Multi-scale models and data for infectious diseases: A systematic review
- Published
- Accepted
- Subject Areas
- Computational Biology, Mathematical Biology, Epidemiology, Infectious Diseases
- Keywords
- mulit-scale modeling, linking mechanism, infectious disease models, SIR models, data-model integration, within-host, between-host, pathogen transmission
- Copyright
- © 2019 Childs et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2019. Multi-scale models and data for infectious diseases: A systematic review. PeerJ Preprints 7:e27485v1 https://doi.org/10.7287/peerj.preprints.27485v1
Abstract
The observed dynamics of infectious diseases are driven by processes across multiple scales. First is within-host, that is how an infection progresses inside a single individual (for instance viral and immune dynamics). Second is how the infection is transmitted between multiple individuals of a host population. The dynamics of each of these may be influenced by the other, particularly across evolutionary time. Thus understanding each of these scales, and the links between them, is necessary for a wholistic understanding of the spread of infectious diseases. One approach to combining these scales is through mathematical modeling. We conducted a systematic review of the published literature on multi-scale mathematical models of disease transmission to determine the extent to which mathematical models are being used to understand across-scale transmission, and the extent to which these models are being confronted with data. Following the PRISMA guidelines for systematic reviews, we identified 19 of 139 qualifying papers across 30 years that include both linked models at the within and between host levels and that used data to parameterize/calibrate models. We find that the approach that incorporates both modeling with data is under-utilized, if increasing. This highlights the need for better communication and collaboration between modelers and empiricists to build well-calibrated models that both improve understanding and may be used for prediction.
Author Comment
This is a submission to PeerJ for review.
Supplemental Information
Journals where included papers appeared
(A) Counts of included papers from different journals. (B) Counts of different journal types. General audience journals included Philosophical Transactions of the Royal Society B: Biological Sciences, PLOS One, and Proceedings of the Royal Society of London B: Biological Sciences. Primarily mathematical and computational journals included American Naturalist, PLOS Computational Biology, and Journal of Theoretical Biology. Specialized journals included Molecular Biology and Evolution and Preventive Veterinary Medicine. Sub-discipline journals included Ecology, Ecological Monographs, Evolution, and Journal of Virology. All journals of the 19 included papers are included.
Reason for exclusion of papers by host species
The reason for exclusion was categorized as no between-host component (gray), no data (orange), no model (blue), or no within-host component (green). All other reasons were included under other (yellow).
Types of focal host species and the modeling type
Types of focal host species used in the within-host and between-host models and the modeling type used to represent the model components. Model types were classified as deterministic (gray), individual-based (orange), statistical (blue) or stochastic (green).
Method used in data fitting at each scale
Three fitting methods were considered: Bayesian inference (gray), least squares (orange), maximum likelihood (blue). All other fitting methods were included under other (green). Different fitting methods could be used in the same papers for different scales.
Supplementary Tables of Question Responses
These tables summarize the answers to each of the screening or evaluation questions.
Table of screening and evaluation questions
Full screening and evaluation questions corresponding to Figure 1B in the main text.
Reference information for included papers
Full bibliographic information for all papers that met the criteria for inclusion in the systematic review.
References for excluded studies
Full bibliographic information for all papers that did not meet the criteria for inclusion in the systematic review, and so were excluded.
Answers to screening questions for excluded papers
Full answers to screening questions for all papers that did not meet the criteria for inclusion in the systematic review.
Answers to screening and evaluation questions for included papers
Full answers to screening and evaluation questions for all papers that met the criteria for inclusion in the systematic review.