The recent outbreak of COVID-19 has dramatically increased interest in mathematical models of infection spread, one for all the celebrated SIR model and its variants. Since its introduction by Kermack and McKendrick [17] , a large number of modifications of SIR have been proposed and implemented to suit the diversity of real world phenomena. The present contribution focuses on a mathematically rigorous derivation of SIR model and some emblematic modifications as a large scale limit of Markovian, individual-based mechanics, a validated strategy in the context of application to life sciences, [4, 23] . It is not our aim to introduce a new model, or to discuss performances of classical ones in practical applications, but instead to consolidate foundations behind used models.
Rather than aiming to achieve the broadest generality, a task made impervious by the topic's vastitude, we focus on mathematical difficulties arising in two settings: a local scaling limit towards the SIR model, and a Mean Field limit producing a non-local version of the latter. The strategy of proofs will make quite clear that our results can be extended to other classes of models, variants of SIR, more realistic for practical applications.
The classical compartimental SIR model, Time evolution of an individual's state will be denoted by A i,t , t ≥ 0. We will also assign a position x i ∈ D ⊂ R 2 to the individual i, D being a bounded domain modelling a geographical region: for the sake of clarity the bulk Date: July 14, 2020. of our discussion will not consider spatial dynamics of individuals -corresponding to diffusion terms at large scales-so positions will not be included in phase space.
Influence of individuals on each other will be encoded in an interaction function τ N : {1, . . . , N } 2 → R, a particular case of which is obtained by considering a function T N : D 2 → R and evaluating T N (x i , x j ), i, j = 1, . . . , N . Functions τ N , T N model contagion rate, the notation hinting to the fact that they might be interpreted as the average time an individual i spends interacting with j, since one can assume that probability of contagion is proportional to such times.
The dynamics is defined by the infinitesimal generator acting on observables 
which, given an initial distribution, defines a continuous time Markov chain on {S, I, R} N . The generator L is split into a non-interacting component L 0 describing the transition from Infected state to Removed, and the more complex interaction part L 1 , modelling transition from Susceptible to Infected. Transition rate p > 0 is a positive number, and notation A Ai→I denotes the state obtained from A by replacing the i-th coordinate with I.
Given a probability measure µ 0 on D × {S, I, R}, let (x n , A n 0 ) n∈N be a sequence of independent identically distributed random variables with law µ 0 with finite first order moment. For any N ≥ 1 we then consider the continuous time Markov chain A N t with initial distribution (A 1 0 , . . . , A N 0 ) and generator L: we associate to it the empirical measure
Notice that the process µ N t thus defined has càdlàg trajectories in M {S,I,R} : we will consider the space of càdlàg M {S,I,R} -valued processes D([0, T ], M {S,I,R} ) with Skorokhod topology.
The Law of Large Numbers implies that µ N 0 converges in probability to µ 0 . Our first result is convergence of the empirical measure µ N t to solutions of a non-local version of (1.1), that is the system for f t : D × {S, I, R} → R, t ≥ 0, x ∈ D,
. The definition of measure-valued solutions to the latter will be reviewed in section 3. Theorem 1.1. Let the interaction function be given by
where T ∈ C b (D × D) is a non negative function and q > 0. The process µ N t converges in probability, on D([0, T ], M {S,I,R} ) with Skorokhod topology, to the unique measure-valued solution µ t of (1.2). Theorem 1.1, which we will prove in section 3, deals with a Mean Field limit: the scaling of the interaction is such that, in the limit, any individual potentially interacts with all the other individuals, and in any case with a relevant fraction of the "infinite" limiting population. The proof in this case is classical and maybe already traced somewhere: we include it here for completeness, and since we could not find a precise reference.
In section 4, we will instead consider a different scaling of the interaction, resulting in a limit in which individuals interact with a negligible part of the whole population. Indeed, the limiting dynamics will be the completely local equation
x) which is simply a collection of SIR equations indexed by x ∈ D, evolving independently at any point.
Let the interaction function be given by
a rotation invariant function. Assume moreover that the initial distribution of points µ 0 has density of class C 2 with respect to Lebesgue's measure on Besides a factor 1 N averaging contributions of all individuals, the interaction function τ N includes a mollifier-like scaling so that, as N increases, each individual interacts with a limited portion of the total population, unlike in Theorem 1.1. In particular, the interaction radius in the limit N → ∞ will be null, and the interaction kernel converge to Dirac's delta. Such scaling intuitively corresponds to the case when there are strong restrictions on movements and thus individuals interact only with their close neighbours.
This case is much more difficult mathematically. In fact, one could distinguish two sub-regimes, one in which an individual interacts only with the strict nearest neighbours, that is β = 1, the other in which it interacts with a large number of nearby individuals, still however an infinitesimal portion of the total population, β < 1. The former case is very difficult and we have not identified yet the solution. Indeed, in Theorem 1.2 we only consider the second case, in the restricted regime β < 1 3 , which we might dub "intermediate interactions", closely related to the result on moderate interactions of [24, 25, 26, 22, 16, 31, 15] .
As already mentioned, the study of compartmental epidemic models such as (1.1) and (1.2) is the object of a rich literature. The main research problem for nonlocal models is the qualitative behaviour of solutions and the emergence of travelling waves, whose relevance is obvious in terms of prediction and control of disease spread. We mention [37, 36] on the equations (1.2) we obtain, [6, 32, 21] on a model including diffusion effects, and [35, 33, 39, 41, 40] considering a delayed, nonlocal contagion spread. Because of the Markovian nature of our individual-based model, we can't include delay in our discussion, and leave it for future developments. Another possible feature we will not address is a more general nonlinear incidence term for the disease term, see [20, 8, 30, 34] . Derivation of SIR model and related ones from individual-based model has also been considered, although from points of view rather different from ours. We mention works on the derivation of SIR from kinetic models, representing interaction of individuals as collisions of hard spheres, [7, 5, 38] and the recent [29] . We refer to [2] for a Markovian epidemic model on graphs whose population is described in the Mean Field limit by SIR: our result in Theorem 1.1 can be regarded as a (broad) generalisation of that work. We also mention that applications of Probability Theory to compartmental models involve furthermore Large Deviation Principles for stochastic models, [27, 19, 28] . Finally, besides topic related literature, we would like to mention some works whose techniques inspired the present one, [10, 11, 14, 1, 12, 13] .
As already mentioned, it is not our purpose to discuss the relevance of our results for real applications, in spite of the emergency of present times. However, to have an intuition, the case of nearest neighbour interaction may be relevant in case of extreme restrictions which limit people to live inside their homes, while the intermediate interaction may correspond to the case in which it is still possible to exit home and walk nearby, go to shops and supermarkets near home. It is a very realistic regime. Of course in real applications the distinction between these regimes is blurred, and a correct description might include both kind of interactions in adequate proportions. We do not mix the two cases just for simplicity, but it is clear that our proofs allow to do so. Let us remark that in real human applications there are also other structures of interactions, more of network type, independent of the position, which escape the framework discussed here.
This work leaves open several foundational questions that we hope to approach in future works. One is the already mentioned strict nearest neighbour case β = 1. Another one is a less well-defined problem, which may be illustrated as follows: since the number of infected people, in the examples we have in mind, is only moderately large, the granular effect due to finite cardinality is important and it is lost in the full limit considered here. One consequence, for instance, is that the distributed parameter system obtained in the case of local (intermediate) interaction does not have traveling waves, as opposed to the finite dimensional system of interacting individuals. If a cluster of infected individuals emerges, it propagates in the microscopic model, but very slowly, at microscopic speed, due to the local interaction. When the limit to the distributed parameter system is taken, propagation is not observed anymore, since its macroscopic speed is zero. It thus would be interesting to identify some intermediate models, not fully macroscopic, but simpler than the particle system, that captures this traveling front.
We denote by M the cone of positive finite measures on D: finite measures on the product space D × {S, I, R} are thus elements of M {S,I,R} = M 3 , and we will adopt the following notation for its elements: 
The total variation |µ| of µ ∈ M 3 corresponds to its total mass, and it is obtained by coupling µ, 1 . We also recall that M is a metric space if endowed with the 1-Wasserstein distance
with Lip 1 (D) the space of 1-Lipschitz functions on D, the analogue holding for M {S,I,R} .
We will consider the following empirical measure associated with the configuration A, which incorporates the position information:
In terms of duality couplings, for φ ∈ C(D) 3 ,
Let us stress the fact that µ N is always a probability measure. In what follows we will denote µ N t the time-dependent empirical measure associated to the Markov chain A t defined above.
It is a general fact that the functional of the continuous time Markov chain A t defined by
In order to derive large-scale limiting equations for the individual-based model, Itō's formula for (couplings of) the empirical measure µ N will play a crucial role. Derivation from the general expression above is straightforward, using the following explicit formulas:
As far as quadratic variation is concerned, the integrand of the right-hand side of (2.2) is made explicit by:
This section is devoted to the proof of Theorem 1.1: we will consider the interaction function defining L 1
is a non negative function and q > 0. The limit dynamics will be described by a non-local measure-valued differential equation, so we begin our analysis with some preliminary results on the latter.
3.1. The Large Scale System. The limiting dynamics will be given by (1.2), which is well-posed in the following sense:
be an integrable function with respect to Lebesgue's measure. There exists a unique (up to almost-everywhere coinci-
Moreover, for any t ≥ 0 the solution remains non negative and integrable, that is for
The proof is in fact elementary: we sketch it because of the somewhat peculiar notion of solution that Proposition 3.1 introduces.
Proof. Assume first that f is a solution of the system in the sense above.
The
The same can be said for the second term in the derivative of f I , whereas the first one is non negative since f S is. Analogous reasoning concludes that also f R remains non negative. We now observe that summing all three equations of (1.2) the right-hand side vanishes:
Since f S , f I , f R are non negative, it follows that f t L 1 (D×{S,I,R}) is constant in t ≥ 0. In sight of this, one can apply Cauchy-Lipschitz-Picard theorem to deduce existence of a unique solution of class C([0, T ], L 1 (D)) for a sufficiently small time horizon T > 0. Indeed, all linear terms of the vector field are trivially Lipschitz in L 1 (D), and
shows that the vector field as a whole is Lipschitz as soon as we restrict to a bounded neighbourhood of the initial datum f 0 in L 1 (D) {S,I,R} , say the ball of ra-
, which we showed above to be a conserved quantity, so we can indefinitely iterate the fixed point scheme to obtain a global-in-time solution.
Passing to a version of the solution f t at time t, we obtain that (1.2) (integrated in time) is satisfied for any t, for almost every x. By considering an increasing sequence of times t n ↑ ∞, we can swap the quantifiers. It only remains to observed that, for every x in the full-measure set for which the system is solved in integrated form, we can apply the fundamental theorem of calculus to show that t → f t (x) A , A ∈ {S, I, R}, are differentiable.
The (quite restrictive) uniqueness we obtained by means of the fixed point scheme above will be greatly strengthened in Theorem 3.3 below. Let us then move to a more general, measure-valued notion of solution for (1.2).
We now prove that measure-valued solutions are unique: existence will follow from Theorem 1.1, in which global-in-time solutions will be obtained as limits of the stochastic evolution described above. 
We will show that for any two solutions µ,μ in the sense of Definition 3.2, with initial data µ 0 =μ 0 , it holds
for all fixed t > 0 and with C > 0 a constant depending only on φ, T (and the numbers p, q). The thesis then follows from Grönwall inequality. In fact, the second summand in the definition of F is linear, so the Grönwall estimate (3.2) is trivial. To control (increments of) the first summand, we need to use the fact that
and the analogue forμ, which is seen directly by taking ϕ ≡ 1 in Definition 3.2.
With that being said, we decompose
from which we obtain
last line making use of (3.3). Integrating in time and taking the supremum over φ with unitary norm we complete the proof of (3.2). Corollary 3.4. If µ S 0 , µ I 0 , µ R 0 are absolutely continuous with respect to the Lebesgue on D, and f S 0 , f I 0 , f R 0 are versions of their densities, the measure-valued solution µ t with initial datum µ 0 is obtained from the unique solution f t of (1.2) with initial datum f 0 .
Proof. It suffices to show existence for (1.2), which follows from Proposition 3.1. Indeed, it is immediate to verify that setting dµ t = f t dx 3 with f t given by Proposition 3.1, µ t 's satisfies Definition 3.2.
3.2. Scaling Limit: Uniform Estimates and Compactness. The crucial step in the proof of Theorem 1.1 is to establish suitable uniform bounds for the increments of the Markov process µ N t . 
The proof of the latter shall be divided in two parts, corresponding to the deterministic and the stochastic (martingale) part of the dynamics of µ N . Indeed, by (2.1), we can always expand the above increments as
is a martingale with quadratic variation given by
We can thus reduce ourselves to bound separately the first and second summands in the right-hand side of (3.5).
Lemma 3.6. In the notation above, it holds
where the constant C > 0 only depends on φ, T and the parameters p, q.
Proof. From the explicit expression for generators L 0 , L 1 derived in subsection 2.1 we have the following estimates, uniform in t ≥ 0:
where constants C, C ′ > 0 only depend on φ, T and parameters p, q. The thesis now trivially follows.
Lemma 3.7. In the notation above, it holds
where the constant C > 0 only depends on φ, T and the parameters p AB , p I .
Proof. Since τ is a stopping time for µ N , it holds
The thesis then follows from the following bounds, using again the explicit expression for L 0 , L 1 of subsection 2.1,
where the constants C, C ′ > 0 only depend on parameters p, q.
The combination of Lemma 3.6 and Lemma 3.7 proves Proposition 3.5, from which we immediately deduce the following tightness result. Regarding the theory of càdlàg processes on a metric space S, D([0, T ], S), we refer to the classical monography [3] . • for any t ∈ [0, T ] the sequence of random variables φ, µ N t is tight; • for any δ > 0 it holds
where T N T denotes the family of stopping times with respect to the filtration of µ N t bounded by T , that is τ ≤ T almost surely; (see again [18, Chapter 4, Section 1]). The former condition is an easy consequence of conservation of total population: since µ N t is always a probability measure on the compact space D, the collection ( φ, µ N t ) N ∈N for fixed t is automatically tight because φ, µ N t ≤ φ ∞ . The second condition, follows immediately from Equation 3.4 and Markov inequality.
Passing to the Limit. Thanks to Corollary 3.8 we know that the sequence {Q N t } N ∈N admits converging subsequences. In order to complete the proof of Theorem 1.1 we only need a characterization of limit points. This is the aim of the next proposition: 
Notice that Ψ φ is continuous on D( Proof of Theorem 1.1. By Corollary 3.8 and Proposition 3.9 we obtain convergence up to subsequences. Since, thanks to Theorem 3.3 solutions are unique, we conclude the convergence of the whole sequence {µ N } N ∈N to the SUKRD system.
We consider in this Section the interaction kernel with local scaling of Theorem 1.2: for β ∈ (0, 1) we thus take
with θ ∈ C ∞ c (D). Although the limit is a very simple model, convergence is way harder to prove than the one of section 3. Indeed, (1.3) can not be written in a weak formulation like (3.1), and we have thus to directly treat convergence towards functions.
Instead of directly proving convergence of empirical measures, we will consider a smoothed version of the latter ones, inspired by the aforementioned theory of moderately interacting particle systems of Oelschläger. It will be convenient to use the same kernel θ N describing the interaction to regularise empirical measures, and we thus define
and analogously the other two components ρ N,I , ρ N,R .
Remark 4.1. For x close enough to ∂D, the support of θ N (x − ·) might not be contained in D. This clearly does not affect the use of θ N as interaction kernel, but it is in fact irrelevant also in its use as a mollifier: we are simply smoothing the empirical measure as a measure on the whole plane and then consider only the restriction to D.
In what follows, we will show that regularised empirical measures ρ N converge to the solution of (1.3), and only then deduce that so do µ N , since they are close to ρ N , in a suitable topology. We thus first focus on the following intermediate result: Let us introduce a convenient notation:
which will be useful because for every x ∈ D, t ∈ [0, T ] and every A ∈ {S, I, R}, it holds
In sight of the latter, we can reduce ourselves to prove many of the required estimates onρ, thus ignoring the state of individuals and focus only on their positions. Let us remark that (4.1) is is uniform with respect to t since particles do not move.
First and foremost we need to describe the time evolution of ρ N , which is given by the action of L 0 , L 1 : for A ∈ {S, I, R},
Let us also compute the quadratic variation of the martingale associated to the observable ρ A N (x),
It holds
We can now derive estimates on ρ N,A (x) in order to prove tightness of processes in Proposition 3.1. We first introduce the following
which differently from ρ N,A , A ∈ {S, I, R} sums up the contributions from all the particles. The latter will prove itself to be useful because of the trivial inequality
for every x ∈ D, t ∈ [0, T ] and for every A ∈ {S, I, R}, while it allows us to forget the state of individuals and focus only on their positions. Also, since particles do not move, the previous inequality is uniform with respect to the parameter t.
Proof. The bound for the first term is classical, positions being independent. We thus focus on the second one, and split the second moment into bias plus variance:
For the the first summand we have
f 0 being the density of positions distribution, so that the uniform bound follows simply by controlling
As for the second summand, the variance term, we use independence of positions to evaluate
From here,
2 ) D f 0 (y) dy leads us to employ the hypothesis on β, which implies 2(β + γβ) ≤ 1 and thus that the estimate is uniform in N .
Notice that in the Lemma above we made essential use of the hypothesis on density of the distribution µ 0 of individuals' positions.
Moreover, for β < 1 3 there exists C ′ > 0 independent of N and A ∈ {S, I, R} such that
The latter follows directly from Lemma 4.3 and the definition ofρ: the first estimate by choosing 2η = 1 + γ, and the other two by Sobolev embedding in dimension 2.
The uniform bounds we produced so far have to be complemented by suitable controls on time evolution, described by L. We thus have to prove a second technical Lemma.
Lemma 4.5. For β ≤ 1 3 , there exists a constant C > 0 independent of x ∈ D and N such that
Proof. We actually prove only the first estimate, the second one following from the former by making use of the additional factor 1 N β . Again we divide the second moment into bias and variance:
Variance is controlled as above using independence of positions,
We only need to control the first term on the right-hand side of
the other one being analogous; we have
where f 0 is the density of positions. Making use of the additional factor 1 N from the last line of (4.6), we have a uniform bound as soon as N 3β N ≤ C, that is the hypothesis β ≤ 1 3 . We can rewrite the bias term of (4.5) as
Therefore, we only need to show that,
is uniformly bounded. However, since particles are independent, it holds
which concludes the proof. Proof. We treat each of the mollified empirical measure separately, since they are different in structure. We first start with the equation for ρ N,S ,
By 
where we used Young's inequality with exponents p/(p−1) and p in the last inequality. The same computations apply for states I, R: repeating them and summing up the three contributions we obtain a Grönwall inequality for the random process
We already know by Corollary 4.4 that E [X 1 ] ≤ C. As for X 2 , we now prove a uniform bound on its term relative to S, the other two being analogous. Since
where the quadratic variation is controlled by
ds.
Since now
which is bounded for β ≤ 1 3 , we conclude
By Corollary 4.4 this shows that E [X 2 ] ≤ C independently of N . Therefore, we know that ∀ε > 0 there exists R 1 , R 2 > 0 such that
Applying Grönwall's Lemma to every fixed sample of the random process φ(t), we obtain sup
For some number C(X 1 , X 2 ) > 0 depending on X 1 , X 2 : we now claim that
As an immediate consequence we have by Sobolev embedding: 
where the constant C > 0 is independent of N, θ and τ .
Proof. It holds
where the quadratic variation of the martingale part can be expressed as
We can thus bound separately the two terms on the right-hand side of
, that is, the first estimate in the statement follows from the latter two. In fact, the second estimate in the thesis follows directly from Corollary 4.6, so only the martingale terms is left. To control it, we follow the same strategy as Lemma 3.7.
Since τ is a stopping time for µ N (and hence also for ρ N ) it holds
.
We conclude directly by corollary Corollary 4.6.
We are now in possession of all the estimates required to apply Aldous' Criterion and obtain tightness of the laws of ρ N . Proof. We follow the same strategy of Corollary 3.8. We apply Theorem 1.3 and Proposition 1.6 of [18, Chapter 4, Section 1]. Hence we need to verify that
where T N T denotes the family of stopping times with respect to the filtration of ρ N t bounded by T , that is τ ≤ T almost surely. The first condition is a consequence of Proposition 4.7 by choosing p = 2, while the second is satisfied by Proposition 4.9.
Measures. Thanks to Proposition 4.10 we know that the sequence {ρ N t } N ∈N admits converging subsequences. In order to complete the proof of Proposition 4.2 we only need a characterization of limit points.
Before moving to the actual proof we briefly highlight the main points. The mollified empirical measure ρ N satisfies (4.4), which differs from (a weak formulation of) (1.3) in two aspects. The first is clearly the martingale term, which will vanish in the limit. The other one is a consequence of the expression for the generator L 1 . In fact from equation (4.3) we see that in the expression for ρ N,S (resp. ρ N,I ) the product term 1
for which the convergence to the product f S f I in the limiting equation (1.3) is clearer. We show in the forthcoming Lemma that their difference θ N * µ N,S (θ N * µ N,I ) − (θ N * µ N,S )(θ N * µ N,I ) = µ N,S , θ N * (θ N * µ N,I ) vanishes in the limit: this can actually be regarded as a result on commutators, akin to the ones common in PDE theory, see for instance [9] .
Lemma 4.11. Let
then it holds for any δ > 0,
Proof. We can rewrite C N t (x) as
in which we can use the Hölder continuity inequality for α < 1 2 ,
where we used the fact that θ N has compact support, with diameter smaller than CN We can finally state and prove the characterization of limit points of ρ N . We notice that Ψ φ is continuous on D([0, T ], L 2 (D) {S,I,R} ) and that Ψ φ vanishes on solutions of (1.3).
Evaluating the functional Ψ φ on the mollified empirical measure ρ N we only get two remainders, as discussed at the beginning of subsection 4.2. Taking now a converging subsequence of {ρ N } N ∈N (that we will still denote ρ N ), if we can prove that for every δ > 0, (4.11) lim inf To show (4.11) we only need to check that the two aforementioned reminders vanish in the limit, i.e. Proof. Let us focus on one component of ρ N t to lighten notation, say ρ N,S t . By definition the above quantity is given by
where, using the fact that D θ N (x − y)dy = 1 for all x, N and symmetry of θ, To conclude we have used the fact that θ N has compact support, with diameter proportional to N −β/2 , so that |x − y| contributes only when it is smaller than N −β/2 . In the last line we have use the fact that total population is preserved for every t ∈ [0, T ].
The combination of the latter Lemma and Proposition 4.2 thus concludes the proof of Theorem 1.2.