Effect of seasonality on the dynamics of an imitation-based vaccination model with public health intervention

Bruno Buonomo; Giuseppe Carbone; Alberto d'Onofrio

doi:10.3934/mbe.2018013

Article Contents

2018, Volume 15, Issue 1: 299-321. Doi: 10.3934/mbe.2018013

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Effect of seasonality on the dynamics of an imitation-based vaccination model with public health intervention

1.
Department of Mathematics and Applications, University of Naples Federico Ⅱ, via Cintia, I-80126 Naples, Italy
2.
International Prevention Research Institute, 95 cours Lafayette, 69006 Lyon, France

* Corresponding author
* Corresponding author

Received: November 18, 2016

Revised: April 02, 2017

Published: February 2018

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Abstract

We extend here the game-theoretic investigation made by d'Onofrio et al (2012) on the interplay between private vaccination choices and actions of the public health system (PHS) to favor vaccine propensity in SIR-type diseases. We focus here on three important features. First, we consider a SEIR-type disease. Second, we focus on the role of seasonal fluctuations of the transmission rate. Third, by a simple population-biology approach we derive -with a didactic aim -the game theoretic equation ruling the dynamics of vaccine propensity, without employing 'economy-related' concepts such as the payoff. By means of analytical and analytical-approximate methods, we investigate the global stability of the of disease-free equilibria. We show that in the general case the stability critically depends on the 'shape' of the periodically varying transmission rate. In other words, the knowledge of the average transmission rate (ATR) is not enough to make inferences on the stability of the elimination equilibria, due to the presence of the class of latent subjects. In particular, we obtain that the amplitude of the oscillations favors the possible elimination of the disease by the action of the PHS, through a threshold condition. Indeed, for a given average value of the transmission rate, in absence of oscillations as well as for moderate oscillations, there is no disease elimination. On the contrary, if the amplitude exceeds a threshold value, the elimination of the disease is induced. We heuristically explain this apparently paradoxical phenomenon as a beneficial effect of the phase when the transmission rate is under its average value: the reduction of transmission rate (for example during holidays) under its annual average over-compensates its increase during periods of intense contacts. We also investigate the conditions for the persistence of the disease. Numerical simulations support the theoretical predictions. Finally, we briefly investigate the qualitative behavior of the non-autonomous system for SIR-type disease, by showing that the stability of the elimination equilibria are, in such a case, determined by the ATR.

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Mathematics Subject Classification: 92D30.

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Figure 1. Sinusoidal fluctuations of the transmission rate. The effective BRN of system (7)-(21), $\mathcal{R}(p_2,\sigma)$, as function of $\bar{\gamma}$ and $\sigma$. Figure (a): $\mathcal{R}(p_2,\sigma)$ vs $\sigma$ with $\bar{\gamma} = 1.4 \times 10^{-4}$. Figure (b): $\mathcal{R}(p_2,\sigma)$ vs $\sigma$ with $\bar{\gamma} = 1.2 \times 10^{-4}$. Figure (c): $\mathcal{R}(p_2,\sigma)$ vs $\bar{\gamma}$ with $\sigma = 0.3$. The other parameter values are taken as described in Section 5.1.

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Figure 2. The stabilizing role of seasonality. Left panels: dynamics of model (7)-(21) for $\sigma = 0.3$. Right panels: dynamics of model (7)-(21) for $\sigma= 0.95$. The initial conditions are $S_0 = 1/R_0$, $E_0 = 9\times 10^{-6}$, $I_0 = 8\times 10^{-6}$, $p_0 = 0.95$. The other parameter values are taken as described in Section 5.1, and $\bar\gamma = 1.41 \times 10^{-4}$.

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Figure 3. Extinction (left panels) and uniform persistence (right panels) of disease. Left panels: dynamics of model (7)-(21) for $\bar\gamma = 1.5 \times 10^{-4}$. Right panels: the dynamics of model (7)-(21) for $\bar\gamma = 1.2 \times 10^{-4}$. The initial condition are $S_0 = 1/R_0$, $E_0 = 9\times 10^{-6}$, $I_0 = 8\times 10^{-6}$, $p_0 = 0.95$. The other parameter values are taken as described in Section 5.1, and $\sigma = 0.3$.

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Figure 4. The stabilizing role of seasonality in case of piecewise contact rate. Plot of the Spectral Radius of the Floquet Matrix $F$ versus the parameter $\eta = c_L/c_H$ measuring the reduction of contacts. Parameters $\gamma$ and $\alpha$ are such that $p_2 = 0.99 p_{*}$. Left Panel: $f=0.5$ (i.e. $c(t)=c_L$ for half year); right panel: $f=0.25$ (i.e. $c(t)=c_L$ for one quarter of year). The other parameter values are taken as described in Section 5.1.

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