Modeling Space and Time: Spatio-Temporal Methods for Epidemics and Climate Forecasting

by Jonathan Reeves, Eliza Harper (Illustrator)

Modeling Space and Spatio-Temporal Methods for Epidemics and Climate Forecasting

How do we understand patterns that unfold across both space and time?

This comprehensive guide introduces the theory and practice of spatio-temporal modeling, offering readers the tools to analyze data that changes not only when events happen but also where they occur. With clear explanations and real-world applications, the book brings together methods from statistics, machine learning, and applied sciences.

Starting with foundational concepts in spatial statistics and time series analysis, the text moves step by step into advanced models such as Gaussian processes, hierarchical frameworks, and Bayesian approaches. Modern machine learning techniques are also explored, showing how neural networks and ensemble methods can enhance forecasting in complex systems.

Applications are given special attention. Readers will find detailed chapters on epidemic modeling, including both traditional compartmental frameworks and their spatial extensions, as well as in-depth treatment of climate science, from data sources and trend detection to forecasting with coupled ocean-atmosphere models.

By combining rigorous methods with accessible case studies, this book provides both the theoretical grounding and practical insight needed to approach spatio-temporal problems with confidence.This book is an essential resource for graduate students, researchers, and professionals in data science, epidemiology, environmental studies, and applied statistics who want to understand and forecast the dynamics of space and time.

Inside you will

Core methods in spatial statistics and time series analysis

How to integrate space and time using covariance structures and stochastic processes

Advanced modeling strategies including hierarchical and Bayesian techniques

Machine learning approaches for large and complex data

Applications in epidemiology, including epidemic forecasting and spatial disease spread

Applications in climate science, including trend detection and forecasting models

Key challenges such as big data scalability, uncertainty quantification, and ethical considerations

190 pages, Kindle Edition

Published September 12, 2025

About the author

Jonathan Reeves was born in Wiltshire, UK, where he grew up as part of a large family. He now lives and works just outside London. As a child growing up in the 1990s, he was immersed within the growing world of video games and the Internet, which combined with his love of reading and writing to give him an unorthodox take on storytelling. His hobbies include cooking and baking, game design, and political activism.