Emergence and control of outbreaks in human populations

Les maladies infectieuses émergentes ont façonné l'histoire de l'espèce humaine. Encore aujourd'hui, l'émergence de nouveaux pathogènes menace la santé publique. Deux axes principaux ont été abordés : la dynamique épidémique des maladies infectieuses émergentes (MIEs) dans les populations humaines et l'impact du comportement humain sur le contrôle des infections. La dynamique épidémique des MIEs est peu connue car elle reste souvent étudiée sans prendre en compte l'effet de leurs caractéristiques, à savoir leur maintien dans un réservoir et leur capacité à émerger chez plusieurs espèces animales. Nous avons modélisé la dynamique des MIEs et mis en évidence que la transmission via réservoir et les populations intermédiaires est aussi importante que la transmission inter humaine pour comprendre les nombreuses et imprévisibles épidémies que l'on peut observer. Par la suite, l'impact du comportement humain sur le contrôle des infections a été étudié en considérant deux aspects, la prise de décision de vaccination et les pratiques culturelles. La considération de biais cognitifs liés à la prise de décision de vaccination et l'interaction entre le comportement et l'épidémiologie peut aboutir aux fluctuations de la couverture vaccinale observées empiriquement. Enfin, l'étude des pratiques culturelles a montré que, bien que souvent considérées comme à l'origine de la propagation de pathogènes dans la population, certaines pratiques peuvent en limiter la transmission. L'ensemble de ces résultats suggère que la prise en compte de l'écologie permet de faire de meilleures prédictions sur l'influence de l'environnement sur l'émergence et la réémergence des maladies infectieuses.Infectious diseases have shaped the history of the human species. Nowadays, the emergence of new pathogens threatens public health. Understanding the interaction between pathogen ecology and human behaviour can help understanding the dynamics observed in human populations. In this thesis, two main axes were studied: the epidemic dynamics of emerging infectious diseases (EID's) in human populations and the impact of human behaviour on the control of infectious diseases. The epidemic dynamics of emerging pathogens is poorly understood because it is often studied without taking into account the effect of their characteristics, namely their persistence in a reservoir population and their ability to emerge in a broad range of species. For the first time, we modeled the dynamics of EID's and highlighted that transmission from both the reservoir and intermediate populations are critically important to consider in order to understand the many and unpredictable outbreaks that can be observed. Thereafter, the impact of human behaviour on infectious diseases control was studied by considering two aspects, vaccination decision-making and cultural practices. We show that consideration of cognitive biases related to vaccination decision-making and the interaction between behaviour and epidemiology can lead to the fluctuations observed in vaccination coverage. Finally, the study of cultural practices has shown that, although often assumed to favour the spread of pathogens in a population, certain practices can limit disease transmission. The results taken together suggest that an ecological approach is key for predicting the dynamics underpinning the emergence and re-emergence of infectious diseases and adapt control strategies

Correction: Beyond Rational Decision-Making: Modelling the Influence of Cognitive Biases on the Dynamics of Vaccination Coverage.

[This corrects the article DOI: 10.1371/journal.pone.0142990.]

Parameters used and their default values.

<p>UT denotes the unit of time which can be expressed in year or ten years.</p><p>Parameters used and their default values.</p

Confirmation bias, conformism and the emergence of oscillations in vaccination coverage.

<p>The emergence of oscillations (o) is depicted as a function of an infection’s reproductive rate (R₀) and the rate of negative side effects from vaccination (<i>δ</i>^<i>v</i>). The emergence of oscillations is contingent on the inclusion of confirmation bias but not conformism and is observed for intermediate values of R₀. Three models are considered: Model 3 (without either confirmation bias or conformism); Model 4 (including confirmation bias) and Model 5 (including both confirmation bias and conformism). If no oscillations emerge, three dynamics can be noted for the distribution of opinions of vaccination: (i) coexistence of 2 opinions with a greater number of individuals with a positive opinion (+), (ii) coexistence of 2 opinions with a greater number of individuals with a negative opinion (-), (iii) all individuals have a positive opinion (*). The models are analysed for 2 different generation times (birth (<i>b</i>) = death (<i>d</i>) = 1 and 0.5) and for different values of the rate of negative side effects from infection (<i>δ</i>^<i>I</i>). 1 million iterations were run with a time step of 0.0001 which correspond to 100 unit of time.</p

Infection and opinion dynamics as a function of the reproductive rate of the infection (model 5).

<p>For each value of R₀ (number of secondary infections which can be understood as the fitness of the pathogen; it must be >1 for the pathogen to invade the population), the time-series of the dynamics of the infection (top panel; the thin line corresponds to the number of infected individuals and the thick line corresponds to the number of vaccinated individuals) and opinion (bottom panel; the dashed grey line indicates the number of individuals who have a positive opinion and the dashed black line indicates individuals who have a negative opinion) are depicted for a rate of negative side effects from vaccination <i>δ</i>^<i>v</i> = 0.7. Alternative changes of opinions of vaccination and oscillations in vaccination coverage emerge for intermediate values of R₀. When the reproductive rate of the disease is large (R₀ >4), the negative opinion of vaccination disappears and vaccination coverage, while reaching high levels, does not reach herd immunity. This is because the infection spreads too quickly. More generally, four dynamics can be observed and a₁ to a₃ represent their limits which can be moved depending of the values of <i>δ</i>^<i>v</i> considered.</p

Components of the different nested models.

<p>Components of the different nested models.</p

Structure of the behaviour-incidence model.

<p>The model is an augmentation of a classic SIR compartmental model [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0142990#pone.0142990.ref030" target="_blank">30</a>]. Individuals are characterized by both their epidemiological status (<i>S</i>: susceptible; <i>I</i>: infected; <i>R</i>^<i>v</i>: recovered through vaccination; <i>R</i>^<i>g</i>: recovered naturally) and for each compartment, their opinion of vaccination (positive: subscript p; non-shaded colours; negative: subscript n; shaded colours). <i>C</i>^<i>V</i> and <i>C</i>^<i>I</i> are compartments that indicate the total recalled number of individuals having suffered negative side effects from vaccination and infection, respectively. <i>β</i> indicates the rate of infection transmission; Ω indicates the rate at which individuals change their opinion (from positive to negative Ω or from negative to positive Ω′); <i>δ</i> indicates the number of individuals suffering side effects from either vaccination <i>δ</i>^<i>V</i> and infection <i>δ</i>^<i>I</i>; <i>θ</i> indicates the rate at which individuals vaccinate. Each individual dies at the same rate <i>d</i>.</p

Conformism and oscillations in vaccination coverage.

<p>The evolution of the number of infected (thin line) and (thick line) vaccinated individuals is depicted. Conformism (illustrated by Model 5) increases the amplitude of oscillations and slows down the rate at which alternative opinions of vaccination alternate. The situation is indicated for infections that are moderately infectious (with a reproductive ratio (R₀) varying from 3 to 5) and with the rate of negative side effects from the vaccination (<i>δ</i>^<i>V</i>) varying from 0.5 to 2.</p