Analysis of a multiscale HIV-1 model coupling within-host viral dynamics and between-host transmission dynamics

Xue, Yuyi; Xiao, Yanni

doi:10.3934/mbe.2020350

Cited by 12 publications

(17 citation statements)

References 27 publications

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“…Developing the mathematical tools for connecting dynamic processes operating at vastly different temporal and spatial scales has been an active focus in infectious disease modeling (Agyingi et al, 2020;Browne and Cheng, 2020;Garabed et al, 2020;Garira, 2020;Jia et al, 2020;Kadelka and M Ciupe, 2019;Rivera et al, 2020;Versypt, 2021;Xue and Xiao, 2020). However, these theoretical innovations have yet to be matched by empirical data generation, providing integrated data sets that consistently document infection processes in the same host-pathogen system across organizational scales.…”

Section: Introductionmentioning

confidence: 99%

“…Multi-scale models of infectious disease dynamics seek to address this challenge by linking mechanistic models representing pathogen-host interactions at cellular to population scales. Developing the mathematical tools for connecting dynamic processes operating at vastly different temporal and spatial scales has been an active focus in infectious disease modeling (Agyingi et al, 2020; Browne and Cheng, 2020; Garabed et al, 2020; Garira, 2020; Jia et al, 2020; Kadelka and M Ciupe, 2019; Rivera et al, 2020; Versypt, 2021; Xue and Xiao, 2020). However, these theoretical innovations have yet to be matched by empirical data generation, providing integrated data sets that consistently document infection processes in the same host-pathogen system across organizational scales.…”

Section: Introductionmentioning

confidence: 99%

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Within-host viral growth and immune response rates predict FMDV transmission dynamics for African Buffalo

Macdonald

Gulbudak²,

Beechler

et al. 2022

Preprint

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Infectious disease dynamics operate across biological scales: pathogens replicate within hosts, but transmit among hosts and populations. Functional changes in the pathogen-host interaction thus generate cascading effects from molecular to landscape scales. We investigated within-host dynamics and among-host transmission of three strains of foot-and-mouth disease viruses (FMDVs) in their wildlife host, African buffalo. We combined data on viral dynamics and host immune responses with mathematical models to ask (i) How do viral and immune dynamics vary among FMDV strains? (SAT1, 2, 3); (ii) Which viral and immune parameters determine viral fitness within hosts?; and (iii) How do within-host dynamics relate to virus transmission among hosts? Our data reveal contrasting within-host dynamics among viral strains. However, SAT2 elicited more rapid and effective immune responses than SAT1 and SAT3. Within-host viral fitness was overwhelmingly determined by variation among hosts in immune response activation rates against FMDVs, but not by variation among individual hosts in viral growth rate. By contrast, our analyses investigating across-scale linkages indicate that viral replication rate in the host correlates with transmission rates among buffalo; and that adaptive immune activation rate determines the infectious period. Together, these parameters define the basic reproductive number of the virus, suggesting that viral invasion potential may be predictable from within-host dynamics. Future work should test the generality of these findings by including additional FMDV strains, and create a multi-scale model to link within-host and between-host dynamics explicitly.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Within-host viral growth and immune response rates predict FMDV transmission dynamics for African Buffalo

Macdonald

Gulbudak²,

Beechler

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…It is well known that most serious epidemics have huge impacts on the human health, social and national stability, e.g., AIDS, Cholera, SARS, Ebola, COVID-19, etc [1][2][3][4][5]. In the past decades, a lot of significant epidemic models were established to study the transmission dynamics of such diseases, which primary focuses consist of the stability of equilibrium (or periodic solution), persistence (or extinction) of disease, the computation of basic reproduction number, bifurcation and chaos, etc [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

An environmentally-driven two-strain mutualistic coinfection model with coupling transmission in vivo and vitro and humoral immunity

Zhang

Sang

et al. 2023

Preprint

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In this paper, a coupling transmission epidemic model with mutualistic two-strain of virus in body and vitro of host is proposed, in which humoral immune response only works for strain 1 due to immunity evasion of mutation. For the within-host subsystem, the global stability of all feasible equilibria with and without environmental influence are discussed. For the between-host subsystem, the basic reproduction number R 0 is obtained. When R 0 < 1 , the disease-free equilibrium is local stable, while the endemic equilibrium is local stable and the disease is uniformly persistent if R 0 > 1 . Meanwhile, backward bifurcation would occur when there exists immune response within host. Finally, numerical examples are provided to illustrate obtained conclusions, by which we find that the mutualism between two strains during co-infection leads to a more persistent disease than single strain, even the basic reproduction number is small than 1 in each single strain.

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“… 2012 ; Coombs et al. 2007 ; Xue and Xiao 2020 ; Shen et al. 2019 , 2019 ; Martcheva and Pilyugin 2006 ; Martcheva 2011 ; Cai et al.…”

Section: Introductionunclassified

Modeling and Research on an Immuno-Epidemiological Coupled System with Coinfection

Gao

et al. 2021

Bull Math Biol

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In this paper, a two-strain model with coinfection that links immunological and epidemiological dynamics across scales is formulated. On the with-in host scale, the two strains eliminate each other with the strain having the larger immunological reproduction number persisting. However, on the population scale coinfection is a common occurrence. Individuals infected with strain one can become coinfected with strain two and similarly for individuals originally infected with strain two. The immunological reproduction numbers , the epidemiological reproduction numbers and invasion reproduction numbers are computed. Besides the disease-free equilibrium, there are strain one and strain two dominance equilibria. The disease-free equilibrium is locally asymptotically stable when the epidemiological reproduction numbers are smaller than one. In addition, each strain dominance equilibrium is locally asymptotically stable if the corresponding epidemiological reproduction number is larger than one and the invasion reproduction number of the other strain is smaller than one. The coexistence equilibrium exists when all the reproduction numbers are greater than one. Simulations suggest that when both invasion reproduction numbers are smaller than one, bistability occurs with one of the strains persisting or the other, depending on initial conditions.

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Analysis of a multiscale HIV-1 model coupling within-host viral dynamics and between-host transmission dynamics

Cited by 12 publications

References 27 publications

Within-host viral growth and immune response rates predict FMDV transmission dynamics for African Buffalo

Within-host viral growth and immune response rates predict FMDV transmission dynamics for African Buffalo

An environmentally-driven two-strain mutualistic coinfection model with coupling transmission in vivo and vitro and humoral immunity

Modeling and Research on an Immuno-Epidemiological Coupled System with Coinfection

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