Prediction of COVID-19 transmission dynamics using a mathematical model considering behavior changes

Kim, Soyoung; Seo, Yu Bin; Jung, Eunok

doi:10.4178/epih.e2020026

Cited by 87 publications

(92 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It is reduced also by population behaviour including social distancing, personal hygiene, for example by wearing a mask in fear of the spread of the virus. This is similar to the effect described by, for example, Kim et al (2020) or Liang (2020).This…”

Section: Theorysupporting

confidence: 86%

Contribution to COVID-19 spread modelling: a physical phenomenological dissipative formalism

Limam

Limam²

2020

Biomech Model Mechanobiol

View full text Add to dashboard Cite

In this study, we propose an evolution law of COVID-19 transmission. An infinite ordered lattice represents population. Epidemic evolution is represented by a wave-like free spread starting from a first case as an epicentre. Free energy of the virus on a given day is defined equal to the natural logarithm of active infected cases number. We postulate a form of free energy built using thermodynamics of irreversible processes in analogy to isotherm wave propagation in solids and non-local elastic damage behaviour of materials. The proposed expression of daily free energy rate leads to dissipation of propagation introducing a parameter quantifying measures taking by governments to restrict transmission. Entropy daily rate representing disorder produced in the initial system is also explicitly defined. In this context, a simple law of evolution of infected cases as function of time is given in an iterative form. The model predicts different effects on peak of infected cases I max and epidemic period, including effects of population size N, effects of measures taking to restrict spread, effects of population density and effect of a parameter T similar to absolute temperature in thermodynamics. Different effects are presented first. The model is then applied to epidemic spread in Tunisia and compared with data registered since the report of the first confirmed case on March 2, 2020. It is shown that the low epidemic size in Tunisia is essentially due to a low population density and relatively strict restriction measures including lockdown and quarantine.

show abstract

Section: Theorysupporting

confidence: 86%

Contribution to COVID-19 spread modelling: a physical phenomenological dissipative formalism

Limam

Limam²

2020

Biomech Model Mechanobiol

View full text Add to dashboard Cite

show abstract

“…Since COVID-19 Pandemic occurred in Indonesia, research on COVID-19 began to be carried out. In the field of mathematical modeling, many experts predict when the pandemic reaches its peak when it ends, and the transmission model of the spread of viruses (Kim et al, 2020;Ndaïrou et al, 2020;Nuraini et al, 2020;Peirlinck et al, 2020;Rahimi & Abadi, 2020;Resmawan & Yahya, 2020;Soewono, 2020;Tang et al, 2020). Whereas in learning mathematics in schools, research on obstacles in the use of e-learning that occurred in schools (Mailizar et al, 2020;Mulenga & Marbán, 2020).…”

Section: Introductionmentioning

confidence: 99%

Challenges During the Pandemic: Use of E-Learning in Mathematics Learning in Higher Education

et al. 2020

View full text Add to dashboard Cite

On March 16, 2020, many universities in Indonesia began implementing online-based learning to replace lectures in the classroom. This is done as a way to reduce the transmission of the Covid-19 outbreak in Indonesia. There is an opinion that with the implementation of online learning, especially in mathematics education study programs, there are many obstacles when learning takes place. This study aims to determine the obstacles that arise after the implementation of online learning in mathematics learning in Higher Education. This research is a qualitative case study, assisted by an online survey. The researcher collected data through an online survey consisting of 27 questions. The survey is aimed at lecturers who teach in Mathematics Education study programs in Indonesia. The survey contains structured questions and leads to three parts, namely; basic skills challenges, teaching and learning challenges, and university challenges. The 27 questions contained questions about the ability of platform mastery to support online learning owned by each lecturer. The research involved 26 lecturers from universities in Sumatra, Java, Kalimantan, and Sulawesi. The results of this study reveal that all lecturers affected by the pandemic use a Learning Management System (LMS) based website as a means of online learning. The learning management system-based platform is the most widely used (google class and Edmodo) while video conferencing is the second choice (Zoom and Skype). What is interesting is that the LMS available on campus is less attractive to lecturers. However, there are obstacles faced such as the limitations of writing mathematical symbols and the limited basic capabilities of the learning management system and multimedia software to support online learning.

show abstract

“…This study was in agreement with several studies of COVID-19 speed of geographical expansion. Another theoretical study mathematically demonstrated that the relatively high per-capita rate of transmission and the low rate of changes in behavior had caused a large-scale transmission of COVID-19 spatially [23]. Due to transmission between persons, the COVID-19 speed of geographical expansion overwhelmed the increase supply system of public health service in the national regions.…”

Section: Discussionmentioning

confidence: 99%

A panel analysis on preventing and controlling efficiency of COVID-19 pandemic

Guan¹

2020

Preprint

View full text Add to dashboard Cite

BACKGROUND: Currently, the globe is making efforts to curb the spreading of coronavirus disease 2019 (COVID-19). However, only a few studies have examined the associations among spreading growth, treatment efficiency, and death increase. This study aimed to examine the associations of COVID-2019 dead cases, healed cases, and recovered cases.METHODS: Data from China Data Lab in Harvard Dataverse with the China (January 22, 2020 to March 18, 2020), United States of America (USA, March 17, 2020 to April 12, 2020), and the world (January 22, 2020 to April 4, 2020) were analyzed. The main variables included in the analysis were the numbers of COVID-2019 total confirmed cases (TC), total dead cases (TD), newly dead cases (ND), newly confirmed cases (NC), newly healed cases (NH), total healed cases (TH), and newly recovered cases (NR). Pooled, dynamic, and event study analyses were conducted to reflect the associations among them.RESULTS: Descriptive analyses showed that NH/NC ratio in China are bigger than NR/NC in USA and the world. Pooled analysis showed that various roles of regions in NH and NR in a specific sample. Dynamic analysis showed significant roles of lags and NC in NH and NR. Panel event study showed that key events influence ND and NH in China significantly and NR in the world rather than NR in USA.CONCLUSION: The findings in this study indirectly confirmed the relationship between spreading growth, treatment efficiency, and death increase. China’s control strategies of COVID-19 pandemic are worth of learning by the globe.

show abstract

Prediction of COVID-19 transmission dynamics using a mathematical model considering behavior changes

Cited by 87 publications

References 2 publications

Contribution to COVID-19 spread modelling: a physical phenomenological dissipative formalism

Contribution to COVID-19 spread modelling: a physical phenomenological dissipative formalism

Challenges During the Pandemic: Use of E-Learning in Mathematics Learning in Higher Education

A panel analysis on preventing and controlling efficiency of COVID-19 pandemic

Contact Info

Product

Resources

About