Work

Social induction dynamics of the causal social R0 on clinical weight loss: Randomized trial evidence of social propagation from Amman, Jordan

Year: 2025

Type: article

Abstract: An unresolved debate persists in public health, concerning a distinction between communicable diseases that are causally linked to microbes or viruses and “non-communicable diseases” that are not. We offer evidence in support of the view that public health categories need rethinking; that more precise and granular ones are needed; and that “non-communicable” and “non-infectious” can be tautological misnomers. The scientific literature suggests an underlying socially “infectious” phenomenon and we put forth a fundamental Social Contagion Hypothesis: that social networks constitute a propagating force; that this force can be quantified in socio-biological terms; and that it can be leveraged for public health. Yet a major question still remains unanswered: if weight loss, like biological pathogens, cascades through host populations, and can be mitigated through interventions targeting risky social behaviors, then can a social reproductive number (Social R0) for metabolic and cardiovascular disease be estimated and modulated? We present the first known evidence from a randomized trial, demonstrating the epidemiological dynamics of socially infectious diseases. We find that health propagation can achieve epidemic proportions (including changes in weight, 1<R0 = 1.3, p<0.01); and that public health systems can intervene to modify the R0 value thereby potentially managing, preventing, or reversing social infections at epidemic scale (yielding population-averaged comparative R0 ratios, 244<Rc<368, p<0.01, for weight change). To facilitate adoption of the methodologies, macros and code for use with various statistical software packages are included. The results indicate that the social induction of health interventions are not only possible, but that propagation can both be isolated causally via trial design and be quantified over time. This bodes enormous promise for developing and quantifying future self-sustaining public health interventions.

Source: PLOS complex systems.

DOI: 10.1371/journal.pcsy.0000031

Authors: Daniel E. Zoughbie, Dillon Huddleston, Eric L. Ding

Institutions: Microclinic International, Institute of International Studies, New England Complex Systems Institute, University of California, Berkeley

Language: English

Topic: Complex Network Analysis Techniques

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,043