COVID-19 has a genetic makeup identical to that of the SARS-CoV and

MERS-CoV however, it di?ers apprehensively in the context of the number of

cases, location and spread rate of this infection; SARS originating in China in

late 2002 had over 8000 cases, death toll of 774 spread across 30 countries worldwide

similarly, MERS prevailed in September of 2012 in Saudi Arabia with 2500,

death toll of 850 circumscribed mainly in the Arabian peninsula. Both

SARS-CoV and MERS-CoV are deemed diminutive to COVID-19 in reference

to number of cases, death toll, mainlands and spread speed: as of April 24, 2019

COVID-19 has recorded of approximately 2:5 million cases, a death toll of 180

thousand and in 213 countries covering all continents but Antartica .

When the spread speed of COVID-19 is juxtaposed with SARS-Co it was found

that the \serial interval", the interval between consecutive infections, was twice

as that of SARS-CoV.

Susceptible Infectious Recovered (SIR) and Susceptible Exposed Infectious Re-

covered (SEIR) are two analogous models that commonly being used in Mathematical

Epidemiology for the modeling of infectious diseases . However,

these models are di?erent in the sense of the latency (Exposed) class located

in the SEIR model, which tends to delay the prevalence of the outbreak; both

models consist of a set of nonlinear di?erential equations solved using numerical

iterative techniques .

The adopted mathematical model in this simulator considers the Susceptible

Exposed Infectious Recovered SEIR with the addition of vital dynamics and a

quarantine class hence the name SEIQR to elicit the true e?ect of COVID-19

on the modeling process: the e?ectiveness of the simulators predictions relies

heavily on adequacy of the mathematical model in predicting values precisely

with minimal complexity. Discrepancies in predictions in previous studies, are

mostly a consequence of failing to separate the con?rmed cases (Quarantine

class) and the anonymous cases (Infectious class) that are directly transmitted

to the Recovered class hence highlighting the importance of the Quarantine

class in our simulator. The adopted model will also add the supposition that the

entire population is inherently Susceptible and transmuting into the Recovered

category does not ensure immunity. The basis on which a Quarantine class was

added is imputed to nature of the data and this categories importance in elucidating

the actual precautions being taken in the Jordanian kingdom; adding the

quarantine category is rather common when modeling infectious disease [9, 10].

Though there isn't a direct relation in the equations between the Quarantine

class and the Infectious class the byproduct of adding this class is most apparent

on the speed of the outbreak along with the contact between the population

which in turn will impact the transmission rate hence repressing the disease.

The SEIQR model encompases several other constants such as the incumbency

period which are reected in the constant rates of the model such as the immunity

rate of the disease. Constant rates deeply impact the credibility of the

model in terms of outbreak value and time, all constant rates have been chosen

to best emulate the actual conditions in the Jordanian kingdom; it's very

common that several constant rate values ?t the model felicitously however give

inconsistent predictions.Optimization of the constant rate values are computed

using mathematical algorithms that ensure convergence of the global minima at

e?ective speed. This simulator will predict the trends of the rates of Susceptible

(S), latent (E), Infectious (I), Quarantined (Q) and recovered (R)groups

overtime prevailing the peak value of the infectious population, the number of

days for the peak of the infectious population and the number of days where the

entire susceptible class population shifts into the recovered class simultaneously

(this value will dismay the mortality rate).