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A switching model for the impact of toxins on the spread of infectious diseases

Abstract

To study the effects of an environmental toxin, such as fine particles in hazy weather, on the spread of infectious diseases, we derive a toxin-dependent dynamic model that incorporates the birth rate with the toxin-dependent switching mode, the mortality rate, and infection rate with the toxin-dependent saturation effect. We analyze the model by showing the positive invariance, existence and stability of equilibria, and bifurcations. Numerical simulation is adopted to verify the mathematical results and exhibit transcritical and Hopf bifurcations. Our theoretical results show that there exists a threshold value of the environmental toxin: if the environmental toxin concentration is lower than the threshold, the system has a disease-free equilibrium and an interior equilibrium; if the environmental toxin concentration is higher than the threshold, the system has the extinction equilibrium. For the case where the disease-induced death is ignored, we show the global stability results. Numerical simulations clearly show that the environmental toxin facilitates the spread of infectious diseases. This study provides a theoretical basis for uncovering the impact of toxins on the spread of infectious diseases and for guiding the decision making by disease control agencies and governments.

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References

  • Brauer M, Hoek G, Smit HA, De Jongste JC, Gerritsen J, Postma DS, Kerkhof M, Brunekreef B (2007) Air pollution and development of asthma, allergy and infections in a birth cohort. Eur Respir J 29(5):879–888

    Article  Google Scholar 

  • Brunekreef B, Holgate ST (2002) Air pollution and health. Lancet 360(9341):1233–1242

    Article  Google Scholar 

  • Chauhan S, Bhatia SK, Gupta S (2015) Effect of pollution on dynamics of SIR model with treatment. Int J Biomath 8(06):1550083

    Article  MathSciNet  Google Scholar 

  • Chowell G, Ammon CE, Hengartner NW, Hyman JM (2006) Transmission dynamics of the great influenza pandemic of 1918 in Geneva, Switzerland: assessing the effects of hypothetical interventions. J Theor Biol 241(2):193–204

    Article  MathSciNet  Google Scholar 

  • Clancy L, Goodman P, Sinclair H, Dockery DW (2002) Effect of air-pollution control on death rates in Dublin, Ireland: an intervention study. Lancet 360(9341):1210–1214

    Article  Google Scholar 

  • Ding YH, Liu YJ (2014) Analysis of long-term variations of fog and haze in China in recent 50 years and their relations with atmospheric humidity. Sci China Earth Sci 57(1):36–46

    Article  Google Scholar 

  • Hernandez-Vargas EA, Wilk E, Canini L, Toapantad FR, Bindera SC, Uvarovskiia A, Rosse TM, Guzmnf CA, Perelsonb AS, Meyer-Hermanna M (2014) Effects of aging on influenza virus infection dynamics. J Virol 88(8):4123–4131

    Article  Google Scholar 

  • Huang QH, Parshotham L, Wang H, Bampfylde C, Lewis MA (2013) A model for the impact of contaminants on fish population dynamics. J Theor Biol 334:71–79

    Article  MathSciNet  Google Scholar 

  • Huang QH, Wang H, Lewis MA (2015) The impact of environmental toxins on predator–prey dynamics. J Theor Biol 378:12–30

    Article  MathSciNet  Google Scholar 

  • Liu B, Duan Y, Luan S (2012) Dynamics of an SI epidemic model with external effects in a polluted environment. Nonlinear Anal Real 13(1):27–38

    Article  MathSciNet  Google Scholar 

  • Ma ZE (1996) Mathematical modelling and study of species ecology. Anhui Education Publishing Company, Anhui

    Google Scholar 

  • Mu Q, Zhang SQ (2013) An evaluation of the economic loss due to the heavy haze during January 2013 in China. China Environ Sci 33(11):2087–2094

    Google Scholar 

  • Pastorok RA, Akçakaya R, Regan H, Ferson S, Bartell SM (2003) Role of ecological modeling in risk assessment. Hum Ecol Risk Assess 9(4):939–972

    Article  Google Scholar 

  • Perko L (2013) Differential equations and dynamical systems. Springer, Berlin

    MATH  Google Scholar 

  • Quan J, Zhang Q, He H, Liu J, Huang M, Jin H (2011) Analysis of the formation of fog and haze in North China Plain (NCP). Atmos Chem Phys 11(15):8205–8214

    Article  Google Scholar 

  • Reynolds JJH, Torremorell M, Craft ME (2014) Mathematical modeling of influenza A virus dynamics within swine farms and the effects of vaccination. PLoS ONE 9(8):e106177

    Article  Google Scholar 

  • Ru SF, Lei ZY (2014) The governance of fog and haze in cities and the transformation of the mode of economic development. J Northwest Univ Philos Soc Sci Ed 2:013

    Google Scholar 

  • Thieme HR (2003) Mathematics in population biology. Princeton University Press, Princeton

    MATH  Google Scholar 

  • Treanor JJ (2014) Influenza viruses. Springer, New York, pp 455–478

    Google Scholar 

  • Wang F, Ma ZE (2004) Persistence and periodic orbits for an SIS model in a polluted environment. Comput Math Appl 47:779–792

    Article  MathSciNet  Google Scholar 

  • Wang Q, Chen X, Liu Z (2013) Study on characteristics of elements in PM2.5 during haze–fog weather in winter in urban Beijing. Spectrosc Spect Anal 33(6):1441–1445

    Google Scholar 

  • Wang F, Ni SS, Liu H (2016) Pollutional haze and COPD: etiology, epidemiology, pathogenesis, pathology, biological markers and therapy. J Thorac Dis 8(1):E20

    Google Scholar 

  • Watt L (2013) Air pollution takes toll on Chinas tourism. ABC News, New York

    Google Scholar 

  • Yaari R, Katriel G, Huppert A, Axelsen JB, Stone L (2013) Modelling seasonal influenza: the role of weather and punctuated antigenic drift. J R Soc Interface 10(84):20130298

    Article  Google Scholar 

  • Yin YW, Cheng JP, Duan YS (2011) Economic evaluation of residents’ health hazard caused by PM 2.5 of haze pollution in a city. J Environ and Health 28(3):250–252

    Google Scholar 

  • Zhang J, Jin Z, Sun GQ, Sun XD, Wang YM, Huang B (2014) Determination of original infection source of H7N9 avian influenza by dynamical model. Sci Rep 4:4846

    Article  Google Scholar 

Download references

Acknowledgements

The project is funded by the National Key Research and Development Program of China (2016YFD0501500), the National Natural Science Foundation of China under Grants (11331009 and 11601292). The third author’s work is partially supported by NSERC.

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Correspondence to Zhen Jin.

Appendix: Proof of Theorem 4.2

Appendix: Proof of Theorem 4.2

Proof

The disease-free equilibrium is \(E_{1} (S_{0},0)\), and the interior equilibrium is \(E_{2}(S^{*},I^{*})\), where \(S_{0}=\frac{(1-\frac{\alpha \sigma A}{\theta \xi })a}{\mu +\frac{k\sigma A}{\theta \xi }}, S^{*}=\frac{\mu +\frac{k\sigma A}{\theta \xi }+\gamma }{\lambda (1+\frac{m\sigma A}{b\theta \xi +\sigma A})}\), \(I^{*}=\frac{(1-\frac{\alpha \sigma A}{\theta \xi })a}{\mu +\frac{k\sigma A}{\theta \xi }}-\frac{\mu +\frac{k\sigma A}{\theta \xi }+\gamma }{\lambda (1+\frac{m\sigma A}{b\theta \xi +\sigma A})}\).

In order to ensure \(I^{*}>0\), we need \(\frac{a\lambda (1-\frac{\alpha \sigma A}{\theta \xi })(1+\frac{m\sigma A}{b\xi \theta +\sigma A})}{(\mu +\frac{k\sigma A}{\xi \theta })(\mu +\gamma +\frac{k\sigma A}{\xi \theta })}>1 \). Let \(R_{0}=\frac{a\lambda (1-\frac{\alpha \sigma A}{\theta \xi })(1+\frac{m\sigma A}{b\xi \theta +\sigma A})}{(\mu +\frac{k\sigma A}{\xi \theta })(\mu +\gamma +\frac{k\sigma A}{\xi \theta })} \), which is called the basic reproductive number. We can obtain \(I^{*}>0\), if \(R_{0}>1\).

The Jacobian matrix at \(E_{1}\) is

$$\begin{aligned} J(E_{1})= \left( \begin{array}{ccc} -\left( \mu +\frac{k\sigma A}{\theta \xi }\right) &{}\quad -\lambda \frac{\left( 1-\frac{\alpha \sigma A}{\theta \xi }\right) a}{\mu +\frac{k\sigma A}{\theta \xi }}\left( 1+\frac{m\sigma A}{b\theta \xi +\sigma A}\right) +\gamma \\ 0&{}\quad \lambda \frac{\left( 1-\frac{\alpha \sigma A}{\theta \xi }\right) a}{\mu +\frac{k\sigma A}{\theta \xi }}\left( 1+\frac{m\sigma A}{b\theta \xi +\sigma A}\right) -\left( \mu +\frac{k\sigma A}{\theta \xi }+\gamma \right) \\ \end{array}\right) , \end{aligned}$$

and the eigenvalues are

$$\begin{aligned} \lambda _{1}= & {} -\left( \mu +\frac{k\sigma A}{\theta \xi }\right) , \\ \lambda _{2}= & {} \lambda \frac{\left( 1-\frac{\alpha \sigma A}{\theta \xi }\right) a}{\mu +\frac{k\sigma A}{\theta \xi }}\left( 1+\frac{m\sigma A}{b\theta \xi +\sigma A}\right) -\left( \mu +\frac{k\sigma A}{\theta \xi }+\gamma \right) . \end{aligned}$$

Obviously, \(\lambda _{1}=-(\mu +\frac{k\sigma A}{\theta \xi })<0\).

If \(R_{0}<1\), we have

$$\begin{aligned} \lambda \frac{\left( 1-\frac{\alpha \sigma A}{\theta \xi }\right) a}{\mu +\frac{k\sigma A}{\theta \xi }}\left( 1+\frac{m\sigma A}{b\theta \xi +\sigma A}\right) -\left( \mu +\frac{k\sigma A}{\theta \xi }+\gamma \right) <0, \end{aligned}$$

thus \(\lambda _{2}<0\).

If \(R_{0}<1\), the equilibrium \(E_{1}\) is locally asymptotically stable, and since the disease-free equilibrium \(E_{1}\) is the only equilibrium under this condition, then \(E_{1}\) is globally asymptotically stable.

The Jacobian matrix at \(E_{2}\) is

$$\begin{aligned}&J(E_{2}) \\&\quad = \left( \begin{array}{ccc} -\lambda I^{*}\left( 1+\frac{m\sigma A}{b\theta \xi +\sigma A}\right) -\left( \mu +\frac{k\sigma A}{\theta \xi }\right) &{}\quad -\lambda S^{*}\left( 1+\frac{m\sigma A}{b\theta \xi +\sigma A}\right) +\gamma \\ \lambda I^{*}\left( 1+\frac{m\sigma A}{b\theta \xi +\sigma A}\right) &{}\quad \lambda S^{*}\left( 1+\frac{m\sigma A}{b\theta \xi +\sigma A}\right) -\left( \mu +\frac{k\sigma A}{\theta \xi }+\gamma \right) \\ \end{array}\right) . \end{aligned}$$

We have

$$\begin{aligned} Tr(J(E_{2}))= & {} \lambda S^{*}\left( 1+\frac{m\sigma A}{b\theta \xi +\sigma A}\right) -\left( \mu +\frac{k\sigma A}{\theta \xi }+\gamma \right) \\&-\lambda I^{*}\left( 1+\frac{m\sigma A}{b\theta \xi +\sigma A}\right) -\left( \mu +\frac{k\sigma A}{\theta \xi }\right) \\= & {} \gamma -\lambda \left( 1+\frac{m\sigma A}{b\theta \xi +\sigma A}\right) \frac{\left( 1-\frac{A\sigma \alpha }{\theta \xi }\right) a}{\mu +\frac{k\sigma A}{\theta \xi }}. \end{aligned}$$

we know \(\frac{\lambda (1-\frac{\alpha \sigma A}{\theta \xi })a(1+\frac{m\sigma A}{b\theta \xi +\sigma A})}{\gamma (\mu +\frac{k\sigma A}{\theta \xi })}>\frac{a\lambda (1-\frac{\alpha \sigma A}{\theta \xi })(1+\frac{m\sigma A}{b\xi \theta +\sigma A})}{(\mu +\frac{k\sigma A}{\xi \theta })(\mu +\gamma +\frac{k\sigma A}{\xi \theta })}\), and if \(R_{0}>1\), we have \(Tr(J(E_{2}))<0\).

On the other hand, we have

$$\begin{aligned} \det (J(E_{2}))= & {} \left( -\lambda I^{*}\left( 1+\frac{m\sigma A}{b\theta \xi +\sigma A}\right) -\left( \mu +\frac{k\sigma A}{\theta \xi }\right) \right) \left( \lambda S^{*} \left( 1+\frac{m\sigma A}{b\theta \xi +\sigma A}\right) \right. \\&-\left. \left( \mu +\frac{k\sigma A}{\theta \xi }+\gamma \right) \right) -\left( \lambda I^{*}\left( 1+\frac{m\sigma A}{b\theta \xi +\sigma A}\right) \right) \\&\left( -\lambda S^{*}\left( 1+\frac{m\sigma A}{b\theta \xi +\sigma A}\right) +\gamma \right) \\= & {} \lambda \left( 1+\frac{m\sigma A}{b\theta \xi +\sigma A}\right) \left( \mu +\frac{k\sigma A}{\theta \xi }\right) \left( \frac{\left( 1-\frac{\alpha \sigma A}{\theta \xi }\right) a}{\mu +\frac{k\sigma A}{\theta \xi }}-\frac{2\left( \mu +\frac{k\sigma A}{\theta \xi }+\gamma \right) }{\lambda \left( 1+\frac{m\sigma A}{b\theta \xi +\sigma A}\right) }\right) \\&+\left( \mu +\frac{k\sigma A}{\theta \xi }+\gamma \right) \left( \mu +\frac{k\sigma A}{\theta \xi }\right) \\= & {} \lambda \left( 1+\frac{m\sigma A}{b\theta \xi +\sigma A}\right) \left( 1-\frac{\alpha \sigma A}{\theta \xi }\right) a-\left( \mu +\frac{k\sigma A}{\theta \xi }+\gamma \right) \left( \mu +\frac{k\sigma A}{\theta \xi }\right) . \end{aligned}$$

Clearly, if \(R_{0}>1\), we have \(\det (J(E_{2}))>0.\)

If \(R_{0}>1\), the interior equilibrium \(E_{2}\) is locally asymptotically stable.

Finally, we will prove that system (8) has no periodic orbits (Chauhan et al. 2015).

Now we choose the Dulac function

$$\begin{aligned} B(S,I)=\frac{\mu +S}{SI}. \end{aligned}$$

Let

$$\begin{aligned} f(S,I)= & {} \frac{dS}{dt}=\left( 1-\frac{\alpha \sigma A}{\theta \xi }\right) a-\left( 1+\frac{m\sigma A}{b\theta \xi +\sigma A}\right) \lambda SI-\left( \mu +\frac{k\sigma A}{\theta \xi }\right) S+\gamma I, \\ g(S,I)= & {} \frac{dI}{dt}=\left( 1+\frac{m\sigma A}{b\theta \xi +\sigma A}\right) \lambda SI-\left( \mu +\frac{k\sigma A}{\theta \xi }\right) I-\gamma I. \end{aligned}$$

We have

$$\begin{aligned} \frac{\partial }{\partial S}(Bf)+\frac{\partial }{\partial I}(Bg)= & {} \frac{\partial }{\partial S} \left( \frac{\mu +S}{SI}\left( \left( 1-\frac{\alpha \sigma A}{\theta \xi }\right) a-\left( 1+\frac{m\sigma A}{b\theta \xi +\sigma A}\right) \lambda SI\right. \right. \\&\left. \left. -\left( \mu +\frac{k\sigma A}{\theta \xi }\right) S+\gamma I\right) \right) \\&+\frac{\partial }{\partial I}\left( \frac{\mu +S}{SI}\left( \left( 1+\frac{m\sigma A}{b\theta \xi +\sigma A}\right) \lambda SI\right. \right. \\&-\left. \left. \left( \mu +\frac{k\sigma A}{\theta \xi }\right) I-\gamma I\right) \right) \\= & {} -\frac{\mu \left( 1-\frac{\alpha \sigma A}{\theta \xi }\right) a}{IS^{2}}-\left( 1+\frac{m\sigma A}{b\theta \xi +\sigma A}\right) \lambda -\frac{\mu +\frac{k\sigma A}{\theta \xi }}{I}, \end{aligned}$$

then

$$\begin{aligned} \frac{\partial }{\partial S}(Bf)+\frac{\partial }{\partial I}(Bg)\le 0. \end{aligned}$$

According to the Bendixson–Dulac criterion, system (8) has no periodic orbits, hence the interior equilibrium \(E_{2}\) is globally asymptotically stable. \(\square \)

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Wang, L., Jin, Z. & Wang, H. A switching model for the impact of toxins on the spread of infectious diseases. J. Math. Biol. 77, 1093–1115 (2018). https://doi.org/10.1007/s00285-018-1245-7

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  • DOI: https://doi.org/10.1007/s00285-018-1245-7

Keywords

  • Switching model
  • Environmental toxin
  • Infectious disease
  • Stability
  • Bifurcation

Mathematics Subject Classification

  • 92D30 (epidemiology)
  • 93D20 (asymptotic stability)
  • 34C23 (bifurcation)