Preprint Article Version 1 This version is not peer-reviewed

Modeling and Analysis of The Early-Growth Dynamics of COVID-19 Transmission

Version 1 : Received: 21 May 2020 / Approved: 23 May 2020 / Online: 23 May 2020 (10:36:33 CEST)

How to cite: Azad, A.K.A.; Hussain, A.M. Modeling and Analysis of The Early-Growth Dynamics of COVID-19 Transmission. Preprints 2020, 2020050372 (doi: 10.20944/preprints202005.0372.v1). Azad, A.K.A.; Hussain, A.M. Modeling and Analysis of The Early-Growth Dynamics of COVID-19 Transmission. Preprints 2020, 2020050372 (doi: 10.20944/preprints202005.0372.v1).

Abstract

As an on-going pandemic caused by the out-break of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or simply COVID-19 sweeps through the globe at an unprecedented rate leaving behind trails of high infection and mortality, it is crucial to understand the propagation dynamics of the virus in a host population in order to take urgent and effective remedial and mitigating steps to save life. It is already observed in many countries and communities that accurate and timely testing, tracing, and tracking of the infection lead to better containment and slowing down of the spread. In this exploratory research, the early growth dynamics of infection within a population is pursued based on real data. The study posits that the early growth in a homogenous population follows an exponential pattern motivating further rigorous quantitative treatment based on a number of analytical models such as logistic model, Richard’s model, and Gompertz model– the acceleration pattern of the outbreak is ascertained from the daily inflection data, and regression analysis against population models yields dynamic growth indices which allow very accurate prediction of the successive outbreak size when calibrated continually with updated data. The performance of the various models is evaluated with the real dataset. More, the basic reproduction number of the COVID-19 virus propagation in the community is estimated based on the on-set phase dataset using multi-compartmental epidemiological model. Also, the maximum infection size, infection doubling time and the scope of the herd immunity are also inferred for COVID-19 in a population.

Subject Areas

COVID-19; SARS-CoV-2; Coronavirus; Pandemic; Epidemiological Analysis; Exponential Growth; Herd Immunity; Doubling Period

Comments (1)

Comment 1
Received: 24 May 2020
Commenter: Monir Uddin Ahmed
The commenter has declared there is no conflict of interests.
Comment: Does this modelling take into account the leadership qualities of the leaders of the country? How much a country will succeed in fighting corona largely depends on the leadership ability of the top leaders and total leadership of the population.
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