Abstract
The aim of this paper is to show that a large class of epidemic models, with both demography and non-permanent immunity incorporated in a rather general manner, can be mathematically formulated as a scalar renewal equation for the force of infection.
1. Introduction
As early as 1927, Kermack and McKendrick published a paper ‘A contribution to the mathematical theory of epidemics’ in the Proceedings of the Royal Society London Ser. A Citation31. The paper became a classic in infectious disease epidemiology and has been cited innumerable times. It was reprinted, with a discussion by Roy Anderson, in a special issue ‘Classics of theoretical biology’ (part two) in the Bulletin of Mathematical Biology Citation34.Footnote†
But how often is it actually read? Judging from an incessant misconception of its contents, one is inclined to conclude: hardly ever! Indeed, even experienced experts often believe that the paper is just about the system
The same issue of the Bulletin of Mathematical Biology has also reprinted two follow-up papers:
• | Contributions to the mathematical theory of epidemics-II. The problem of endemicity [32,35 . | ||||
• | Contributions to the mathematical theory of epidemics-III. Further studies of the problem of endemicity [33,36 . |
In the spirit of Inaba Citation29, the aim of the present paper is to take up ‘the problem of endemicity’ and to show that, actually, one can formulate it in a way that is very reminiscent of the general epidemic model of 1927, provided one takes the force of infection as the primary unknown. In particular, we show that one can derive a scalar nonlinear renewal equation for the force of infection under rather general assumptions on both the demographic turnover and the waning of immunity. In order to introduce this approach, we first rederive in Section 2 the main results of the 1927 paper by employing the formulation in terms of the renewal equation for the force of infection. In this ‘epidemic’ setting, the population is demographically closed and infection leads to permanent immunity. In Section 3, we relax the first of these assumptions and introduce newborn individuals, which are susceptible, at a constant rate B. Rather than introducing an age-independent per capita death rate, we describe the survival probability till at least age a by a general decreasing function in order to be able to incorporate the rather flat age distributions that characterize modern developed countries. We derive the unsurprising result that there is a unique endemic equilibrium when R 0>1 and no endemic equilibrium when R 0<1. For the special case of a constant per capita death rate μ (so ), we show that the endemic equilibrium is locally asymptotically stable, no matter how infectiousness depends on time since infection. We are unable to extend this result to general survival functions and wonder whether or not one might have the possibility of a Hopf bifurcation in that setting.
In Section 4, we drop the assumption of permanent immunity and allow for re-infection of the same individual. We note that there are multiple options for describing partial protection but that, whichever of these we choose, the force of infection is determined by a scalar renewal equation (the difference is in the kernel). We briefly discuss the existence and uniqueness of an endemic state, but essentially leave this as an open problem (for ourselves), and we do not even start to discuss the question of local stability. Our plan is to investigate these issues in the near future. Moreover, we would like to incorporate vaccination schedules and multiple strains (perhaps in Adaptive Dynamics spirit) in order to study how (and on what time scale) vaccination may lead to strain replacement in populations with realistic demographic structure.
In the appendix, we show how the SISFootnote1 compartmental model fits into the framework described in this paper.
2. An epidemic in a closed population
We denote
Quite in general, a delay equation is a rule for extending a function of time towards the future on the basis of the (assumed to be) known past. Such equations lead to dynamical systems by considering the shift along the extended function, see Citation15 Citation16. The renewal Equationequation (1), with Equation(2) substituted, fits into this framework!
But before we rederive the main conclusion of the Kermack–McKendrick 1927 paper from EquationEquation (1), let us briefly discuss how one would choose the function A of time-since-infection τ.
First of all, we emphasize that familiar compartmental epidemic models are included in the present framework:
• | contact intensity | ||||
• | infectiousness (i.e. probability of transmission, given a contact with a susceptible). |
In order to be mathematically precise, we assume
Using elementary properties of the function and simple graphical arguments, one proves that EquationEquation (5) has a unique strictly positive solution if R 0>1 and no strictly positive solution if R 0≤1. In order to translate this information into the Kermack–McKendrick threshold theorem, we first observe that EquationEquation (2) implies
(i) when R 0>1, introduction of the infective agent leads to an outbreak with the final size given by EquationEquation (7), where y(∞) is the strictly positive solution of EquationEquation (5) | |||||
(ii) when R 0≤1, introduction of the infective agent leads to an outbreak with the final size close to zero. |
Concerning (i): this is the deterministic formulation that takes as a starting point that a small but positive fraction of the large population is infected. When we start with a small number of infected individuals, we should describe the initial stages by a branching process. The condition R 0>1 then amounts to the branching process being supercritical. As a branching process may go extinct, even when supercritical, we may get a minor outbreak. EquationEquation (7) describes the expected size of a major outbreak. See, for instance, the textbook of Diekmann and Heesterbeek Citation13.
Concerning (ii): for R 0≤1, the relevant solution of EquationEquation (7) is zero, so where does the ‘close to zero’ come from? In deriving EquationEquation (5), we assumed that y satisfied EquationEquation (4) for all . When providing an initial condition that captures the introduction of a small number of infectives, we essentially replace EquationEquation (4) by
(ii′) when R 0≤1, the positive solution y(∞) of EquationEquation (9) converges to zero when . |
If transmission occurs on a time scale which is fast relative to the time scale of demographic changes (birth, death and migration), we may conceive the host population as static during the outbreak. If, moreover, infection confers permanent immunity, the fraction of the population that is susceptible necessarily decreases monotonically and the concept of ‘final size’ makes sense. In this section, we have shown that the expected final size can be characterized in terms of the scalar Equationequation (5) involving only one parameter, the basic reproduction number R 0. In turn, R 0 is defined by EquationEquation (6), that is, as the product of the original density of susceptibles and the expected total contribution to the force of infection of one newly infected individual. EquationEquation (5) was derived from a nonlinear scalar renewal equation. The entire content of this section is presented in the 1927 paper of Kermack–McKendrick! The only thing we have done is to rewrite the derivation in such a way that the transition to the next sections, dealing with demographic turnover and with waning immunity, is as smooth as possible.
3. The endemic balance
Even if individuals remain immune for the rest of their life after an infection, new susceptibles arise as a result of reproduction. In this section, we consider the situation where, at the population level, there is a constant birth rate B. So from the point of view of the infectious agent, there is a constant inflow at rate B of ‘resource’.
Individuals come and go: we now also need to specify how long would newborn individuals live. We do so in terms of the probability that an individual stays alive for at least a units of time (see Citation20 for a very early use of this description in terms of ℱ in the context of an epidemic model). In particular, we assume that this survival probability does not depend on whether or not the individual becomes infected (so we investigate the influence of demographic turnover on disease transmission and not the influence of a deadly infectious disease such as HIV on population dynamics). We would like to keep ℱ general and not restrict to the special family with μ>0, simply since in modern developed countries the observed ℱ is hugely different from exponential. Note that we have introduced two more model ingredients (on top of A(τ)), viz., B and . We assume that , that ℱ is monotonically non-increasing and that exponentially for . We consider B>0.
If no infectious disease is circulating, we observe the stable age distribution
If we substitute EquationEquation (10) into EquationEquation (11), we arrive at a nonlinear scalar renewal equation for the unknown F. Apart from the disease-free state F(t)≡0, there might be an endemic steady state. To find it, we need to solve
The Principle of Exchange of Stability Citation37 guarantees that the endemic steady state is stable for R 0 slightly greater than one. To ascertain the local stability for larger values of R 0, we linearize (11)–(10) and look for solutions of the form for the linearized equation
Notation: Ā is the Laplace transform of A, that is, .
Theorem 3.1
Assume that with μ>0 and that R 0>1 where
Proof
By the theory developed in Citation12 Citation16, it suffices to show that all roots of EquationEquation (13) have a negative real part. A straightforward computation, using also EquationEquation (14), shows that EquationEquation (13) takes the form
Remark
The proof given above is a much simplified version of the elaboration of [Exercise 3.10]Citation13. See [Theorem 5]Citation52 for a different proof and various generalizations. Global stability has been shown in Citation42 and a text-book version of that proof can be found in [Section 9.9]Citation49.
In this section, we have shown that when demographic turnover is described by a constant population birth rate and a general survival function, we can still capture the infectious disease dynamics by a scalar renewal equation for the force of infection. This equation has a positive steady state if and only if R 0>1 and there is at most one such steady state. For the special case of a constant per capita death rate, we can find that this steady state is locally asymptotically stable. Whether or not it is for general survival is an open problem. But note that Andreasen Citation1, Magal and Ruan Citation43 and Thieme Citation50 found that instability is certainly possible if we allow the susceptibility or the infectiousness of an individual to depend on its age.
4. Waning immunity
When immunity reduces susceptibility, but has no impact on infectiousness once the infection occurs, we can stick to the already introduced A(τ) to describe the expected contribution to the force of infection at time τ after infection, given survival.
There are at least two ways to describe temporary partial immunity. One may assume that all individuals, at time τ after the last infection took place, have their susceptibility reduced by a factor Q(τ), meaning that the probability of getting infected upon contact with an infectious individual is Q(τ) times what it is for a fully susceptible individual (so Q(τ)=0 corresponds to full protection and Q(τ)=1 to full susceptibility). Another possibility is to allow only for full protection or full susceptibility and to describe the probability that at time τ after the last infection an individual is fully susceptible again by P(τ). Note that SIS and SIRS compartment models (see Appendix) are of this type. Note that Inaba Citation28–30 followed Kermack and McKendrick Citation32 Citation33 Citation35 Citation36 in assuming that a new clock starts when an event transforms an infected individual into a recovered individual and that the reduction in susceptibility is a function of the time shown by the second clock (see Citation54 for a similar approach; also see Citation18 Citation43 Citation46 Citation53). Also note that one can, of course, introduce a compartment of intermediate protection (see e.g. Citation39 Citation48 and the references given there) and thus obtain a description that combines features of both gradual return of susceptibility and variability in the length of the time window of protection. One might also assume that susceptibility does not simply depend on the time since last infection, but also depends on the history of previous infections Citation39.
Note that we have to make sure that an infected individual that contributes to the positivity of P(τ) should not contribute to the positivity of A(σ) for , as otherwise the possibility of re-infection does have an influence on expected infectiousness (for compartmental models of SIS and SIRS type, etc., this is indeed guaranteed). When we work with Q, the safest assumption is to require that Q(τ)=0 for . In any case, we can assume that EquationEquation (11), which we repeat here as
Another important observation is that, implicitly, P and Q refer to an individual while conditioning that this individual is not yet re-infected. The possibility of re-infection leads, in the case of partial susceptibility, to the conclusion that of the individuals that become infected at time t−τ, a fraction
We are now ready to replace expression Equation(10) by the equation
If we define q(t, a) by the relation
In the special case that , EquationEquation (24) reduces to
5. Concluding remarks
Often when epidemic models are formulated as delay equations, the aim is to derive conditions for the (in)stability of the endemic steady state and to find out, by way of the Hopf bifurcation theorem, when one should expect to find persistent oscillations Citation5 Citation6 Citation10 Citation14,Citation22–26,Citation29 Citation43 Citation55 Citation56.
The trigger for the approach sketched in the present paper is somewhat different: our ultimate goal is to find conditions for strain replacement as a result of mass vaccination Citation17 Citation19 Citation27 Citation38 Citation44. In this context, the need arises to incorporate
(i) a realistic form of demographic turnover, | |||||
(ii) the waning of immunity and | |||||
(iii) cross-immunity. |
Acknowledgements
We are grateful to Horst Thieme for providing us with a substantial list of very relevant references! Comments by Horst and by Hisashi Inaba have been very helpful.
Notes
†Dedicated to Simon Levin on the occasion of his 70th birthday. Thank you Simon for being an organizing centre for mathematical biology, a source of inspiration for two of the authors for many years.
, , and .
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Appendix
Here, we show how the familiar SIS compartmental model described by the ODE system
For a given function F=F(t), let M i (t; s), i=1, 2, be defined by
By using for each of the two equations of (A3) the variation-of-constants formula, we obtain
From Equation (A4), we have