Abstract
The ability of disease to invade a community network that is connected by environmental pathogen movement is examined. Each community is modeled by a susceptible–infectious–recovered (SIR) framework that includes an environmental pathogen reservoir, and the communities are connected by pathogen movement on a strongly connected, weighted, directed graph. Disease invasibility is determined by the basic reproduction number \(\mathcal{{R}}_0\) for the domain. The domain \(\mathcal{{R}}_0\) is computed through a Laurent series expansion, with perturbation parameter corresponding to the ratio of the pathogen decay rate to the rate of water movement. When movement is fast relative to decay, \(\mathcal{{R}}_0\) is determined by the product of two weighted averages of the community characteristics. The weights in these averages correspond to the network structure through the rooted spanning trees of the weighted, directed graph. Clustering of disease “hot spots” influences disease invasibility. In particular, clustering hot spots together according to a generalization of the group inverse of the Laplacian matrix facilitates disease invasion.
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References
Avrachenkov KE, Haviv M, Howlett PG (2001) Inversion of analytic matrix functions that are singular at the origin. SIAM J Matrix Anal Appl 22(4):1175–1189
Ben-Israel A, Greville TNE (2003) Generalized inverses: theory and applications, 2nd edn. Springer, New York
Berman A, Plemmons RJ (1979) Nonnegative matrices in the mathematical sciences. Academic Press, New York
Bertuzzo E, Casagrandi R, Gatto M, Rodriguez-Iturbe I, Rinaldo A (2010) On spatially explicit models of cholera epidemics. J R Soc Interface 7(43):321–333
Chao DL, Halloran ME, Longini IM Jr (2011) Vaccination strategies for epidemic cholera in Haiti with implications for the developing world. Proc Natl Acad Sci USA 108(17):7081–7085
Codeço C (2001) Endemic and epidemic dynamics of cholera: the role of the aquatic reservoir. BMC Infect Dis 1:1
DeVille REL, Peskin CS (2012) Synchrony and asynchrony for neuronal dynamics defined on complex networks. Bull Math Biol 74(4):769–802
Diekmann O, Heesterbeek JAP (2000) Mathematical epidemiology of infectious diseases: model building, analysis, and interpretation. Wiley, New York
Dowell SF, Braden CR (2011) Implications of the introduction of cholera to Haiti. Emerg Infect Dis 17(7):1299–1300
Eisenberg MC, Shuai Z, Tien JH, van den Driessche P (2013) A cholera model in a patchy environment with water and human movement. Math Biosci 246(1):105–112
Galvani AP, May RM (2005) Dimensions of superspreading. Nature 438:293–295
Guo H, Li MY, Shuai Z (2006) Global stability of the endemic equilibrium of multigroup SIR epidemic models. Can Appl Math Q 14:259–284
Hethcote HW, Yorke JA (1984) Gonorrhea transmission dynamics and control, Lecture Notes in Biomathematics, vol 56. Springer, New York
Horn RA, Johnson CR (1985) Matrix analysis. Cambridge University Press, Cambridge
Kaper JB, Morris JG Jr, Levine MM (1995) Cholera. Clin Microbiol Rev 8(1):48–86
Keeling MJ (1999) The effect of local spatial structure on epidemiological invasions. Proc R Soc Lond B 266:859–867
Kleinberg JM (1999) Authoritative sources in a hyperlinked environment. J Assoc Comput Mach 46:604–632
Langenhop CE (1971) The Laurent expansion for a nearly singular matrix. Linear Algebra Appl 4:329–340
Meyer CDJ (1975) The role of the group generalized inverse in the theory of finite Markov chains. SIAM Rev 17(3):443–464
Moon JW (1970) Counting labelled trees. William Clowes and Sons Limited, London
Newman M (2010) Networks: an introduction. Oxford University Press, Oxford
Newman M, Barabasi A-L, Watts DJ (2006) The structure and dynamics of networks. Princeton University Press, Princeton
Newman MEJ, Strogatz SH, Watts DJ (2001) Random graphs with arbitrary degree distributions and their applications. Phys Rev E 64:026118
Piarroux R, Barrais R, Faucher B, Haus R, Piarroux M, Gaudart J, Magloire R, Raoult D (2011) Understanding the cholera epidemic, Haiti. Emerg Infect Dis 17(7):1161–1168
Rothblum UG (1981) Resolvent expansions of matrices and applications. Linear Algebra Appl 38:33–49
Schweitzer P, Stewart GW (1993) The Laurent expansion of pencils that are singular at the origin. Linear Algebra Appl 183:237–254
van den Driessche P, Watmough J (2002) Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission. Math Biosci 180:29–48
Variano EA, Ho DT, Engel VC, Schmeider PJ, Reid MC (2009) Flow and mixing dynamics in a patterned wetland: kilometer-scale tracer releases in the Everglades. Water Resour Res 45:W08422
Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393(6684):440–442
West DB (2001) Introduction to graph theory, 2nd edn. Prentice Hall, Upper Saddle River
Acknowledgments
JHT and MCE acknowledge support from the National Science Foundation (OCE-1115881) and the Mathematical Biosciences Institute (DMS-0931642). The research of PvdD is partially supported through a Discovery Grant from the Natural Science and Engineering Research Council of Canada (NSERC). ZS acknowledges support from the University of Central Florida through a start-up grant. The authors are grateful to the anonymous reviewers for their thoughtful, constructive comments. This paper was improved by discussions at a Research in Teams meeting (13rit168) held at the Banff International Research Station.
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Appendices
Appendix A: Graph-theoretic background
For the convenience of the reader, we present some definitions and standard results from graph theory used throughout the paper. Further information can be found in graph theory textbooks such as West (2001). We include also a statement of the matrix tree theorem for weighted, directed graphs. A proof can be found in Moon (1970).
A directed graph (digraph) \(\mathcal {G}=(V,E)\) consists of a set \(V=\{1, 2, \ldots , n\}\) of vertices and a set \(E=E(\mathcal {G})\) of directed arcs \((i,j)\) from vertex \(i\) to vertex \(j\). A directed graph \(\mathcal {G}\) is weighted if each arc \((j,i)\) is assigned a positive weight \(m_{ij}\); the weighted, directed graph is denoted as \((\mathcal {G}, M)\), with nonnegative weight matrix \(M=[m_{ij}]\) and \(m_{ij}>0\) if and only if \((j,i)\in E(\mathcal {G})\). For example, the weight matrix \(M\) may correspond to the movement matrix in the community network, and thus the weighted directed graph \((\mathcal {G}, M)\) corresponds to the community network. A directed graph is strongly connected if for any ordered pair of vertices, there exists a directed path from one to the other. A weighted directed graph \((\mathcal {G}, M)\) is strongly connected if and only if the weight matrix \(M\) is irreducible (Berman and Plemmons 1979).
A rooted spanning tree (in-tree) \(\mathcal {T}\) is a subgraph of \(\mathcal {G}\) on \(n\) vertices such that \(\mathcal {T}\) is connected with no cycles, and has a root vertex such that every directed path between a non-root vertex and the root is oriented towards the root. The weight of a rooted spanning tree is the product of all arcs in the rooted spanning tree. To illustrate, consider the star graph shown in Fig. 5, with arc weights \(a\) from periphery to center, and \(b\) from center to periphery. Figure 5b shows the single spanning in-tree rooted at the center. This tree possesses \(n\) arcs with weight \(a\), giving a tree weight of \(a^{n}\). Each peripheral vertex roots a single spanning in-tree, shown in Fig. 5c. Trees rooted at the periphery have \(n-1\) arcs with weight \(a\) and a single arc with weight \(b\), giving a tree weight of \(a^{n-1}b\).
Let \((\mathcal {G}, M)\) be a weighted, directed graph. The Laplacian matrix \(L\) of the graph is then defined as:
Note that each of the columns of \(L\) sum to zero, implying both that \(L\) is singular, and that the cofactors of \(L\) are the same within each column. The matrix tree theorem states that any cofactor corresponding to column \(k\) of \(L\) is equal to the sum of the weights of the spanning in-trees rooted at vertex \(k\). For a proof see Moon (Section 5.5 and Theorem 5.5, Moon 1970).
Theorem 2
(Matrix Tree Theorem)
Let \((\mathcal {G}, M)\) be a weighted, directed graph, and let \(L\) be the Laplacian matrix of \((\mathcal {G}, M)\). Let \(c_{kk}\) denote the \((k,k)\) cofactor of \(L\). Then the cofactors of \(L\) are related to the rooted spanning trees of \(\mathcal{G}\) by the following:
where \(\mathbb {T}_k\) is the set of all spanning in-trees rooted at vertex \(k, E(\mathcal {T})\) is the arc set of rooted spanning in-tree \(\mathcal {T}\), and \(m_{ij}\) the weight of the arc from \(j\) to \(i\).
A direct consequence of the matrix tree theorem is a relationship between \(\ker L\) and the rooted spanning trees of \(\mathcal{G}\). This is pointed out, for example, in Lemma 2.1 of Guo et al. (2006). Consider the vector of cofactors \(c = (c_{11}, \dots , c_{nn})^T\). Then \((Lc)_i = \det L = 0\) for \(i=1,\dots ,n\), so \(c\) belongs to \(\ker L\). The dimension of the nullspace of \(L\) is equal to the number of connected components of \(\mathcal{G}\), so for strongly connected digraphs, \(L\) has a one dimensional nullspace spanned by \(c\). For strongly connected digraphs, there is at least one in-tree rooted at each vertex, and thus the entries of \(c\) are all positive for digraphs with non-negative arc weights. Let \(u_i = c_{ii} / \sum _{j=1}^n c_{jj}\), and let \(u = (u_1, \dots , u_n)^T\). Then \(u\) provides a basis vector for \(\ker L\), with all positive entries and normalized so that \(\sum _{i=1}^n u_i = 1\).
A weighted directed graph \((\mathcal {G}, M)\) is balanced if the net inflow is equal to the net outflow at each vertex, i.e. for each \(i, \sum _{j\not =i} m_{ij} = \sum _{j\not =i}m_{ji}\). For balanced community networks, both the row sums and column sums of \(L\) are equal to zero, giving that all cofactors of \(L\) are equal. This in turn implies that all entries of \(u\) are equal to \(1/n\) for balanced networks.
Appendix B: \(X_0\) and the group inverse
In the equal pathogen decay case, \(G = (L+\varepsilon Id)\). The resolvent expansion for matrices of this form is considered by Rothblum (1981), who shows that the zeroth order term in the series is given by the Drazin inverse of \(L\). For matrices of index 1, the Drazin inverse is equal to the group inverse (e.g. Ben-Israel and Greville 2003, Chapter 4). As pointed out by Schweitzer and Stewart (1993), \(X_0\) in the general case is a generalization of the Drazin (here, group) inverse. It is interesting to consider the relationship between \(X_0\) in Proposition 2 and the group inverse in more detail.
The group inverse of a square matrix \(M\) of index 1 is the unique matrix \(M^\#\) that satisfies the following three conditions (Ben-Israel and Greville 2003, Section 4.4):
The matrix \(X_0\) in Proposition 2 satisfies conditions (39) and (40) with \(M=L\), but in general not (41). In the language of generalized inverses, \(X_0\) is said to be a “\(\lbrace 1,2 \rbrace \)-inverse” of \(L\) (Ben-Israel and Greville 2003).
We first show that \(X_0\) acts as a right inverse of \(L\) on \(\mathop {\mathrm {range}}L\).
Lemma 5
Let \(X_0\) be given as in Proposition 2. Then
Proof
Let \(x \in \mathop {\mathrm {range}}L\). As \(L: N_{1,0} \rightarrow \mathop {\mathrm {range}}L\) a bijection (Theorem 1(c)), there exists \(y \in N_{1,0}\) such that \(Ly = x\). Then
Finally, note that \(X_0\) sends \(R_{1,0}\) to 0 (Theorem 1(e)), and thus \(LX_0x = 0\) for all \(x \in R_{1,0}\). \(\square \)
We can now show that \(X_0\) is a \(\lbrace 1,2 \rbrace \)-inverse of \(L\), and determine where \(X_0\) and the group inverse coincide.
Proposition 3
Let \(X_0\) be given as in Proposition 2. Then \(X_0\) is a \(\lbrace 1,2 \rbrace \)-inverse of \(L\) (i.e. satisfies conditions (39) and (40)), and commutes with \(L\) on \(N_{1,0} \cap \mathop {\mathrm {range}}L\).
Proof
For any \(x \in \mathbb {R}^n\), take \(x = y+z\), where \(y \in N_{1,0}\) and \(z \in \ker L\). From Proposition 2,
Multiplying on the left by \(L\) then gives
and thus \(X_0\) satisfies (39).
Similarly, for any \(x \in \mathbb {R}^n\) take \(x = \tilde{y} + \tilde{z}\), where \(\tilde{y} \in \mathop {\mathrm {range}}L\) and \(\tilde{z} \in R_{1,0}\). Using Lemma 5,
so \(X_0\) satisfies (40).
Finally, consider \(x \in N_{1,0} \cap \mathop {\mathrm {range}}L\). As \(x \in \mathop {\mathrm {range}}L\), Lemma 5 gives \(x = L X_0 x\). But \(x \in N_{1,0}\) as well, so property (e) in Theorem 1 gives \(x = X_0 L x\), and thus \(X_0\) and \(L\) commute on \(N_{1,0} \cap \mathop {\mathrm {range}}L\). \(\square \)
Proposition 3 shows that \(X_0\) and the group inverse coincide on \(N_{1,0} \cap \mathop {\mathrm {range}}L\). Of course, this intersection may be trivial. In the case where \(D=Id\) (i.e. equal pathogen decay rates), \(N_{1,0} = \mathop {\mathrm {range}}L\), and \(X_0 = (Id-X_{-1}D)(L-X_{-1}D)^{-1}\) is equal to the group inverse of \(L\).
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Tien, J.H., Shuai, Z., Eisenberg, M.C. et al. Disease invasion on community networks with environmental pathogen movement. J. Math. Biol. 70, 1065–1092 (2015). https://doi.org/10.1007/s00285-014-0791-x
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DOI: https://doi.org/10.1007/s00285-014-0791-x