Controlling Epidemic Spread: Reducing Economic Losses with Targeted Closures: The Effective Reproduction Rate Is Below One if and only if a Linear Matrix Inequality in the Activity Levels HoldsResearch Paper
Why targeted closures matter
A city can reduce contacts by limiting activity in selected neighborhoods. Such a policy affects more than the residents of the closed neighborhood: people travel across neighborhood boundaries, meet there, and carry infection back to their home neighborhoods. A citywide closure ignores these differences in movement and economic cost. Birge, Candogan, and Feng model these links and ask how to curb infection while preserving activity where it is most valuable. Their 2020 working paper gives separate optimization formulations for a large infection regime and a small infection regime; this mission concerns the latter. The paper later uses movement and economic data for New York City, while the theorem here is independent of those data. Birge, Candogan, and Feng, BFI Working Paper 2020-57.
The spatial epidemic model
There are neighborhoods. Neighborhood has population , and its residents spend a fraction of their time in neighborhood . The row sum is at most one; remaining time is spent in isolation. An activity level controls contact at destination . This makes the number of infectious contacts depend on where residents of every neighborhood meet, not only where they live.
Each neighborhood has susceptible, exposed, clinically infected, subclinically infected, and recovered populations. The effective contact rate is , the progression rate from exposed to infected is , the clinical fraction is , the relative infectiousness of subclinical cases is , the removal rate is , and the natural birth and death rate is . The model uses and . At the disease-free equilibrium, the exposed and infected populations vanish and the susceptible population in neighborhood equals .
The paper separates the infected-coordinate dynamics into a vector of new infections and a vector of other transfers. In the coordinate order exposed, clinical, subclinical, their Jacobians at the disease-free equilibrium are and , each a real matrix. Define as the greatest modulus of a complex eigenvalue of . The effective reproduction rate in condition (6) is . This is a property of the full infected-coordinate system. Model and condition (6), pp. 7–13.
Formalization targets
The intermediate targets identify the displayed inverse of , the smaller matrix sharing the nonzero eigenvalues of , and its diagonal similarity to a symmetric matrix. They connect condition (6) with a bound on the spectral radius of the reduced matrix. Each target is taken from the proof of Lemma 2.1 in the working paper.
The goal is the paper's Lemma 2.1. Let , , , and . With meaning that is positive definite, the target is
The strict inequality is the stability condition stated in the paper. The semidefinite program labelled (P2) uses a non-strict inequality to close its feasible region; that optimization problem is outside this mission. Lemma 2.1 and (P2), pp. 13–14.
What the result provides
The left side of Lemma 2.1 is a spectral test on a next generation matrix. The right side is a linear matrix inequality in the activity variables because is diagonal and depends linearly on . It therefore supplies the mathematical constraint used by the paper's small infection optimization problem. The theorem states equivalence for the paper's spatial epidemic model; it does not assert that the optimization problem has a solution or that a chosen activity vector minimizes economic loss. Birge, Candogan, and Feng, pp. 13–14.
The working paper proves the lemma in ordinary mathematics. The formalization task is to give its matrices and hypotheses precise Lean definitions, then prove the spectral and positive definiteness statements. The definition of the reproduction rate and the intermediate matrix identities can also support later formal statements about related compartmental models. No machine-checked proof of these draft items is claimed here.
Where the difficulty lies
The matrix is generally nonsymmetric and has three blocks of infected coordinates. A direct comparison between its entries and a symmetric positive definite matrix does not express the theorem. The paper first relates its nonzero eigenvalues to those of an matrix, then uses a diagonal similarity to obtain a symmetric matrix. The final equivalence relies on the activity and mobility bounds that make this symmetric matrix positive semidefinite. A matrix statement that starts directly from the reduced symmetric form would omit the spectral reduction at the heart of the lemma.
Formalization scope
Lean uses Fin n for neighborhoods and Fin 3 × Fin n for infected coordinates, in exposed, clinical, subclinical order. All model matrices are real; takes the spectral radius after mapping a matrix to complex entries, so complex eigenvalues are included. The inverse in is Mathlib's matrix inverse, and is represented by positive definiteness of . The only numerical instance in the sanity file has one neighborhood, but the theorem quantifies over arbitrary finite .
The standing assumptions include positive populations and rates, nonnegative travel fractions with row sums at most one, clinical and subclinical fractions in , and activity levels in . The formalization also requires : the paper divides by this quantity without stating the exclusion of . If no one visits destination , its mixing denominator is zero. Under the standing assumptions that entire column of vanishes, so Lean's value has no effect on the theorem. These conventions are part of the model, not optional proof shortcuts.
The proposal includes the full and , their displayed inverse, the paper's reduced matrix, the diagonal similarity, and the Loewner form. Contributions may develop spectral identities for block matrices, diagonal similarity, and positive definite matrix bounds. The large infection linear program, the stability theorem cited from earlier epidemic literature, and the New York City numerical application are beyond this target.
Selected references
- John R. Birge, Ozan Candogan, and Yiding Feng, Controlling Epidemic Spread: Reducing Economic Losses with Targeted Closures, Becker Friedman Institute Working Paper No. 2020-57, May 18, 2020 (preliminary), especially §2.1, §2.3, and Lemma 2.1. Working paper PDF.
- John R. Birge, Ozan Candogan, and Yiding Feng, Controlling Epidemic Spread: Reducing Economic Losses with Targeted Closures, Management Science 68(5), 2022, pp. 3175–3195. DOI. This mission's statements and page numbers follow the 2020 working paper.