Evidence map›Paper›PMID 26211717›Full record

ReviewPhysics of life reviews2015

Coupled disease-behavior dynamics on complex networks: A review.

Zhen Wang, Michael A Andrews, Zhi-Xi Wu, Lin Wang, Chris T Bauch

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Physics of life reviews, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 104 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
104citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

104 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Paradoxes in the coevolution of contagions and institutions.Proceedings. Biological sciences · 2024
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. The impact of threshold decision mechanisms of collective behavior on disease spread.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article

44 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Zhen WangSchool of Automation, Northwestern Polytechnical University, Xi'an 710072, China; Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Fukuoka, 816-8580, Japan. Electronic address: zhenwang0@gmail.com.
Michael A AndrewsDepartment of Mathematics and Statistics, University of Guelph, Guelph, ON, N1G 2W1, Canada. Electronic address: mandre04@uoguelph.ca.
Zhi-Xi WuInstitute of Computational Physics and Complex Systems, Lanzhou University, Lanzhou, Gansu 730000, China. Electronic address: wuzhx@lzu.edu.cn.
Lin WangSchool of Computer and Communication Engineering, Tianjin University of Technology, Tianjin 300384, China. Electronic address: wanglinieee@outlook.com.
Chris T BauchDepartment of Applied Mathematics, University of Waterloo, Waterloo, ON N2L 3G1, Canada. Electronic address: cbauch@uwaterloo.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

It is increasingly recognized that a key component of successful infection control efforts is understanding the complex, two-way interaction between disease dynamics and human behavioral and social dynamics. Human behavior such as contact precautions and social distancing clearly influence disease prevalence, but disease prevalence can in turn alter human behavior, forming a coupled, nonlinear system. Moreover, in many cases, the spatial structure of the population cannot be ignored, such that social and behavioral processes and/or transmission of infection must be represented with complex networks. Research on studying coupled disease-behavior dynamics in complex networks in particular is growing rapidly, and frequently makes use of analysis methods and concepts from statistical physics. Here, we review some of the growing literature in this area. We contrast network-based approaches to homogeneous-mixing approaches, point out how their predictions differ, and describe the rich and often surprising behavior of disease-behavior dynamics on complex networks, and compare them to processes in statistical physics. We discuss how these models can capture the dynamics that characterize many real-world scenarios, thereby suggesting ways that policy makers can better design effective prevention strategies. We also describe the growing sources of digital data that are facilitating research in this area. Finally, we suggest pitfalls which might be faced by researchers in the field, and we suggest several ways in which the field could move forward in the coming years.

Indexed as

Models, TheoreticalSpatial BehaviorDiseaseHumansPopulation DynamicsDecision-makingDisease–behavior dynamicsNetworksSocial distancingVaccination

Identifiers

PMID26211717
PMCPMC7105224

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.