Predictive Modeling Of Infectious Disease Spread In Pakistan Using Logistic Regression And Sir Models

Raza, Wasim (2024) Predictive Modeling Of Infectious Disease Spread In Pakistan Using Logistic Regression And Sir Models. Masters thesis, Institut Teknologi Sepuluh Nopember Surabaya Indonesia.

	Text Master's Thesis Book_Wasim Raza_2024.pdf - Accepted Version Restricted to Repository staff only until 1 October 2026. Download (3MB)
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Abstract

This research work therefore compares two differential modeling perspectives namely the logistic growth regression model and the modifed SIR model in the measurement of the COVID-19 transmission in the regions of Pakistan. As a result, the final epidemic sizes, peak time and growth rate are forecasted based on the logistic growth regression model in different geographic regions in Pakistan, Gilgit Baltistan, and Islamabad. Some parameters of the model are accomplished by the non-linear fits of the data; comparative estimations are visible in the regions with the curves’ flattening. Thus, contradicting the recent tendencies that appeared to lack the continuation of the deceleration of the augmenting infection rates in Pakistan, the logistic growth regression model offers accurate estimations, which point to Pakistan’s distinct COVID picture. On the other hand, the SIR model provides theoretical roots that can be applied to uncover the patterns of COVID- 19 spread among populace. The SIR model is especially helpful because it gives understanding that goes beyond raw numbers by taking into account essential factors associated with the disease’s progress in time and space. In this study, data obtained before and after strict control measures instituted on June 27, 2021, as well as the SIR model including data from 24 June 2021 to 29 July 2021, different factors such as susceptible, infective, and the number of people to be eradicated up to August 2021 were estimated. This on identifies and outlines the patterns of the cases or the infection levels in this case from the comparative analysis of the two models. While the logistic growth regression model offers persuasive estimations based on current trends, the SIR model provides a valuable tool for simulating the spread of COVID-19, enabling researchers to assess the disease’s potential impact and make accurate prognoses. Ultimately, this research underscores the importance of employing multiple modeling techniques to comprehensively understand and address the challenges posed by the COVID-19 pandemic.
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Penelitian ini menyajikan analisis komparatif dari dua teknik pemodelan yang berbeda, yaitu model regresi pertumbuhan logistik dan model rentan-terinfeksi-sembuh (SIR) yang dimodifikasi, untuk menilai penyebaran COVID-19 di wilayah-wilayah Pakistan. Model regresi pertumbuhan logistik digunakan untuk memperkirakan ukuran akhir epidemi, waktu puncak, dan laju pertumbuhan di berbagai lokasi geografis di Pakistan, termasuk Gilgit Baltistan dan Islamabad. Parameter model diperkirakan melalui penyesuaian non-linear terhadap data, mengungkapkan perkiraan komparatif di wilayah-wilayah di mana terdapat bukti kurva yang melandai. Meskipun tren terbaru menunjukkan penurunan laju infeksi yang berkepanjangan di Pakistan, model regresi pertumbuhan logistik memberikan perkiraan yang meyakinkan, mencerminkan lintasan pandemi yang unik di negara tersebut. Sebaliknya, model SIR menawarkan dasar analitis untuk memeriksa penyebaran COVID-19 dalam populasi. Model SIR memberikan wawasan di luar data yang tercatat dengan mempertimbangkan elemen-elemen penting terkait perkembangan penyakit dari waktu ke waktu dan ruang. Menggunakan data yang dikumpulkan antara 24 Juni 2021 hingga 29 Juli 2021, termasuk periode sebelum dan sesudah penerapan tindakan pengendalian ketat, model SIR memproyeksikan faktor-faktor seperti jumlah populasi rentan, terinfeksi, dan sembuh hingga Agustus 2021. Analisis komparatif dari kedua model tersebut mengungkap wawasan tentang pola kasus infeksi. Sementara model regresi pertumbuhan logistik menawarkan perkiraan yang meyakinkan berdasarkan tren saat ini, model SIR menyediakan alat yang berharga untuk mensimulasikan penyebaran COVID-19, memungkinkan para peneliti untuk menilai potensi dampak penyakit dan membuat prognosis yang akurat. Pada akhirnya, penelitian ini menekankan pentingnya menggunakan berbagai teknik pemodelan untuk memahami dan mengatasi tantangan yang dihadapi oleh pandemi COVID-19 secara komprehensif.

Item Type:	Thesis (Masters)
Subjects:	Q Science > QA Mathematics > QA273.6 Weibull distribution. Logistic distribution. Q Science > QA Mathematics > QA276 Mathematical statistics. Time-series analysis. Failure time data analysis. Survival analysis (Biometry) Q Science > QA Mathematics > QA278.2 Regression Analysis. Logistic regression Q Science > QA Mathematics > QA401 Mathematical models.
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Mathematics > 44101-(S2) Master Thesis
Depositing User:	Wasim Raza
Date Deposited:	15 Jul 2024 01:31
Last Modified:	15 Jul 2024 01:31
URI:	http://repository.its.ac.id/id/eprint/108286

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