Mathematical model for the impact of awareness on the dynamics of infectious diseases

Agaba, G O; Kyrychko, Yuliya N.; Blyuss, Konstantin B.

doi:10.1016/j.mbs.2017.01.009

Cited by 81 publications

(73 citation statements)

References 44 publications

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“…In recent years, a growing number of studies have focused on understanding and quantifying the impact of such behaviour influencing factors on the spread of infectious diseases [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. A number of studies have employed mathematical models to assess the impact of media reports on emerging infectious disease prevention and control [4,5,[19][20][21][22][23][24][25][26][27][28][29][30]. Recently, Greenhalgh et al [19] presented a brief and nice commentary on the literature related to awareness and their effects on the dynamics of diseases.…”

Section: Introductionmentioning

confidence: 99%

“…For example, in [19], the authors proposed a mathematical model by inducing behavioural changes in the population through delineation of the susceptible class into unaware susceptible and aware susceptible subpopulations. [16] and [29] explicitly introduced distinct compartments for unaware and aware individuals in each of the disease states, and transitions between respective unaware and aware compartments took place at constant rates.…”

Section: Introductionmentioning

confidence: 99%

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Optimal media reporting intensity on mitigating spread of an emerging infectious disease

2019

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Mass media reports can induce individual behaviour change during a disease outbreak, which has been found to be useful as it reduces the force of infection. We propose a compartmental model by including a new compartment of the intensity of the media reports, which extends existing models by considering a novel media function, which is dependent both on the number of infected individuals and on the intensity of mass media. The existence and stability of the equilibria are analyzed and an optimal control problem of minimizing the total number of cases and total cost is considered, using reduction or enhancement in the media reporting rate as the control. With the help of Pontryagin’s Maximum Principle, we obtain the optimal media reporting intensity. Through parameterization of the model with the 2009 A/H1N1 influenza outbreak data in the 8th Hospital of Xi’an in Shaanxi Province of China, we obtain the basic reproduction number for the formulated model with two particular media functions. The optimal media reporting intensity obtained here indicates that during the early stage of an epidemic we should quickly enhance media reporting intensity, and keep it at a maximum level until it can finally weaken when epidemic cases have decreased significantly. Numerical simulations show that media impact reduces the number of cases during an epidemic, but that the number of cases is further mitigated under the optimal reporting intensity. Sensitivity analysis implies that the outbreak severity is more sensitive to the weight α1 (weight of media effect sensitive to infected individuals) than weight α2 (weight of media effect sensitive to media items).

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal media reporting intensity on mitigating spread of an emerging infectious disease

2019

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“…Historically, human behaviour has been considered as an important factor in the spread of infectious diseases, becoming a reasonably well studied topic in human, and to some extent, animal diseases [12][13][14][15][16][17]. Models on the interplay between human behaviour and disease epidemics have been developed on the basis of direct observations of the disease state [14,16,18], contact networks [19][20][21][22], mean-field with global mixing between individuals in the population [14] and spatial cellular automata models [23] to mention some. Previous work on the interplay between human behaviour and the spread of animal and human diseases have largely assumed that decisions are based on cost-benefit [14,24].…”

Section: Introductionmentioning

confidence: 99%

What makes or breaks a campaign to stop an invading plant pathogen?

et al. 2020

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Diseases in humans, animals and plants remain an important challenge in our society. Effective control of invasive pathogens often requires coordinated concerted action of a large group of stakeholders. Both epidemiological and human behavioural factors influence the outcome of a disease control campaign. In mathematical models that are frequently used to guide such campaigns, human behaviour is often ill-represented, if at all. Existing models of human, animal and plant disease that do incorporate participation or compliance are often driven by pay-offs or direct observations of the disease state. It is however very well known that opinion is an important driving factor of human decision making. Here we consider the case study of Citrus Huanglongbing disease (HLB), which is an acute bacterial disease that threatens the sustainability of citrus production across the world. We show how by coupling an epidemiological model of this invasive disease with an opinion dynamics model we are able to answer the question: What makes or breaks the effectiveness of a disease control campaign? Frequent contact between stakeholders and advisors is shown to increase the probability of successful control. More surprisingly, we show that informing stakeholders about the effectiveness of control methods is of much greater importance than prematurely increasing their perceptions of the risk of infection. We discuss the overarching consequences of this finding and the effect on human as well as plant disease epidemics.

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“…Understandably, the spread of awareness can play both a positive role, resulting in the containment or eradication of a disease, and a negative role, as evidence by the failure of an HPV campaign in Romania due to negative press coverage [21], or the spread of plague in one of the states in India due to panic and anxiety [22]. A number of mathematical models have looked at the roles of different factors associated with the simultaneous spread of disease and awareness, using a mean-field approach (see [1,7,13], and [9,17] for recent reviews of some of the existing models) or network [6,7,8,10,11,12,24,33,35] models that can often provide a more detailed information about contacts between individuals.…”

Section: Introductionmentioning

confidence: 99%

Time-delayed SIS epidemic model with population awareness

Agaba

Kyrychko

Blyuss

2017

Ecological Complexity

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This paper analyses the dynamics of infectious disease with a concurrent spread of disease awareness. The model includes local awareness due to contacts with aware individuals, as well as global awareness due to reported cases of infection and awareness campaigns. We investigate the effects of time delay in response of unaware individuals to available information on the epidemic dynamics by establishing conditions for the Hopf bifurcation of the endemic steady state of the model. Analytical results are supported by numerical bifurcation analysis and simulations.Comment: 15 pages, 5 figure

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Mathematical model for the impact of awareness on the dynamics of infectious diseases

Cited by 81 publications

References 44 publications

Optimal media reporting intensity on mitigating spread of an emerging infectious disease

Optimal media reporting intensity on mitigating spread of an emerging infectious disease

What makes or breaks a campaign to stop an invading plant pathogen?

Time-delayed SIS epidemic model with population awareness

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