root Relative Squared Error and the Percentage Deviance Explained measured how well the models fit the data. A local min-max function was used to identify all local maxima in the fitted values. The pure Markov-Switching and the family of Markov-Switching GARCH models were used to identify structural breaks in the COVID-19 time series. Finally, a quadrants system to identify countries that are more/less efficient in the short/long term in controlling the spread of the virus and the number of deaths was developed. Such methods were applied in the time series of 189 countries, collected from the Centre for Systems Science and Engineering at Johns Hopkins University.ResultsOur methodology proves more effective in explaining the evolution of COVID-19 than growth functions worldwide, in addition to standardizing the entire estimation process in a single type of function. Besides, it highlights several inflection points and regime-switching moments, as a consequence of people’s diminished commitment to fighting the pandemic. Although Europe is the most developed continent in the world, it is home to most countries with an upward trend and considered inefficient, for confirmed cases and deaths.ConclusionsThe new outcomes presented in this research will allow key stakeholders to check whether or not public policies and interventions in the fight against COVID-19 are having an effect, easily identifying examples of best practices and promote such policies more widely around the world.

Artificial Intelligence Models and Techniques Applied to COVID-19: A Review

The rapid spread of SARS-CoV-2 and the consequent global COVID-19 pandemic has prompted the public administrations of different countries to establish health procedures and protocols based on information generated through predictive techniques and models, which, in turn, are based on technology such as artificial intelligence (AI) and machine learning (ML). This article presents some AI tools and computational models used to collaborate in the control and detection of COVID-19 cases. In addition, the main features of the Epidempredict project regarding COVID-19 in Panama are presented. This initiative consists of the planning and design of a digital platform, with cloud-based technology, to manage the ingestion, analysis, visualization and exportation of data regarding the evolution of COVID-19 in Panama. The methodology for the design of predictive algorithms is based on a hybrid model that combines the dynamics associated with population data of an SIR model of differential equations and extrapolation with recurrent neural networks. The technological solution developed suggests that adjustments can be made to the rules implemented in the expert processes that are considered. Furthermore, the resulting information is displayed and explored through user-friendly dashboards, contributing to more meaningful decision-making processes.

Long-term sequelae are highly prevalent one year after hospitalization for severe COVID-19

Many coronavirus disease 2019 (Covid-19) survivors show symptoms months after acute illness. The aim of this work is to describe the clinical evolution of Covid-19, one year after discharge. We performed a prospective cohort study on 238 patients previously hospitalized for Covid-19 pneumonia in 2020 who already underwent clinical follow-up 4 months post-Covid-19. 200 consented to participate to a 12-months clinical assessment, including: pulmonary function tests with diffusing lung capacity for carbon monoxide (DLCO); post-traumatic stress (PTS) symptoms evaluation by the Impact of Event Scale (IES); motor function evaluation (by Short Physical Performance Battery and 2 min walking test); chest Computed Tomography (CT). After 366 [363–369] days, 79 patients (39.5%) reported at least one symptom. A DLCO < 80% was observed in 96 patients (49.0%). Severe DLCO impairment (< 60%) was reported in 20 patients (10.2%), related to extent of CT scan abnormalities. Some degree of motor impairment was observed in 25.8% of subjects. 37/200 patients (18.5%) showed moderate-to-severe PTS symptoms. In the time elapsed from 4 to 12 months after hospital discharge, motor function improves, while respiratory function does not, being accompanied by evidence of lung structural damage. Symptoms remain highly prevalent one year after acute illness.

A Bayesian-Deep Learning Model for Estimating COVID-19 Evolution in Spain

This work proposes a semi-parametric approach to estimate the evolution of COVID-19 (SARS-CoV-2) in Spain. Considering the sequences of 14-day cumulative incidence of all Spanish regions, it combines modern Deep Learning (DL) techniques for analyzing sequences with the usual Bayesian Poisson-Gamma model for counts. The DL model provides a suitable description of the observed time series of counts, but it cannot give a reliable uncertainty quantification. The role of expert elicitation of the expected number of counts and its reliability is DL predictions’ role in the proposed modelling approach. Finally, the posterior predictive distribution of counts is obtained in a standard Bayesian analysis using the well known Poisson-Gamma model. The model allows to predict the future evolution of the sequences on all regions or estimates the consequences of eventual scenarios.

The evolution of clot strength in critically-ill COVID-19 patients: a prospective observational thromboelastography study

BackgroundFew studies detail the evolution of COVID-19 associated coagulopathy. We performed serial thromboelastography (TEG) and laboratory coagulation studies in 40 critically-ill, mechanically ventilated COVID-19 patients over a 14-day period and analysed differences between 30-day survivors and non-survivors.MethodsSingle-center prospective, observational study including 40 patients with severe COVID-19 pneumonia admitted to the intensive care unit (ICU) for mechanical ventilation. TEG analysis was performed on days 1, 7 and 14 of ICU admission and laboratory coagulation studies were performed on days 1 and 14. Coagulation variables were evaluated for change over the 14-day observation period. Differences between survivors and non-survivors at 30-days were analysed and compared.ResultsOn admission, TEG maximum amplitude (MA) with heparinase correction was above the upper limit of the reference range in 32 (80%) patients while 33 (82.5%) presented with absent clot lysis at 30 min. The functional fibrinogen MA was also elevated above the upper limit of the reference range in 37 (92.5%) patients. All patients had elevated D-dimer and fibrinogen levels, mildly prolonged prothrombin times (PT), normal platelet counts and normal activated partial thromboplastin times (aPTT). The heparinase MA decreased significantly with time and normalised after 14 days (p = < 0.001) while the increased fibrin contribution to clot strength persisted with time (p = 0.113). No significant differences in TEG analysis were noted between 30-day survivors and non-survivors at all time points. No patients developed disseminated intravascular coagulopathy (DIC) after 14-days, however thrombosis and bleeding were each reported in 3 (7.5%) patients.ConclusionCritically-ill patients with COVID-19 present in a hypercoagulable state characterised by an increased clot strength. This state normalises after 14 days despite a persistently increased fibrin contribution to clot strength. We were unable to demonstrate any significant differences in TEG parameters between 30-day survivors and non-survivors at all time points.

Managing the resource allocation for the COVID-19 pandemic in healthcare institutions: a pluralistic perspective

PurposeAs COVID-19 outbreak has created a global crisis, treating patients with minimum resources and traditional methods has become a hectic task. In this technological era, the rapid growth of coronavirus has affected the countries in lightspeed manner. Therefore, the present study proposes a model to analyse the resource allocation for the COVID-19 pandemic from a pluralistic perspective.Design/methodology/approachThe present study has combined data analytics with the K-mean clustering and probability queueing theory (PQT) and analysed the evolution of COVID-19 all over the world from the data obtained from public repositories. By using K-mean clustering, partitioning of patients’ records along with their status of hospitalization can be mapped and clustered. After K-mean analysis, cluster functions are trained and modelled along with eigen vectors and eigen functions.FindingsAfter successful iterative training, the model is programmed using R functions and given as input to Bayesian filter for predictive model analysis. Through the proposed model, disposal rate; PPE (personal protective equipment) utilization and recycle rate for different countries were calculated.Research limitations/implicationsUsing probabilistic queueing theory and clustering, the study was able to predict the resource allocation for patients. Also, the study has tried to model the failure quotient ratio upon unsuccessful delivery rate in crisis condition.Practical implicationsThe study has gathered epidemiological and clinical data from various government websites and research laboratories. Using these data, the study has identified the COVID-19 impact in various countries. Further, effective decision-making for resource allocation in pluralistic setting has being evaluated for the practitioner's reference.Originality/valueFurther, the proposed model is a two-stage approach for vulnerability mapping in a pandemic situation in a healthcare setting for resource allocation and utilization.

A Survey Conducted during COVID-19 Pandemic Among Brazilian Chronic Myeloid Leukemia and Philadelphia-Negative Myeloproliferative Neoplasms Patients

Introduction: COVID-19 has severely affected the Brazilian population. By July 2021, the incidence was 19,9 million cases and 556.000 deaths. Recent studies suggest that patients with MPN have higher infection and death rates than the general population. Older age and comorbidities are risk factors for severe COVID-19 in CML and MPN.ObjectivesThis study aimed to evaluate the incidence and clinical evolution of COVID-19 in a cohort of CML and MPN Brazilian patients.MethodsThis is a prospective, observational, ongoing study. All patients signed informed consent and answered two structured questionnaires within a six months interval. The questionnaire included questions about patient's behavior during pandemic, symptoms, contacts, COVID-19 infection, and vaccination data in the last six months. In addition, demographic data, CML and NMP treatment, comorbidities, laboratory tests, COVID severity, and outcome were collected from the medical records.ResultsFrom September 2020 to July 2021, 370 patients answered the first questionnaire, and 153 answered the second: 225 with CML and 145 with MPN (45% essential thrombocythemia, 27.6%, polycythemia vera, 23% myelofibrosis, and 4.8% not classified). In the CML population, the median age was 56 (19-90). Most were receiving tyrosine kinase inhibitors (88,5%) and 26 (11,5%) no treatment, in treatment-free remission (TFR). 80% of the patients were practicing social distancing, and 30% had at least one family member or close contact diagnosed with COVID-19. Comorbidities: hypertension (35%), diabetes (14%), pulmonary disease (6%), cardiac disease (16%), renal disease (7%), other (18%). A total of 28/225 (12.4%) patients had confirmed COVID-19 diagnosis (by serology or PCR), while 10 were suspect. The median age was 47 years, 68% were male, and 41% were not respecting social distancing. Thirty-five percent had comorbidities: 25% hypertension; 68% had a history of close contact with an infected person. One patient was in the accelerated phase, and 27 were in the chronic phase; 4 had a complete cytogenetic response, 13 major molecular response (MMR), 3 MR4.0, and 7 MR4.5. COVID-19 was mild/moderate in 27 and in severe in one case, resulting in death. This patient was a male, 71-year-old, with hypertension, in MMR with nilotinib. At COVID-19 onset, 16 pts were receiving imatinib, five dasatinib; five nilotinib e 2 were in TFR. There was one reinfection, in a 54 years old male patient, with no comorbidities. To date, 84 (37%) patients (pts) have received vaccines against COVID-19: 32 CORONAVAC (Sinovac/Butantan), 51 ChAdOx1nCov-19-Covishield (Astrazeneca/Oxford), and one BNT162 (Pfizer). All COVID-19 cases occurred before vaccination.Among the 145 MPN pts, the median age was 67 years (29-90), and 86% had comorbidities (52% hypertension, 17.5% diabetes, and 13% cardiac diseases). Social distancing was 83%. Nine out of 145 (6.2%) had confirmed COVID-19 diagnosis, and 3% suspect. The median age of these pts was 43 years (28-80). Seven patients had ET and 2 PV. Seven were female. Four pts received Hydroxyurea (HU) and aspirin, four aspirin, one HU, and one no treatment. There were seven mild and two moderate cases requiring hospitalization, none requiring oxygen or mechanical ventilation, none with thrombosis. Two COVID cases occurred after the first dose of vaccines (CORONAVAC and Covishield). In the whole MPN group, 11% have received two doses (57% CORONAVAC, 40% Covishield, and 3.6% BNT 162).ConclusionsCOVID-19 cases occurred more frequently in younger patients. COVID-19 incidence was higher in the CML than in the MPN population, probably because MPN patients were less exposed, and the older pts were the first to receive vaccines. The impact of the vaccination on the prevention of new cases will be evaluated during the follow-up. DisclosuresBortolini: Novartis: Speakers Bureau. Pagnano: Novartis: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Astellas: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Pintpharma: Other: Lecture; EMS: Other: Lecture; Jansenn: Other: Lecture.