Correlated host movements can reshape spatio-temporal disease dynamics: modeling the contributions of space use to transmission risk using movement data

Despite decades of epidemiological theory making relatively simple assumptions about host movements, it is increasingly clear that non-random movements drastically affect disease transmission. To better predict transmission risk, theory needs to simultaneously account for how the environment affects host space use and how social dynamics affect correlation in space use. We develop new theory that decomposes the relative contributions of fine-scale space use and correlated movements to spatio-temporal transmission risk. Using analytical results, simulations, and empirical movement data, we show that even weak correlations can increase transmission risk by orders of magnitude compared to independent movement. Accounting for correlation is especially critical for pathogens with direct transmission or short environmental persistence. Our theory provides clear expectations for what has been observed empirically but largely ignored in disease models—movement correlation can reshape epidemiological landscapes, creating transmission hotspots whose magnitude and location are not necessarily predictable from spatial overlap alone.