UNDERSTANDING INFECTIOUS DISEASE TRANSMISSION: A GENERALIZED COMPARTMENTAL TRANSMISSION MODEL (GCTM) APPROACH
DOI:
https://doi.org/10.29121/shodhkosh.v5.i1.2024.2726Keywords:
Data Analytics, COVID-19, Pandemic, Preventive Measures, Ordinary Differential Calculus, SEIR, SEIVRAbstract [English]
Background
Infectious diseases pose significant public health challenges worldwide, affect- ing millions and straining healthcare systems. Accurate modeling of disease transmission dynamics is crucial for effective intervention strategies. These models help understand how diseases spread, identify potential hotspots, and predict future outbreaks. By developing robust models, public health officials can design targeted interventions, allocate resources efficiently, and implement measures to mitigate the impact of infectious diseases, ultimately protecting public health and preventing the spread of infections.
Objective
This study aims to develop a Generalized Compartmental Transmission Model (GCTM) to analyze the spread of infectious diseases, incorporating depen- dencies such as population density, age, and comorbidities.
Methods
The GCTM divides the infection process into five stages: initial stage, rapid spread, peak spread, slowing spread, and decline. Each stage is character- ized by different infection rates. Data from the World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC) were used, along with demographic data on population density, age distribution, and comorbidities. Logistic growth equations were employed to model the infection rates, and numerical methods were used to solve the differential equations.
Results
The model revealed key insights into the dynamics of disease transmission across different stages. The incorporation of dependencies such as population density, age, and comorbidities provided a more accurate representation of the infection dynamics
Conclusion
The GCTM offers a comprehensive approach to understanding and managing infectious disease outbreaks. By leveraging data-driven decision-making and incorporating key dependencies, the model enhances our ability to predict and control disease spread.
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