On the time shift phenomena in epidemic models
(Ayşe Peker Dobie, Ali Demirci, Ayşe Hümeyra Bilge, Semra Ahmetolan)

In the standard Susceptible-Infected-Removed (SIR) and Susceptible-Exposed-Infected-Removed (SEIR) models, the peak of infected individuals coincides with the in ection point of removed individuals. Nevertheless, a survey based on the data of the 2009 H1N1 epidemic in Istanbul, Turkey [19] displayed an unexpected time shift between the hospital referrals and fatalities. With the motivation of investigating the underlying reason, we use multistage SIR and SEIR models to provide an explanation for this time shift. Numerical solutions of these models present strong evidences that the delay is approximately half of the infection period of the epidemic disease. In addition, graphs of the classical SIR and the multistage SIR models; and the classical SEIR and the multistage SEIR models are compared for various epidemic parameters. Depending on the number of stages, it is observed that the delay varies for relatively small stage numbers whereas it does not change for large numbers in multistage systems. One important result that follows immediately from this observation is that this fixed delay for large numbers explains the time shift. Additionally, depending on the stage number and the duration of the epidemic disease, the distance between the points where each infectious stage reaches its maximum is found approximately both graphically and qualitatively for both systems. Variations of the time shift, the maximum point of the sum of all infectious stages, and the in ection point of the removed stage are observed subject to the stage number N and it is shown that these variations stay unchanged for greater values of N.

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What Can We Estimate from Fatality and Infectious Case Data? A case Study of Covid-19 Pandemic
(Semra Ahmetolan, Ayşe Hümeyra Bilge, Ali Demici, Ayse Peker Dobie, Önder Ergönül)

Daily case reports and daily fatalities for China, South Korea, France, Germany, Italy, Spain, Iran, Turkey, the United Kingdom and the United States over the period January 22, 2020 - April 20, 2020 are analysed using the Susceptible-Infected-Removed (SIR) model. For each country, the Susceptible-Infected-Removed (SIR) models fitting cumulative infective case data within 5% error are analysed. It is shown that the quantity that can be the most robustly estimated from the normalized data, is the timing of the maximum and timings of the inflection points of the proportion of infected individuals.

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