Computational Epidemiology Modeling

#Infection #data #covid19 #omicron #India #machinelearning #distribution #ARIMA #model In a joint multi-disciplinary research effort, together with Dr. @Subhash Yadav (Department of Statistics, Babasaheb Bhimrao Ambedkar University) and Dr. Vinit Kumar, PhD (Department of Library & Information Science, Babasaheb Bhimrao Ambedkar University), we analyzed COVID-19 Omicron variant infection data in the top ten Indian provinces by dissemination, encompassing periods both before and during strict control measures. Using a combination of mathematical, statistical and artificial intelligence models, including the SIR model, ARIMA, Random Forest, and distribution fitting, we aimed to accurately analyze the Omicron infection spread and provide insights for future interventions. Our findings, particularly the estimates of the scale of infection spread, can guide health agencies and policymakers in developing targeted strategies to combat future outbreaks of similar infectious agents. The work is published in the journal, Spatial and Spatio-temporal Epidemiology (Elsevier). Full text: https://lnkd.in/ggeM7SzJ

A simple ML model for forecasting the pandemic This article is about Scientific ML - an emerging field that combines mechanistic models' interpretability with ML models' predictive power. Take the example of COVID. How would we model the spread of COVID in a population? SIR model is one of the simplest yet foundational models in epidemiology that categorizes a population into three compartments: Susceptible (S), Infected (I), and Recovered (R). Initially, the entire population is susceptible except for a few infected individuals. As the virus spreads, susceptible individuals become infected, and over time, infected individuals recover and gain immunity. This transition is modeled using a system of three ordinary differential equations, each representing the rate of change of the S, I, and R populations. The model uses parameters like τ_SI (the rate at which susceptible people become infected) and τ_IR (the recovery rate). One of the biggest problems with the SIR model is that the interaction parameters τ_SI and τ_IR are difficult to estimate in real life. So how about using a neural network to overcome these limitations? We can use neural networks to create a mapping from initial conditions such as S(t=0), I(t=0), and R(t=0) to the number of infections at the end of a month. For this, we will need real-world data from different regions of the world. However, traditional machine learning models are black boxes and don’t leverage the known structure of these scientific equations. In the SIR model, we have some information. Although the interaction parameters may be unknown, we can still deduce the presence of S, I, and R terms in the 3 equations using logic. So why should we throw away this knowledge and just use a black box? Scientific ML bridges this gap by integrating known physical or epidemiological relationships (like those in the SIR model) into the learning process, resulting in models that are both interpretable and more accurate, even with partially unknown parameters. Universal Differential Equations (UDEs) in SciML preserve the structure of known scientific laws - such as the system of ordinary differential equations (ODEs) in the SIR model - while replacing unknown or hard-to-estimate terms (like the interaction parameters τ_SI and τ_IR) with trainable neural networks. I have made a lecture video on UDEs (incorporating the SIR model) to forecast the spread of a pandemic and hosted it on Vizuara’s YouTube channel. Do check this out. I hope you enjoy watching this lecture as much as I enjoyed making it: https://lnkd.in/gMdquisq If you wish to conduct research in SciML, check this out: https://lnkd.in/dSxFNzPb

The Susceptible-Infected-Recovered (SIR) model is a foundational tool in epidemiology and systems biology. It provides a simplified framework to study the spread of infectious diseases within a population. By dividing the population into three compartments—susceptible (S), infected (I), and recovered (R)—the SIR model uses differential equations to track how individuals transition between these states over time. This model has been instrumental in understanding disease dynamics, predicting outbreaks, and informing control strategies. For students of bioinformatics and systems biology, mastering the SIR model opens doors to its application in real-world scenarios ranging from public health planning to research in host-pathogen interactions. In the latest article on Decoding Biology, linked below, you'll learn how the SIR model works, how to implement it in Python, and how to modify your code to simulate different disease outbreaks, interventions, and environmental factors. If you enjoy this article, and want to see more like it, please let me know in the comments below. Thank you! #compbio #biotech #bioinformatics #datascience #sirmodel #python #epidemiology #systemsbiology

🚀 Unleashing the Power of Count Data Modeling in Public Health Research! 📊 In a world where data is the new oil, understanding the right tools for the right data is key. Today, I reflect on three statistical warriors that have transformed how we analyze count data—especially in epidemiology, public health, and beyond: 🔹 Poisson Regression When our data is about counts—number of hospital visits, cases of infection, or traffic accidents—Poisson regression is our go-to model. It's simple, powerful, and assumes that the mean equals the variance. But real-world data is rarely this neat... 🔹 Quasi-Poisson Regression ...and when the variance is higher than the mean (a phenomenon known as overdispersion), Poisson struggles. Quasi-Poisson steps in as a flexible upgrade, allowing us to adjust the standard errors without changing the mean structure. It's like putting on glasses—same data, sharper insight! 👓 🔹 Negative Binomial Regression For serious overdispersion, where variability can’t just be "tweaked," we bring in the negative binomial model. Think of it as Poisson's smarter cousin—built to handle extra variation due to unobserved heterogeneity in the data. It gives us better estimates, better confidence, and ultimately, better decisions. 🔍 These models don’t just run numbers—they reveal patterns, predict future risks, and guide policy. Whether it's modeling disease incidence, hospital readmissions, or community outreach programs, they help us answer one fundamental question: 👉 "How can we use data to save lives and allocate resources wisely?" As I continue my journey in health data science, I am more convinced than ever that mastering these models isn't just about analytics—it's about impact. 📈❤️ #DataScience #PublicHealth #PoissonRegression #NegativeBinomial #HealthAnalytics #Epidemiology #CountData #StatisticalModeling #PredictiveAnalytics #ResearchThatMatters

Novel Nonlinear Epidemiological Model with Applications in Public Health I'm thrilled to announce the publication of our new preprint, titled "Mathematical Modeling and Hyers-Ulam Stability for a Nonlinear Epidemiological Model with Φᵖ Operator and Mittag-Leffler Kernel," available here: https://lnkd.in/d9cNc_8m This joint work with Achraf ZINIHI and Moulay Rchid Sidi Ammi delves into a novel nonlinear singular fractional SI model, incorporating the Φᵖ operator and the Mittag-Leffler kernel. It holds significant potential for improving our understanding of disease dynamics and informing public health interventions. Key highlights of the paper: - Novel Model Formulation: We propose a new mathematical framework for modeling infectious diseases using the fractional calculus framework. It will be extended in the future by adding more compartments and terms accounting for uncertainty, time-delay, waning effects, etc. - Rigorous Analysis: We establish the existence, uniqueness, boundedness, and non-negativity of the model's solutions. - Hyers-Ulam Stability: We ensure the model's robustness to small perturbations using the powerful Hyers-Ulam stability theorem. - Optimal Control Analysis: We design optimal control strategies to minimize infection spread and maximize the susceptible population, aiding public health decision-making. - Numerical Validation: We showcase the model's practical applicability through comprehensive numerical simulations. I believe this research has significant implications for various fields, including: - Public health officials aiming to develop effective disease control strategies. - Mathematicians interested in applying fractional calculus to real-world problems. - Researchers seeking to advance the understanding of nonlinear epidemiological models. I encourage you to read the full preprint and join the discussion! I'm eager to hear your thoughts and potential applications of this novel modeling approach. #mathematics #analysis #publichealth #epidemiology #optimalcontrol #mathematicalmodeling #fractionalcalculus #research