{'fraction_non_alphanumeric': 0.035101498308361526, 'fraction_numerical': 0.011478975350410826, 'mean_word_length': 4.512154512154512, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 4, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Arrival Time Statistics in Global Disease Spread

Metapopulation models describing cities with different populations coupled by the travel of individuals are of great importance in the understanding of disease spread on a large scale. An important example is the Rvachev-Longini model [{\it Math. Biosci.} {\bf 75}, 3-22 (1985)] which is widely used in computational epidemiology. Few analytical results are however available and in particular little is known about paths followed by epidemics and disease arrival times. We study the arrival time of a disease in a city as a function of the starting seed of the epidemics. We propose an analytical Ansatz, test it in the case of a spreading on the world wide air transportation network, and show that it predicts accurately the arrival order of a disease in world-wide cities.

Metapopulation models describing cities with different populations coupled by the travel of individuals are of great importance in the understanding of disease spread on a large scale. An important example is the Rvachev-Longini model [Math. Biosci. 75, 3-22 (1985)] which is widely used in computational epidemiology. Few analytical results are however available and in particular little is known about paths followed by epidemics and disease arrival times. We study the arrival time of a disease in a city as a function of the starting seed of the epidemics. We propose an analytical Ansatz, test it in the case of a spreading on the world wide air transportation network, and show that it predicts accurately the arrival order of a disease in world-wide cities. In modern societies, individuals can easily travel over a wide range of spatial and temporal scales. The interconnections of areas and populations through various means of transport have important effects on the geographical spread of epidemics. In particular, the structure and the different complexity levels of the air-transportation network are responsible for the heterogeneous and seemingly erratic outbreak patterns observed in the worldwide propagation of diseases [1] as recently documented for SARS [2,3]. In order to describe such a complex phenomenon and to obtain powerful numerical forecasting tools, different levels of description are possible, ranging from a simple global mean-field to detailed agent-based simulations [4,5,6,7,8,9,10] that recreate entire populations and their dynamics at the scale of the single individual [10].
At large scale, such as the world-wide level, a very important class of models in modern epidemiology are the so-called metapopulation models which use a description at two levels by dividing the global population into interconnected subpopulations. Within each subpopulation, a mean-field like model of epidemic spreading is used, while the spread from one subpopulation to another is due to the travel of individuals. Agents of each subpopulation can be in various states (healthy, infectious, recovered...), change state by contact with other agents, and diffuse on the transportation network between subpopulations. Metapopulation models can thus be considered as reaction-diffusion processes, which opens very interesting perspectives and issues [11] within the global framework of dynamical phenomena occurring on complex networks [12,13,14,15]. For the description of worldwide epidemic spreading, the subpopulations are cities connected by a transportation network in which links correspond to the existence of passenger flows described by the worldwide air-transportation network (WAN). The WAN represents a major channel for the worldwide spread of infectious diseases [1,3] and its complex, heterogeneous features at various levels (degree distribution, traffic, populations) have recently been characterized [16,17].
In this Letter, we focus, in the framework of such metapopulation models, on the issue of the arrival time in a city of the first infectious individual. In particular, we study how this time depends on the origin of the disease and on the network characteristics. This problem is more complex than the one of random walks on complex networks [18], since the number of infectious individuals diffusing on the network is constantly evolving due to the inner-city epidemic dynamics. We also note that references [19,20] were also concerned with the arrival time problem for an epidemic spreading on a complex network, but in a different framework: each network node was an individual (susceptible or infectious), while in our case each node represents a whole subpopulation. After the precise definition of the model, we will first consider the simple case of a one-dimensional topology for the transportation network in order to gain analytical insights into this problem. This will allow us to propose an analytical form for the arrival time in arbitrary networks. We then test this form in the case of the WAN, by simulating numerically a stochastic spreading phenomenon on the network, and show that we can indeed predict with a good accuracy the spreading phenomenon and the arrival order of a disease in various cities at a world-wide level. While the precise model describing the epidemic spreading at the subpopulation level could be refined at will in order to describe a particular disease, we are here interested in generic and fundamental aspects of the metapopulation modeling approach. We therefore restrict our study to a simple SI disease model in which individuals are either healthy (susceptible, S) or can become infectious (I) if in contact with an infectious individual. The Rvachev-Longini SI model [21] describes the evolution of the number of infectious I i (t) individuals (and also of S i (t)) in each city i through where the first term K of the right hand side describes the (epidemic) reaction process inside each subpopulation (city), due to the interaction of individuals in the various possible states. In our case X ∈ {S, I} (we have checked that more involved models, such as SIS or SIR, give consistent results [22]) and the standard homogeneous mixing assumption in each city gives [4]: where N i is the population of city i and λ the spreading rate. The second term Ω represents the evolution due to the arrival or departure of infectious individuals from or to other cities and is determined by passenger flows on the transportation network. This model therefore considers a simplified mechanistic approach with a widely used markovian assumption in which individuals are not labeled according to their original subpopulation, and at each time step the same traveling probability applies to all individuals in the subpopulation, without memory of their origin [1,3,21]. Denoting by w ij the average number of passengers traveling from i to j per unit of time (w ij = 0 if there is no direct connection), the probability per unit time that an individual travels from city i to city j is then given by w ij /N i . The full metapopulation model is therefore described by This original formulation considers only expectation values, which can take continuous values, so that "fractions" of infectious individuals can travel and infect neighboring cities arbitrarily fast [23]. To investigate arrival times, one therefore needs to take into account the inherent stochasticity of the spreading. We thus consider in all our numerical simulations the stochastic generalization described in [1,3] where the number of individuals traveling on each connection is an integer variable randomly extracted at each time step of length ∆t, with average ∆tw ij I i /N i (in the numerical simulations we will use ∆t = 1 day); for simplicity we keep the endogenous growth deterministic since we are mainly concerned with the effect of travel, but we have checked that inclusion of stochastic effects as in [1] do not change our results [22]. Note that in real cases such as the WAN, most weights are symmetric (w ij = w ji ) [16] but the probabilities of travel from one city to another are not since they depend on the populations of the various cities: the travel effectively occurs as a random diffusion with non-symmetric rates on the transportation network. The topological distance thus does not contain all the information needed to characterize such a process. Moreover, since most transportation networks are small-world networks, many cities lie at the same topological distance from a given seed, but will potentially be reached at very different times. Before turning to numerical simulations of the described model, we present an analytical approach to the determination of arrival times. Let us first consider the simple case of two cities (0 and 1), with populations N 0 , N 1 which are connected by a passenger flux w 01 = w. We assume that at t = 0, there are I 0 = 1 infectious people in the city 0. Let us first consider that the travel events occur as instantaneous jumps of probability p = w N0 ∆t, at discretized times, in units of ∆t. The probability that the time of arrival t 1 of the epidemic in the city 1 is equal to t = n∆t is then In order to obtain the density probability P (t) of the arrival time in the city 1 we consider the limit ∆t → 0, using the following assumptions: (i) I 0 (t) ≪ N , which is realistic for usual diseases, in which only small fractions of the population are infectious; (ii) the continuous limit for I 0 (t) can be used which reads 1 λ ≪ t 1 . Within these assumptions, we obtain (the last assumption then reads 1 ≪ ln( N0λ w )). We recognize in (4) a Gumbel distribution with average where γ is the Euler constant. The variance is V ar(t 1 ) = π √ 6λ and does not depend of w N0 (the non physical contribution of the negative values of t in the distribution has to be negligible which is satisfied if 0 −∞ P (t)dt = w N0λ ≪ 1). Within these assumptions, we obtain a good agreement between results of numerical simulations using discretized travel events [1] and the theory which uses continuous approximations (see Fig. 1). We now consider the case of a one-dimensional line of cities connected by passenger fluxes of random intensity. We assume that the spreading process starts at city 0 and we denote by t n the arrival time in the city n. The quantities having the same unit as t n are 1/λ and N i /w i , where w i is the number of passengers traveling from i to i + 1 per unit time. Dimensional analysis then implies that the probability distribution of the adimensional quantity λt n must be a function of the other adimensional quantities which are the w i /(lambdaN i ): P (λt n ) = G n (λt n , { wi Niλ }), where G n is an unknown function. One can write t n as a sum of random variables, ∆ i = t i − t i−1 which are however correlated since each local infection process depends on the history of the epidemics in all previously infected cities. While a complete study of P (λt n ) is left for future work [22], numerical simulations of the spreading show (Fig. 2) that it obeys important invariance properties. For heterogeneous populations and travels (w i and N i are distributed uniformly in [10,2000] and [10 5 , 2.10 7 ], respectively), the whole distribution is invariant when one replaces (i) all the random weights by their geometrical mean w = ( n−1 i=0 w i ) 1/n ; (ii) all the random populations by their geometrical mean N = ( n−1 i=0 N i ) 1/n ; (iii) all weights by w and all populations by N . The ratios of the average times for these different sets stay very close to 1, with deviations at most of the order of 5%.
The average arrival time can thus be written as λ t n = F ({ wi Niλ }) where F (x 1 , . . . , x n ) is a symmetric function of its variables which depends only on the product x i , and such that t 1 is the average of the Gumbel distribution (4). This leads to the following Ansatz (C) uniform populations Ni =N and weights wi =w. We use a small value of n since most real complex networks are small-world: any node lies at a small distance from the seed. Figure 3 shows that the average arrival time in a city is indeed determined by χ to a very good extent (while the arrival time at a given topological distance from the seed can vary a lot). More quantitatively, χ is approximately proportional to λ t , which it slightly overestimates since we neglect the flow of infectious individuals from n − 2 to n − 1 with respect to the endogenous increase of I n−1 during [t n−1 ; t n ] [22]. We now consider a generic transportation network between the cities. The quantity (5) can easily be computed on any path of length n on the network. While the spread can a priori follow multiple paths from one city to another, we can reasonably assume that the most probable path is the one which minimizes the value of χ computed on it, leading to the smallest arrival time possible (a more refined Ansatz taking into account multiple paths does not lead to strong differences in the final results [22]). We thus obtain the following Ansatz for the arrival time at a city t of a disease starting at node s χ(s, t) = min where {P sj } is the set of all possible paths connecting s to t, and the sum is over the links (k, l) on the paths. In other terms, we have introduced a new (non symmetric) weight ln(N i λ/w ij ) − γ on each oriented link (i, j) of the network. We have simulated, using the model developed in [1], and summarized above, a spreading phenomenon on a subnetwork of the WAN, composed of the 2, 400 nodes for which the populations are larger than 10, 000 inhabitants and which corresponds to 98% of the total traffic [24]. The arrival times are computed by solving numerically the equations of the Rvachev-Longini model with discretized random travel events, and averaging over 1, 000 realizations of the spreading with the same seed (one infectious individual in a given city). Figure 4 shows the obtained values of λ t versus χ for various initial seeds. We observe that the average arrival time is indeed determined by the value of χ in a given city: various cities with the same χ are reached at the same time by the disease propagation. While χ quantitatively overestimates the arrival time, the two quantities are correlated strongly enough, in order to obtain with a good confidence the order of arrival of the disease in different cities. More precisely, if we denote ∆χ(i, j) =| χ(j) − χ(i) |, we show in Fig. 5 the probability f c (∆χ) that the arrival times in one realization of the spread t (i) and t (j) starting in different cities (whose name is specified in each graph). Each red circle corresponds to a city and averages are done over 1, 000 realizations of the spreading. Crosses are an average over cities with the same χ. When the initial seed is a hub, the average arrival time is larger than χ in the first reached cities, due to the multiplicity of possible paths [22]. follow the same order as given by χ(i) and χ(j) [ie.
(t (i) − t (j) )(χ(i) − χ(j)) > 0]. In other words, f c is the probability that the disease arrival rank for the two cities i and j is correctly predicted by χ. If ∆χ(i, j) is equal to 0, no prediction is possible and we indeed obtain f c (0) = 0.5. For ∆χ > 10, almost all node couples are correctly ranked. This result has however to be weighted by the number of couples with such a large ∆χ. We thus plot on the same figure the cumulative distribution p > (∆χ) of the number of couples of nodes with a given value of ∆χ. We see for example that approximately 80% of the couples of cities have a ∆χ > 2 and more than 70% of these couples are correctly sorted (instead of just 50% on average if no information is available). From a theoretical point of view, metapopulation models go far beyond classical random walks and deserve many further theoretical investigations. In this Letter, we have proposed an Ansatz for the arrival time of a disease in a city, knowing the starting point of the spread. This Ansatz is a good approximation and predicts with accuracy the arrival order of the disease in the different cities, even if they are at the same topological distance from the seed [22]. Containment strategies could use such information to target the cities most at risk of rapid infection, and therefore deploy limited supplies of vaccine or antivirals in an efficient way. Further developments could include more sophisticated compartmental or metapopulation models, and the systematic investigation of various structures of complex networks [22]. Finally, it would be interesting to extend this study to other scales, like the urban scale, where nodes are locations such as homes, offices or malls [10].