Multivariate Prediction Network Model for epidemic progression to study the effects of lockdown time and coverage on a closed community on theoretical and real scenarios of COVID-19

Dm, Abramov; Sg, Junior

doi:10.1101/2020.05.04.20090712

Cited by 3 publications

(6 citation statements)

References 14 publications

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“…The first term on the right side of the equation (1) indicates that positive cases have logistical growth, but are reduced by the term p(N) described in equation (2). In this case R: growth rate in the number of infected individuals, K: carrying support, A: inflection point of the logistic curve e B: maximum value of function p(N).…”

Section: A Mathematical Model For Covid-19mentioning

confidence: 99%

“…In this case R: growth rate in the number of infected individuals, K: carrying support, A: inflection point of the logistic curve e B: maximum value of function p(N). Using expansion of the terms in (1) and (2) the complete model is…”

Section: A Mathematical Model For Covid-19mentioning

confidence: 99%

“…(2) With K, b and from step (1), identify A e B from equation (2). Considering the points of p(N) the identification of A and B is easy using, for instance, least squares method, Kalman filter or genetic algorithm.…”

Section: A Mathematical Model For Covid-19mentioning

confidence: 99%

“…The seven points are highlighted in Table-7 For point (1) on February 6, it was calculated and observed that ( ,r) was within the bistable region on February 6. Figure-21 shows the configuration of the points, where it can be seen that when (κ, r) was in (1) the number of new cases in China on February 6 was 3,697 and for point (2), outside the bistable region the reduction was abrupt decreasing 50% in seven days. This confirms the downward trajectory that existed in China, in Figure-20, when the peak of cases reported and reviewed after the point (2) occurs.…”

Section: F China 2020mentioning

confidence: 99%

“…presents the function p(N) from(2) using difference between predictions D and D+1.Figure-4shows parameters A e B fit from equation(2). Parameter A is inflexion point of function p(N) and B is maximum value for p(N).…”

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Can Catastrophe Theory explain expansion and contagious of Covid-19?

Caetano

2021

Preprint

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Since SARS-Cov-2 started spreading in China and turned into a pandemic disease called Covid-19, many articles about prediction with mathematical model have appeared in the literature. In addition to models in specialized journals, a significant amount of software was made available, presenting with dashboards spreading of the pandemic for each new. These models are solved by computer simulation of traditional exponential models as a representation of the growth of cases and deaths. Some more accurate models are based on existing variations of SIR model (Susceptible, Infected and Recovered). A third class of study is developed in spatial or probabilistic models as a way of forecasting the effect of Covid-19 around the world. Data on the number of positive cases in all countries, show that SARS-Cov-2 shows great resistance even after strategies of lockdown or social distancing. The purpose of this article is to show how the bifurcation theory, known as Catastrophe Theory, can help to understand why Covid-19 expansion rates change so much and even with low values for a longtime trigger contagion quickly and abruptly. The Catastrophe Theory was conceived by the mathematician Rene Thom in the 60s with wide applications in works in the 70s. The outbreak of spruce budworm in Canada revealed a very interesting opportunity to test Catastrophe Theory whose explanation for the phenomenon was widely debated in the academic world. Inspired by the same mathematical approach to this phenomenon in Canada in the 1970s, we applied the Catastrophe Theory in the current Covid-19 pandemic. We observed that sudden outbreaks occur when the carrying capacity and the rate of expansion of the virus reach a region of bifurcation on the cusp surface. With actual Covid-19 data obtained from WHO, we fitted the dynamic model using the particle swarm technique and compared the results in the bifurcation plan with the Covid-19 outbreaks in different regions of the world. It is possible in many cases to observe the trajectory of the parameters between limit points in the bistable region and the consequent explosion of cases observed for each country assessed.

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Section: A Mathematical Model For Covid-19mentioning

confidence: 99%

Section: A Mathematical Model For Covid-19mentioning

confidence: 99%

Section: A Mathematical Model For Covid-19mentioning

confidence: 99%

Section: F China 2020mentioning

confidence: 99%

mentioning

confidence: 99%

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Can Catastrophe Theory explain expansion and contagious of Covid-19?

Caetano

2021

Preprint

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Factors Influencing Human Mobility During The COVID-19 Pandemic in Selected Countries of Europe and North America

Hosseini

Gittler

2020

2020 IEEE International Conference on Big Data (Big Data)

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A novel coronavirus was first reported in Wuhan, China in December 2109 and was declared a global pandemic by the World Health Organization on 11 March 2020. Identification of the critical factors that predict reduced mobility and human interaction is critical to developing successful transmission mitigation efforts globally. Governments and localities around the world have responded with wide-ranging policies related to containment and closure such as travel restrictions and stay-at-home-orders, as well as economic benefits, expanded testing, and public health education, among others. Anonymized GPSenabled smartphone data is a novel tool to track human mobility and is becoming widely available. This study explores the relationships between containment and closure policies, disease trends, and human mobility patterns in 40 countries in Western, Eastern, Northern, and Southern Europe and North America. The principal component analysis was applied to reduce variable dimensions, followed by multivariate multiple regression. The model parameter estimations indicate that total cases, canceling activities (school, work, events), mask policies and the pandemic declaration all were significant predictors (p<.001) of change in workplace mobility from baseline.

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Evaluation of the Physical Activity, Sleep Quality, Depression, and Life Satisfaction of University Students During the COVID-19

Dinler

Badat

Kocamaz

et al. 2020

International Journal of Disabilities Sports and Health Sciences

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Background/aim: The aim of this study is to evaluate the levels of self-awareness, physical activity, sleep quality, depression and life satisfaction of university students during the Covid-19 pandemic period. Materials and methods: 200 university students (133 female, and 67 male) who agreed to participate in the study voluntarily were included. The average age of the students was 21.57±2.03. The students replied about the effects of pandemic process with Likert scale that 14 questions. The International Physical Activity Questionnaire (Short Form) (IPAQ-SF), Pittsburgh Sleep Quality Index (PSQI), Beck Depression Scale (BDI), Life Satisfaction Questionnaire (SLWS) were applied to students. Results: According to the results of the study; In the term of Covid-19, it was determined that physical activity and exercise habits were decreased. The participants with good sleep quality. Among the questions in the self-awareness questionnaire, the most frequently participated question by the students was 66% increase in sitting time and the least agreed 2% decrease in sitting time. Among these questions, the significance level is p = 0.01. Conclusion: During the Covid-19 process, students' self-awareness, physical activity levels, sleep quality and life satisfaction found to be high, and the severity of depression low during the online / distance education period. We think that long-term follow-up studies during the pandemic process will contribute to the literature.

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Multivariate Prediction Network Model for epidemic progression to study the effects of lockdown time and coverage on a closed community on theoretical and real scenarios of COVID-19

Cited by 3 publications

References 14 publications

Can Catastrophe Theory explain expansion and contagious of Covid-19?

Can Catastrophe Theory explain expansion and contagious of Covid-19?

Factors Influencing Human Mobility During The COVID-19 Pandemic in Selected Countries of Europe and North America

Evaluation of the Physical Activity, Sleep Quality, Depression, and Life Satisfaction of University Students During the COVID-19

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