- Monte Carlo sampling and computational analysis of a three component tumor radiotherapy mathematical model
Cancer is commonly acknowledged to be among the leading causes of death, and mathematical modeling has the potential to dramatically improve experimental cancer research. To investigate the impact of quiescent cells, we present a 3-C tumor growth model that extends the conventional Gompertz model. We used the Monte Carlo sampling technique, namely the Latin Hypercube Sampling (LHS), to determine the most critical parameters in the model dynamics. Our findings suggest that radiation therapy can be influenced by a variety of factors, including the volume of quiescent cells and the radiation sensitivity coefficient. Furthermore, in some situations, quiescent cells might transform into dividing cells, which can have a significant impact on tumor progression.; December 2023
- Downscaling epidemiological time series data for improving forecasting accuracy: An algorithmic approach
Data scarcity and discontinuity are common occurrences in the healthcare and epidemiological dataset and often is needed to form an educative decision and forecast the upcoming scenario. Often to avoid these problems, these data are processed as monthly/yearly aggregate where the prevalent forecasting tools like Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average (SARIMA), and TBATS often fail to provide satisfactory results. Artificial data synthesis methods have been proven to be a powerful tool for tackling these challenges. The paper aims to propose a novel algorithm named Stochastic Bayesian Downscaling (SBD) algorithm based on the Bayesian approach that can regenerate downscaled time series of varying time lengths from aggregated data, preserving most of the statistical characteristics and the aggregated sum of the original data. The paper presents two epidemiological time series case studies of Bangladesh (Dengue, Covid-19) to showcase the workflow of the algorithm. The case studies illustrate that the synthesized data agrees with the original data regarding its statistical properties, trend, seasonality, and residuals. In the case of forecasting performance, using the last 12 years data of Dengue infection data in Bangladesh, we were able to decrease error terms up to 72.76% using synthetic data over actual aggregated data.; December 2023
- Isolation Effect on Age-Stratified Compartmental Model of the COVID-19
Focusing on the dynamics of the most recent outbreak of COVID-19, we formulate an age-distributed model with five different components in terms of nonlinear partial differential equations. The model has been analyzed by studying the stability of the equilibrium points and their reproduction number. To explore the disease effect on different ages more efficiently, we apply the recent estimated data in the formulated model and analyze it numerically. We observed more susceptibility and infection among the older population from the model's numerical solution profiles. Also studied the effectiveness of isolation in controlling illness and death.; April 2021
- The Perspective of Acquired Immunity to Combat against Infectious Diseases: An Overview
There is a long ritual of acquired immunity using physical exercise, a balanced
diet, and pharmaceutical medication to generate immunity against a
particular disease insight into the human body. This paper has extensively reviewed
the impact of exercise, daily life practice, food selection, and several
other issues to improve the immune system that combat infection. Studying
the effect of exercise in varying degrees on the immunity system of humans is
well developed and exhibit in this study. It investigates the prevention of
pandemics due to herd immunity and finds the perfect amount of exercise to
boost immunity to its maximum. Besides the life practice, it is also explored
that vaccination can improve and optimize herd immunity.; September 2021
