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Anam V, Guerrero BV, Srivastav AK, Stollenwerk N, Aguiar M. Within-host models unravelling the dynamics of dengue reinfections. Infect Dis Model 2024; 9:458-473. [PMID: 38385021 PMCID: PMC10879676 DOI: 10.1016/j.idm.2024.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 02/03/2024] [Accepted: 02/03/2024] [Indexed: 02/23/2024] Open
Abstract
Caused by four serotypes, dengue fever is a major public health concern worldwide. Current modeling efforts have mostly focused on primary and heterologous secondary infections, assuming that lifelong immunity prevents reinfections by the same serotype. However, recent findings challenge this assumption, prompting a reevaluation of dengue immunity dynamics. In this study, we develop a within-host modeling framework to explore different scenarios of dengue infections. Unlike previous studies, we go beyond a deterministic framework, considering individual immunological variability. Both deterministic and stochastic models are calibrated using empirical data on viral load and antibody (IgM and IgG) concentrations for all dengue serotypes, incorporating confidence intervals derived from stochastic realizations. With good agreement between the mean of the stochastic realizations and the mean field solution for each model, our approach not only successfully captures primary and heterologous secondary infection dynamics facilitated by antibody-dependent enhancement (ADE) but also provides, for the first time, insights into homotypic reinfection dynamics. Our study discusses the relevance of homotypic reinfections in dengue transmission at the population level, highlighting potential implications for disease prevention and control strategies.
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Affiliation(s)
- Vizda Anam
- Basque Center for Applied Mathematics, Basque Country, Spain
- Department of Mathematics and Statistics, University of Basque Country, Basque Country, Spain
| | | | | | | | - Maíra Aguiar
- Basque Center for Applied Mathematics, Basque Country, Spain
- Ikerbasque, Basque Foundation for Science, Basque Country, Spain
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2
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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.
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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
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3
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Fung T, Clapham HE, Chisholm RA. Temporary Cross-Immunity as a Plausible Driver of Asynchronous Cycles of Dengue Serotypes. Bull Math Biol 2023; 85:124. [PMID: 37962713 DOI: 10.1007/s11538-023-01226-4] [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: 01/27/2023] [Accepted: 10/16/2023] [Indexed: 11/15/2023]
Abstract
Many infectious diseases exist as multiple variants, with interactions between variants potentially driving epidemiological dynamics. These diseases include dengue, which infects hundreds of millions of people every year and exhibits complex multi-serotype dynamics. Antibodies produced in response to primary infection by one of the four dengue serotypes can produce a period of temporary cross-immunity (TCI) to infection by other serotypes. After this period, the remaining antibodies can facilitate the entry of heterologous serotypes into target cells, thus enhancing severity of secondary infection by a heterologous serotype. This represents antibody-dependent enhancement (ADE). In this study, we analyze an epidemiological model to provide novel insights into the importance of TCI and ADE in producing cyclic outbreaks of dengue serotypes. Our analyses reveal that without TCI, such cyclic outbreaks are synchronous across serotypes and only occur when ADE produces high transmission rates. In contrast, the presence of TCI allows asynchronous cycles of serotypes by inducing a time lag between recovery from primary infection by one serotype and secondary infection by another, with such cycles able to occur without ADE. Our results suggest that TCI is a fundamental driver of asynchronous cycles of dengue serotypes and possibly other multi-variant diseases.
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Affiliation(s)
- Tak Fung
- Department of Biological Sciences, National University of Singapore, 16 Science Drive 4, Singapore, 117558, Singapore.
| | - Hannah E Clapham
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, 12 Science Drive 2, Singapore, 117549, Singapore
| | - Ryan A Chisholm
- Department of Biological Sciences, National University of Singapore, 16 Science Drive 4, Singapore, 117558, Singapore
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4
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Aguiar M, Anam V, Blyuss KB, Estadilla CDS, Guerrero BV, Knopoff D, Kooi BW, Mateus L, Srivastav AK, Steindorf V, Stollenwerk N. Prescriptive, descriptive or predictive models: What approach should be taken when empirical data is limited? Reply to comments on "Mathematical models for Dengue fever epidemiology: A 10-year systematic review". Phys Life Rev 2023; 46:56-64. [PMID: 37245453 DOI: 10.1016/j.plrev.2023.05.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 05/07/2023] [Indexed: 05/30/2023]
Affiliation(s)
- Maíra Aguiar
- Basque Center for Applied Mathematics, Alameda de Mazarredo 14, Bilbao, E-48009, Basque Country, Spain; Ikerbasque, Basque Foundation for Science, Bilbao, Spain.
| | - Vizda Anam
- Basque Center for Applied Mathematics, Alameda de Mazarredo 14, Bilbao, E-48009, Basque Country, Spain
| | | | - Carlo Delfin S Estadilla
- Basque Center for Applied Mathematics, Alameda de Mazarredo 14, Bilbao, E-48009, Basque Country, Spain; Preventive Medicine and Public Health Department, University of the Basque Country (UPV/EHU), Leioa, Basque Country Spain
| | - Bruno V Guerrero
- Basque Center for Applied Mathematics, Alameda de Mazarredo 14, Bilbao, E-48009, Basque Country, Spain
| | - Damián Knopoff
- Centro de Investigaciones y Estudios de Matemática CIEM, CONICET, Córdoba, Argentina; Intelligent Biodata SL, San Sebastián, Spain
| | - Bob W Kooi
- Basque Center for Applied Mathematics, Alameda de Mazarredo 14, Bilbao, E-48009, Basque Country, Spain; VU University, Faculty of Science, De Boelelaan 1085, NL 1081, HV Amsterdam, the Netherlands
| | - Luís Mateus
- Basque Center for Applied Mathematics, Alameda de Mazarredo 14, Bilbao, E-48009, Basque Country, Spain
| | - Akhil Kumar Srivastav
- Basque Center for Applied Mathematics, Alameda de Mazarredo 14, Bilbao, E-48009, Basque Country, Spain
| | - Vanessa Steindorf
- Basque Center for Applied Mathematics, Alameda de Mazarredo 14, Bilbao, E-48009, Basque Country, Spain
| | - Nico Stollenwerk
- Basque Center for Applied Mathematics, Alameda de Mazarredo 14, Bilbao, E-48009, Basque Country, Spain
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5
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Macdonald JC, Gulbudak H. Forward hysteresis and Hopf bifurcation in an Npzd model with application to harmful algal blooms. J Math Biol 2023; 87:45. [PMID: 37589908 DOI: 10.1007/s00285-023-01969-7] [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: 03/12/2023] [Revised: 06/21/2023] [Accepted: 07/12/2023] [Indexed: 08/18/2023]
Abstract
Nutrient-Phytoplankton-Zooplankton-Detritus (NPZD) models, describing the interactions between phytoplankton, zooplankton systems, and their ecosystem, are used to predict their ecological and evolutionary population dynamics. These organisms form the base two trophic levels of aquatic ecosystems. Hence understanding their population dynamics and how disturbances can affect these systems is crucial. Here, starting from a base NPZ modeling framework, we incorporate the harmful effects of phytoplankton overpopulation on zooplankton-representing a crucial next step in harmful algal bloom (HAB) modeling-and split the nutrient compartment to formulate an NPZD model. We then mathematically analyze the NPZ system upon which this new model is based, including local and global stability of equilibria, Hopf bifurcation condition, and forward hysteresis, where the bi-stability occurs with multiple attractors. Finally, we extend the threshold analysis to the NPZD model, which displays both forward hysteresis with bi-stability and Hopf bifurcation under different parameter regimes. We also examine ecological implications after incorporating seasonality and ecological disturbances. Ultimately, we quantify ecosystem health in terms of the relative values of the robust persistence thresholds for phytoplankton and zooplankton and find (i) ecosystems sufficiently favoring phytoplankton, as quantified by the relative values of the plankton persistence numbers, are vulnerable to both HABs and (local) zooplankton extinction (ii) even healthy ecosystems are extremely sensitive to nutrient depletion over relatively short time scales.
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Affiliation(s)
- J C Macdonald
- Department of Mathematics, University of Louisiana at Lafayette, 1401 Johnston Street, Lafayette, LA, 70504, USA.
- Faculty of Life Sciences, School of Zoology, Tel Aviv University, Tel Aviv-Yafo, Israel.
| | - H Gulbudak
- Department of Mathematics, University of Louisiana at Lafayette, 1401 Johnston Street, Lafayette, LA, 70504, USA.
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6
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Sun M, Fu X. Competitive dual-strain SIS epidemiological models with awareness programs in heterogeneous networks: two modeling approaches. J Math Biol 2023; 87:14. [PMID: 37336794 DOI: 10.1007/s00285-023-01945-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Revised: 04/06/2023] [Accepted: 06/02/2023] [Indexed: 06/21/2023]
Abstract
Epidemic diseases and media campaigns are closely associated with each other. Considering most epidemics have multiple pathogenic strains, in this paper, we take the lead in proposing two multi-strain SIS epidemic models in heterogeneous networks incorporating awareness programs due to media. For the first model, we assume that the transmission rates for strain 1 and strain 2 depend on the level of awareness campaigns. For the second one, we further suppose that awareness divides susceptible population into two different subclasses. After defining the basic reproductive numbers for the whole model and each strain, we obtain the analytical conditions that determine the extinction, coexistence and absolute dominance of two strains. Moreover, we also formulate its optimal control problem and identify an optimal implementation pair of awareness campaigns using optimal control theory. Given the complexity of the second model, we use the numerical simulations to visualize its different types of dynamical behaviors. Through theoretical and numerical analysis of these two models, we discover some new phenomena. For example, during the persistence analysis of the first model, we find that the characteristic polynomials of two boundary equilibria may have a pair of pure imaginary roots, implying that Hopf bifurcation and periodic solutions may appear. Most strikingly, multistability occurs in the second model and the growth rate of awareness programs (triggered by the infection prevalence) has a multistage impact on the final size of two strains. The numerical results suggest that the spread of a two-strain epidemic can be controlled (even be eradicated) by taking the measures of enhancing awareness transmission, reducing memory fading of aware individuals and ensuring high-level influx and rapid growth of awareness programs appropriately.
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Affiliation(s)
- Mengfeng Sun
- Department of Mathematics, Shanghai University, Shanghai, 200444, China.
| | - Xinchu Fu
- Department of Mathematics, Shanghai University, Shanghai, 200444, China
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7
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de Araújo RGS, Jorge DCP, Dorn RC, Cruz-Pacheco G, Esteva MLM, Pinho STR. Applying a multi-strain dengue model to epidemics data. Math Biosci 2023; 360:109013. [PMID: 37127090 DOI: 10.1016/j.mbs.2023.109013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 04/17/2023] [Accepted: 04/24/2023] [Indexed: 05/03/2023]
Abstract
Dengue disease transmission is a complex vector-borne disease, mainly due to the co-circulation of four serotypes of the virus. Mathematical models have proved to be a useful tool to understand the complexity of this disease. In this work, we extend the model studied by Esteva et al., 2003, originally proposed for two serotypes, to four circulating serotypes. Using epidemic data of dengue fever in Iquitos (Peru) and San Juan (Puerto Rico), we estimate numerically the co-circulation parameter values for selected outbreaks using a bootstrap method, and we also obtained the Basic Reproduction Number, R0, for each serotype, using both analytical calculations and numerical simulations. Our results indicate that the impact of co-circulation of serotypes in population dynamics of dengue infection is such that there is a reduced effect from DENV-3 to DENV-4 in comparison to no-cross effect for epidemics in Iquitos. Concerning San Juan epidemics, also comparing to no-cross effect, we also observed a reduced effect from the predominant serotype DENV-3 to both DENV-2 and DENV-1 epidemics neglecting the very small number of cases of DENV-4.
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Affiliation(s)
| | - Daniel C P Jorge
- Instituto de Física, Universidade Federal da Bahia, Salvador, Brazil; Instituto de Física Teórica, Universidade Estadual Paulista, São Paulo, Brazil.
| | - Rejane C Dorn
- Instituto de Física, Universidade Federal da Bahia, Salvador, Brazil.
| | - Gustavo Cruz-Pacheco
- Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Autónoma de México, Cuidad de México, Mexico.
| | - M Lourdes M Esteva
- Facultad de Ciências, Universidad Autónoma de México, Cuidad de México, Mexico.
| | - Suani T R Pinho
- Instituto de Física, Universidade Federal da Bahia, Salvador, Brazil; Instituto Nacional de Ciência e Tecnologia - Sistemas Complexos, Brazil.
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8
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Kenne C, Mophou G, Zongo P. A nested model with boosting and waning of immunity from Tilapia Lake Virus infection with distributed resistance to pathogens carrier-state. J Math Biol 2023; 86:67. [PMID: 37009960 DOI: 10.1007/s00285-023-01906-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 02/28/2023] [Accepted: 03/16/2023] [Indexed: 04/04/2023]
Abstract
This paper proposes and analyzes an immune-structured population model of tilapia subject to Tilapia Lake Virus (TiLV) disease. The model incorporates within-host dynamics, used to describe the interaction between the pathogen, the immune system and the waning of immunity. Individuals infected with a low dose acquire a low immunity level and those infected with a high dose acquire a high level of immunity. Since individuals' immune status plays an important role in the spread of infectious diseases at the population level, the within-host dynamics are connected to the between-host dynamics in the population. We define an explicit formula for the reproductive number [Formula: see text] and show that the disease-free equilibrium is locally asymptotically stable when [Formula: see text], while it is unstable when [Formula: see text]. Furthermore, we prove that an endemic equilibrium exists. We also study the influence of the initial distribution of host resistance on the spread of the disease, and find that hosts' initial resistance plays a crucial role in the disease dynamics. This suggests that the genetic selection aiming to improve hosts' initial resistance to TiLV could help fight the disease. The results also point out the crucial role played by the inoculum size. We find that the higher the initial inoculum size, the faster the dynamics of infection. Moreover, if the initial inoculum size is below a certain threshold, it may not result in an outbreak at the between-host level. Finally, the model shows that there is a strong negative correlation between heterogeneity and the probability of pathogen invasion.
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Affiliation(s)
- Cyrille Kenne
- Department of Mathematics, Laboratoire LAMIA, Université des Antilles, Campus Fouillole, 97159, Pointe-à-Pitre, Guadeloupe.
- University of Buea, Buea, Cameroon.
| | - Gisèle Mophou
- Department of Mathematics, Laboratoire LAMIA, Université des Antilles, Campus Fouillole, 97159, Pointe-à-Pitre, Guadeloupe
- Laboratoire MAINEGE, Université Ouaga 3S, 06 BP 10347, Ouagadougou, Burkina Faso
| | - Pascal Zongo
- Laboratoire L3MA, UFR STE et IUT, Université des Antilles, 97275, Schoelcher, Martinique
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9
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Knowledge, attitudes and practices of dengue prevention between dengue sustained hotspots and non-sustained hotspots in Singapore: a cross-sectional study. Sci Rep 2022; 12:18426. [PMID: 36319678 PMCID: PMC9626577 DOI: 10.1038/s41598-022-22776-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 10/19/2022] [Indexed: 11/07/2022] Open
Abstract
Dengue sustained hotspots (SHS) have resulted in a significant public health burden. In our study, we aimed to (1) compare knowledge, attitudes and practices (KAP) scores between SHS and non-sustained hotspots (NSHS); and (2) identify and describe gaps and factors associated with KAP of dengue prevention among SHS residents residing in Singapore. A cross-sectional study with convenience sampling was conducted using digital survey in randomly selected SHS and NSHS residential areas, consisting of residents aged 21 or older and who had been residing in their existing housing unit in 2019 and 2020. Chi-square test and T-test were used for comparison analysis of categorical and continuous variables, respectively. A total of 466 respondents completed the self-administered, anonymous survey. There were no significant difference in mean scores for Knowledge [SHS(24.66) vs. NSHS(24.37); P: 0.18], Attitudes [SHS(10.38) vs NSHS(10.16); P: 0.08] and Practices [SHS(9.27) vs NSHS(8.80); P: 0.16] sections. Significant SHS-associated factors identified were age group 41-50 years old [95%CI: 1.25-5.03], Malay (95%CI: 0.17-0.98), up to secondary school education (95%CI: 0.07-0.65), private condominium (95%CI: 1.17-3.39), residing in same household unit for 2-5 years (95%CI: 2.44-6.88), respondents who know that mosquito can breed in open container with stagnant water (95%CI: 0.06-0.98), disagree that reducing Aedes mosquitoes is the only way to prevent dengue: (95%CI: 1.19-3.00) and go to clinic/hospital even without severe symptoms: (95%CI: 0.39-0.95). These independent factors associated with dengue sustained hotspots may influence the risk of dengue transmission in residential areas.
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10
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Aguiar M, Anam V, Blyuss KB, Estadilla CDS, Guerrero BV, Knopoff D, Kooi BW, Srivastav AK, Steindorf V, Stollenwerk N. Mathematical models for dengue fever epidemiology: A 10-year systematic review. Phys Life Rev 2022; 40:65-92. [PMID: 35219611 PMCID: PMC8845267 DOI: 10.1016/j.plrev.2022.02.001] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 02/08/2022] [Indexed: 01/11/2023]
Abstract
Mathematical models have a long history in epidemiological research, and as the COVID-19 pandemic progressed, research on mathematical modeling became imperative and very influential to understand the epidemiological dynamics of disease spreading. Mathematical models describing dengue fever epidemiological dynamics are found back from 1970. Dengue fever is a viral mosquito-borne infection caused by four antigenically related but distinct serotypes (DENV-1 to DENV-4). With 2.5 billion people at risk of acquiring the infection, it is a major international public health concern. Although most of the cases are asymptomatic or mild, the disease immunological response is complex, with severe disease linked to the antibody-dependent enhancement (ADE) - a disease augmentation phenomenon where pre-existing antibodies to previous dengue infection do not neutralize but rather enhance the new infection. Here, we present a 10-year systematic review on mathematical models for dengue fever epidemiology. Specifically, we review multi-strain frameworks describing host-to-host and vector-host transmission models and within-host models describing viral replication and the respective immune response. Following a detailed literature search in standard scientific databases, different mathematical models in terms of their scope, analytical approach and structural form, including model validation and parameter estimation using empirical data, are described and analyzed. Aiming to identify a consensus on infectious diseases modeling aspects that can contribute to public health authorities for disease control, we revise the current understanding of epidemiological and immunological factors influencing the transmission dynamics of dengue. This review provide insights on general features to be considered to model aspects of real-world public health problems, such as the current epidemiological scenario we are living in.
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Affiliation(s)
- Maíra Aguiar
- Basque Center for Applied Mathematics, Alameda de Mazarredo 14, Bilbao, E-48009, Basque Country, Spain; Dipartimento di Matematica, Università degli Studi di Trento, Via Sommarive 14, Povo, Trento, 38123, Italy; Ikerbasque, Basque Foundation for Science, Bilbao, Spain.
| | - Vizda Anam
- Basque Center for Applied Mathematics, Alameda de Mazarredo 14, Bilbao, E-48009, Basque Country, Spain
| | - Konstantin B Blyuss
- VU University, Faculty of Science, De Boelelaan 1085, NL 1081, HV Amsterdam, the Netherlands
| | - Carlo Delfin S Estadilla
- Basque Center for Applied Mathematics, Alameda de Mazarredo 14, Bilbao, E-48009, Basque Country, Spain
| | - Bruno V Guerrero
- Basque Center for Applied Mathematics, Alameda de Mazarredo 14, Bilbao, E-48009, Basque Country, Spain
| | - Damián Knopoff
- Basque Center for Applied Mathematics, Alameda de Mazarredo 14, Bilbao, E-48009, Basque Country, Spain; Centro de Investigaciones y Estudios de Matemática CIEM, CONICET, Medina Allende s/n, Córdoba, 5000, Argentina
| | - Bob W Kooi
- University of Sussex, Department of Mathematics, Falmer, Brighton, UK
| | - Akhil Kumar Srivastav
- Basque Center for Applied Mathematics, Alameda de Mazarredo 14, Bilbao, E-48009, Basque Country, Spain
| | - Vanessa Steindorf
- Basque Center for Applied Mathematics, Alameda de Mazarredo 14, Bilbao, E-48009, Basque Country, Spain
| | - Nico Stollenwerk
- Basque Center for Applied Mathematics, Alameda de Mazarredo 14, Bilbao, E-48009, Basque Country, Spain; Dipartimento di Matematica, Università degli Studi di Trento, Via Sommarive 14, Povo, Trento, 38123, Italy
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11
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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}
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\begin{document}$$4\%$$\end{document}4% increase in infectiousness of hosts when the reproduction number \documentclass[12pt]{minimal}
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\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.
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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
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12
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Alamo T, G Reina D, Millán Gata P, Preciado VM, Giordano G. Data-driven methods for present and future pandemics: Monitoring, modelling and managing. ANNUAL REVIEWS IN CONTROL 2021; 52:448-464. [PMID: 34220287 PMCID: PMC8238691 DOI: 10.1016/j.arcontrol.2021.05.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 05/24/2021] [Accepted: 05/27/2021] [Indexed: 05/29/2023]
Abstract
This survey analyses the role of data-driven methodologies for pandemic modelling and control. We provide a roadmap from the access to epidemiological data sources to the control of epidemic phenomena. We review the available methodologies and discuss the challenges in the development of data-driven strategies to combat the spreading of infectious diseases. Our aim is to bring together several different disciplines required to provide a holistic approach to epidemic analysis, such as data science, epidemiology, and systems-and-control theory. A 3M-analysis is presented, whose three pillars are: Monitoring, Modelling and Managing. The focus is on the potential of data-driven schemes to address three different challenges raised by a pandemic: (i) monitoring the epidemic evolution and assessing the effectiveness of the adopted countermeasures; (ii) modelling and forecasting the spread of the epidemic; (iii) making timely decisions to manage, mitigate and suppress the contagion. For each step of this roadmap, we review consolidated theoretical approaches (including data-driven methodologies that have been shown to be successful in other contexts) and discuss their application to past or present epidemics, such as Covid-19, as well as their potential application to future epidemics.
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Affiliation(s)
- Teodoro Alamo
- Departamento de Ingeniería de Sistemas y Automática, Universidad de Sevilla, Escuela Superior de Ingenieros, Sevilla, Spain
| | - Daniel G Reina
- Departamento de Ingeniería Electrónica, Universidad de Sevilla, Escuela Superior de Ingenieros, Sevilla, Spain
| | - Pablo Millán Gata
- Departamento de Ingeniería, Universidad Loyola Andalucía, Seville, Spain
| | - Victor M Preciado
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, USA
| | - Giulia Giordano
- Department of Industrial Engineering, University of Trento, Trento, Italy
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Sarmah DT, Bairagi N, Chatterjee S. Tracing the footsteps of autophagy in computational biology. Brief Bioinform 2020; 22:5985288. [PMID: 33201177 PMCID: PMC8293817 DOI: 10.1093/bib/bbaa286] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2020] [Revised: 09/29/2020] [Accepted: 09/30/2020] [Indexed: 12/11/2022] Open
Abstract
Autophagy plays a crucial role in maintaining cellular homeostasis through the degradation of unwanted materials like damaged mitochondria and misfolded proteins. However, the contribution of autophagy toward a healthy cell environment is not only limited to the cleaning process. It also assists in protein synthesis when the system lacks the amino acids’ inflow from the extracellular environment due to diet consumptions. Reduction in the autophagy process is associated with diseases like cancer, diabetes, non-alcoholic steatohepatitis, etc., while uncontrolled autophagy may facilitate cell death. We need a better understanding of the autophagy processes and their regulatory mechanisms at various levels (molecules, cells, tissues). This demands a thorough understanding of the system with the help of mathematical and computational tools. The present review illuminates how systems biology approaches are being used for the study of the autophagy process. A comprehensive insight is provided on the application of computational methods involving mathematical modeling and network analysis in the autophagy process. Various mathematical models based on the system of differential equations for studying autophagy are covered here. We have also highlighted the significance of network analysis and machine learning in capturing the core regulatory machinery governing the autophagy process. We explored the available autophagic databases and related resources along with their attributes that are useful in investigating autophagy through computational methods. We conclude the article addressing the potential future perspective in this area, which might provide a more in-depth insight into the dynamics of autophagy.
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Affiliation(s)
| | - Nandadulal Bairagi
- Centre for Mathematical Biology and Ecology, Department of Mathematics, Jadavpur University, Kolkata, India
| | - Samrat Chatterjee
- Translational Health Science and Technology Institute, Faridabad, India
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