Analysis of the outbreak of COVID-19 in Japan by SIQR model

Abstract

The SIQR model is exploited to analyze the outbreak of COVID-19 in Japan where the number of the daily confirmed new cases is explicitly treated as an observable. It is assumed that the society consists of four compartments; susceptible individuals (S), infected individuals at large (I), quarantined patients (Q) and recovered individuals (R), and the time evolution of the pandemic is described by a set of ordinary differential equations. It is shown that the quarantine rate can be determined from the time dependence of the daily confirmed new cases, from which the number of infected individuals can be estimated. The infection rate and quarantine rate are determined for the period from mid-February to mid-April in Japan and transmission characteristics of the initial stages of the outbreak in Japan are analyzed in connection with the policies employed by the government. The effectiveness of different measures is discussed for controlling the outbreak and it is shown that identifying patients through PCR (Polymerase Chain Reaction) testing and isolating them in a quarantine is more effective than lockdown measures aimed at inhibiting social interactions of the general population. An effective reproduction number for infected individuals at large is introduced which is appropriate to epidemics controlled by quarantine measures.

Self-organized wavy infection curve of COVID-19

“…Since the waiting time distribution function can be assumed to be a well behaved function with a single peak 10 , 11 , the convolution can be evaluated by the saddle-point method of integration and it is given by 8 where is a characteristic time representing the peak position of and . Therefore, in the time period where does not depend on , I can show that …”

“…The first wave of the outbreak of COVID-19 in various countries has been analyzed on the basis of Eq. ( 11 ), where is approximated by a piece-wise simple exponential function 8 , 9 , 12 – 16 .…”

Modelling virus spreading in ride-pooling networks

“…We adopt a SIQR model to represent the four compartments characteristic of the COVID-19 pandemic: Susceptible ( S ), Infected ( I ), Quarantined ( Q ) and Recovered ( R ), recently, directly applied to tackle COVID-19 propagation in other studies (Italy 44 and Japan 45 ). Following the argumentation of Pedersen and Meneghini 44 we do not explicitly designate the E state, given the evidence suggesting that the COVID-19 virus can be propagated without first exhibiting visual symptoms.…”

Classification of the infection status of COVID-19 in 190 countries

“…(1). It is known [2] that ¡É´Øµ is given by a convolution of the incubation period (from infection to tested positive) distribution function and the number of infecteds. Furthermore, since the incubation period distribution is a well behaved function with a single peak [4,5], it is shown that ¡É´Øµ » Á´Ø µ (5) holds.…”