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1.
PLoS One ; 16(12): e0261776, 2021.
Article in English | MEDLINE | ID: covidwho-1631646

ABSTRACT

The Coronavirus Disease 2019 has resulted in a transition from physical education to online learning, leading to a collapse of the established educational order and a wisdom test for the education governance system. As a country seriously affected by the pandemic, the health of the Indian higher education system urgently requires assessment to achieve sustainable development and maximize educational externalities. This research systematically proposes a health assessment model from four perspectives, including educational volume, efficiency, equality, and sustainability, by employing the Technique for Order Preference by Similarity to an Ideal Solution Model, Principal Component Analysis, DEA-Tobit Model, and Augmented Solow Model. Empirical results demonstrate that India has high efficiency and an absolute health score in the higher education system through multiple comparisons between India and the other selected countries while having certain deficiencies in equality and sustainability. Additionally, single-target and multiple-target path are simultaneously proposed to enhance the Indian current education system. The multiple-target approach of the India-China-Japan-Europe-USA process is more feasible to achieve sustainable development, which would improve the overall health score from .351 to .716. This finding also reveals that the changes are relatively complex and would take 91.5 years considering the relationship between economic growth rates and crucial indicators. Four targeted policies are suggested for each catching-up period, including expanding and increasing the social funding sources, striving for government expenditure support to improve infrastructures, imposing gender equality in education, and accelerating the construction of high-quality teachers.


Subject(s)
COVID-19/epidemiology , Education, Distance/methods , Educational Status , Models, Theoretical , Pandemics , SARS-CoV-2 , Sustainable Development , COVID-19/virology , China/epidemiology , Europe/epidemiology , Humans , India/epidemiology , Japan/epidemiology , Principal Component Analysis/methods , United States/epidemiology
2.
PLoS One ; 17(1): e0261851, 2022.
Article in English | MEDLINE | ID: covidwho-1613359

ABSTRACT

Perceived risk clearly impacts travel behavior, including destination selection and satisfaction, but it is unclear how or why its effect is only significant in certain cases. This is because existing studies have undervalued the mediating factors of risk aversion, government initiatives, and media influence as well as the multiple forms or dimensions of risk that can mask its direct effect. This study constructs a structural equation model of perceived risk's impact on destination image and travel intention for a more nuanced model of the perceived risk mechanism in tourism, based on 413 e-questionnaires regarding travel to Chengdu, China during the COVID-19 pandemic, using the Bootstrap method to analyze suppressing effect. It finds that while perceived risk has a significant negative impact on destination image and travel intention, this is complexly mediated so as to appear insignificant. Furthermore, different mediating factors and dimensions of perceived risk operate differently according to their varied combinations in actual circumstances. This study is significant because it provides a theoretical interpretation of tourism risk, elucidates the mechanisms or paths by which perceived risk affects travel intention, and expands a framework for research on destination image and travel intention into the realms of psychology, political, and communication science. It additionally encourages people to pay greater attention to the negative impact of crises and focuses on the important role of internal and external responses in crisis management, which can help improve the effectiveness of crisis management and promote the sustainable development of the tourism industry.


Subject(s)
COVID-19/psychology , Travel/trends , China/epidemiology , Humans , Intention , Models, Theoretical , Pandemics , Perception , Risk Factors , SARS-CoV-2/pathogenicity , Tourism , Travel/psychology
3.
Cad. Saúde Pública (Online) ; 37(10): e00119021, 2021. graf
Article in Portuguese | LILACS (Americas) | ID: covidwho-1609136

ABSTRACT

Este ensaio tem como objetivo apresentar e discutir o quadro teórico da sindemia da COVID-19. Na primeira parte, são apresentados os fundamentos e princípios da teoria sindêmica. Adotou-se o conceito de sindemia como processo de interação sinérgica entre duas ou mais doenças, no qual os efeitos se potencializam mutuamente. Foram discutidas as três principais tipologias de interação sindêmica: epidemias mutuamente causais; epidemias interagindo sinergicamente; e epidemias causais em série. Na segunda parte, a COVID-19 é analisada como uma sindemia resultante da interação entre vários grupos de doenças e o contexto socioeconômico. O modelo teórico considerou a interação entre COVID-19 e doenças crônicas não transmissíveis, doenças infecciosas e parasitárias e problemas de saúde mental. Abordou-se como as iniquidades sociais e as condições de vulnerabilidade atuam em diversos níveis e potencializam a atuação da COVID-19 e das demais pandemias. Na última seção, discute-se a necessidade de respostas abrangentes, multisetoriais e integradas ao enfrentamento da COVID-19. Foi apresentado um modelo de intervenção envolvendo as dimensões assistencial e socioeconômica. No âmbito assistencial, defendeu-se a estruturação de sistemas de saúde fortes, responsivos e acessíveis a toda a população. A dimensão econômica e social abordou o resgate dos ideais de solidariedade, da estratégia da promoção da saúde e a ênfase sobre os determinantes sociais. Conclui-se que as lições aprendidas com a abordagem sindêmica da COVID-19 exortam governos e a sociedade para o desenvolvimento de políticas que articulem intervenções clínicas, sanitárias, socioeconômicas e ambientais.


This essay aims to present and discuss the theoretical framework for the COVID-19 syndemic. The first part presents the foundations and principles of syndemic theory. For the purposes of this essay, syndemic was defined as a process of synergic interaction between two or more diseases, in which the effects are mutually enhanced. We discussed the three principal typologies of syndemic interaction: mutually causal epidemics; epidemics interacting synergically; and serial causal epidemics. In the second part, COVID-19 is analyzed as a syndemic resulting from the interaction between various groups of diseases and the socioeconomic context. The theoretical model considered the interaction between COVID-19 and chronic noncommunicable diseases, infectious and parasitic diseases, and mental health problems. The essay addressed how social iniquities and conditions of vulnerability act at various levels to increase the effect of COVID-19 and other pandemics. The last section discusses the need for comprehensive, multisector, and integrated responses to COVID-19. A model for intervention was presented that involves the patient care and socioeconomic dimensions. In the sphere of patient care, the authors defend the structuring of strong and responsive health systems, accessible to the entire population. The economic and social dimension addressed the issue of reclaiming the ideals of solidarity, the health promotion strategy, and emphasis on social determinants of health. In conclusion, the lessons learned from the syndemic approach to COVID-19 call on government and society to develop policies that link clinical, sanitary, socioeconomic, and environmental interventions.


Este ensayo tiene como objetivo presentar y discutir el cuadro teórico de la sindemia de la COVID-19. En la primera parte, se presentan los fundamentos y principios de la teoría sindémica. Se adoptó el concepto de sindemia como un proceso de interacción sinérgica entre dos o más enfermedades, en el que los efectos se potencializan mutuamente. Se discutieron las tres principales tipologías de interacción sindémica: epidemias mutuamente causales; epidemias interactuando sinérgicamente; y epidemias causales en serie. En la segunda parte, la COVID-19 es analizada como una sindemia resultante de la interacción entre varios grupos de enfermedades y el contexto socioeconómico. El modelo teórico consideró la interacción entre COVID-19 y enfermedades crónicas no transmisibles, enfermedades infecciosas y parasitarias, así como problemas de salud mental. Se abordó cómo las inequidades sociales y las condiciones de vulnerabilidad actúan en diversos niveles y potencializan la actuación de la COVID-19 y de las demás pandemias. En la última sección, se discute la necesidad de respuestas integrales, multisectoriales e integradas en el combate a la COVID-19. Se presentó un modelo de intervención implicando las dimensiones asistencial y socioeconómica. En el ámbito asistencial, se defendió la conformación de sistemas de salud fuertes, con capacidad de respuesta y accesibles a toda la población. La dimensión económica y social abordó el rescate de los ideales de solidaridad, de la estrategia de promoción de la salud, así como el énfasis sobre los determinantes sociales. Se concluye que las lecciones aprendidas con el abordaje sindémico de la COVID-19 exhortan a gobiernos y sociedad a que desarrollen políticas que implementen y coordinen intervenciones clínicas, sanitarias, socioeconómicas y ambientales.


Subject(s)
Humans , Syndemic , COVID-19 , Brazil , SARS-CoV-2 , Models, Theoretical
4.
JAMA Netw Open ; 5(1): e2142057, 2022 01 04.
Article in English | MEDLINE | ID: covidwho-1604871

ABSTRACT

Importance: Closure of day care centers has been implemented globally to contain the COVID-19 pandemic but has negative effects on children's health and psychosocial well-being. Objective: To investigate the feasibility of surveillance among children and childcare workers and to model the efficacy of surveillance on viral spread prevention. Design, Setting, and Participants: This nonrandomized controlled trial was conducted at 9 day care centers in Wuerzburg, Germany, from October 2020 to March 2021. Participants included children attending day care, childcare workers, and household members. Participating day care centers were assigned to different surveillance modules in a nonrandomized feasibility study. A mathematical model for SARS-CoV-2 spread in day care centers was developed to identify optimal surveillance. Interventions: Modules 1, 2, and 3 involved continuous surveillance of asymptomatic children and childcare workers by SARS-CoV-2 polymerase chain reaction testing of either midturbinate nasal swabs twice weekly (module 1) or once weekly (module 2) or self-sampled saliva samples twice weekly (module 3). Module 4 involved symptom-based, on-demand testing of children, childcare workers, and their household members by oropharyngeal swabs. All participants underwent SARS-CoV-2 antibody status testing before and after the sampling period. Questionnaires on attitudes and perception of the pandemic were administered in weeks 1, 6, and 12. Mathematical modeling was used to estimate SARS-CoV-2 spread in day care centers. Main Outcomes and Measures: The primary outcomes were acceptance of the respective surveillance protocols (feasibility study) and the estimated number of secondary infections (mathematical modeling). Results: Of 954 eligible individuals (772 children and 182 childcare workers), 592 (62%), including 442 children (median [IQR] age, 3 [2-4] years; 214 [48.6%] female) and 150 childcare workers (median [IQR] age, 29 [25-44] years; 129 [90.8%] female) participated in the surveillance. In total, 4755 tests for SARS-CoV-2 detected 2 infections (1 childcare worker and 1 adult household member). Acceptance for continuous surveillance was highest for biweekly saliva testing (150 of 221 eligible individuals [67.9%; 95% CI, 61.5%-73.7%]) compared with biweekly (51 of 117 individuals [43.6%; 95% CI, 35.0%-52.6%]) and weekly (44 of 128 individuals [34.4%; 95% CI, 26.7%-43.0%]) midturbinate swabbing (P < .001). Dropout rates were higher for midturbinate swabbing (biweekly, 11 of 62 participants [18%]; once weekly, 11 of 55 participants [20%]) than for saliva testing (6 of 156 participants [4%]). Mathematical modeling based on study and literature data identified biweekly testing of at least 50% of children and childcare workers as minimal requirements to limit secondary infections. Conclusions and Relevance: In this nonrandomized controlled trial, surveillance for SARS-CoV-2 in 9 German day care centers was feasible and well accepted. Mathematical modeling estimated that testing can minimize the spread of SARS-CoV-2 in day care centers. These findings enable setup of surveillance programs to maintain institutional childcare. Trial Registration: German Registry for Clinical Trials Identifier: DRKS00023721.


Subject(s)
COVID-19 Testing , COVID-19/prevention & control , Caregivers , Child Care , Child Day Care Centers , Child Health , Adult , COVID-19/diagnosis , COVID-19/virology , Child , Child, Preschool , Feasibility Studies , Female , Germany , Humans , Male , Models, Theoretical , Pandemics , Patient Acceptance of Health Care , Polymerase Chain Reaction , SARS-CoV-2 , Saliva , Specimen Handling
5.
Sci Rep ; 11(1): 24498, 2021 12 30.
Article in English | MEDLINE | ID: covidwho-1597845

ABSTRACT

When a virus spreads, it may mutate into, e.g., vaccine resistant or fast spreading lineages, as was the case for the Danish Cluster-5 mink variant (belonging to the B.1.1.298 lineage), the British B.1.1.7 lineage, and the South African B.1.351 lineage of the SARS-CoV-2 virus. A way to handle such spreads is through a containment strategy, where the population in the affected area is isolated until the spread has been stopped. Under such circumstances, it is important to monitor whether the mutated virus is extinct via massive testing for the virus sub-type. If successful, the strategy will lead to lower and lower numbers of the sub-type, and it will eventually die out. An important question is, for how long time one should wait to be sure the sub-type is extinct? We use a hidden Markov model for infection spread and an approximation of a two stage sampling scheme to infer the probability of extinction. The potential of the method is illustrated via a simulation study. Finally, the model is used to assess the Danish containment strategy when SARS-CoV-2 spread from mink to man during the summer of 2020, including the Cluster-5 sub-type. In order to avoid further spread and mink being a large animal virus reservoir, this situation led to the isolation of seven municipalities in the Northern part of the country, the culling of the entire Danish 17 million large mink population, and a bill to interim ban Danish mink production until the end of 2021.


Subject(s)
COVID-19 , Models, Theoretical , Pandemics , SARS-CoV-2/genetics , Animals , COVID-19/epidemiology , COVID-19/virology , Humans , Probability
6.
Sci Rep ; 11(1): 24467, 2021 12 28.
Article in English | MEDLINE | ID: covidwho-1596771

ABSTRACT

Mobility restrictions are successfully used to contain the diffusion of epidemics. In this work we explore their effect on the epidemic growth by investigating an extension of the Susceptible-Infected-Removed (SIR) model in which individual mobility is taken into account. In the model individual agents move on a chessboard with a Lévy walk and, within each square, epidemic spreading follows the standard SIR model. These simple rules allow to reproduce the sub-exponential growth of the epidemic evolution observed during the Covid-19 epidemic waves in several countries and which cannot be captured by the standard SIR model. We show that we can tune the slowing-down of the epidemic spreading by changing the dynamics of the agents from Lévy to Brownian and we investigate how the interplay among different containment strategies mitigate the epidemic spreading. Finally we demonstrate that we can reproduce the epidemic evolution of the first and second COVID-19 waves in Italy using only 3 parameters, i.e , the infection rate, the removing rate, and the mobility in the country. We provide an estimate of the peak reduction due to imposed mobility restrictions, i. e., the so-called flattening the curve effect. Although based on few ingredients, the model captures the kinetic of the epidemic waves, returning mobility values that are consistent with a lock-down intervention during the first wave and milder limitations, associated to a weaker peak reduction, during the second wave.


Subject(s)
COVID-19/epidemiology , Models, Theoretical , Movement , COVID-19/virology , Epidemics , Humans , Italy/epidemiology , SARS-CoV-2/isolation & purification
7.
Sci Rep ; 11(1): 24171, 2021 12 17.
Article in English | MEDLINE | ID: covidwho-1593554

ABSTRACT

The transmission of COVID-19 is dependent on social mixing, the basic rate of which varies with sociodemographic, cultural, and geographic factors. Alterations in social mixing and subsequent changes in transmission dynamics eventually affect hospital admissions. We employ these observations to model and predict regional hospital admissions in Sweden during the COVID-19 pandemic. We use an SEIR-model for each region in Sweden in which the social mixing is assumed to depend on mobility data from public transport utilisation and locations for mobile phone usage. The results show that the model could capture the timing of the first and beginning of the second wave of the pandemic 3 weeks in advance without any additional assumptions about seasonality. Further, we show that for two major regions of Sweden, models with public transport data outperform models using mobile phone usage. We conclude that a model based on routinely collected mobility data makes it possible to predict future hospital admissions for COVID-19 3 weeks in advance.


Subject(s)
Algorithms , COVID-19/transmission , Cell Phone/statistics & numerical data , Hospitalization/statistics & numerical data , Models, Theoretical , Patient Admission/statistics & numerical data , COVID-19/epidemiology , COVID-19/virology , Disease Transmission, Infectious/statistics & numerical data , Forecasting/methods , Geography , Hospitalization/trends , Humans , Pandemics/prevention & control , Patient Admission/trends , Retrospective Studies , SARS-CoV-2/physiology , Sweden/epidemiology , Travel/statistics & numerical data
8.
Sci Rep ; 11(1): 24491, 2021 12 29.
Article in English | MEDLINE | ID: covidwho-1591547

ABSTRACT

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.


Subject(s)
COVID-19/epidemiology , COVID-19/mortality , Data Mining/methods , Brazil/epidemiology , COVID-19/pathology , Cities/epidemiology , Humans , Models, Theoretical , Morbidity , Pandemics/prevention & control , SARS-CoV-2/pathogenicity
9.
Front Immunol ; 12: 776933, 2021.
Article in English | MEDLINE | ID: covidwho-1581333

ABSTRACT

The efficacy of COVID-19 vaccines appears to depend in complex ways on the vaccine dosage and the interval between the prime and boost doses. Unexpectedly, lower dose prime and longer prime-boost intervals have yielded higher efficacies in clinical trials. To elucidate the origins of these effects, we developed a stochastic simulation model of the germinal center (GC) reaction and predicted the antibody responses elicited by different vaccination protocols. The simulations predicted that a lower dose prime could increase the selection stringency in GCs due to reduced antigen availability, resulting in the selection of GC B cells with higher affinities for the target antigen. The boost could relax this selection stringency and allow the expansion of the higher affinity GC B cells selected, improving the overall response. With a longer dosing interval, the decay in the antigen with time following the prime could further increase the selection stringency, amplifying this effect. The effect remained in our simulations even when new GCs following the boost had to be seeded by memory B cells formed following the prime. These predictions offer a plausible explanation of the observed paradoxical effects of dosage and dosing interval on vaccine efficacy. Tuning the selection stringency in the GCs using prime-boost dosages and dosing intervals as handles may help improve vaccine efficacies.


Subject(s)
B-Lymphocytes/immunology , COVID-19 Vaccines/immunology , COVID-19/immunology , Clonal Selection, Antigen-Mediated/immunology , Germinal Center/immunology , Host-Pathogen Interactions/immunology , SARS-CoV-2/immunology , Antigens/immunology , B-Lymphocytes/metabolism , COVID-19/virology , COVID-19 Vaccines/administration & dosage , Dose-Response Relationship, Immunologic , Germinal Center/metabolism , Humans , Immunization, Secondary , Models, Theoretical , Vaccination
10.
Viruses ; 13(12)2021 12 19.
Article in English | MEDLINE | ID: covidwho-1580421

ABSTRACT

Vaccination is considered the best strategy for limiting and eliminating the COVID-19 pandemic. The success of this strategy relies on the rate of vaccine deployment and acceptance across the globe. As these efforts are being conducted, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is continuously mutating, which leads to the emergence of variants with increased transmissibility, virulence, and resistance to vaccines. One important question is whether surveillance testing is still needed in order to limit SARS-CoV-2 transmission in a vaccinated population. In this study, we developed a multi-scale mathematical model of SARS-CoV-2 transmission in a vaccinated population and used it to predict the role of testing in an outbreak with variants of increased transmissibility. We found that, for low transmissibility variants, testing was most effective when vaccination levels were low to moderate and its impact was diminished when vaccination levels were high. For high transmissibility variants, widespread vaccination was necessary in order for testing to have a significant impact on preventing outbreaks, with the impact of testing having maximum effects when focused on the non-vaccinated population.


Subject(s)
COVID-19 Testing , COVID-19/diagnosis , COVID-19/prevention & control , Models, Theoretical , Vaccination , COVID-19 Vaccines , Diagnostic Tests, Routine , Humans , SARS-CoV-2/isolation & purification , Virulence
11.
Sci Rep ; 11(1): 23378, 2021 12 16.
Article in English | MEDLINE | ID: covidwho-1585808

ABSTRACT

Emissions of black carbon (BC) particles from anthropogenic and natural sources contribute to climate change and human health impacts. Therefore, they need to be accurately quantified to develop an effective mitigation strategy. Although the spread of the emission flux estimates for China have recently narrowed under the constraints of atmospheric observations, consensus has not been reached regarding the dominant emission sector. Here, we quantified the contribution of the residential sector, as 64% (44-82%) in 2019, using the response of the observed atmospheric concentration in the outflowing air during Feb-Mar 2020, with the prevalence of the COVID-19 pandemic and restricted human activities over China. In detail, the BC emission fluxes, estimated after removing effects from meteorological variability, dropped only slightly (- 18%) during Feb-Mar 2020 from the levels in the previous year for selected air masses of Chinese origin, suggesting the contributions from the transport and industry sectors (36%) were smaller than the rest from the residential sector (64%). Carbon monoxide (CO) behaved differently, with larger emission reductions (- 35%) in the period Feb-Mar 2020, suggesting dominance of non-residential (i.e., transport and industry) sectors, which contributed 70% (48-100%) emission during 2019. The estimated BC/CO emission ratio for these sectors will help to further constrain bottom-up emission inventories. We comprehensively provide a clear scientific evidence supporting mitigation policies targeting reduction in residential BC emissions from China by demonstrating the economic feasibility using marginal abatement cost curves.


Subject(s)
Air Pollutants/analysis , Air Pollution/analysis , COVID-19/prevention & control , Particulate Matter/analysis , SARS-CoV-2/isolation & purification , Soot/analysis , Algorithms , Atmosphere/analysis , COVID-19/epidemiology , COVID-19/virology , China , Climate Change , Environmental Monitoring/methods , Environmental Monitoring/statistics & numerical data , Geography , Human Activities , Humans , Models, Theoretical , Pandemics , Residence Characteristics , SARS-CoV-2/physiology , Seasons , Wind
12.
Sci Rep ; 11(1): 24124, 2021 12 16.
Article in English | MEDLINE | ID: covidwho-1585805

ABSTRACT

The quantification of spreading heterogeneity in the COVID-19 epidemic is crucial as it affects the choice of efficient mitigating strategies irrespective of whether its origin is biological or social. We present a method to deduce temporal and individual variations in the basic reproduction number directly from epidemic trajectories at a community level. Using epidemic data from the 98 districts in Denmark we estimate an overdispersion factor k for COVID-19 to be about 0.11 (95% confidence interval 0.08-0.18), implying that 10 % of the infected cause between 70 % and 87 % of all infections.


Subject(s)
Algorithms , Basic Reproduction Number/statistics & numerical data , COVID-19/transmission , Models, Theoretical , SARS-CoV-2/isolation & purification , COVID-19/epidemiology , COVID-19/virology , Denmark/epidemiology , Epidemics/prevention & control , Geography , Humans , SARS-CoV-2/physiology
13.
Sci Rep ; 11(1): 24051, 2021 12 15.
Article in English | MEDLINE | ID: covidwho-1585803

ABSTRACT

Since the onset of the coronavirus disease 2019 (COVID-19) pandemic, different mitigation and management strategies limiting economic and social activities have been implemented across many countries. Despite these strategies, the virus continues to spread and mutate. As a result, vaccinations are now administered to suppress the pandemic. Current COVID-19 epidemic models need to be expanded to account for the change in behaviour of new strains, such as an increased virulence and higher transmission rate. Furthermore, models need to account for an increasingly vaccinated population. We present a network model of COVID-19 transmission accounting for different immunity and vaccination scenarios. We conduct a parameter sensitivity analysis and find the average immunity length after an infection to be one of the most critical parameters that define the spread of the disease. Furthermore, we simulate different vaccination strategies and show that vaccinating highly connected individuals first is the quickest strategy for controlling the disease.


Subject(s)
COVID-19 Vaccines/administration & dosage , COVID-19/prevention & control , Mass Vaccination/psychology , COVID-19/transmission , Humans , Mass Vaccination/statistics & numerical data , Models, Theoretical , SARS-CoV-2/genetics , SARS-CoV-2/pathogenicity , Social Interaction
14.
Sci Rep ; 11(1): 23959, 2021 12 14.
Article in English | MEDLINE | ID: covidwho-1585800

ABSTRACT

Evidence that patients may avoid healthcare facilities for fear of COVID-19 infection has heightened the concern that true rates of myocardial infarctions have been under-ascertained and left untreated. We analyzed data from the National Emergency Medical Services Information System (NEMSIS) and incident COVID-19 infections across the United States (US) between January 1, 2020 and April 30, 2020. Grouping events by US Census Division, multivariable adjusted negative binomial regression models were utilized to estimate the relationship between COVID-19 and EMS cardiovascular activations. After multivariable adjustment, increasing COVID-19 rates were associated with less activations for chest pain and non-ST-elevation myocardial infarctions. Simultaneously, increasing COVID-19 rates were associated with more activations for cardiac arrests, ventricular fibrillation, and ventricular tachycardia. Although direct effects of COVID-19 infections may explain these discordant observations, these findings may also arise from patients delaying or avoiding care for myocardial infarction, leading to potentially lethal consequences.


Subject(s)
Arrhythmias, Cardiac/epidemiology , COVID-19/epidemiology , Chest Pain/epidemiology , Arrhythmias, Cardiac/etiology , COVID-19/complications , Chest Pain/etiology , Humans , Models, Theoretical , Non-ST Elevated Myocardial Infarction/epidemiology , Non-ST Elevated Myocardial Infarction/genetics , United States/epidemiology
15.
Sci Rep ; 11(1): 24073, 2021 12 15.
Article in English | MEDLINE | ID: covidwho-1585794

ABSTRACT

Mitigating the devastating effect of COVID-19 is necessary to control the infectivity and mortality rates. Hence, several strategies such as quarantine of exposed and infected individuals and restricting movement through lockdown of geographical regions have been implemented in most countries. On the other hand, standard SEIR based mathematical models have been developed to understand the disease dynamics of COVID-19, and the proper inclusion of these restrictions is the rate-limiting step for the success of these models. In this work, we have developed a hybrid Susceptible-Exposed-Infected-Quarantined-Removed (SEIQR) model to explore the influence of quarantine and lockdown on disease propagation dynamics. The model is multi-compartmental, and it considers everyday variations in lockdown regulations, testing rate and quarantine individuals. Our model predicts a considerable difference in reported and actual recovered and deceased cases in qualitative agreement with recent reports.


Subject(s)
COVID-19/prevention & control , Humans , Models, Theoretical , Quarantine , Stochastic Processes
16.
Sci Rep ; 11(1): 24224, 2021 12 20.
Article in English | MEDLINE | ID: covidwho-1585790

ABSTRACT

Since 2019, a large number of people worldwide have been infected with severe acute respiratory syndrome coronavirus 2. Among those infected, a limited number develop severe coronavirus disease 2019 (COVID-19), which generally has an acute onset. The treatment of patients with severe COVID-19 is challenging. To optimize disease prognosis and effectively utilize medical resources, proactive measures must be adopted for patients at risk of developing severe COVID-19. We analyzed the data of COVID-19 patients from seven medical institutions in Tokyo and used mathematical modeling of patient blood test results to quantify and compare the predictive ability of multiple prognostic indicators for the development of severe COVID-19. A machine learning logistic regression model was used to analyze the blood test results of 300 patients. Due to the limited data set, the size of the training group was constantly adjusted to ensure that the results of machine learning were effective (e.g., recognition rate of disease severity > 80%). Lymphocyte count, hemoglobin, and ferritin levels were the best prognostic indicators of severe COVID-19. The mathematical model developed in this study enables prediction and classification of COVID-19 severity.


Subject(s)
COVID-19/pathology , Models, Theoretical , Adolescent , Adult , Aged , C-Reactive Protein/analysis , COVID-19/virology , Female , Ferritins/analysis , Hemoglobins/analysis , Humans , Lymphocyte Count , Machine Learning , Male , Middle Aged , Prognosis , Retrospective Studies , Risk Factors , SARS-CoV-2/isolation & purification , Severity of Illness Index , Young Adult
17.
Sci Rep ; 11(1): 24326, 2021 12 21.
Article in English | MEDLINE | ID: covidwho-1585785

ABSTRACT

We develop a site-bond percolation model, called PERCOVID, in order to describe the time evolution of all epidemics propagating through respiratory tract or by skin contacts in human populations. This model is based on a network of social relationships representing interconnected households experiencing governmental non-pharmaceutical interventions. As a very first testing ground, we apply our model to the understanding of the dynamics of the COVID-19 pandemic in France from December 2019 up to December 2021. Our model shows the impact of lockdowns and curfews, as well as the influence of the progressive vaccination campaign in order to keep COVID-19 pandemic under the percolation threshold. We illustrate the role played by social interactions by comparing two typical scenarios with low or high strengths of social relationships as compared to France during the first wave in March 2020. We investigate finally the role played by the α and δ variants in the evolution of the epidemic in France till autumn 2021, paying particular attention to the essential role played by the vaccination. Our model predicts that the rise of the epidemic observed in July and August 2021 would not result in a new major epidemic wave in France.


Subject(s)
COVID-19/epidemiology , Interpersonal Relations , Models, Theoretical , COVID-19/prevention & control , COVID-19/virology , France/epidemiology , Humans , Pandemics , SARS-CoV-2/isolation & purification , Vaccination
18.
Sci Rep ; 11(1): 24443, 2021 12 27.
Article in English | MEDLINE | ID: covidwho-1585775

ABSTRACT

We investigate, through a data-driven contact tracing model, the transmission of COVID-19 inside buses during distinct phases of the pandemic in a large Brazilian city. From this microscopic approach, we recover the networks of close contacts within consecutive time windows. A longitudinal comparison is then performed by upscaling the traced contacts with the transmission computed from a mean-field compartmental model for the entire city. Our results show that the effective reproduction numbers inside the buses, [Formula: see text], and in the city, [Formula: see text], followed a compatible behavior during the first wave of the local outbreak. Moreover, by distinguishing the close contacts of healthcare workers in the buses, we discovered that their transmission, [Formula: see text], during the same period, was systematically higher than [Formula: see text]. This result reinforces the need for special public transportation policies for highly exposed groups of people.


Subject(s)
COVID-19/transmission , Contact Tracing/methods , Brazil/epidemiology , COVID-19/epidemiology , COVID-19/virology , Disease Outbreaks , Health Personnel/statistics & numerical data , Humans , Models, Theoretical , SARS-CoV-2/isolation & purification , Transportation
19.
Medicine (Baltimore) ; 100(50): e28134, 2021 Dec 17.
Article in English | MEDLINE | ID: covidwho-1583960

ABSTRACT

BACKGROUND: The COVID-19 pandemic caused >0.228 billion infected cases as of September 18, 2021, implying an exponential growth for infection worldwide. Many mathematical models have been proposed to predict the future cumulative number of infected cases (CNICs). Nevertheless, none compared their prediction accuracies in models. In this work, we compared mathematical models recently published in scholarly journals and designed online dashboards that present actual information about COVID-19. METHODS: All CNICs were downloaded from GitHub. Comparison of model R2 was made in 3 models based on quadratic equation (QE), modified QE (OE-m), and item response theory (IRT) using paired-t test and analysis of variance (ANOVA). The Kano diagram was applied to display the association and the difference in model R2 on a dashboard. RESULTS: We observed that the correlation coefficient was 0.48 (t = 9.87, n = 265) between QE and IRT models based on R2 when modeling CNICs in a short run (dated from January 1 to February 16, 2021). A significant difference in R2 was found (P < .001, F = 53.32) in mean R2 of 0.98, 0.92, and 0.84 for IRT, OE-mm, and QE, respectively. The IRT-based COVID-19 model is superior to the counterparts of QE-m and QE in model R2 particularly in a longer period of infected days (i.e., in the entire year in 2020). CONCLUSION: An online dashboard was demonstrated to display the association and difference in prediction accuracy among predictive models. The IRT mathematical model was recommended to make projections about the evolution of CNICs for each county/region in future applications, not just limited to the COVID-19 epidemic.


Subject(s)
COVID-19 , Models, Theoretical , COVID-19/epidemiology , Forecasting , Humans , Pandemics , SARS-CoV-2
20.
PLoS One ; 16(3): e0247686, 2021.
Article in English | MEDLINE | ID: covidwho-1574773

ABSTRACT

OBJECTIVES: The aim of this study was to investigate possible patterns of demand for chest imaging during the first wave of the SARS-CoV-2 pandemic and derive a decision aid for the allocation of resources in future pandemic challenges. MATERIALS AND METHODS: Time data of requests for patients with suspected or confirmed coronavirus disease 2019 (COVID-19) lung disease were analyzed between February 27th and May 27th 2020. A multinomial logistic regression model was used to evaluate differences in the number of requests between 3 time intervals (I1: 6am - 2pm, I2: 2pm - 10pm, I3: 10pm - 6am). A cosinor model was applied to investigate the demand per hour. Requests per day were compared to the number of regional COVID-19 cases. RESULTS: 551 COVID-19 related chest imagings (32.8% outpatients, 67.2% in-patients) of 243 patients were conducted (33.3% female, 66.7% male, mean age 60 ± 17 years). Most exams for outpatients were required during I2 (I1 vs. I2: odds ratio (OR) = 0.73, 95% confidence interval (CI) 0.62-0.86, p = 0.01; I2 vs. I3: OR = 1.24, 95% CI 1.04-1.48, p = 0.03) with an acrophase at 7:29 pm. Requests for in-patients decreased from I1 to I3 (I1 vs. I2: OR = 1.24, 95% CI 1.09-1.41, p = 0.01; I2 vs. I3: OR = 1.16, 95% CI 1.05-1.28, p = 0.01) with an acrophase at 12:51 pm. The number of requests per day for outpatients developed similarly to regional cases while demand for in-patients increased later and persisted longer. CONCLUSIONS: The demand for COVID-19 related chest imaging displayed distinct distribution patterns depending on the sector of patient care and point of time during the SARS-CoV-2 pandemic. These patterns should be considered in the allocation of resources in future pandemic challenges with similar disease characteristics.


Subject(s)
COVID-19/diagnostic imaging , Diagnostic Imaging/trends , Thorax/diagnostic imaging , Adult , Aged , COVID-19/epidemiology , Diagnostic Tests, Routine/trends , Female , Humans , Male , Middle Aged , Models, Theoretical , Pandemics , Pilot Projects , SARS-CoV-2/pathogenicity , Thorax/virology
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