Modeling and Optimization of Dynamical Systems in Epidemiology using Sparse Grid Interpolation

Infectious diseases pose a perpetual threat across the globe, devastating communities, and straining public health resources to their limit. The ease and speed of modern communications and transportation networks means policy makers are often playing catch-up to nascent epidemics, formulating critical, yet hasty, responses with insufficient, possibly inaccurate, information. In light of these difficulties, it is crucial to first understand the causes of a disease, then to predict its course, and finally to develop ways of controlling it. Mathematical modeling provides a methodical, in silico solution to all of these challenges, as we explore in this work. We accomplish these tasks with the aid of a surrogate modeling technique known as sparse grid interpolation, which approximates dynamical systems using a compact polynomial representation. Our contributions to the disease modeling community are encapsulated in the following endeavors. We first explore transmission and recovery mechanisms for disease eradication, identifying a relationship between the reproductive potential of a disease and the maximum allowable disease burden. We then conduct a comparative computational study to improve simulation fits to existing case data by exploiting the approximation properties of sparse grid interpolants both on the global and local levels. Finally, we solve a joint optimization problem of periodically selecting field sensors and deploying public health interventions to progressively enhance the understanding of a metapopulation-based infectious disease system using a robust model predictive control scheme

From Novice to Expert: A Journey into Training Machine Learning Models

Machine learning has evolved into a priceless asset for tackling complex obstacles across a wide range of disciplines, including Computer Vision(CV), Natural Language Processing(NLP), healthcare, and finance. At the core of machine learning lies the training process, wherein model parameters are optimized to make precise predictions on unseen data. For beginners venturing into this domain, it is crucial to grasp the fundamentals of training machine learning models. This article serves as a comprehensive guide, specifically focusing on training machine learning models using Python. Step-by-step instructions and explanations are provided to facilitate a thorough understanding of the training process. By following this article, beginners will gain practical knowledge and confidence in training their own machine learning models

Exploratory Data Analysis on Autopilot: Python's Automatic Solutions

Python has gained immense popularity in the fields of data science and machine learning due to its extensive libraries and efficient coding capabilities, enabling time-saving solutions. This article presents a comprehensive tutorial on Automatic Exploratory Data Analysis (EDA) using Python. By leveraging Python libraries, we can swiftly extract valuable insights and statistical information from datasets, reducing the manual effort involved in data exploration. The article aims to equip readers with the knowledge and tools to efficiently analyze data, revealing hidden patterns and trends, all accomplished through just a few lines of code. By the end of this article, readers will have a clear understanding of how Python's automated EDA techniques can revolutionize the data analysis process, maximizing efficiency and productivity

Data Driven Exploration: Unleashing Topic Modelling Using Python

Topic Modeling is a crucial technique in natural language processing that involves assigning topic labels to a set of text documents. The primary objective of topic modeling is to unveil the latent themes or subjects present within the textual data. This article serves as a comprehensive guide for individuals seeking to acquire knowledge on performing topic modeling using machine learning algorithms with the aid of Python. Through this article, readers will gain insights into the fundamental concepts of topic modeling, various machine learning techniques used in the process, and a step-by-step implementation using Python programming language. By the end of this article, readers will have a solid foundation in topic modeling and the necessary skills to explore and extract meaningful topics from their own text data

Numeric Metamorphosis: Converting Categorical Features With Python

In data preprocessing for machine learning, converting categorical features to numerical values is a crucial step. Python offers various techniques to achieve this transformation. One common approach is Label Encoding, where each category is assigned a unique integer. This method is suitable when there's a meaningful ordinal relationship between categories. Alternatively, One-Hot Encoding can be used to create binary columns for each category, which is ideal when there's no inherent order among them. These conversions enable machine learning algorithms to work with categorical data efficiently, making them an essential part of the data preparation process, ultimately leading to more accurate and effective predictive models

A Forest of Possibilities: Decision Trees and Beyond

Decision trees are fundamental in machine learning due to their interpretability and versatility. They are hierarchical structures used for classification and regression tasks, making decisions by recursively splitting data based on features. This abstract explores decision tree algorithms, tree construction, pruning to prevent overfitting, and ensemble methods like Random Forests. Additionally, it covers handling categorical data, imbalanced datasets, missing values, and hyperparameter tuning. Decision trees are valuable for feature selection and model interpretability. However, they have drawbacks, such as overfitting and sensitivity to data variations. Nevertheless, they find applications in fields like finance, medicine, and natural language processing, making them a critical topic in machine learning

Python's AutoTS: Your Co-Pilot for Time Series Analysis

AutoTS is a powerful automatic machine learning library designed specifically for automatic time series forecasting in Python. With its intuitive functionality and versatility, this library empowers users to effortlessly engage in various time series forecasting tasks, such as predicting stock prices for a specified number of days ahead. In this article, we present a comprehensive tutorial on utilizing the AutoTS library in Python. By following this guide, readers will gain insights into the library's capabilities, enabling them to harness its potential for accurate and efficient time series forecasting in their projects. Whether you are a seasoned data scientist or a beginner in the field, AutoTS offers a seamless and accessible solution for tackling complex time series analysis with ease

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Impact of COVID-19 on cardiovascular testing in the United States versus the rest of the world

Objectives: This study sought to quantify and compare the decline in volumes of cardiovascular procedures between the United States and non-US institutions during the early phase of the coronavirus disease-2019 (COVID-19) pandemic. Background: The COVID-19 pandemic has disrupted the care of many non-COVID-19 illnesses. Reductions in diagnostic cardiovascular testing around the world have led to concerns over the implications of reduced testing for cardiovascular disease (CVD) morbidity and mortality. Methods: Data were submitted to the INCAPS-COVID (International Atomic Energy Agency Non-Invasive Cardiology Protocols Study of COVID-19), a multinational registry comprising 909 institutions in 108 countries (including 155 facilities in 40 U.S. states), assessing the impact of the COVID-19 pandemic on volumes of diagnostic cardiovascular procedures. Data were obtained for April 2020 and compared with volumes of baseline procedures from March 2019. We compared laboratory characteristics, practices, and procedure volumes between U.S. and non-U.S. facilities and between U.S. geographic regions and identified factors associated with volume reduction in the United States. Results: Reductions in the volumes of procedures in the United States were similar to those in non-U.S. facilities (68% vs. 63%, respectively; p = 0.237), although U.S. facilities reported greater reductions in invasive coronary angiography (69% vs. 53%, respectively; p < 0.001). Significantly more U.S. facilities reported increased use of telehealth and patient screening measures than non-U.S. facilities, such as temperature checks, symptom screenings, and COVID-19 testing. Reductions in volumes of procedures differed between U.S. regions, with larger declines observed in the Northeast (76%) and Midwest (74%) than in the South (62%) and West (44%). Prevalence of COVID-19, staff redeployments, outpatient centers, and urban centers were associated with greater reductions in volume in U.S. facilities in a multivariable analysis. Conclusions: We observed marked reductions in U.S. cardiovascular testing in the early phase of the pandemic and significant variability between U.S. regions. The association between reductions of volumes and COVID-19 prevalence in the United States highlighted the need for proactive efforts to maintain access to cardiovascular testing in areas most affected by outbreaks of COVID-19 infection

Local Interpretable Model Agnostic Shap Explanations for machine learning models

With the advancement of technology for artificial intelligence (AI) based solutions and analytics compute engines, machine learning (ML) models are getting more complex day by day. Most of these models are generally used as a black box without user interpretability. Such complex ML models make it more difficult for people to understand or trust their predictions. There are variety of frameworks using explainable AI (XAI) methods to demonstrate explainability and interpretability of ML models to make their predictions more trustworthy. In this manuscript, we propose a methodology that we define as Local Interpretable Model Agnostic Shap Explanations (LIMASE). This proposed ML explanation technique uses Shapley values under the LIME paradigm to achieve the following (a) explain prediction of any model by using a locally faithful and interpretable decision tree model on which the Tree Explainer is used to calculate the shapley values and give visually interpretable explanations. (b) provide visually interpretable global explanations by plotting local explanations of several data points. (c) demonstrate solution for the submodular optimization problem. (d) also bring insight into regional interpretation e) faster computation compared to use of kernel explainer.Comment: Under revie