This work owes its existence to the incredible Takashi Okada, with support from
@qinqinyu.bsky.social, @giulioisac.bsky.social, and invaluable input from friends and experts. Read more: www.medrxiv.org/content/10.1...

With NIH (esp. NIAID) funding under threat, this work underscores the importance of genomic epidemiology for global health. Supporting such analyses is vital—any suggestions on alternative funding sources?

Why is this important? Knowing these networks can enhance epidemic forecasting, inform targeted interventions like vaccination campaigns, explain why some regions contribute more to pathogen evolution.

Applied to SARS-CoV-2 data from 🇬🇧England & 🇺🇸the USA, our method revealed: Networks mirror geography / Long-range interactions have greater impact than expected based on mobility data alone / Importation networks shift across variant waves

The massive rise in genome surveillance during the pandemic, allowed lead author Takashi Okada to infer entire importation networks, using an HMM to filter out genetic drift and sampling noise.

Neutral allele frequency time series can tell. Consider two communities (A & B) under transient travel restrictions: Allele frequencies X_A(t), X_B(t) drift independently during isolation but converge post-lockdown - the convergence rate precisely measures the importation rate.

The pandemic showed us that disease doesn’t respect boundaries. But how do we map hidden transmission pathways, especially the crucial rare ones between distant communities? 🌍