Learning virulence-transmission relationships using causal inference

TL;DR

This paper introduces LETR, a data-driven causal inference framework that uncovers how pathogen traits like virulence influence transmission over time, challenging simplistic models and revealing complex evolutionary dynamics.

Contribution

LETR uniquely combines causal discovery with dynamical modeling to identify directional trait relationships and their evolution, applicable to diverse biological systems.

Findings

LETR reliably recovers known virulence-transmission influences in synthetic data.

In COVID-19 data, past virulence predicts future transmission better than vice versa.

Long-term trends show decreasing virulence and transmission, with bimodal distributions indicating ecological or host heterogeneity.

Abstract

The relationship between traits that influence pathogen virulence and transmission is part of the central canon of the evolution and ecology of infectious disease. However, identifying directional and mechanistic relationships among traits remains a key challenge in various subfields of biology, as models often assume static, fixed links between characteristics. Here, we introduce learning evolutionary trait relationships (LETR), a data-driven framework that applies Granger-causality principles to determine which traits drive others and how these relationships change over time. LETR integrates causal discovery with generative mapping and transfer-operator analysis to link short-term predictability with long-term trait distributions. Using a synthetic myxomatosis virus-host data set, we show that LETR reliably recovers known directional influences, such as virulence driving transmission. Applying the framework to global pandemic (SARS-CoV-2) data, we find that past virulence improves future transmission prediction, while the reverse effect is weak. Invariant-density estimates reveal a long-term trend toward low virulence and transmission, with bimodality in virulence suggesting ecological influences or host heterogeneity. In summary, this study provides a blueprint for learning the relationship between how harmful a pathogen is and how well it spreads, which is highly idiosyncratic and context-dependent. This finding undermines simplistic models and encourages the development of new theory for the constraints underlying pathogen evolution. Further, by uniting causal inference with dynamical modeling, the LETR framework offers a general approach for uncovering mechanistic trait linkages in complex biological systems of various kinds.