Controlling propagation of epidemics via mean-field games

The coronavirus disease 2019 (COVID-19) pandemic is changing and impacting lives on a global scale. In this paper, we introduce a mean-field game model in controlling the propagation of epidemics on a spatial domain. The control variable, the spatial velocity, is first introduced for the classical disease models, such as the SIR model. For this proposed model, we provide fast numerical algorithms based on proximal primal-dual methods. Numerical experiments demonstrate that the proposed model illustrates how to separate infected patients in a spatial domain effectively.

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1. Controlling propagation of epidemics: Mean-field SIR games Stanley Osher Joint work with Wonjun Lee, Siting Liu, Hamidou Tembine and Wuchen Li
2. COVID 19 As of May 30, 2020, the total case of COVID 19 has reached: 2
3. COVID 19 in US 3
4. Goals Fight against COVID-19 by optimal transport and mean field games. 4
5. Classic Epidemic Model The classical Epidemic model is the SIR model (Kermack and McKendrick, 1927) 8 > > > > > < > > > > > : dS dt = SI dI dt = SI I dR dt = I where S, I,R : [0, T] ! [0, 1] represent the density of the susceptible population, infected population, and recovered population, respectively, given time t. The nonnegative constants and represent the rates of susceptible becoming infected and infected becoming recovered. 5
6. Spatial SIR To model the spatial e↵ect of virus spreading ,the spatial SIR model is considered: 8 > > > > > > > < > > > > > > > : @ t ⇢ S (t, x) + ⇢ S (t, x) Z ⌦ K(x, y)⇢ I (t, y)dy ⌘2 S 2 ⇢ S (t, x) = 0 @ t ⇢ I (t, x) ⇢ I (x) Z ⌦ K(x, y)⇢ S (t, y)dy + ⇢ I (t, x) ⌘2 I 2 ⇢ I (t, x) = 0 @ t ⇢ R (t, x) ⇢ I (t, x) ⌘2 R 2 ⇢ R (t, x) = 0 Here ⌦ is a given spatial domain and K(x, y) is a symmetric positive definite kernel modeling the physical distancing. E.g. R Kd⇢ I is the exposure to infectious agents. 6
7. Optimal control of population behaviors Optimal control of population behaviors have been widely considered in optimal transport and mean field games. Long story short, it refers to an optimal control problem in density space: min Running cost of a population s.t. Evolution of population dynamics E.g. 7
8. Goals: Mean field game spatial SIR model Questions To balance the social cost and saving lives under this COVID epidemic daily life, we need to control or allocate S,I, R populations in a spatial domain. Solutions: We propose a mean field control problem for spatial SIR models and introduce an e cient numerical scheme. 8
9. MFG Related I Introduced by Jovanovic & Rosenthal[JR88], M. Huang, P. Caines, R. Malham´ e [HMC06] and P.-L. Lions, J.-M. Lasry [LL06a, LL06b] to model huge populations of identical agents playing non-cooperative di↵erential games. I Wide applications to various fields: in economics, Finance, crowd motion, industrial engineering, data science, material dynamics, and more [GNP15, BDFMW13, LLLL16, AL19]. I Computational methods developed to solve high dimensional problems. [BC15, BnAKS18, EHL18, LFL+20, ROL+19, LJL+20]. 9
10. Related Study on COVID-19 I Study traveling waves to understand the propagation of epidemics. In [BRR20], they introduce a SIRT model to study the e↵ects of the presence of a road on the spatial propagation the epidemic. I Optimal control with control measures on medicare (vaccination) I Machine Learning, Data Driven + Epidemic model Figure: Social Distancing https://www.wfla.com/news/by-the-numbers/tampa-bay-counties-earn-d-and-f-grades-for-social-distancing/ https://s.hdnux.com/photos/01/12/06/10/19423760/5/1024x1024.jpg Understand connection between the society (global) and the individuals (local) . 10
11. Spatial SIR variational problems Construct the following variational problem to balance virus spreading and “social” cost. min ⇢i,vi E(⇢ I (T, ·)) + Z T 0 Z ⌦ X i=S,I,R ↵ i 2 ⇢ ikv ik 2 + c 2 (⇢ S + ⇢ I + ⇢ R )2dxdt subject to 8 > > > > > > > > > < > > > > > > > > > : @ t ⇢ S + r · (⇢ S v S ) + ⇢ S ⇢ I ⌘2 S 2 ⇢ S = 0 @ t ⇢ I + r · (⇢ I v I ) ⇢ S ⇢ I + ⇢ I ⌘2 I 2 ⇢ I = 0 @ t ⇢ R + r · (⇢ R v R ) ⇢ I ⌘2 R 2 ⇢ R = 0 ⇢ S (0, ·), ⇢ I (0, ·), ⇢ R (0, ·) are given. 11
12. Spatial convolution SIR variation Consider min ⇢i,vi E(⇢ I (T, ·)) + Z T 0 Z ⌦ X i=S,I,R ↵ i 2 ⇢ ikv ik 2 + c 2 (⇢ S + ⇢ I + ⇢ R )2dxdt subject to 8 > > > > > > > > > < > > > > > > > > > : @ t ⇢ S + r · (⇢ S v S ) + ⇢ S K ⇤ ⇢ I ⌘2 S 2 ⇢ S = 0 @ t ⇢ I + r · (⇢ I v I ) K ⇤ ⇢ S ⇢ I + ⇢ I ⌘2 I 2 ⇢ I = 0 @ t ⇢ R + r · (⇢ R v R ) ⇢ I ⌘2 R 2 ⇢ R = 0 ⇢ S (0, ·), ⇢ I (0, ·), ⇢ R (0, ·) are given. Here K is the normalized positive definite symmetric convolution kernel. Kendall (1965) introduced this kernel for modeling pandemic dynamics without optimization. 12
13. Mean-field game SIR systems 8 > > > > > > > > > > > > > > > > > > > > > > > > > < > > > > > > > > > > > > > > > > > > > > > > > > > : @ t S ↵ S 2 |r S| 2 + ⌘2 S 2 S + c(⇢ S + ⇢ I + ⇢ R ) + (K ⇤ ( I ⇢ I ) S K ⇤ ⇢ I ) = 0 @ t I ↵ I 2 |r I| 2 + ⌘2 I 2 I + c(⇢ S + ⇢ I + ⇢ R ) + ( I K ⇤ ⇢ S K ⇤ ( S ⇢ S )) + ⇢( R I ) = 0 @ t R ↵ R 2 |r R| 2 + ⌘2 R 2 R + c(⇢ S + ⇢ I + ⇢ R ) = 0 @ t ⇢ S 1 ↵ S r · (⇢ Sr S ) + ⇢ S K ⇤ ⇢ I ⌘2 S 2 ⇢ S = 0 @ t ⇢ I 1 ↵ I r · (⇢ Ir I ) ⇢ I K ⇤ ⇢ S + ⇢ I ⌘2 I 2 ⇢ I = 0 @ t ⇢ R 1 ↵ R r · (⇢ Rr R ) ⇢ I ⌘2 R 2 ⇢ R = 0. 13
14. Review on PDHG method Consider a saddle point problem min x sup y {L(x, y) := hAx, yi + g(x) f⇤(y)} . Here, f and g are convex functions with respect to a variable x, A is a continuous linear operator. For each iteration, the algorithm finds the minimizer x ⇤ by gradient descent method and the maximizer y ⇤ by gradient ascent method. Thus, the minimizer and maximizer are calculated by iterating ( xk+1 = argmin x L(x, yk) + 1 2⌧ kx xkk2 yk+1 = argmax y L(xk+1, y) + 1 2 ky ykk2 where ⌧ and are step sizes for the algorithm. 14
15. Review on G-Proximal Here G-Prox PDHG is a modified version of PDHG that solves the minimization problem by choosing the most appropriate norms for updating x and y. Choosing the appropriate norms allows us to choose larger step sizes. Hence, we get a faster convergence rate. In details, ( xk+1 = argmin x L(x, yk) + 1 2⌧ kx xkk2 H yk+1 = argmax y L(xk+1, y) + 1 2 ky ykk2 G where H and G are some Hilbert spaces with the inner product (u1, u2) G = (Au1, Au2) H . 15
16. Algorithm: Primal-Dual updates In particular, we use G-Prox PDHG to solve the variational SIR model by x = (⇢ S , ⇢ I , ⇢ R , m S , m I , m R ), g(x) = F(⇢ i , m i ) i=S,I,R , f(Ax) = ( 0 if Ax = (0, 0, ⇢ I ) 1 otherwise. Ax = (@ t ⇢ S + r · m S ⌘2 S 2 ⇢ S + ⇢ S K ⇤ ⇢ I , @ t ⇢ I + r · m I ⌘2 2 ⇢ I ⇢ I K ⇤ ⇢ S + ⇢ I , @ t ⇢ R + r · m R ⌘2 2 ⇢ R ). 16
17. Variational formulation Denote m i = ⇢ i v i . Define the Lagrangian functional for Mean field game SIR problem by L((⇢ i , m i , i ) i=S,I,R ) =P(⇢ i , m i ) i=S,I,R Z T 0 Z ⌦ X i=S,I,R i ✓ @ t ⇢ i + r · m i ⌘2 i 2 ⇢ i ◆ dxdt + Z T 0 Z ⌦ I ⇢ I K ⇤ ⇢ S S ⇢ S K ⇤ ⇢ I + ⇢ I ( R I )dxdt. Using this Lagrangian functional, we convert the minimization problem into a saddle problem. inf (⇢i,mi)i=S,I,R sup ( i)i=S,I,R L((⇢ i , m i , i ) i=S,I,R ). 17
18. Algorithm Algorithm: PDHG for mean field game SIR system Input: ⇢ i (0, ·) (i = S, I, R) Output: ⇢ i , m i , i (i = S, I, R) for x 2 ⌦, t 2 [0, T] While relative error > tolerance ⇢(k+1) i = argmin ⇢ L(⇢, m(k) i , (k) i ) + 1 2⌧i k⇢ ⇢(k) i k2 L2 m(k+1) i = argmin m L(⇢(k+1), m, (k) i ) + 1 2⌧i km m(k) i k2 L2 (k+ 1 2 ) i = argmax L(⇢(k+1), m(k+1) i , ) 1 2 i k (k) i k2 H2 (k+1) i = 2 (k+ 1 2 ) i (k) i end 18
19. Discussions Importance of spatial SIR variational problems. I Consider more status of populations, going beyond S, I, R. I Construct discrete spatial domain model, including airport, train station, hospital, school etc. I Propose inverse mean field SIR problems. Learning parameters in the model by daily life data. I Combine mean field game SIR models with AI and machine learning algorithms, including APAC, Neural variational ODE, Neural Fokker-Planck equations, etc. 23
20. Examples I 19 Small recovery rate
21. Example II 20 Large recovery rate
22. Example III 21 Small recovery rate
23. Example IV 22 Large recovery rate
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