Propagation Of COVID-19 Research Articles

Study of Dynamics of a COVID-19 Model for Saudi Arabia with Vaccination Rate, Saturated Treatment Function and Saturated Incidence Rate

This paper proposes, validates and analyzes the dynamics of the susceptible exposed infectious recovered (SEIR) model for the propagation of COVID-19 in Saudi Arabia, which recorded the largest number of cases in the Arab world. The model incorporates a saturated incidence rate, a constant vaccination rate and a nonlinear treatment function. The rate of treatment is assumed to be proportional to the number of infected persons when this number is low and reaches a fixed value for large number of infected individuals. The expression of the basic reproduction number is derived, and the model basic stability properties are studied. We show that when the basic reproduction number is less than one the model can predict both a Hopf and backward bifurcations. Simulations are also provided to fit the model to COVID-19 data in Saudi Arabia and to study the effects of the parameters of the treatment function and vaccination rate on disease control.

Dynamics and optimal control of a stochastic coronavirus (COVID-19) epidemic model with diffusion.

In view of the facts in the infection and propagation of COVID-19, a stochastic reaction–diffusion epidemic model is presented to analyse and control this infectious diseases. Stationary distribution and Turing instability of this model are discussed for deriving the sufficient criteria for the persistence and extinction of disease. Furthermore, the amplitude equations are derived by using Taylor series expansion and weakly nonlinear analysis, and selection of Turing patterns for this model can be determined. In addition, the optimal quarantine control problem for reducing control cost is studied, and the differences between the two models are compared. By applying the optimal control theory, the existence and uniqueness of the optimal control and the optimal solution are obtained. Finally, these results are verified and illustrated by numerical simulation.

Interferon-alpha or -beta facilitates SARS-CoV-2 pulmonary vascular infection by inducing ACE2

Severe viral pneumonia caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is characterized by a hyperinflammatory state typified by elevated circulating pro-inflammatory cytokines, frequently leading to potentially lethal vascular complications including thromboembolism, disseminated intracellular coagulopathy and vasculitis. Though endothelial infection and subsequent endothelial damage have been described in patients with fatal COVID-19, the mechanism by which this occurs remains elusive, particularly given that, under naïve conditions, pulmonary endothelial cells demonstrate minimal cell surface expression of the SARS-CoV-2 binding receptor ACE2. Herein we describe SARS-CoV-2 infection of the pulmonary endothelium in postmortem lung samples from individuals who died of COVID-19, demonstrating both heterogeneous ACE2 expression and endothelial damage. In primary endothelial cell cultures, we show that SARS-CoV-2 infection is dependent on the induction of ACE2 protein expression and that this process is facilitated by type 1 interferon-alpha (IFNα) or -beta(β)—two of the main anti-viral cytokines induced in severe SARS-CoV-2 infection—but not significantly by other cytokines (including interleukin 6 and interferon γ/λ). Our findings suggest that the stereotypical anti-viral interferon response may paradoxically facilitate the propagation of COVID-19 from the respiratory epithelium to the vasculature, raising concerns regarding the use of exogenous IFNα/β in the treatment of patients with COVID-19.

Special issue on communications and networking approaches for combating COVID-19

The COVID-19 pandemic has had an impact across the globe. This includes the many lives lost and the severe disruption to economies and daily lives. Researchers in a variety of fields have been motivated to use their expertise to help address the many challenges of this on-going pandemic. Communication and Networking researchers are among these. Communications and networking technologies can be utilized to help facilitate contact tracing and other forms of remote health monitoring. Epidemic models developed for studying the propagation of information and viruses in communication networks can be adapted to study the propagation of a true epidemic like COVID-19. Techniques such as machine learning can be utilized to make better predictions from limited public health data. Approaches for security and privacy can be leveraged to protect confidential health information. The goal of this special issue of the Journal of Communications and Networks is to highlight some of the work being done in these important areas.

1492Ordering the chaos: The global clustering of COVID-19 incidence and mortality

Abstract Background The propagation of COVID-19 has been dynamic across countries and time. We utilised a temporal clustering approach in exploring trends of incidence and mortality across 202 countries. Methods COVID-19 case and death data between 1 January 2020 and 30 April 2021 were extracted from open-source data repositories. A partitional clustering algorithm, using Euclidean distances and partition around medoids, was utilised in exploring 14-day incidence and mortality rates across 202 countries. Inter-cluster comparisons were carried out using the 14-day incidence and mortality rates across clusters. Results Country-specific trends of incidence and mortality across the study period were agglomerated into one of six clusters. The overall trend of incidence and mortality during this period peaked between November 2020 and January 2021. However, four of the six clusters have an upward trajectory. Countries in cluster four, mostly situated in Europe, reported the highest overall incidence of 192 cases per 100,000 population (95% CI: 166, 220). Countries in cluster three, a mix of countries from South America, Eastern Europe, and Africa, were observed to have the highest overall mortality rate of 32 deaths per 1,000,000 population (95% CI: 23, 45). Conclusions The high global burden of disease and inequity in vaccine access highlights the need for a consolidated global effort in mitigating the pandemic. Key messages Increasing trajectories of incidence and mortality in Asia, South America, and Africa suggest that the worst of the pandemic may be ahead of us.

Understanding spatial propagation using metric geometry with application to the spread of COVID-19 in the United States

This paper introduces a novel approach to spatio-temporal data analysis using metric geometry to study the propagation of COVID-19 across the United States. Using a geodesic Wasserstein metric, we analyse discrepancies between the density functions of new case counts on any given day, incorporating the geographic spread of cases. First, we apply this to identify the periods during which the changes in the geographic distribution of COVID-19 were most profound. The greatest shift occurred between May and June of 2020, when COVID-19 shifted from mostly dominating the Northeastern states to a wider distribution across the country. We support our findings with a new measure of the extent of geodesic variance of a distribution, demonstrating that the geographic imprint of COVID-19 was most concentrated in May 2020. Next, we investigate whether the epidemic exhibited meaningful patterns of spatial reversion, where similar geographic distributions return later. We identify broad similarity between the spread of COVID-19 across the US between the second and third waves, and to a lesser extent, the reemergence of the first wave's Northeastern dominance closer to the present day. This methodology could provide new insights for analysts to monitor the dynamical spread of epidemics and enable regional policymakers to protect their localities. More broadly, the framework we introduce could be applied to a variety of problems evolving over space and time.

Economic Diversity and the Propagation of COVID-19: How Vulnerable is Your City?

The heterogeneity of social and economic systems in known to play an important role in the propagation of contagious pathogens such as the SARS-CoV-2 virus. This heterogeneity, usually attached directly to individuals can also be found systemically in age groups, income brackets, and other important population characteristics. In this article, we identify a set of heterogeneous factors, associated not with individuals but with geographic units that include metropolitan and larger conurbations, that correlate with worse outcomes with respect to the COVID-19 pandemic. A key predictive factor is a lack economic diversity reflected in narrower occupational and industrial compositions. Although the exact mechanism by which these factors induce worse outcomes regarding the control of COVID-19 is not clear, one candidate explanation emerges in the stubbornness of work mobility, where units with lower economic diversity have populations that continue to go to work more than higher diversity units. The effect we identify is, in practice, as strong as many typically accepted drivers of disease propagation such as population.

Facial Self-Touching and the Propagation of COVID-19: The Role of Gloves in the Dental Practice

Background: Despite facial self–touching being a possible source of transmission of SARS–Co–V–2 its role in dental practice has not been studied. Factors such as anxiety symptoms or threat perception of COVID-19 may increase the possibility of contagion. The objective was to compare the impact of control measures, such as gloves or signs in the reduction in facial self–touching. Methods: An intra–subject design was undertaken with 150 adults. The patients’ movements in the waiting room were monitored with Microsoft Kinect software on three occasions: without any control measures, using plastic gloves or using advisory signs against self–touching. Additionally, the participants completed the sub–scale of STAI (State–Anxiety) and the BIP–Q5 (Brief Illness Perception Questionnaire); their blood pressure and heart rate were recorded. Results: The lowest incidence of facial self–touching occurred in the experimental situation in which gloves were introduced. The subjects with elevated anxiety symptoms realized more facial self–touching regardless of the control measures. However, the threat perception of COVID-19 is associated negatively with facial self–touching. Conclusions: The use of gloves is a useful control measure in the reduction in facial touching. However, people with anxiety symptoms regardless of whether they have greater threat perception for COVID-19 exhibit more facial touching.

Containing pandemics through targeted testing of households

BackgroundWhile invasive social distancing measures have proven efficient to control the spread of pandemics failing wide-scale deployment of vaccines, they carry vast societal costs. The development of a diagnostic methodology for identifying COVID-19 infection through simple testing was a reality only a few weeks after the novel virus was officially announced. Thus, we were interested in exploring the ability of regular testing of non-symptomatic people to reduce cases and thereby offer a non-pharmaceutical tool for controlling the spread of a pandemic.MethodsWe developed a data-driven individual-based epidemiological network model in order to investigate epidemic countermeasures. This models is based on high-resolution demographic data for each municipality in Norway, and each person in the model is subject to Susceptible-Exposed-Infectious-Recovered (SEIR) dynamics. The model was calibrated against hospitalization data in Oslo, Norway, a city with a population of 700k which we have used as the simulations focus.ResultsFinding that large households function as hubs for the propagation of COVID-19, we assess the intervention efficiency of targeted pooled household testing (TPHT) repeatedly. For an outbreak with reproductive number R=1.4, we find that weekly TPHT of the 25% largest households brings R below unity. For the case of R=1.2, our results suggest that TPHT with the largest 25% of households every three days in an urban area is as effective as a lockdown in curbing the outbreak. Our investigations of different disease parameters suggest that these results are markedly improved for disease variants that more easily infect young people, and when compliance with self-isolation rules is less than perfect among suspected symptomatic cases. These results are quite robust to changes in the testing frequency, city size, and the household-size distribution. Our results are robust even with only 50% of households willing to participate in TPHT, provided the total number of tests stay unchanged.ConclusionsPooled and targeted household testing appears to be a powerful non-pharmaceutical alternative to more invasive social-distancing and lock-down measures as a localized early response to contain epidemic outbreaks.

Influence of transportation network on transmission heterogeneity of COVID-19 in China

In this paper, we propose a novel approach to model spatial heterogeneity for epidemic spreading, which combines the relevance of transport proximity in human movement and the excellent estimation accuracy of deep neural network. We apply this model to investigate the effects of various transportation networks on the heterogeneous propagation of COVID-19 in China. We further apply it to predict the development of COVID-19 in China in two scenarios, i.e., i) assuming that different types of traffic restriction policies are conducted and ii) assuming that the epicenter of the COVID-19 outbreak is in Beijing, so as to illustrate the potential usage of the model in generating various policy insights to help the containment of the further spread of COVID-19. We find that the most effective way to prevent the coronavirus from spreading quickly and extensively is to control the routes linked to the epicenter at the beginning of the pandemic. But if the virus has been widely spread, setting restrictions on hub cities would be much more efficient than imposing the same travel ban across the whole country. We also show that a comprehensive consideration of the epicenter location is necessary for disease control.

PNS13 Comparative Network Efficiency Analysis of Brazil Response to COVID-19 at State Level

Efficiency analyses of health systems are often limited to hospital applications or macroeconomic evaluations considering a single production process. This application using a two-stage prevention and cure network perspective highlights nonparametric frontier estimations more coherently with the current COVID-19 pandemic scenario. This two-stage Data Envelopment Analysis methodology estimates cost efficiencies of preventing an increasing propagation of COVID-19 using the Federal Government COVID-19 expenditures as input and the inverse rate of cases as output (standardized by population) in the first stage. The second stage for the technical efficiency (cure model) has the number of ICUs, ventilators and medical staff (nurses, assistants and physicians) as input and success rate (the difference between the number of infected and deaths) as output. Two efficient states on both prevention and cure models (Acre and Roraima). Pernambuco is deemed efficient in preventing COVID-19 (3 states total), and São Paulo, Mato Grosso, Amapá, Santa Catarina and Rio Grande do Sul (7 states) in cure. About 1400 ICU admissions, 590 ventilators and more than 100 thousand medical staff can be spared directed by best practices from these seven efficient states. The most inefficient states in preventing the pandemic are São Paulo (0.028), Bahia (0.076), Rio Grande do Sul (0.087) and Paraná (0.098), also reporting low social isolation. The most inefficient states in cure are Rio de Janeiro (0.231), Pernambuco (0.277), and Rio Grande do Norte (0.439). The proposed methodology offers an interesting framework combining both public health perspectives as responses to the COVID-19 pandemic. Brief comments can be made on Pernambuco's mapping technologies available for the general population; the cooperation between the government and NGOs in Roraima for providing EPIs, tests, food and hygiene items for indigenous districts (reducing potential propagations without expanding governments expenditures); and Acre's campaigns for basic hygiene, and pandemic control.

Predicting the propagation of COVID-19 at an international scale: extension of an SIR model

ObjectivesSeveral epidemiological models have been published to forecast the spread of the COVID-19 pandemic, yet many of them have proven inaccurate for reasons that remain to be fully determined. We...

Relationship analysis between the spread of COVID-19 and the multidimensional poverty index in the city of Manizales, Colombia

COVID-19 has forced government and health agencies to take measures to mitigate the spread of the disease and thus safeguard as many lives as possible. These measures have initially impacted the economy of many countries, and therefore they have been forced to gradually return to a new normalcy, in what they have called reopening. For reopening policies to be effective, it is necessary that the people in charge of drawing up these policies know the local behavior of the propagation of COVID-19, and beyond this they can understand that between the cases of COVID-19 and the socioeconomic conditions of their population there is a relationship. For this reason, in this article a case study is presented, which allowed to evaluate the relationship between positive cases of COVID-19 and the multidimensional poverty index (MPI) in the city of Manizales, Colombia. The results of an exploratory analysis, obtained with the use of remote sensing data, are presented, which allowed to confirm the relationship in mention, and it is hoped that this can serve the municipal administration in its decision making.

The impact of COVID-19 lockdown - case report

IntroductionIn early 2020, governments started to implement different forms of public health measures, from physical distancing recommendations, to stay-at-home orders, to limit the propagation of COVID-19. Here we report the case of a 41-year-old woman, with a diagnosis of panic disorder. During the end of the lockdown, the patient presented psychopathological worsening, from her fear of Covid-19 infection, stemming from a heart failure disease and concerns regarding the hygiene and safety measures of those around her.ObjectivesPresentation of a clinical vignette.MethodsSelection and analisis of clinical case and review of the literature using PubMed database.ResultsThe COVID-19 pandemic and the measures adopted to prevent the spread of the disease had a huge impact on a personal, social, and economic level for the world population. The rise of fear and anxiety among people due to uncertainty about the disease are coupled with essential yet disruptive measures such as lockdowns and quarantines. The chronically ill population are especially vulnerable during such circumstances and require addressing their physical health and any psychological difficulties they might experience, being at higher risk of suffering physically from the pandemic’s disease as well as psychologically from the implemented countermeasures.ConclusionsThis vignette provides a case where a person’s psychiatric conditions are worsened due to the end of a pandemic lockdown, rather than the lockdown itself. Additional work should aim at comparing the experiences of the different countries affected by the pandemic in order to understand the size of the psychological impact, the potential risk and protective factors.DisclosureNo significant relationships.

Spatial Propagation of COVID-19 in the Democratic Republic of Congo（コンゴ民主共和国における新型コロナウイルスの空間的拡散）

Spatial Propagation of COVID-19 in the Democratic Republic of Congo（コンゴ民主共和国における新型コロナウイルスの空間的拡散）

Simulation of COVID-19 Propagation Scenarios in the Madrid Metropolitan Area.

This work presents simulation results for different mitigation and confinement scenarios for the propagation of COVID-19 in the metropolitan area of Madrid. These scenarios were implemented and tested using EpiGraph, an epidemic simulator which has been extended to simulate COVID-19 propagation. EpiGraph implements a social interaction model, which realistically captures a large number of characteristics of individuals and groups, as well as their individual interconnections, which are extracted from connection patterns in social networks. Besides the epidemiological and social interaction components, it also models people's short and long-distance movements as part of a transportation model. These features, together with the capacity to simulate scenarios with millions of individuals and apply different contention and mitigation measures, gives EpiGraph the potential to reproduce the COVID-19 evolution and study medium-term effects of the virus when applying mitigation methods. EpiGraph, obtains closely aligned infected and death curves related to the first wave in the Madrid metropolitan area, achieving similar seroprevalence values. We also show that selective lockdown for people over 60 would reduce the number of deaths. In addition, evaluate the effect of the use of face masks after the first wave, which shows that the percentage of people that comply with mask use is a crucial factor for mitigating the infection's spread.

Sentiment analysis of user feedback on the HSE\u2019s Covid-19 contact tracing app

BackgroundDigital Contact Tracing is seen as a key tool in reducing the propagation of Covid-19. But it requires high uptake and continued participation across the population to be effective. To achieve sufficient uptake/participation, health authorities should address, and thus be aware of, user concerns.AimThis work manually analyzes user reviews of the Irish Heath Service Executive’s (HSE) Contact Tracker app, to identify user concerns and to lay the foundations for subsequent, large-scale, automated analyses of reviews. While this might seem tightly scoped to the Irish context, the HSE app provides the basis for apps in many jurisdictions in the USA and Europe.MethodsManual analysis of (1287) user reviews from the Google/Apple playstores was performed, to identify the aspects of the app that users focused on, and the positive/negative sentiment expressed.ResultsThe findings suggest a largely positive sentiment towards the app, and that users thought it handled data protection and transparency aspects well. But feedback suggests that users would appreciate more targeted feedback on the incidence of the virus, and facilities for more proactive engagement, like notifications that prompt users to submit their health status daily. Finally, the analysis suggests that the “android battery” issue and the backward-compatibility issue with iPhones seriously impacted retention/uptake of the app respectively.ConclusionThe HSE have responded to the public’s desire for targeted feedback in newer versions, but should consider increasing the app’s proactive engagement. The results suggest they should also raise the backward compatibility issue, regarding older iPhones, with Apple.

Spatial modeling of COVID-19 transmission in Bangladesh

The purpose of the research was to investigate and identify the demographic risk factors behind the transmission of COVID-19 in Bangladesh based on spatial and statistical modeling. Number of COVID-19 confirmed cases per thousand population as the dependent variable and nine demographic explanatory variables were considered. Different spatial (i.e., Spatial Lag and Spatial Error Model) and non-spatial (Classic Model) regression techniques were employed in the research to detect the geographical relevance of potential risk factors affecting the transmission of COVID-19. Results indicate that population density was crucial for explaining the pattern of COVID-19 transmission in Bangladesh. Spatial Auto-correlation suggests that the spatial pattern of population density were significantly clustered at a confidence interval of 95%. Again, the regression analysis also shows that population density is an influential determinant for the propagation of COVID-19 in Bangladesh, with densely populated districts like Dhaka and Narayanganj also being among the worst affected areas. The findings of this research will help the government agencies and communities for effective and well-informed decision making in order to develop and implement strategies to contain the further spread of COVID-19 in Bangladesh.

A Modelization of the Propagation of COVID-19 in Regions of Spain and Italy with Evaluation of the Transmission Rates Related to the Intervention Measures.

Simple SummaryTwo different mathematical models are proposed in order to describe the spreading of COVID-19 through the different provinces and regions of Spain and Italy. The models will divide the populations of both countries in three categories: the subpopulation susceptible to be infected of the disease, the subpopulation which is already infected and thus is infectious, and the subpopulation which has already recovered from the disease so is considered immune. The transmission rate is calculated within these models while considering the different locations and, more importantly, the lockdown measures implemented during 2020. The efficiency of these measures is compared between the areas of infection and the different levels of lockdown.Two discrete mathematical SIR models (Susceptible-Infectious-Recovered) are proposed for modelling the propagation of the SARS-CoV-2 (COVID-19) through Spain and Italy. One of the proposed models is delay-free while the other one considers a delay in the propagation of the infection. The objective is to estimate the transmission, also known as infectivity rate, through time taking into account the infection evolution data supplied by the official health care systems in both countries. Such a parameter is estimated through time at different regional levels and it is seen to be strongly dependent on the intervention measures such as the total (except essential activities) or partial levels of lockdown. Typically, the infectivity rate evolves towards a minimum value under total lockdown and it increases again when the confinement measures are partially or totally removed.

Using equity market reactions and network analysis to infer global supply chain interdependencies in the context of COVID-19

While the COVID-19 pandemic has exposed the vulnerabilities of “just-in-time” international supply chains and is set to further accelerate their reconfigurations, very little is known about how global production networks are linked together at the firm level, making it difficult to assess where the dependencies lie. In this study, we use firm-level data on customer-supplier relationships from over 170 economies and network analysis tools to map global supply chain dependencies and shed light on the complex interconnections that facilitated the propagation of COVID-19’s economic impact. Leveraging the early virus outbreak in China as an exogenous shock, we first study the equity price responses of China-linked firms to news of virus-induced lockdowns, finding that markets priced in significant transmission effects of the coronavirus shock across global supply chains. Then, we dive deeper into the production networks of three globalised industries, whose unique and convoluted network structures may serve as a reminder that “reshoring” supply chains is easier said than done.