Predicting Epidemic Diseases using Mathematical Modelling of SIR

Mathematical models can give us information on number of cases, deaths caused by infectious diseases. In this study, we aimed to predict the effects of Crimean-Congo Hemorrhagic Fever (CCHF), tuberculosis (TB), measles for Turkey and the efficiency of SI and SIS mathematical models were defined in the prediction of the number of infected people for these diseases. In CCHF, predictions for 2014 gave 0% error in both models. As for TB, SIS model predicted the exact number and SI model predicted a few more cases than that of SIS model. Again SI and SIS models gave the exact values for measles. According to these predictions it seems that CCHF and TB cases will continue to increase slightly while measles cases will approach zero. These models can predict exact numbers for each year, in a long term and in normal conditions (unless there are external parameters such as natural disaster, war, emigration and terrorism) they can predict the trend for the diseases and can tell when to disappear. Therefore, updating data are of importance to achieve the powerful prediction.

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“…SIS turns into SIR model (Susceptible-Infected-Recovered) if (R) Recovery is calculated [12]. In SIS, R is excluded.…”

Section: Formulation Of Si and Sis Modelsmentioning

confidence: 99%

Investigation of Various Infectious Diseases in Turkey by Mathematical Models SI and SIS

Çilli

Ergen²

2019

International Journal of Computational and Experimental Science and Engineering

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“…Bu çalışmada iyileşen (kurtarılan) grup dâhil edilmemiştir. SIR modeli ile uygulama başka çalışmalarda bulunmaktadır [10].…”

Section: Matematiksel Modelleme Metoduunclassified

Salgın Hastalıkların Tahmininde Kullanılan SI ve SIS Modellerin Uygulamaları

Çilli

Ergen

2019

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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ÖzDünyada ve Türkiye'de görülen birçok salgın hastalığın gelecekte artma ve azalma oranını tahmin etmek için çeşitli matematiksel modeller kullanılmaktadır. Bu modellerden bazıları SI, SIS, SIR, MSIRS vb. isimlerle tanımlanmıştır. Bu çalışmada, Tüberküloz, Kırım Kongo Kanamalı Ateşi, AIDS, Sıtma, Kızamık salgın hastalıkları ele alınmıştır. Bu salgın hastalıklara SI ve SIS modelleri uygulanarak gelecekteki durumu (artışı veya azalışı) incelenmiştir. Sonuç olarak, bu çalışmanın hastalığın ilerlemesi veya gerilemesi açısından ülkelere ve toplumlara ışık tutacağını düşünmekteyiz.

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“…This model can be also seen as the probabilistic alternative of the ordinary differential equation model [8], the extended version of the probabilistic Boolean model [9] and the lasso-type of the generalized additive model [10]. In GGM, if the normality assumption does not hold for the data matrix, the copula can solve the problem by combining data in a way that their joint distribution is Gaussian with the same covariance matrix.…”

Section: Copula Gaussian Graphical Modelmentioning

confidence: 99%

Gibbs Sampling in Inference of Copula Gaussian Graphical Model Adapted to Biological Networks

Purutçuoğlu

Farnoudkia

2017

Acta Phys. Pol. A

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Markov chain Monte Carlo methods (MCMC) are iterative algorithms that are used in many Bayesian simulation studies, where the inference cannot be easily obtained directly through the defined model. Reversible jump MCMC methods belong to a special type of MCMC methods, in which the dimension of parameters can change in each iteration. In this study, we suggest Gibbs sampling in place of RJMCMC, to decrease the computational demand of the calculation of high dimensional systems. We evaluate the performance of the suggested algorithm in three real benchmark datasets, by comparing the accuracy and the computational demand with its strong alternatives, namely, birth-death MCMC, RJMCMC and QUIC algorithms. From the comparative analyses, we detect that Gibbs sampling improves the computational cost of RJMCMC without losing the accuracy.

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Predicting Epidemic Diseases using Mathematical Modelling of SIR

Cited by 14 publications

References 8 publications

Investigation of Various Infectious Diseases in Turkey by Mathematical Models SI and SIS

Investigation of Various Infectious Diseases in Turkey by Mathematical Models SI and SIS

Salgın Hastalıkların Tahmininde Kullanılan SI ve SIS Modellerin Uygulamaları

Gibbs Sampling in Inference of Copula Gaussian Graphical Model Adapted to Biological Networks

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