1
|
Gubbins S. Quantifying the relationship between within-host dynamics and transmission for viral diseases of livestock. J R Soc Interface 2024; 21:20230445. [PMID: 38379412 PMCID: PMC10879856 DOI: 10.1098/rsif.2023.0445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 01/18/2024] [Indexed: 02/22/2024] Open
Abstract
Understanding the population dynamics of an infectious disease requires linking within-host dynamics and between-host transmission in a quantitative manner, but this is seldom done in practice. Here a simple phenomenological model for viral dynamics within a host is linked to between-host transmission by assuming that the probability of transmission is related to log viral titre. Data from transmission experiments for two viral diseases of livestock, foot-and-mouth disease virus in cattle and swine influenza virus in pigs, are used to parametrize the model and, importantly, test the underlying assumptions. The model allows the relationship between within-host parameters and transmission to be determined explicitly through their influence on the reproduction number and generation time. Furthermore, these critical within-host parameters (time and level of peak titre, viral growth and clearance rates) can be computed from more complex within-host models, raising the possibility of assessing the impact of within-host processes on between-host transmission in a more detailed quantitative manner.
Collapse
Affiliation(s)
- Simon Gubbins
- The Pirbright Institute, Ash Road, Pirbright, Surrey GU24 0NF, UK
| |
Collapse
|
2
|
Bai Y, Du Z, Wang L, Lau EHY, Fung ICH, Holme P, Cowling BJ, Galvani AP, Krug RM, Meyers LA. Public Health Impact of Paxlovid as Treatment for COVID-19, United States. Emerg Infect Dis 2024; 30:262-269. [PMID: 38181800 PMCID: PMC10826746 DOI: 10.3201/eid3002.230835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2024] Open
Abstract
We evaluated the population-level benefits of expanding treatment with the antiviral drug Paxlovid (nirmatrelvir/ritonavir) in the United States for SARS-CoV-2 Omicron variant infections. Using a multiscale mathematical model, we found that treating 20% of symptomatic case-patients with Paxlovid over a period of 300 days beginning in January 2022 resulted in life and cost savings. In a low-transmission scenario (effective reproduction number of 1.2), this approach could avert 0.28 million (95% CI 0.03-0.59 million) hospitalizations and save US $56.95 billion (95% CI US $2.62-$122.63 billion). In a higher transmission scenario (effective reproduction number of 3), the benefits increase, potentially preventing 0.85 million (95% CI 0.36-1.38 million) hospitalizations and saving US $170.17 billion (95% CI US $60.49-$286.14 billion). Our findings suggest that timely and widespread use of Paxlovid could be an effective and economical approach to mitigate the effects of COVID-19.
Collapse
|
3
|
Tatsukawa Y, Arefin MR, Kuga K, Tanimoto J. An agent-based nested model integrating within-host and between-host mechanisms to predict an epidemic. PLoS One 2023; 18:e0295954. [PMID: 38100436 PMCID: PMC10723725 DOI: 10.1371/journal.pone.0295954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Accepted: 12/01/2023] [Indexed: 12/17/2023] Open
Abstract
The COVID-19 pandemic has remarkably heightened concerns regarding the prediction of communicable disease spread. This study introduces an innovative agent-based modeling approach. In this model, the quantification of human-to-human transmission aligns with the dynamic variations in the viral load within an individual, termed "within-host" and adheres to the susceptible-infected-recovered (SIR) process, referred to as "between-host." Variations in the viral load over time affect the infectivity between individual agents. This model diverges from the traditional SIR model, which employs a constant transmission probability, by incorporating a dynamic, time-dependent transmission probability influenced by the viral load in a host agent. The proposed model retains the time-integrated transmission probability characteristic of the conventional SIR model. As observed in this model, the overall epidemic size remains consistent with the predictions of the standard SIR model. Nonetheless, compared to predictions based on the classical SIR process, notable differences existed in the peak number of the infected individuals and the timing of this peak. These nontrivial differences are induced by the direct correlation between the time-evolving transmission probability and the viral load within a host agent. The developed model can inform targeted intervention strategies and public health policies by providing detailed insights into disease spread dynamics, crucial for effectively managing epidemics.
Collapse
Affiliation(s)
- Yuichi Tatsukawa
- Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Fukuoka, Japan
- MRI Research Associates Inc., Tokyo, Japan
| | - Md. Rajib Arefin
- Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Fukuoka, Japan
- Department of Mathematics, University of Dhaka, Dhaka, Bangladesh
| | - Kazuki Kuga
- Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Fukuoka, Japan
- Faculty of Engineering Sciences, Kyushu University, Fukuoka, Japan
| | - Jun Tanimoto
- Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Fukuoka, Japan
- Faculty of Engineering Sciences, Kyushu University, Fukuoka, Japan
| |
Collapse
|
4
|
Doran JWG, Thompson RN, Yates CA, Bowness R. Mathematical methods for scaling from within-host to population-scale in infectious disease systems. Epidemics 2023; 45:100724. [PMID: 37976680 DOI: 10.1016/j.epidem.2023.100724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 06/29/2023] [Accepted: 10/26/2023] [Indexed: 11/19/2023] Open
Abstract
Mathematical modellers model infectious disease dynamics at different scales. Within-host models represent the spread of pathogens inside an individual, whilst between-host models track transmission between individuals. However, pathogen dynamics at one scale affect those at another. This has led to the development of multiscale models that connect within-host and between-host dynamics. In this article, we systematically review the literature on multiscale infectious disease modelling according to PRISMA guidelines, dividing previously published models into five categories governing their methodological approaches (Garira (2017)), explaining their benefits and limitations. We provide a primer on developing multiscale models of infectious diseases.
Collapse
Affiliation(s)
- James W G Doran
- Centre for Mathematical Biology, Department of Mathematical Sciences, University of Bath, Bath, BA2 7AY, United Kingdom.
| | - Robin N Thompson
- Mathematics Institute, Zeeman Building, University of Warwick, Coventry, CV4 7AL, United Kingdom; Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, CV4 7AL, United Kingdom; Mathematical Institute, University of Oxford, Oxford, OX2 6GG, United Kingdom
| | - Christian A Yates
- Centre for Mathematical Biology, Department of Mathematical Sciences, University of Bath, Bath, BA2 7AY, United Kingdom
| | - Ruth Bowness
- Centre for Mathematical Biology, Department of Mathematical Sciences, University of Bath, Bath, BA2 7AY, United Kingdom
| |
Collapse
|
5
|
Bai Y, Du Z, Wang L, Lau EHY, Fung ICH, Holme P, Cowling BJ, Galvani AP, Krug RM, Meyers LA. The public health impact of Paxlovid COVID-19 treatment in the United States. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.16.23288870. [PMID: 37732213 PMCID: PMC10508801 DOI: 10.1101/2023.06.16.23288870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
The antiviral drug Paxlovid has been shown to rapidly reduce viral load. Coupled with vaccination, timely administration of safe and effective antivirals could provide a path towards managing COVID-19 without restrictive non-pharmaceutical measures. Here, we estimate the population-level impacts of expanding treatment with Paxlovid in the US using a multi-scale mathematical model of SARS-CoV-2 transmission that incorporates the within-host viral load dynamics of the Omicron variant. We find that, under a low transmission scenario R e ∼ 1.2 treating 20% of symptomatic cases would be life and cost saving, leading to an estimated 0.26 (95% CrI: 0.03, 0.59) million hospitalizations averted, 30.61 (95% CrI: 1.69, 71.15) thousand deaths averted, and US$52.16 (95% CrI: 2.62, 122.63) billion reduction in health- and treatment-related costs. Rapid and broad use of the antiviral Paxlovid could substantially reduce COVID-19 morbidity and mortality, while averting socioeconomic hardship.
Collapse
Affiliation(s)
- Yuan Bai
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Zhanwei Du
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Lin Wang
- Department of Genetics, University of Cambridge, Cambridge, UK
| | - Eric H. Y. Lau
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Isaac Chun-Hai Fung
- Department of Biostatistics, Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA 30460, USA
| | - Petter Holme
- Department of Computer Science, Aalto University, Espoo, FI 00076, Finland
- Center for Computational Social Science, Kobe University, Nada, Kobe 657-8501, Japan
| | - Benjamin J. Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Alison P. Galvani
- Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT, USA
| | - Robert M. Krug
- Department of Molecular Biosciences, John Ring LaMontagne Center for Infectious Disease, Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, TX 78712, USA
| | - Lauren Ancel Meyers
- Department of Integrative Biology, University of Texas at Austin, Austin, TX 78712, USA
- Santa Fe Institute, Santa Fe, NM 87507, USA
| |
Collapse
|
6
|
Pike VL, Stevens EJ, Griffin AS, King KC. Within- and between-host dynamics of producer and non-producer pathogens. Parasitology 2023; 150:805-812. [PMID: 37394480 PMCID: PMC10478067 DOI: 10.1017/s0031182023000586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 05/25/2023] [Accepted: 05/28/2023] [Indexed: 07/04/2023]
Abstract
For infections to be maintained in a population, pathogens must compete to colonize hosts and transmit between them. We use an experimental approach to investigate within-and-between host dynamics using the pathogen Pseudomonas aeruginosa and the animal host Caenorhabditis elegans. Within-host interactions can involve the production of goods that are beneficial to all pathogens in the local environment but susceptible to exploitation by non-producers. We exposed the nematode host to ‘producer’ and two ‘non-producer’ bacterial strains (specifically for siderophore production and quorum sensing), in single infections and coinfections, to investigate within-host colonization. Subsequently, we introduced infected nematodes to pathogen-naive populations to allow natural transmission between hosts. We find that producer pathogens are consistently better at colonizing hosts and transmitting between them than non-producers during coinfection and single infection. Non-producers were poor at colonizing hosts and between-host transmission, even when coinfecting with producers. Understanding pathogen dynamics across these multiple levels will ultimately help us predict and control the spread of infections, as well as contribute to explanations for the persistence of cooperative genotypes in natural populations.
Collapse
Affiliation(s)
| | | | | | - Kayla C. King
- Department of Biology, University of Oxford, Oxford, UK
- Department of Zoology, University of British Columbia, Vancouver, Canada
- Department of Microbiology & Immunology, University of British Columbia, Vancouver, Canada
| |
Collapse
|
7
|
Xu J, Carruthers J, Finnie T, Hall I. Simplified within-host and Dose-response Models of SARS-CoV-2. J Theor Biol 2023; 565:111447. [PMID: 36898624 PMCID: PMC9993737 DOI: 10.1016/j.jtbi.2023.111447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 02/20/2023] [Accepted: 02/24/2023] [Indexed: 03/12/2023]
Abstract
Understanding the mechanistic dynamics of transmission is key to designing more targeted and effective interventions to limit the spread of infectious diseases. A well-described within-host model allows explicit simulation of how infectiousness changes over time at an individual level. This can then be coupled with dose-response models to investigate the impact of timing on transmission. We collected and compared a range of within-host models used in previous studies and identified a minimally-complex model that provides suitable within-host dynamics while keeping a reduced number of parameters to allow inference and limit unidentifiability issues. Furthermore, non-dimensionalised models were developed to further overcome the uncertainty in estimates of the size of the susceptible cell population, a common problem in many of these approaches. We will discuss these models, and their fit to data from the human challenge study (see Killingley et al. (2022)) for SARS-CoV-2 and the model selection results, which has been performed using ABC-SMC. The parameter posteriors have then used to simulate viral-load based infectiousness profiles via a range of dose-response models, which illustrate the large variability of the periods of infection window observed for COVID-19.
Collapse
Affiliation(s)
- Jingsi Xu
- Department of Mathematics, University of Manchester, United Kingdom.
| | | | - Thomas Finnie
- PHAGE Joint Modelling Team, UK Health Security Agency, United Kingdom
| | - Ian Hall
- Department of Mathematics, University of Manchester, United Kingdom; PHAGE Joint Modelling Team, UK Health Security Agency, United Kingdom.
| |
Collapse
|
8
|
Waites W, Cavaliere M, Danos V, Datta R, Eggo RM, Hallett TB, Manheim D, Panovska-Griffiths J, Russell TW, Zarnitsyna VI. Compositional modelling of immune response and virus transmission dynamics. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022; 380:20210307. [PMID: 35965463 PMCID: PMC9376723 DOI: 10.1098/rsta.2021.0307] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Transmission models for infectious diseases are typically formulated in terms of dynamics between individuals or groups with processes such as disease progression or recovery for each individual captured phenomenologically, without reference to underlying biological processes. Furthermore, the construction of these models is often monolithic: they do not allow one to readily modify the processes involved or include the new ones, or to combine models at different scales. We show how to construct a simple model of immune response to a respiratory virus and a model of transmission using an easily modifiable set of rules allowing further refining and merging the two models together. The immune response model reproduces the expected response curve of PCR testing for COVID-19 and implies a long-tailed distribution of infectiousness reflective of individual heterogeneity. This immune response model, when combined with a transmission model, reproduces the previously reported shift in the population distribution of viral loads along an epidemic trajectory. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.
Collapse
Affiliation(s)
- W. Waites
- Department of Computer and Information Sciences, University of Strathclyde, Glasgow, UK
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene and Tropical Medicine, London, UK
| | - M. Cavaliere
- Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, UK
| | - V. Danos
- Département d’Informatique, École Normale Supérieure, Paris, France
| | - R. Datta
- Datta Enterprises LLC, San Francisco, CA, USA
| | - R. M. Eggo
- Department of Computer and Information Sciences, University of Strathclyde, Glasgow, UK
| | - T. B. Hallett
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - D. Manheim
- Technion, Israel Institute of Technology, Haifa, Israel
| | - J. Panovska-Griffiths
- The Big Data Institute and the Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The Queen’s College, University of Oxford, Oxford, UK
| | - T. W. Russell
- Department of Computer and Information Sciences, University of Strathclyde, Glasgow, UK
| | - V. I. Zarnitsyna
- Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, GA, USA
| |
Collapse
|
9
|
Kirk D, O’Connor MI, Mordecai EA. Scaling effects of temperature on parasitism from individuals to populations. J Anim Ecol 2022; 91:2087-2102. [PMID: 35900837 PMCID: PMC9532350 DOI: 10.1111/1365-2656.13786] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 07/15/2022] [Indexed: 11/27/2022]
Abstract
Parasitism is expected to change in a warmer future, but whether warming leads to substantial increases in parasitism remains unclear. Understanding how warming effects on parasitism in individual hosts (e.g. parasite load) translate to effects on population-level parasitism (e.g. prevalence, R0 ) remains a major knowledge gap. We conducted a literature review and identified 24 host-parasite systems that had information on the temperature dependence of parasitism at both individual host and host population levels: 13 vector-borne systems and 11 environmentally transmitted systems. We found a strong positive correlation between the thermal optima of individual- and population-level parasitism, although several of the environmentally transmitted systems exhibited thermal optima >5°C apart between individual and population levels. Parasitism thermal optima were close to vector performance thermal optima in vector-borne systems but not hosts in environmentally transmitted systems, suggesting these thermal mismatches may be more common in certain types of host-parasite systems. We also adapted and simulated simple models for both types of transmission modes and found the same pattern across the two modes: thermal optima were more strongly correlated across scales when there were more traits linking individual- to population-level processes. Generally, our results suggest that information on the temperature dependence, and specifically the thermal optimum, at either the individual or population level should provide a useful-although not quantitatively exact-baseline for predicting temperature dependence at the other level, especially in vector-borne parasite systems. Environmentally transmitted parasitism may operate by a different set of rules, in which temperature dependence is decoupled in some systems, requiring the need for trait-based studies of temperature dependence at individual and population levels.
Collapse
Affiliation(s)
- Devin Kirk
- Department of Biology, Stanford University, Stanford, USA
- Department of Zoology, University of British Columbia, Vancouver, Canada
| | - Mary I. O’Connor
- Department of Zoology, University of British Columbia, Vancouver, Canada
| | | |
Collapse
|
10
|
Wells K, Flynn R. Managing host-parasite interactions in humans and wildlife in times of global change. Parasitol Res 2022; 121:3063-3071. [PMID: 36066742 PMCID: PMC9446624 DOI: 10.1007/s00436-022-07649-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 08/30/2022] [Indexed: 11/24/2022]
Abstract
Global change in the Anthropocene has modified the environment of almost any species on earth, be it through climate change, habitat modifications, pollution, human intervention in the form of mass drug administration (MDA), or vaccination. This can have far-reaching consequences on all organisational levels of life, including eco-physiological stress at the cell and organism level, individual fitness and behaviour, population viability, species interactions and biodiversity. Host-parasite interactions often require highly adapted strategies by the parasite to survive and reproduce within the host environment and ensure efficient transmission among hosts. Yet, our understanding of the system-level outcomes of the intricate interplay of within host survival and among host parasite spread is in its infancy. We shed light on how global change affects host-parasite interactions at different organisational levels and address challenges and opportunities to work towards better-informed management of parasite control. We argue that global change affects host-parasite interactions in wildlife inhabiting natural environments rather differently than in humans and invasive species that benefit from anthropogenic environments as habitat and more deliberate rather than erratic exposure to therapeutic drugs and other control efforts.
Collapse
Affiliation(s)
- Konstans Wells
- Department of Biosciences, Swansea University, Swansea, SA28PP, UK.
| | - Robin Flynn
- Graduate Studies Office, South East Technological University, Cork Road Campus, Waterford, X91 K0EK, Ireland
| |
Collapse
|
11
|
Páez DJ, McKenney D, Purcell MK, Naish KA, Kurath G. Variation in within-host replication kinetics among virus genotypes provides evidence of specialist and generalist infection strategies across three salmonid host species. Virus Evol 2022; 8:veac079. [PMID: 36101884 PMCID: PMC9463992 DOI: 10.1093/ve/veac079] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 08/23/2022] [Indexed: 02/12/2024] Open
Abstract
Theory of the evolution of pathogen specialization suggests that a specialist pathogen gains high fitness in one host, but this comes with fitness loss in other hosts. By contrast, a generalist pathogen does not achieve high fitness in any host, but gains ecological fitness by exploiting different hosts, and has higher fitness than specialists in nonspecialized hosts. As a result, specialist pathogens are predicted to have greater variation in fitness across hosts, and generalists would have lower fitness variation across hosts. We test these hypotheses by measuring pathogen replicative fitness as within-host viral loads from the onset of infection to the beginning of virus clearance, using the rhabdovirus infectious hematopoietic necrosis virus (IHNV) in salmonid fish. Based on field prevalence and virulence studies, the IHNV subgroups UP, MD, and L are specialists, causing infection and mortality in sockeye salmon, steelhead, and Chinook salmon juveniles, respectively. The UC subgroup evolved naturally from a UP ancestor and is a generalist infecting all three host species but without causing severe disease. We show that the specialist subgroups had the highest peak and mean viral loads in the hosts in which they are specialized, and they had low viral loads in nonspecialized hosts, resulting in large variation in viral load across hosts. Viral kinetics show that the mechanisms of specialization involve the ability to both maximize early virus replication and avoid clearance at later times, with different mechanisms of specialization evident in different host-virus combinations. Additional nuances in the data included different fitness levels for nonspecialist interactions, reflecting different trade-offs for specialist viruses in other hosts. The generalist UC subgroup reached intermediate viral loads in all hosts and showed the smallest variation in fitness across hosts. The evolution of the UC generalist from an ancestral UP sockeye specialist was associated with fitness increases in steelhead and Chinook salmon, but only slight decreases in fitness in sockeye salmon, consistent with low- or no-cost generalism. Our results support major elements of the specialist-generalist theory, providing evidence of a specialist-generalist continuum in a vertebrate pathogen. These results also quantify within-host replicative fitness trade-offs resulting from the natural evolution of specialist and generalist virus lineages in multi-host ecosystems.
Collapse
Affiliation(s)
- David J Páez
- School of Aquatic and Fishery Sciences, The University of Washington, 1122 NE Boat St, Box 355020, Seattle, WA 98195, USA
- U.S. Geological Survey, Western Fisheries Research Center, Marrowstone Marine Field Station, 616 Marrowstone Point Road, Nordland, WA 98358, USA
| | - Douglas McKenney
- U.S. Geological Survey, Western Fisheries Research Center, 6505 NE 65th Street, Seattle, WA 98115, USA
| | - Maureen K Purcell
- U.S. Geological Survey, Western Fisheries Research Center, 6505 NE 65th Street, Seattle, WA 98115, USA
| | - Kerry A Naish
- School of Aquatic and Fishery Sciences, The University of Washington, 1122 NE Boat St, Box 355020, Seattle, WA 98195, USA
| | - Gael Kurath
- U.S. Geological Survey, Western Fisheries Research Center, 6505 NE 65th Street, Seattle, WA 98115, USA
| |
Collapse
|
12
|
Silk MJ, Wilber MQ, Fefferman NH. Capturing complex interactions in disease ecology with simplicial sets. Ecol Lett 2022; 25:2217-2231. [PMID: 36001469 DOI: 10.1111/ele.14079] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 04/21/2022] [Accepted: 06/29/2022] [Indexed: 11/28/2022]
Abstract
Network approaches have revolutionized the study of ecological interactions. Social, movement and ecological networks have all been integral to studying infectious disease ecology. However, conventional (dyadic) network approaches are limited in their ability to capture higher-order interactions. We present simplicial sets as a tool that addresses this limitation. First, we explain what simplicial sets are. Second, we explain why their use would be beneficial in different subject areas. Third, we detail where these areas are: social, transmission, movement/spatial and ecological networks and when using them would help most in each context. To demonstrate their application, we develop a novel approach to identify how pathogens persist within a host population. Fourth, we provide an overview of how to use simplicial sets, highlighting specific metrics, generative models and software. Finally, we synthesize key research questions simplicial sets will help us answer and draw attention to methodological developments that will facilitate this.
Collapse
Affiliation(s)
- Matthew J Silk
- NIMBioS, University of Tennessee, Knoxville, Tennessee, USA.,CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France
| | - Mark Q Wilber
- Department of Forestry, Wildlife and Fisheries, University of Tennessee, Knoxville, Tennessee, USA
| | - Nina H Fefferman
- NIMBioS, University of Tennessee, Knoxville, Tennessee, USA.,Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, Tennessee, USA.,Department of Mathematics, University of Tennessee, Knoxville, Tennessee, USA
| |
Collapse
|
13
|
Du Z, Wang S, Bai Y, Gao C, Lau EHY, Cowling BJ. Within-host dynamics of SARS-CoV-2 infection: A systematic review and meta-analysis. Transbound Emerg Dis 2022; 69:3964-3971. [PMID: 35907777 PMCID: PMC9353427 DOI: 10.1111/tbed.14673] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 07/23/2022] [Accepted: 07/28/2022] [Indexed: 02/04/2023]
Abstract
Within-host model specified by viral dynamic parameters is a mainstream tool to understand SARS-CoV-2 replication cycle in infected patients. The parameter uncertainty further affects the output of the model, such as the efficacy of potential antiviral drugs. However, gathering empirical data on these parameters is challenging. Here, we aim to conduct a systematic review of viral dynamic parameters used in within-host models by calibrating the model to the viral load data measured from upper respiratory specimens. We searched the PubMed, Embase and Web of Science databases (between 1 December 2019 and 10 February 2022) for within-host modelling studies. We identified seven independent within-host models from the above nine studies, including Type I interferon, innate response, humoral immune response or cell-mediated immune response. From these models, we extracted and analyse seven widely used viral dynamic parameters including the viral load at the point of infection or symptom onset, the rate of viral particles infecting susceptible cells, the rate of infected cells releasing virus, the rate of virus particles cleared, the rate of infected cells cleared and the rate of cells in the eclipse phase can become productively infected. We identified seven independent within-host models from nine eligible studies. The viral load at symptom onset is 4.78 (95% CI:2.93, 6.62) log(copies/ml), and the viral load at the point of infection is -1.00 (95% CI:-1.94, -0.05) log(copies/ml). The rate of viral particles infecting susceptible cells and the rate of infected cells cleared have the pooled estimates as -6.96 (95% CI:-7.66, -6.25) log([copies/ml]-1 day-1 ) and 0.92 (95% CI:-0.09, 1.93) day-1 , respectively. We found that the rate of infected cells cleared was associated with the reported model in the meta-analysis by including the model type as a categorical variable (p < .01). Joint viral dynamic parameters estimates when parameterizing within-host models have been published for SARS-CoV-2. The reviewed viral dynamic parameters can be used in the same within-host model to understand SARS-CoV-2 replication cycle in infected patients and assess the impact of pharmaceutical interventions.
Collapse
Affiliation(s)
- Zhanwei Du
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of MedicineThe University of Hong Kong, Hong Kong Special Administrative RegionChina,Laboratory of Data Discovery for Health Limited, Hong Kong Science Park, Hong Kong Special Administrative RegionChina
| | - Shuqi Wang
- Laboratory of Data Discovery for Health Limited, Hong Kong Science Park, Hong Kong Special Administrative RegionChina
| | - Yuan Bai
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of MedicineThe University of Hong Kong, Hong Kong Special Administrative RegionChina,Laboratory of Data Discovery for Health Limited, Hong Kong Science Park, Hong Kong Special Administrative RegionChina
| | - Chao Gao
- School of Artificial Intelligence, Optics, and Electronics (iOPEN)Northwestern Polytechnical UniversityXianChina
| | - Eric H. Y. Lau
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of MedicineThe University of Hong Kong, Hong Kong Special Administrative RegionChina,Laboratory of Data Discovery for Health Limited, Hong Kong Science Park, Hong Kong Special Administrative RegionChina
| | - Benjamin J. Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of MedicineThe University of Hong Kong, Hong Kong Special Administrative RegionChina,Laboratory of Data Discovery for Health Limited, Hong Kong Science Park, Hong Kong Special Administrative RegionChina
| |
Collapse
|
14
|
Affiliation(s)
- Amy R. Sweeny
- Institute of Evolutionary Biology University of Edinburgh Edinburgh Scotland
| | - Gregory F. Albery
- Department of Biology Georgetown University Washington DC USA
- Wissenschaftskolleg zu Berlin Berlin Germany
| |
Collapse
|
15
|
Wilber MQ, Yang A, Boughton R, Manlove KR, Miller RS, Pepin KM, Wittemyer G. A model for leveraging animal movement to understand spatio-temporal disease dynamics. Ecol Lett 2022; 25:1290-1304. [PMID: 35257466 DOI: 10.1111/ele.13986] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 12/27/2021] [Accepted: 02/04/2022] [Indexed: 12/19/2022]
Abstract
The ongoing explosion of fine-resolution movement data in animal systems provides a unique opportunity to empirically quantify spatial, temporal and individual variation in transmission risk and improve our ability to forecast disease outbreaks. However, we lack a generalizable model that can leverage movement data to quantify transmission risk and how it affects pathogen invasion and persistence on heterogeneous landscapes. We developed a flexible model 'Movement-driven modelling of spatio-temporal infection risk' (MoveSTIR) that leverages diverse data on animal movement to derive metrics of direct and indirect contact by decomposing transmission into constituent processes of contact formation and duration and pathogen deposition and acquisition. We use MoveSTIR to demonstrate that ignoring fine-scale animal movements on actual landscapes can mis-characterize transmission risk and epidemiological dynamics. MoveSTIR unifies previous work on epidemiological contact networks and can address applied and theoretical questions at the nexus of movement and disease ecology.
Collapse
Affiliation(s)
- Mark Q Wilber
- Forestry, Wildlife, and Fisheries, Institute of Agriculture, University of Tennessee, Knoxville, Tennessee, USA
| | - Anni Yang
- United States Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, National Wildlife Research Center, Fort Collins, Colorado, USA.,Department of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, Colorado, USA.,Department of Geography and Environmental Sustainability, University of Oklahoma, Norman, Oklahoma, USA
| | - Raoul Boughton
- Archbold Biological Station, Buck Island Ranch, Lake Placid, Florida, USA
| | - Kezia R Manlove
- Department of Wildland Resources and Ecology Center, Utah State University, Logan, Utah, USA
| | - Ryan S Miller
- United States Department of Agriculture, Animal and Plant Health Inspection Service, Veterinary Service, Center for Epidemiology and Animal Health, Fort Collins, Colorado, USA
| | - Kim M Pepin
- United States Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, National Wildlife Research Center, Fort Collins, Colorado, USA
| | - George Wittemyer
- Department of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, Colorado, USA
| |
Collapse
|
16
|
Loo SL, Tanaka MM. The role of a programmatic immune response on the evolution of pathogen traits. J Theor Biol 2022; 534:110962. [PMID: 34822803 DOI: 10.1016/j.jtbi.2021.110962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 11/07/2021] [Accepted: 11/11/2021] [Indexed: 11/26/2022]
Abstract
In modelling pathogen evolution during epidemics, it is important to understand the interactions between within-host infection dynamics and between-host pathogen transmission. Multiscale models often assume an immune response that is highly responsive to pathogen dynamics. Empirical evidence, however, suggests that the immune response in acute infections is triggered and programmatic. This leads to somewhat more predictable infection trajectories where transition times and, consequently, the infectious window are non-exponentially distributed. Here, we develop a within-host model where the immune response is triggered by pathogen growth but otherwise develops independently, and use this to obtain analytic expressions for the infectious period and peak pathogen load. This allows us to model the basic reproductive number in terms of explicit functional relationships among within-host traits including the growth rate of the pathogen. We find that the dependence of pathogen load and the infectious window on within-host parameters constrains the evolution of the pathogen growth rate. At low growth rate, selection favours a higher pathogen load and therefore increasing pathogen growth rate. At high growth rates, selection for a longer infectious window trades off against selection against the effects of virulence. At intermediate growth rates the basic reproductive number is relatively insensitive to changes in the growth rate. The resulting "flat" region of the pathogen fitness landscape is due to the stability of the programmatic immune response in clearing the infection.
Collapse
Affiliation(s)
- Sara L Loo
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW 2052, Australia; Evolution & Ecology Research Centre, University of New South Wales, Sydney, NSW 2052, Australia.
| | - Mark M Tanaka
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW 2052, Australia; Evolution & Ecology Research Centre, University of New South Wales, Sydney, NSW 2052, Australia
| |
Collapse
|
17
|
Gulbudak H, Qu Z, Milner F, Tuncer N. Sensitivity Analysis in an Immuno-Epidemiological Vector-Host Model. Bull Math Biol 2022; 84:27. [PMID: 34982249 PMCID: PMC8724773 DOI: 10.1007/s11538-021-00979-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 11/23/2021] [Indexed: 11/30/2022]
Abstract
Sensitivity Analysis (SA) is a useful tool to measure the impact of changes in model parameters on the infection dynamics, particularly to quantify the expected efficacy of disease control strategies. SA has only been applied to epidemic models at the population level, ignoring the effect of within-host virus-with-immune-system interactions on the disease spread. Connecting the scales from individual to population can help inform drug and vaccine development. Thus the value of understanding the impact of immunological parameters on epidemiological quantities. Here we consider an age-since-infection structured vector-host model, in which epidemiological parameters are formulated as functions of within-host virus and antibody densities, governed by an ODE system. We then use SA for these immuno-epidemiological models to investigate the impact of immunological parameters on population-level disease dynamics such as basic reproduction number, final size of the epidemic or the infectiousness at different phases of an outbreak. As a case study, we consider Rift Valley Fever Disease utilizing parameter estimations from prior studies. SA indicates that \documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$1\%$$\end{document}1% increase in within-host pathogen growth rate can lead up to \documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$8\%$$\end{document}8% increase in \documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$\mathcal R_0,$$\end{document}R0, up to \documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$1 \%$$\end{document}1% increase in steady-state infected host abundance, and up to \documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$4\%$$\end{document}4% increase in infectiousness of hosts when the reproduction number \documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$\mathcal R_0$$\end{document}R0 is larger than one. These significant increases in population-scale disease quantities suggest that control strategies that reduce the within-host pathogen growth can be important in reducing disease prevalence.
Collapse
Affiliation(s)
- Hayriye Gulbudak
- Department of Mathematics, University of Louisiana at Lafayette, 217 Maxim Doucet Hall, Lafayette, LA, P.O. Box 43568, USA.
| | - Zhuolin Qu
- Department of Mathematics, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX, 78249, USA
| | - Fabio Milner
- School of Mathematical and Statistical Sciences, Arizona State University, 825 Wexler Hall, P.O. Box 871804, Tempe, AZ, 85287, USA
| | - Necibe Tuncer
- Department of Mathematical Sciences, Florida Atlantic University, Science Building, Room 234 777 Glades Road, Boca Raton, FL, 33431, USA
| |
Collapse
|
18
|
Ke R, Zitzmann C, Ho DD, Ribeiro RM, Perelson AS. In vivo kinetics of SARS-CoV-2 infection and its relationship with a person's infectiousness. Proc Natl Acad Sci U S A 2021; 118:e2111477118. [PMID: 34857628 PMCID: PMC8670484 DOI: 10.1073/pnas.2111477118] [Citation(s) in RCA: 85] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/25/2021] [Indexed: 01/11/2023] Open
Abstract
The within-host viral kinetics of SARS-CoV-2 infection and how they relate to a person's infectiousness are not well understood. This limits our ability to quantify the impact of interventions on viral transmission. Here, we develop viral dynamic models of SARS-CoV-2 infection and fit them to data to estimate key within-host parameters such as the infected cell half-life and the within-host reproductive number. We then develop a model linking viral load (VL) to infectiousness and show a person's infectiousness increases sublinearly with VL and that the logarithm of the VL in the upper respiratory tract is a better surrogate of infectiousness than the VL itself. Using data on VL and the predicted infectiousness, we further incorporated data on antigen and RT-PCR tests and compared their usefulness in detecting infection and preventing transmission. We found that RT-PCR tests perform better than antigen tests assuming equal testing frequency; however, more frequent antigen testing may perform equally well with RT-PCR tests at a lower cost but with many more false-negative tests. Overall, our models provide a quantitative framework for inferring the impact of therapeutics and vaccines that lower VL on the infectiousness of individuals and for evaluating rapid testing strategies.
Collapse
Affiliation(s)
- Ruian Ke
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545
- New Mexico Consortium, Los Alamos, NM 87544
| | - Carolin Zitzmann
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545
| | - David D Ho
- Aaron Diamond AIDS Research Center, Columbia University Vagelos College of Physicians and Surgeons, New York, NY 10032
| | - Ruy M Ribeiro
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545
| | - Alan S Perelson
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545;
- New Mexico Consortium, Los Alamos, NM 87544
| |
Collapse
|
19
|
Kepler L, Hamins-Puertolas M, Rasmussen DA. Decomposing the sources of SARS-CoV-2 fitness variation in the United States. Virus Evol 2021; 7:veab073. [PMID: 34642604 PMCID: PMC8499931 DOI: 10.1093/ve/veab073] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 08/13/2021] [Accepted: 08/20/2021] [Indexed: 12/14/2022] Open
Abstract
The fitness of a pathogen is a composite phenotype determined by many different factors influencing growth rates both within and between hosts. Determining what factors shape fitness at the host population-level is especially challenging because both intrinsic factors like pathogen genetics and extrinsic factors such as host behavior influence between-host transmission potential. This challenge has been highlighted by controversy surrounding the population-level fitness effects of mutations in the SARS-CoV-2 genome and their relative importance when compared against non-genetic factors shaping transmission dynamics. Building upon phylodynamic birth-death models, we develop a new framework to learn how hundreds of genetic and non-genetic factors have shaped the fitness of SARS-CoV-2. We estimate the fitness effects of all amino acid variants and several structural variants that have circulated in the United States between February 2020 and March 2021 from viral phylogenies. We also estimate how much fitness variation among pathogen lineages is attributable to genetic versus non-genetic factors such as spatial heterogeneity in transmission rates. Before September 2020, most fitness variation between lineages can be explained by background spatial heterogeneity in transmission rates across geographic regions. Starting in late 2020, genetic variation in fitness increased dramatically with the emergence of several new lineages including B.1.1.7, B.1.427, B.1.429 and B.1.526. Our analysis also indicates that genetic variants in less well-explored genomic regions outside of Spike may be contributing significantly to overall fitness variation in the viral population.
Collapse
Affiliation(s)
- Lenora Kepler
- Bioinformatics Research Center, North Carolina State University, 1 Lampe Drive, Raleigh, NC 27607, USA
| | - Marco Hamins-Puertolas
- Biomathematics Graduate Program, North Carolina State University, Campus Box 8213, Raleigh, NC 27695, USA
| | - David A Rasmussen
- Bioinformatics Research Center, North Carolina State University, 1 Lampe Drive, Raleigh, NC 27607, USA
- Department of Entomology and Plant Pathology, North Carolina State University, Campus Box 7613, Raleigh, NC 27695, USA
| |
Collapse
|
20
|
Stewart Merrill TE, Rapti Z, Cáceres CE. Host Controls of Within-Host Disease Dynamics: Insight from an Invertebrate System. Am Nat 2021; 198:317-332. [PMID: 34403315 DOI: 10.1086/715355] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
AbstractWithin-host processes (representing the entry, establishment, growth, and development of a parasite inside its host) may play a key role in parasite transmission but remain challenging to observe and quantify. We develop a general model for measuring host defenses and within-host disease dynamics. Our stochastic model breaks the infection process down into the stages of parasite exposure, entry, and establishment and provides associated probabilities for a host's ability to resist infections with barriers and clear internal infections. We tested our model on Daphnia dentifera and the parasitic fungus Metschnikowia bicuspidata and found that when faced with identical levels of parasite exposure, Daphnia patent (transmitting) infections depended on the strength of internal clearance. Applying a Gillespie algorithm to the model-estimated probabilities allowed us to visualize within-host dynamics, within which signatures of host defense could be clearly observed. We also found that early within-host stages were the most vulnerable to internal clearance, suggesting that hosts have a limited window during which recovery can occur. Our study demonstrates how pairing longitudinal infection data with a simple model can reveal new insight into within-host dynamics and mechanisms of host defense. Our model and methodological approach may be a powerful tool for exploring these properties in understudied host-parasite interactions.
Collapse
|
21
|
Kirk D, Greischar M, Mideo N, Krkošek M. Environmental variability affects optimal trade‐offs in ecological immunology. Ecosphere 2021. [DOI: 10.1002/ecs2.3654] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Affiliation(s)
- Devin Kirk
- Department of Ecology and Evolutionary Biology University of Toronto Toronto OntarioM5S 1A1Canada
| | - Megan Greischar
- Department of Ecology and Evolutionary Biology University of Toronto Toronto OntarioM5S 1A1Canada
| | - Nicole Mideo
- Department of Ecology and Evolutionary Biology University of Toronto Toronto OntarioM5S 1A1Canada
| | - Martin Krkošek
- Department of Ecology and Evolutionary Biology University of Toronto Toronto OntarioM5S 1A1Canada
| |
Collapse
|
22
|
Towards an ecosystem model of infectious disease. Nat Ecol Evol 2021; 5:907-918. [PMID: 34002048 DOI: 10.1038/s41559-021-01454-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 03/25/2021] [Indexed: 02/03/2023]
Abstract
Increasingly intimate associations between human society and the natural environment are driving the emergence of novel pathogens, with devastating consequences for humans and animals alike. Prior to emergence, these pathogens exist within complex ecological systems that are characterized by trophic interactions between parasites, their hosts and the environment. Predicting how disturbance to these ecological systems places people and animals at risk from emerging pathogens-and the best ways to manage this-remains a significant challenge. Predictive systems ecology models are powerful tools for the reconstruction of ecosystem function but have yet to be considered for modelling infectious disease. Part of this stems from a mistaken tendency to forget about the role that pathogens play in structuring the abundance and interactions of the free-living species favoured by systems ecologists. Here, we explore how developing and applying these more complete systems ecology models at a landscape scale would greatly enhance our understanding of the reciprocal interactions between parasites, pathogens and the environment, placing zoonoses in an ecological context, while identifying key variables and simplifying assumptions that underly pathogen host switching and animal-to-human spillover risk. As well as transforming our understanding of disease ecology, this would also allow us to better direct resources in preparation for future pandemics.
Collapse
|
23
|
Ke R, Zitzmann C, Ho DD, Ribeiro RM, Perelson AS. In vivo kinetics of SARS-CoV-2 infection and its relationship with a person's infectiousness. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021:2021.06.26.21259581. [PMID: 34230935 PMCID: PMC8259912 DOI: 10.1101/2021.06.26.21259581] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
The within-host viral kinetics of SARS-CoV-2 infection and how they relate to a person's infectiousness are not well understood. This limits our ability to quantify the impact of interventions on viral transmission. Here, we develop data-driven viral dynamic models of SARS-CoV-2 infection and estimate key within-host parameters such as the infected cell half-life and the within-host reproductive number. We then develop a model linking VL to infectiousness, showing that a person's infectiousness increases sub-linearly with VL. We show that the logarithm of the VL in the upper respiratory tract (URT) is a better surrogate of infectiousness than the VL itself. Using data on VL and the predicted infectiousness, we further incorporated data on antigen and reverse transcription polymerase chain reaction (RT-PCR) tests and compared their usefulness in detecting infection and preventing transmission. We found that RT-PCR tests perform better than antigen tests assuming equal testing frequency; however, more frequent antigen testing may perform equally well with RT-PCR tests at a lower cost, but with many more false-negative tests. Overall, our models provide a quantitative framework for inferring the impact of therapeutics and vaccines that lower VL on the infectiousness of individuals and for evaluating rapid testing strategies. SIGNIFICANCE Quantifying the kinetics of SARS-CoV-2 infection and individual infectiousness is key to quantitatively understanding SARS-CoV-2 transmission and evaluating intervention strategies. Here we developed data-driven within-host models of SARS-CoV-2 infection and by fitting them to clinical data we estimated key within-host viral dynamic parameters. We also developed a mechanistic model for viral transmission and show that the logarithm of the viral load in the upper respiratory tract serves an appropriate surrogate for a person's infectiousness. Using data on how viral load changes during infection, we further evaluated the effectiveness of PCR and antigen-based testing strategies for averting transmission and identifying infected individuals.
Collapse
Affiliation(s)
- Ruian Ke
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
- New Mexico Consortium, 4200 West Jemez Road, Los Alamos, NM 87544
| | - Carolin Zitzmann
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
| | - David D. Ho
- Aaron Diamond AIDS Research Center, Columbia University Vagelos College of Physicians and Surgeons, New York, NY 10032
| | - Ruy M. Ribeiro
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
| | - Alan S. Perelson
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
- New Mexico Consortium, 4200 West Jemez Road, Los Alamos, NM 87544
| |
Collapse
|
24
|
Turner WC, Kamath PL, van Heerden H, Huang YH, Barandongo ZR, Bruce SA, Kausrud K. The roles of environmental variation and parasite survival in virulence-transmission relationships. ROYAL SOCIETY OPEN SCIENCE 2021; 8:210088. [PMID: 34109041 PMCID: PMC8170194 DOI: 10.1098/rsos.210088] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Disease outbreaks are a consequence of interactions among the three components of a host-parasite system: the infectious agent, the host and the environment. While virulence and transmission are widely investigated, most studies of parasite life-history trade-offs are conducted with theoretical models or tractable experimental systems where transmission is standardized and the environment controlled. Yet, biotic and abiotic environmental factors can strongly affect disease dynamics, and ultimately, host-parasite coevolution. Here, we review research on how environmental context alters virulence-transmission relationships, focusing on the off-host portion of the parasite life cycle, and how variation in parasite survival affects the evolution of virulence and transmission. We review three inter-related 'approaches' that have dominated the study of the evolution of virulence and transmission for different host-parasite systems: (i) evolutionary trade-off theory, (ii) parasite local adaptation and (iii) parasite phylodynamics. These approaches consider the role of the environment in virulence and transmission evolution from different angles, which entail different advantages and potential biases. We suggest improvements to how to investigate virulence-transmission relationships, through conceptual and methodological developments and taking environmental context into consideration. By combining developments in life-history evolution, phylogenetics, adaptive dynamics and comparative genomics, we can improve our understanding of virulence-transmission relationships across a diversity of host-parasite systems that have eluded experimental study of parasite life history.
Collapse
Affiliation(s)
- Wendy C. Turner
- US Geological Survey, Wisconsin Cooperative Wildlife Research Unit, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Pauline L. Kamath
- School of Food and Agriculture, University of Maine, Orono, ME 04469, USA
| | - Henriette van Heerden
- Faculty of Veterinary Science, Department of Veterinary Tropical Diseases, University of Pretoria, Onderstepoort, South Africa
| | - Yen-Hua Huang
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Zoe R. Barandongo
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Spencer A. Bruce
- Department of Biological Sciences, University at Albany, State University of New York, Albany, NY 12222, USA
| | - Kyrre Kausrud
- Section for Epidemiology, Norwegian Veterinary Institute, Ullevålsveien 68, 0454 Oslo, Norway
| |
Collapse
|
25
|
Tao Y, Hite JL, Lafferty KD, Earn DJD, Bharti N. Transient disease dynamics across ecological scales. THEOR ECOL-NETH 2021; 14:625-640. [PMID: 34075317 PMCID: PMC8156581 DOI: 10.1007/s12080-021-00514-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Accepted: 05/04/2021] [Indexed: 11/25/2022]
Abstract
Analyses of transient dynamics are critical to understanding infectious disease transmission and persistence. Identifying and predicting transients across scales, from within-host to community-level patterns, plays an important role in combating ongoing epidemics and mitigating the risk of future outbreaks. Moreover, greater emphases on non-asymptotic processes will enable timely evaluations of wildlife and human diseases and lead to improved surveillance efforts, preventive responses, and intervention strategies. Here, we explore the contributions of transient analyses in recent models spanning the fields of epidemiology, movement ecology, and parasitology. In addition to their roles in predicting epidemic patterns and endemic outbreaks, we explore transients in the contexts of pathogen transmission, resistance, and avoidance at various scales of the ecological hierarchy. Examples illustrate how (i) transient movement dynamics at the individual host level can modify opportunities for transmission events over time; (ii) within-host energetic processes often lead to transient dynamics in immunity, pathogen load, and transmission potential; (iii) transient connectivity between discrete populations in response to environmental factors and outbreak dynamics can affect disease spread across spatial networks; and (iv) increasing species richness in a community can provide transient protection to individuals against infection. Ultimately, we suggest that transient analyses offer deeper insights and raise new, interdisciplinary questions for disease research, consequently broadening the applications of dynamical models for outbreak preparedness and management. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s12080-021-00514-w.
Collapse
Affiliation(s)
- Yun Tao
- Intelligence Community Postdoctoral Research Fellowship Program, Department of Ecology, Evolution and Marine Biology, University of California, Santa Barbara, CA 93106 USA
| | - Jessica L. Hite
- School of Veterinary Medicine, Department of Pathobiological Sciences, University of Wisconsin, Madison, WI 53706 USA
| | - Kevin D. Lafferty
- Western Ecological Research Center at UCSB Marine Science Institute, U.S. Geological Survey, CA 93106 Santa Barbara, USA
| | - David J. D. Earn
- Department of Mathematics and Statistics, McMaster University, Hamilton, ON L8S 4K1 Canada
| | - Nita Bharti
- Department of Biology Center for Infectious Disease Dynamics, Penn State University, University Park, PA 16802 USA
| |
Collapse
|
26
|
Calhoun AC, Harrod AE, Bassingthwaite TA, Sadd BM. Testing the multiple stressor hypothesis: chlorothalonil exposure alters transmission potential of a bumblebee pathogen but not individual host health. Proc Biol Sci 2021; 288:20202922. [PMID: 33784861 DOI: 10.1098/rspb.2020.2922] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Numerous threats are putting pollinator health and essential ecosystem pollination services in jeopardy. Although individual threats are widely studied, their co-occurrence may exacerbate negative effects, as posited by the multiple stressor hypothesis. A prominent branch of this hypothesis concerns pesticide-pathogen co-exposure. A landscape analysis demonstrated a positive association between local chlorothalonil fungicide use and microsporidian pathogen (Nosema bombi) prevalence in declining bumblebee species (Bombus spp.), suggesting an interaction deserving further investigation. We tested the multiple stressor hypothesis with field-realistic chlorothalonil and N. bombi exposures in worker-produced B. impatiens microcolonies. Chlorothalonil was not avoided in preference assays, setting the stage for pesticide-pathogen co-exposure. However, contrary to the multiple stressor hypothesis, co-exposure did not affect survival. Bees showed surprising tolerance to Nosema infection, which was also unaffected by chlorothalonil exposure. However, previously fungicide-exposed infected bees carried more transmission-ready spores. Our use of a non-declining bumblebee and potential higher chlorothalonil exposures under some scenarios could mean stronger individual or interactive effects in certain field settings. Yet, our results alone suggest consequences of pesticide co-exposure for pathogen dynamics in host communities. This underlies the importance of considering both within- and between-host processes when addressing the multiple stressor hypothesis in relation to pathogens.
Collapse
Affiliation(s)
- Austin C Calhoun
- School of Biological Sciences, Illinois State University, Normal, IL 61790, USA
| | - Audrey E Harrod
- School of Biological Sciences, Illinois State University, Normal, IL 61790, USA
| | | | - Ben M Sadd
- School of Biological Sciences, Illinois State University, Normal, IL 61790, USA
| |
Collapse
|
27
|
Perelson AS, Ke R. Mechanistic Modeling of SARS-CoV-2 and Other Infectious Diseases and the Effects of Therapeutics. Clin Pharmacol Ther 2021; 109:829-840. [PMID: 33410134 DOI: 10.1002/cpt.2160] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Accepted: 12/24/2020] [Indexed: 12/11/2022]
Abstract
Modern viral kinetic modeling and its application to therapeutics is a field that attracted the attention of the medical, pharmaceutical, and modeling communities during the early days of the AIDS epidemic. Its successes led to applications of modeling methods not only to HIV but a plethora of other viruses, such as hepatitis C virus (HCV), hepatitis B virus and cytomegalovirus, which along with HIV cause chronic diseases, and viruses such as influenza, respiratory syncytial virus, West Nile virus, Zika virus, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which generally cause acute infections. Here we first review the historical development of mathematical models to understand HIV and HCV infections and the effects of treatment by fitting the models to clinical data. We then focus on recent efforts and contributions of applying these models towards understanding SARS-CoV-2 infection and highlight outstanding questions where modeling can provide crucial insights and help to optimize nonpharmaceutical and pharmaceutical interventions of the coronavirus disease 2019 (COVID-19) pandemic. The review is written from our personal perspective emphasizing the power of simple target cell limited models that provided important insights and then their evolution into more complex models that captured more of the virology and immunology. To quote Albert Einstein, "Everything should be made as simple as possible, but not simpler," and this idea underlies the modeling we describe below.
Collapse
Affiliation(s)
- Alan S Perelson
- Los Alamos National Laboratory, Theoretical Biology and Biophysics Group, Los Alamos, New Mexico, USA.,New Mexico Consortium, Los Alamos, New Mexico, USA
| | - Ruian Ke
- Los Alamos National Laboratory, Theoretical Biology and Biophysics Group, Los Alamos, New Mexico, USA.,New Mexico Consortium, Los Alamos, New Mexico, USA
| |
Collapse
|
28
|
Fain B, Dobrovolny HM. Initial Inoculum and the Severity of COVID-19: A Mathematical Modeling Study of the Dose-Response of SARS-CoV-2 Infections. EPIDEMIOLGIA (BASEL, SWITZERLAND) 2020; 1:5-15. [PMID: 36417207 PMCID: PMC9620883 DOI: 10.3390/epidemiologia1010003] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 09/30/2020] [Accepted: 10/14/2020] [Indexed: 12/14/2022]
Abstract
SARS-CoV-2 (Severe acute respiratory syndrome coronavirus 2) causes a variety of responses in those who contract the virus, ranging from asymptomatic infections to acute respiratory failure and death. While there are likely multiple mechanisms triggering severe disease, one potential cause of severe disease is the size of the initial inoculum. For other respiratory diseases, larger initial doses lead to more severe outcomes. We investigate whether there is a similar link for SARS-CoV-2 infections using the combination of an agent-based model (ABM) and a partial differential equation model (PDM). We use the model to examine the viral time course for different sizes of initial inocula, generating dose-response curves for peak viral load, time of viral peak, viral growth rate, infection duration, and area under the viral titer curve. We find that large initial inocula lead to short infections, but with higher viral titer peaks; and that smaller initial inocula lower the viral titer peak, but make the infection last longer.
Collapse
|
29
|
Hernandez-Vargas EA, Velasco-Hernandez JX. In-host Mathematical Modelling of COVID-19 in Humans. ANNUAL REVIEWS IN CONTROL 2020; 50:448-456. [PMID: 33020692 PMCID: PMC7526677 DOI: 10.1016/j.arcontrol.2020.09.006] [Citation(s) in RCA: 93] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 09/26/2020] [Accepted: 09/27/2020] [Indexed: 05/14/2023]
Abstract
COVID-19 pandemic has underlined the impact of emergent pathogens as a major threat to human health. The development of quantitative approaches to advance comprehension of the current outbreak is urgently needed to tackle this severe disease. Considering different starting times of infection, mathematical models are proposed to represent SARS-CoV-2 dynamics in infected patients. Based on the target cell limited model, the within-host reproductive number for SARS-CoV-2 is consistent with the broad values of human influenza infection. The best model to fit the data was including immune cell response, which suggests a slow immune response peaking between 5 to 10 days post-onset of symptoms. The model with the eclipse phase, time in a latent phase before becoming productively infected cells, was not supported. Interestingly, model simulations predict that SARS-CoV-2 may replicate very slowly in the first days after infection, and viral load could be below detection levels during the first 4 days post infection. A quantitative comprehension of SARS-CoV-2 dynamics and the estimation of standard parameters of viral infections is the key contribution of this pioneering work. These models can serve for future evaluation of control theoretical approaches to tailor new drugs against COVID-19.
Collapse
Affiliation(s)
- Esteban A Hernandez-Vargas
- Instituto de Matemáticas, Universidad Nacional Autonoma de Mexico, Boulevard Juriquilla 3001, Querétaro, Qro., 76230, México
- Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany
| | - Jorge X Velasco-Hernandez
- Instituto de Matemáticas, Universidad Nacional Autonoma de Mexico, Boulevard Juriquilla 3001, Querétaro, Qro., 76230, México
| |
Collapse
|
30
|
Kendig AE, Borer ET, Boak EN, Picard TC, Seabloom EW. Host nutrition mediates interactions between plant viruses, altering transmission and predicted disease spread. Ecology 2020; 101:e03155. [PMID: 32745231 DOI: 10.1002/ecy.3155] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Accepted: 06/18/2020] [Indexed: 01/24/2023]
Abstract
Interactions among co-infecting pathogens are common across host taxa and can affect infectious disease dynamics. Host nutrition can mediate these among-pathogen interactions, altering the establishment and growth of pathogens within hosts. It is unclear, however, how nutrition-mediated among-pathogen interactions affect transmission and the spread of disease through populations. We manipulated the nitrogen (N) and phosphorus (P) supplies to oat plants in growth chambers and evaluated interactions between two aphid-vectored Barley and Cereal Yellow Dwarf Viruses: PAV and RPV. We quantified the effect of each virus on the other's establishment, within-plant density, and transmission. Co-inoculation significantly increased PAV density when N and P supplies were low and tended to increase RPV density when N supply was high. Co-infection increased PAV transmission when N and P supplies were low and tended to increase RPV transmission when N supply was high. Despite the parallels between the effects of among-pathogen interactions on density and transmission, changes in virus density only partially explained changes in transmission, suggesting that virus density-independent processes contribute to transmission. A mathematical model describing the spread of two viruses through a plant population, parameterized with empirically derived transmission values, demonstrated that nutrition-mediated among-pathogen interactions could affect disease spread. Interactions that altered transmission through virus density-independent processes determined overall disease dynamics. Our work suggests that host nutrition alters disease spread through among-pathogen interactions that modify transmission.
Collapse
Affiliation(s)
- Amy E Kendig
- Department of Ecology, Evolution, and Behavior, University of Minnesota, St. Paul, Minnesota, 55108, USA.,Agronomy Department, University of Florida, Gainesville, Florida, 32611, USA
| | - Elizabeth T Borer
- Department of Ecology, Evolution, and Behavior, University of Minnesota, St. Paul, Minnesota, 55108, USA
| | - Emily N Boak
- Department of Ecology, Evolution, and Behavior, University of Minnesota, St. Paul, Minnesota, 55108, USA.,Department of Horticultural Sciences, Texas A&M University, College Station, Texas, 77843, USA
| | - Tashina C Picard
- Department of Ecology, Evolution, and Behavior, University of Minnesota, St. Paul, Minnesota, 55108, USA
| | - Eric W Seabloom
- Department of Ecology, Evolution, and Behavior, University of Minnesota, St. Paul, Minnesota, 55108, USA
| |
Collapse
|
31
|
Hite JL. Host age alters disease life history. A case study in zooplankton and a castrating pathogen. Funct Ecol 2020. [DOI: 10.1111/1365-2435.13620] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Affiliation(s)
- Jessica L. Hite
- School of Biological Sciences University of Nebraska Lincoln NE USA
| |
Collapse
|
32
|
Abstract
A current challenge for disease modeling and public health is understanding pathogen dynamics across scales since their ecology and evolution ultimately operate on several coupled scales. This is particularly true for vector-borne diseases, where within-vector, within-host, and between vector–host populations all play crucial roles in diversity and distribution of the pathogen. Despite recent modeling efforts to determine the effect of within-host virus-immune response dynamics on between-host transmission, the role of within-vector viral dynamics on disease spread is overlooked. Here, we formulate an age-since-infection-structured epidemic model coupled to nonlinear ordinary differential equations describing within-host immune-virus dynamics and within-vector viral kinetics, with feedbacks across these scales. We first define the within-host viral-immune response and within-vector viral kinetics-dependent basic reproduction number [Formula: see text] Then we prove that whenever [Formula: see text] the disease-free equilibrium is locally asymptotically stable, and under certain biologically interpretable conditions, globally asymptotically stable. Otherwise, if [Formula: see text] it is unstable and the system has a unique positive endemic equilibrium. In the special case of constant vector to host inoculum size, we show the positive equilibrium is locally asymptotically stable and the disease is weakly uniformly persistent. Furthermore, numerical results suggest that within-vector-viral kinetics and dynamic inoculum size may play a substantial role in epidemics. Finally, we address how the model can be utilized to better predict the success of control strategies such as vaccination and drug treatment.
Collapse
Affiliation(s)
- HAYRIYE GULBUDAK
- Department of Mathematics, University of Louisiana at Lafayette, 104 E. University Circle, Lafayette, LA 70503, USA
| |
Collapse
|
33
|
Hart WS, Maini PK, Yates CA, Thompson RN. A theoretical framework for transitioning from patient-level to population-scale epidemiological dynamics: influenza A as a case study. J R Soc Interface 2020; 17:20200230. [PMID: 32400267 DOI: 10.1098/rsif.2020.0230] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Multi-scale epidemic forecasting models have been used to inform population-scale predictions with within-host models and/or infection data collected in longitudinal cohort studies. However, most multi-scale models are complex and require significant modelling expertise to run. We formulate an alternative multi-scale modelling framework using a compartmental model with multiple infected stages. In the large-compartment limit, our easy-to-use framework generates identical results compared to previous more complicated approaches. We apply our framework to the case study of influenza A in humans. By using a viral dynamics model to generate synthetic patient-level data, we explore the effects of limited and inaccurate patient data on the accuracy of population-scale forecasts. If infection data are collected daily, we find that a cohort of at least 40 patients is required for a mean population-scale forecasting error below 10%. Forecasting errors may be reduced by including more patients in future cohort studies or by increasing the frequency of observations for each patient. Our work, therefore, provides not only an accessible epidemiological modelling framework but also an insight into the data required for accurate forecasting using multi-scale models.
Collapse
Affiliation(s)
- W S Hart
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Woodstock Road, Oxford OX2 6GG, UK
| | - P K Maini
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Woodstock Road, Oxford OX2 6GG, UK
| | - C A Yates
- Centre for Mathematical Biology, University of Bath, Claverton Down, Bath BA2 7AY, UK
| | - R N Thompson
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Woodstock Road, Oxford OX2 6GG, UK.,Christ Church, University of Oxford, Saint Aldate's, Oxford OX1 1DP, UK
| |
Collapse
|
34
|
McKay B, Ebell M, Dale AP, Shen Y, Handel A. Virulence-mediated infectiousness and activity trade-offs and their impact on transmission potential of influenza patients. Proc Biol Sci 2020; 287:20200496. [PMID: 32396798 DOI: 10.1098/rspb.2020.0496] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Communicable diseases are often virulent, i.e. they cause morbidity symptoms in those infected. While some symptoms may be transmission-enhancing, other symptoms are likely to reduce transmission potential. For human diseases, the reduction in transmission opportunities is commonly caused by reduced activity. There is limited data regarding the potential impact of virulence on transmission potential. We performed an exploratory data analysis of 324 influenza patients at a university health centre during the 2016/2017 influenza season. We classified symptoms as infectiousness-related or morbidity-related and calculated two scores. The scores were used to explore the relationship between infectiousness, morbidity (virulence), and activity level. We found a decrease in the activity level with increasing morbidity scores. There was no consistent pattern between an activity level and an infectiousness score. We also found a positive correlation between morbidity and infectiousness scores. Overall, we find that increasing virulence leads to increased infectiousness and reduced activity, suggesting a trade-off that can impact overall transmission potential. Our findings indicate that a reduction of systemic symptoms may increase host activity without reducing infectiousness. Therefore, interventions should target both systemic- and infectiousness-related symptoms to reduce overall transmission potential. Our findings can also inform simulation models that investigate the impact of different interventions on transmission.
Collapse
Affiliation(s)
- Brian McKay
- Department of Epidemiology and Biostatistics, The University of Georgia, Athens, GA, USA
| | - Mark Ebell
- Department of Epidemiology and Biostatistics, The University of Georgia, Athens, GA, USA
| | | | - Ye Shen
- Department of Epidemiology and Biostatistics, The University of Georgia, Athens, GA, USA
| | - Andreas Handel
- Department of Epidemiology and Biostatistics and Health Informatics Institute and Center for the Ecology of Infectious Diseases, The University of Georgia, Athens, GA, USA
| |
Collapse
|
35
|
A Mathematical Framework for Predicting Lifestyles of Viral Pathogens. Bull Math Biol 2020; 82:54. [PMID: 32350621 PMCID: PMC7189636 DOI: 10.1007/s11538-020-00730-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2017] [Accepted: 03/31/2020] [Indexed: 11/26/2022]
Abstract
Despite being similar in structure, functioning, and size, viral pathogens enjoy very different, usually well-defined ways of life. They occupy their hosts for a few days (influenza), for a few weeks (measles), or even lifelong (HCV), which manifests in acute or chronic infections. The various transmission routes (airborne, via direct physical contact, etc.), degrees of infectiousness (referring to the viral load required for transmission), antigenic variation/immune escape and virulence define further aspects of pathogenic lifestyles. To survive, pathogens must infect new hosts; the success determines their fitness. Infection happens with a certain likelihood during contact of hosts, where contact can also be mediated by vectors. Besides structural aspects of the host-contact network, three parameters appear to be key: the contact rate and the infectiousness during contact, which encode the mode of transmission, and third the immunity of susceptible hosts. On these grounds, what can be said about the reproductive success of viral pathogens? This is the biological question addressed in this paper. The answer extends earlier results of the author and makes explicit connection to another basic work on the evolution of pathogens. A mathematical framework is presented that models intra- and inter-host dynamics in a minimalistic but unified fashion covering a broad spectrum of viral pathogens, including those that cause flu-like infections, childhood diseases, and sexually transmitted infections. These pathogens turn out as local maxima of numerically simulated fitness landscapes. The models involve differential and integral equations, agent-based simulation, networks, and probability.
Collapse
|
36
|
Hite JL, Cressler CE. Parasite-Mediated Anorexia and Nutrition Modulate Virulence Evolution. Integr Comp Biol 2020; 59:1264-1274. [PMID: 31187120 DOI: 10.1093/icb/icz100] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
Temporary but substantial reductions in voluntary food intake routinely accompany parasite infection in hosts ranging from insects to humans. This "parasite-mediated anorexia" drives dynamic nutrient-dependent feedbacks within and among hosts, which should alter the fitness of both hosts and parasites. Yet, few studies have examined the evolutionary and epidemiological consequences of this ubiquitous but overlooked component of infection. Moreover, numerous biomedical, veterinary, and farming practices (e.g., rapid biomass production via high-calorie or high-fat diets, low-level antibiotics to promote growth, nutritional supplementation, nonsteroidal anti-inflammatory drugs like Ibuprofen) directly or indirectly alter the magnitude of host anorexia-while also controlling host diet and therefore the nutrients available to hosts and parasites. Here, we show that anorexia can enhance or diminish disease severity, depending on whether the current dietary context provides nutrients that bolster or inhibit immune function. Feedbacks driven by nutrition-mediated competition between host immune function and parasite production can create a unimodal relationship between anorexia and parasite fitness. Subsequently, depending on the host's diet, medical or husbandry practices that suppress anorexia could backfire, and inadvertently select for more virulent parasites and larger epidemics. These findings carry implications for the development of integrated treatment programs that consider links between host feeding behavior, nutrition, and disease severity.
Collapse
Affiliation(s)
- Jessica L Hite
- School of Biological Sciences, University of Nebraska, Lincoln, NE 68588, USA
| | - Clayton E Cressler
- School of Biological Sciences, University of Nebraska, Lincoln, NE 68588, USA
| |
Collapse
|
37
|
Hall RJ. Modeling the Effects of Resource-Driven Immune Defense on Parasite Transmission in Heterogeneous Host Populations. Integr Comp Biol 2020; 59:1253-1263. [PMID: 31127280 DOI: 10.1093/icb/icz074] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Individuals experience heterogeneous environmental conditions that can affect within-host processes such as immune defense against parasite infection. Variation among individuals in parasite shedding can cause some hosts to contribute disproportionately to population-level transmission, but we currently lack mechanistic theory that predicts when environmental conditions can result in large disease outbreaks through the formation of immunocompromised superspreading individuals. Here, I present a within-host model of a microparasite's interaction with the immune system that links an individual host's resource intake to its infectious period. For environmental scenarios driving population-level heterogeneity in resource intake (resource scarcity and resource subsidy relative to baseline availability), I generate a distribution of infectious periods and simulate epidemics on these heterogeneous populations. I find that resource scarcity can result in large epidemics through creation of superspreading individuals, while resource subsidies can reduce or prevent transmission of parasites close to their invasion threshold by homogenizing resource allocation to immune defense. Importantly, failure to account for heterogeneity in competence can result in under-prediction of outbreak size, especially when parasites are close to their invasion threshold. More generally, this framework suggests that differences in conditions experienced by individual hosts can lead to superspreading via differences in resource allocation to immune defense alone, even in the absence of other heterogeneites such as host contacts.
Collapse
Affiliation(s)
- Richard J Hall
- Odum School of Ecology, University of Georgia, Athens, GA, USA.,Department of Infectious Diseases, College of Veterinary Medicine, University of Georgia, Athens, GA, USA.,Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA, USA
| |
Collapse
|
38
|
Becker DJ, Downs CJ, Martin LB. Multi-Scale Drivers of Immunological Variation and Consequences for Infectious Disease Dynamics. Integr Comp Biol 2020; 59:1129-1137. [PMID: 31559436 DOI: 10.1093/icb/icz138] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
The immune system is the primary barrier to parasite infection, replication, and transmission following exposure, and variation in immunity can accordingly manifest in heterogeneity in traits that govern population-level infectious disease dynamics. While much work in ecoimmunology has focused on individual-level determinants of host immune defense (e.g., reproductive status and body condition), an ongoing challenge remains to understand the broader evolutionary and ecological contexts of this variation (e.g., phylogenetic relatedness and landscape heterogeneity) and to connect these differences into epidemiological frameworks. Ultimately, such efforts could illuminate general principles about the drivers of host defense and improve predictions and control of infectious disease. Here, we highlight recent work that synthesizes the complex drivers of immunological variation across biological scales of organization and scales these within-host differences to population-level infection outcomes. Such studies note the limitations involved in making species-level comparisons of immune phenotypes, stress the importance of spatial scale for immunology research, showcase several statistical tools for translating within-host data into epidemiological parameters, and provide theoretical frameworks for linking within- and between-host scales of infection processes. Building from these studies, we highlight several promising avenues for continued work, including the application of machine learning tools and phylogenetically controlled meta-analyses to immunology data and quantifying the joint spatial and temporal dependencies in immune defense using range expansions as model systems. We also emphasize the use of organismal traits (e.g., host tolerance, competence, and resistance) as a way to interlink various scales of analysis. Such continued collaboration and disciplinary cross-talk among ecoimmunology, disease ecology, and mathematical modeling will facilitate an improved understanding of the multi-scale drivers and consequences of variation in host defense.
Collapse
Affiliation(s)
- Daniel J Becker
- Department of Biology, Indiana University, Bloomington, IN 47405, USA.,Center for the Ecology of Infectious Disease, University of Georgia, Athens, GA 30602, USA
| | - Cynthia J Downs
- Department of Biology, Hamilton College, Clinton, NY 13323, USA
| | - Lynn B Martin
- Department of Global and Planetary Health, University of South Florida, Tampa, FL 33620, USA
| |
Collapse
|
39
|
Gulbudak H, Browne CJ. Infection severity across scales in multi-strain immuno-epidemiological Dengue model structured by host antibody level. J Math Biol 2020; 80:1803-1843. [PMID: 32157381 DOI: 10.1007/s00285-020-01480-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Revised: 01/19/2020] [Indexed: 01/08/2023]
Abstract
Infection by distinct Dengue virus serotypes and host immunity are intricately linked. In particular, certain levels of cross-reactive antibodies in the host may actually enhance infection severity leading to Dengue hemorrhagic fever (DHF). The coupled immunological and epidemiological dynamics of Dengue calls for a multi-scale modeling approach. In this work, we formulate a within-host model which mechanistically recapitulates characteristics of antibody dependent enhancement in Dengue infection. The within-host scale is then linked to epidemiological spread by a vector-host partial differential equation model structured by host antibody level. The coupling allows for dynamic population-wide antibody levels to be tracked through primary and secondary infections by distinct Dengue strains, along with waning of cross-protective immunity after primary infection. Analysis of both the within-host and between-host systems are conducted. Stability results in the epidemic model are formulated via basic and invasion reproduction numbers as a function of immunological variables. Additionally, we develop numerical methods in order to simulate the multi-scale model and assess the influence of parameters on disease spread and DHF prevalence in the population.
Collapse
Affiliation(s)
- Hayriye Gulbudak
- Mathematics Department, University of Louisiana at Lafayette, Lafayette, LA, USA.
| | - Cameron J Browne
- Mathematics Department, University of Louisiana at Lafayette, Lafayette, LA, USA
| |
Collapse
|
40
|
KhudaBukhsh WR, Choi B, Kenah E, Rempała GA. Survival dynamical systems: individual-level survival analysis from population-level epidemic models. Interface Focus 2019; 10:20190048. [PMID: 31897290 PMCID: PMC6936005 DOI: 10.1098/rsfs.2019.0048] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/07/2019] [Indexed: 12/20/2022] Open
Abstract
In this paper, we show that solutions to ordinary differential equations describing the large-population limits of Markovian stochastic epidemic models can be interpreted as survival or cumulative hazard functions when analysing data on individuals sampled from the population. We refer to the individual-level survival and hazard functions derived from population-level equations as a survival dynamical system (SDS). To illustrate how population-level dynamics imply probability laws for individual-level infection and recovery times that can be used for statistical inference, we show numerical examples based on synthetic data. In these examples, we show that an SDS analysis compares favourably with a complete-data maximum-likelihood analysis. Finally, we use the SDS approach to analyse data from a 2009 influenza A(H1N1) outbreak at Washington State University.
Collapse
Affiliation(s)
- Wasiur R KhudaBukhsh
- Mathematical Biosciences Institute, The Ohio State University, Columbus, OH, USA
| | - Boseung Choi
- Division of Economics and Statistics, Department of National Statistics, Korea University Sejong campus, Sejong Special Autonomous City, Republic of Korea
| | - Eben Kenah
- Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH, USA
| | - Grzegorz A Rempała
- Division of Biostatistics, College of Public Health and Mathematical Biosciences Institute, The Ohio State University, Columbus, OH, USA
| |
Collapse
|
41
|
Rynkiewicz EC, Fenton A, Pedersen AB. Linking community assembly and structure across scales in a wild mouse parasite community. Ecol Evol 2019; 9:13752-13763. [PMID: 31938479 PMCID: PMC6953566 DOI: 10.1002/ece3.5785] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 08/23/2019] [Accepted: 09/21/2019] [Indexed: 01/03/2023] Open
Abstract
Understanding what processes drive community structure is fundamental to ecology. Many wild animals are simultaneously infected by multiple parasite species, so host-parasite communities can be valuable tools for investigating connections between community structures at multiple scales, as each host can be considered a replicate parasite community. Like free-living communities, within-host-parasite communities are hierarchical; ecological interactions between hosts and parasites can occur at multiple scales (e.g., host community, host population, parasite community within the host), therefore, both extrinsic and intrinsic processes can determine parasite community structure. We combine analyses of community structure and assembly at both the host population and individual scales using extensive datasets on wild wood mice (Apodemus sylvaticus) and their parasite community. An analysis of parasite community nestedness at the host population scale provided predictions about the order of infection at the individual scale, which were then tested using parasite community assembly data from individual hosts from the same populations. Nestedness analyses revealed parasite communities were significantly more structured than random. However, observed nestedness did not differ from null models in which parasite species abundance was kept constant. We did not find consistency between observed community structure at the host population scale and within-host order of infection. Multi-state Markov models of parasite community assembly showed that a host's likelihood of infection with one parasite did not consistently follow previous infection by a different parasite species, suggesting there is not a deterministic order of infection among the species we investigated in wild wood mice. Our results demonstrate that patterns at one scale (i.e., host population) do not reliably predict processes at another scale (i.e., individual host), and that neutral or stochastic processes may be driving the patterns of nestedness observed in these communities. We suggest that experimental approaches that manipulate parasite communities are needed to better link processes at multiple ecological scales.
Collapse
Affiliation(s)
- Evelyn C. Rynkiewicz
- Department of Science and MathematicsFashion Institute of TechnologyState University of New YorkNew YorkNYUSA
- Institute of Evolutionary Biology & Centre for Immunity, Infection and EvolutionSchool of Biological ScienceUniversity of EdinburghEdinburghUK
| | - Andy Fenton
- Institute of Integrative BiologyUniversity of LiverpoolLiverpoolUK
| | - Amy B. Pedersen
- Institute of Evolutionary Biology & Centre for Immunity, Infection and EvolutionSchool of Biological ScienceUniversity of EdinburghEdinburghUK
| |
Collapse
|
42
|
Hite JL, Pfenning AC, Cressler CE. Starving the Enemy? Feeding Behavior Shapes Host-Parasite Interactions. Trends Ecol Evol 2019; 35:68-80. [PMID: 31604593 DOI: 10.1016/j.tree.2019.08.004] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Revised: 08/05/2019] [Accepted: 08/07/2019] [Indexed: 01/09/2023]
Abstract
The loss of appetite that typically accompanies infection or mere exposure to parasites is traditionally considered a negative byproduct of infection, benefitting neither the host nor the parasite. Numerous medical and veterinary practices directly or indirectly subvert this 'illness-mediated anorexia'. However, the ecological factors that influence it, its effects on disease outcomes, and why it evolved remain poorly resolved. We explore how hosts use anorexia to defend against infection and how parasites manipulate anorexia to enhance transmission. Then, we use a coevolutionary model to illustrate how shifts in the magnitude of anorexia (e.g., via drugs) affect disease dynamics and virulence evolution. Anorexia could be exploited to improve disease management; we propose an interdisciplinary approach to minimize unintended consequences.
Collapse
Affiliation(s)
- Jessica L Hite
- School of Biological Sciences, University of Nebraska, Lincoln, NE 68588, USA.
| | - Alaina C Pfenning
- School of Biological Sciences, University of Nebraska, Lincoln, NE 68588, USA
| | - Clayton E Cressler
- School of Biological Sciences, University of Nebraska, Lincoln, NE 68588, USA
| |
Collapse
|
43
|
Pepin KM, Hopken MW, Shriner SA, Spackman E, Abdo Z, Parrish C, Riley S, Lloyd-Smith JO, Piaggio AJ. Improving risk assessment of the emergence of novel influenza A viruses by incorporating environmental surveillance. Philos Trans R Soc Lond B Biol Sci 2019; 374:20180346. [PMID: 31401963 PMCID: PMC6711309 DOI: 10.1098/rstb.2018.0346] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Reassortment is an evolutionary mechanism by which influenza A viruses (IAV) generate genetic novelty. Reassortment is an important driver of host jumps and is widespread according to retrospective surveillance studies. However, predicting the epidemiological risk of reassortant emergence in novel hosts from surveillance data remains challenging. IAV strains persist and co-occur in the environment, promoting co-infection during environmental transmission. These conditions offer opportunity to understand reassortant emergence in reservoir and spillover hosts. Specifically, environmental RNA could provide rich information for understanding the evolutionary ecology of segmented viruses, and transform our ability to quantify epidemiological risk to spillover hosts. However, significant challenges with recovering and interpreting genomic RNA from the environment have impeded progress towards predicting reassortant emergence from environmental surveillance data. We discuss how the fields of genomics, experimental ecology and epidemiological modelling are well positioned to address these challenges. Coupling quantitative disease models and natural transmission studies with new molecular technologies, such as deep-mutational scanning and single-virus sequencing of environmental samples, should dramatically improve our understanding of viral co-occurrence and reassortment. We define observable risk metrics for emerging molecular technologies and propose a conceptual research framework for improving accuracy and efficiency of risk prediction. This article is part of the theme issue 'Dynamic and integrative approaches to understanding pathogen spillover'.
Collapse
Affiliation(s)
- Kim M. Pepin
- National Wildlife Research Center, USDA-APHIS, Fort Collins, CO 80521, USA
- e-mail:
| | - Matthew W. Hopken
- National Wildlife Research Center, USDA-APHIS, Fort Collins, CO 80521, USA
- Colorado State University, Fort Collins, CO 80523, USA
| | - Susan A. Shriner
- National Wildlife Research Center, USDA-APHIS, Fort Collins, CO 80521, USA
| | - Erica Spackman
- Exotic and Emerging Avian Viral Diseases Research, USDA-ARS, Athens, GA 30605, USA
| | - Zaid Abdo
- Colorado State University, Fort Collins, CO 80523, USA
| | - Colin Parrish
- Baker Institute for Animal Health, Department of Microbiology and Immunology, Cornell University, Ithaca, NY 14853, USA
| | - Steven Riley
- MRC Centre for Global Infectious Disease Analysis, Imperial College, London, SW7 2AZ, UK
| | - James O. Lloyd-Smith
- UCLA, Los Angeles, CA 90095, USA
- Department of Ecology and Evolutionary Biology, Fogarty International Center, National Institutes of Health, Bethesda MD 20892, USA
| | | |
Collapse
|
44
|
Clay PA, Rudolf VHW. How parasite interaction strategies alter virulence evolution in multi-parasite communities. Evolution 2019; 73:2189-2203. [PMID: 31506940 DOI: 10.1111/evo.13843] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2019] [Revised: 06/15/2019] [Accepted: 07/06/2019] [Indexed: 12/11/2022]
Abstract
The majority of organisms host multiple parasite species, each of which can interact with hosts and competitors through a diverse range of direct and indirect mechanisms. These within-host interactions can directly alter the mortality rate of coinfected hosts and alter the evolution of virulence (parasite-induced host mortality). Yet we still know little about how within-host interactions affect the evolution of parasite virulence in multi-parasite communities. Here, we modeled the virulence evolution of two coinfecting parasites in a host population in which parasites interacted through cross immunity, immune suppression, immunopathology, or spite. We show (1) that these within-host interactions have different effects on virulence evolution when all parasites interact with each other in the same way versus when coinfecting parasites have unique interaction strategies, (2) that these interactions cause the evolution of lower virulence in some hosts, and higher virulence in other hosts, depending on the hosts infection status, and (3) that for cross immunity and spite, whether parasites increase or decrease the evolutionarily stable virulence in coinfected hosts depended on interaction strength. These results improve our understanding of virulence evolution in complex parasite communities, and show that virulence evolution must be understood at the community scale.
Collapse
|
45
|
Strauss AT, Shoemaker LG, Seabloom EW, Borer ET. Cross‐scale dynamics in community and disease ecology: relative timescales shape the community ecology of pathogens. Ecology 2019; 100:e02836. [DOI: 10.1002/ecy.2836] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Revised: 05/15/2019] [Accepted: 06/25/2019] [Indexed: 11/10/2022]
Affiliation(s)
- Alexander T. Strauss
- Department of Ecology, Evolution, and Behavior University of Minnesota St. Paul Minnesota 55108 USA
| | - Lauren G. Shoemaker
- Department of Ecology, Evolution, and Behavior University of Minnesota St. Paul Minnesota 55108 USA
| | - Eric W. Seabloom
- Department of Ecology, Evolution, and Behavior University of Minnesota St. Paul Minnesota 55108 USA
| | - Elizabeth T. Borer
- Department of Ecology, Evolution, and Behavior University of Minnesota St. Paul Minnesota 55108 USA
| |
Collapse
|
46
|
Childs LM, El Moustaid F, Gajewski Z, Kadelka S, Nikin-Beers R, Smith JW, Walker M, Johnson LR. Linked within-host and between-host models and data for infectious diseases: a systematic review. PeerJ 2019; 7:e7057. [PMID: 31249734 PMCID: PMC6589080 DOI: 10.7717/peerj.7057] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Accepted: 04/28/2019] [Indexed: 12/17/2022] Open
Abstract
The observed dynamics of infectious diseases are driven by processes across multiple scales. Here we focus on two: within-host, that is, how an infection progresses inside a single individual (for instance viral and immune dynamics), and between-host, that 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 holistic 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 (as defined by combining within-host and between-host scales) 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 24 of 197 qualifying papers across 30 years that include both linked models at the within and between host scales 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.
Collapse
Affiliation(s)
- Lauren M Childs
- Department of Mathematics, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USA
| | - Fadoua El Moustaid
- Department of Biological Sciences, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USA.,Global Change Center, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USA
| | - Zachary Gajewski
- Department of Biological Sciences, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USA.,Global Change Center, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USA.,Department of Statistics, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USA
| | - Sarah Kadelka
- Department of Mathematics, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USA
| | - Ryan Nikin-Beers
- Department of Mathematics, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USA.,Department of Mathematics, University of Florida, Gainesville, FL, USA
| | - John W Smith
- Department of Statistics, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USA
| | - Melody Walker
- Department of Mathematics, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USA
| | - Leah R Johnson
- Department of Biological Sciences, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USA.,Global Change Center, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USA.,Department of Statistics, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USA.,Computational Modeling and Data Analytics, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USA
| |
Collapse
|
47
|
Azevedo F, Amaku M, Coutinho FAB, Lopez LF, Massad E. The effect of the infection within the individual host on its propagation in the population. Infect Dis Model 2019; 3:345-361. [PMID: 30839922 PMCID: PMC6326235 DOI: 10.1016/j.idm.2018.11.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2018] [Revised: 11/02/2018] [Accepted: 11/13/2018] [Indexed: 10/27/2022] Open
Abstract
We consider nested or multiscale models to study the effect of the temporal evolution of the disease within the host in the population dynamics of the disease, for one and two infectious agents. We assumed a coupling between the within-host infection rate and the between-host transmission rate. The age of infection within each individual in a population affects the probability of transmission of the disease to a susceptible host and this will affect the temporal evolution of the disease in the host population. To analyze the infection within the host, we consider bacterial-like and viral-like infections. In the model for two infectious agents, we found that, when strain 2 has a basic reproduction number R 02 greater than the basic reproduction number R 01 of strain 1, strain 2 replaces strain 1 in the population. However, if R 02 > R 01 but the values are closer, the replacement does not occur immediately and both strains can coexist for a long time. We applied the model to a scenario in which patients infected with the hepatitis C virus (HCV) are cleared of HCV when super-infected with the hepatitis A virus (HAV). We compared the time for the replacement of HCV by HAV in the population considering instantaneous and non-instantaneous replacement within the individuals. The model developed can be generalized for more than two infectious agents.
Collapse
Affiliation(s)
- Franciane Azevedo
- Faculdade de Engenharia da Computação e Engenharia Elétrica, Universidade Federal do Sul e Sudeste do Pará, Marabá, PA, Brazil.,Faculdade de Medicina, Universidade de São Paulo e LIM01-HCFMUSP, Av. Dr. Arnaldo 455, São Paulo, SP, 01246-903, Brazil
| | - Marcos Amaku
- Faculdade de Medicina, Universidade de São Paulo e LIM01-HCFMUSP, Av. Dr. Arnaldo 455, São Paulo, SP, 01246-903, Brazil.,Faculdade de Medicina Veterinária e Zootecnia, Universidade de São Paulo, São Paulo, SP, 05508-270, Brazil
| | | | - Luis Fernandez Lopez
- Faculdade de Medicina, Universidade de São Paulo e LIM01-HCFMUSP, Av. Dr. Arnaldo 455, São Paulo, SP, 01246-903, Brazil
| | - Eduardo Massad
- Faculdade de Medicina, Universidade de São Paulo e LIM01-HCFMUSP, Av. Dr. Arnaldo 455, São Paulo, SP, 01246-903, Brazil.,London School of Hygiene and Tropical Medicine, London, UK.,Escola de Matemática Aplicada, Fundação Getúlio Vargas, Rio de Janeiro, Brazil.,College of Natural and Life Sciences, The University of Derby, Derby, UK
| |
Collapse
|
48
|
Hite JL, Cressler CE. Resource-driven changes to host population stability alter the evolution of virulence and transmission. Philos Trans R Soc Lond B Biol Sci 2019. [PMID: 29531142 DOI: 10.1098/rstb.2017.0087] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
What drives the evolution of parasite life-history traits? Recent studies suggest that linking within- and between-host processes can provide key insight into both disease dynamics and parasite evolution. Still, it remains difficult to understand how to pinpoint the critical factors connecting these cross-scale feedbacks, particularly under non-equilibrium conditions; many natural host populations inherently fluctuate and parasites themselves can strongly alter the stability of host populations. Here, we develop a general model framework that mechanistically links resources to parasite evolution across a gradient of stable and unstable conditions. First, we dynamically link resources and between-host processes (host density, stability, transmission) to virulence evolution, using a 'non-nested' model. Then, we consider a 'nested' model where population-level processes (transmission and virulence) depend on resource-driven changes to individual-level (within-host) processes (energetics, immune function, parasite production). Contrary to 'non-nested' model predictions, the 'nested' model reveals complex effects of host population dynamics on parasite evolution, including regions of evolutionary bistability; evolution can push parasites towards strongly or weakly stabilizing strategies. This bistability results from dynamic feedbacks between resource-driven changes to host density, host immune function and parasite production. Together, these results highlight how cross-scale feedbacks can provide key insights into the structuring role of parasites and parasite evolution.This article is part of the theme issue 'Anthropogenic resource subsidies and host-parasite dynamics in wildlife'.
Collapse
Affiliation(s)
- Jessica L Hite
- School of Biological Sciences, University of Nebraska, Lincoln, NE 68588, USA
| | - Clayton E Cressler
- School of Biological Sciences, University of Nebraska, Lincoln, NE 68588, USA
| |
Collapse
|
49
|
Martin LB, Addison B, Bean AGD, Buchanan KL, Crino OL, Eastwood JR, Flies AS, Hamede R, Hill GE, Klaassen M, Koch RE, Martens JM, Napolitano C, Narayan EJ, Peacock L, Peel AJ, Peters A, Raven N, Risely A, Roast MJ, Rollins LA, Ruiz-Aravena M, Selechnik D, Stokes HS, Ujvari B, Grogan LF. Extreme Competence: Keystone Hosts of Infections. Trends Ecol Evol 2019; 34:303-314. [PMID: 30704782 PMCID: PMC7114649 DOI: 10.1016/j.tree.2018.12.009] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Revised: 12/10/2018] [Accepted: 12/13/2018] [Indexed: 12/20/2022]
Abstract
Individual hosts differ extensively in their competence for parasites, but traditional research has discounted this variation, partly because modeling such heterogeneity is difficult. This discounting has diminished as tools have improved and recognition has grown that some hosts, the extremely competent, can have exceptional impacts on disease dynamics. Most prominent among these hosts are the superspreaders, but other forms of extreme competence (EC) exist and others await discovery; each with potentially strong but distinct implications for disease emergence and spread. Here, we propose a framework for the study and discovery of EC, suitable for different host-parasite systems, which we hope enhances our understanding of how parasites circulate and evolve in host communities.
Collapse
Affiliation(s)
- Lynn B Martin
- Global and Planetary Health, University of South Florida, Tampa, Florida 33620, USA.
| | - BriAnne Addison
- School of Life and Environmental Sciences, Deakin University, Geelong Waurn Ponds, VIC 3216, Australia
| | - Andrew G D Bean
- CSIRO Health & Biosecurity at the Australian Animal Health Laboratory, Geelong, VIC 3220, Australia
| | - Katherine L Buchanan
- School of Life and Environmental Sciences, Deakin University, Geelong Waurn Ponds, VIC 3216, Australia
| | - Ondi L Crino
- School of Life and Environmental Sciences, Deakin University, Geelong Waurn Ponds, VIC 3216, Australia
| | - Justin R Eastwood
- School of Biological Sciences, Monash University, VIC 3800, Australia
| | - Andrew S Flies
- Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS 7008, Australia
| | - Rodrigo Hamede
- School of Natural Sciences, University of Tasmania, Hobart, TAS 7001, Australia
| | - Geoffrey E Hill
- Department of Biological Sciences, Auburn University, Auburn, AL 36849, USA
| | - Marcel Klaassen
- School of Life and Environmental Sciences, Deakin University, Geelong Waurn Ponds, VIC 3216, Australia
| | - Rebecca E Koch
- School of Biological Sciences, Monash University, VIC 3800, Australia
| | - Johanne M Martens
- School of Life and Environmental Sciences, Deakin University, Geelong Waurn Ponds, VIC 3216, Australia
| | | | - Edward J Narayan
- School of Science and Health, Western Sydney University, Penrith, NSW 2751, Australia
| | - Lee Peacock
- School of Biological Sciences, Monash University, VIC 3800, Australia
| | - Alison J Peel
- Environmental Futures Research Institute, Griffith University, Nathan, QLD 4111, Australia
| | - Anne Peters
- School of Biological Sciences, Monash University, VIC 3800, Australia
| | - Nynke Raven
- School of Life and Environmental Sciences, Deakin University, Geelong Waurn Ponds, VIC 3216, Australia
| | - Alice Risely
- School of Life and Environmental Sciences, Deakin University, Geelong Waurn Ponds, VIC 3216, Australia
| | - Michael J Roast
- School of Biological Sciences, Monash University, VIC 3800, Australia
| | - Lee A Rollins
- School of Biological, Earth and Environmental Sciences, Evolution & Ecology Research Centre, University of New South Wales, Sydney, NSW 2052, Australia
| | - Manuel Ruiz-Aravena
- School of Natural Sciences, University of Tasmania, Hobart, TAS 7001, Australia
| | - Dan Selechnik
- School of Life and Environmental Sciences (SOLES), University of Sydney, Sydney, NSW 2006, Australia
| | - Helena S Stokes
- School of Life and Environmental Sciences, Deakin University, Geelong Waurn Ponds, VIC 3216, Australia
| | - Beata Ujvari
- School of Life and Environmental Sciences, Deakin University, Geelong Waurn Ponds, VIC 3216, Australia
| | - Laura F Grogan
- Environmental Futures Research Institute, Griffith University, Nathan, QLD 4111, Australia
| |
Collapse
|
50
|
Almocera AES, Hernandez-Vargas EA. Coupling multiscale within-host dynamics and between-host transmission with recovery (SIR) dynamics. Math Biosci 2019; 309:34-41. [PMID: 30658088 DOI: 10.1016/j.mbs.2019.01.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2018] [Revised: 08/31/2018] [Accepted: 01/11/2019] [Indexed: 11/25/2022]
Abstract
Multiscale models that link within-host infection to between-host transmission are valuable tools to progress understanding of viral infectious diseases. In this paper, we present two multiscale models that couple within-host infection to a susceptible-infected-recovered (SIR) model. A disease-induced transmission rate bridges the scales from within to between-host. Our stability analysis on the first model (influenza infection) reveals two equilibrium points for the SIR model that describe endemic scenarios where both susceptible and infected cases maintain nonzero population sizes. Consequently, the between-host system has two bifurcations determined by the corresponding basic reproduction number of the within-host and the size of the infected population at the interior equilibrium point. Analysis on the second model (Ebola infection) reveals the limited transient inhibitory effect of antibodies on viral replication, which influences the time window from infection to a potential outbreak. Simulations numerically illustrate our results.
Collapse
Affiliation(s)
- Alexis Erich S Almocera
- Frankfurt Institute for Advanced Studies, Ruth-Moufang-Straße 1, Frankfurt am Main 60438, Germany; Division of Physical Sciences and Mathematics, University of The Philippines Visayas, Miag-ao, Iloilo, Philippines
| | | |
Collapse
|