DELENDA: Differentiable Epidemiology for Latent-state Estimation and Nonlinear Decision Analysis

Malaria subnational tailoring is often a population-level allocation problem: which interventions should be prioritized, at what coverage, and under what budget and uncertainty assumptions? We present D elenda , a differentiable compartmental model of Plasmodium falciparum transmission designed for posterior calibration and intervention-mix optimization. We fit a NUTS posterior jointly to age-stratified prevalence and clinical-incidence data from five sub-Saharan African sites plus three pre-intervention Garki Project villages, spanning a broad entomological inoculation rate (EIR) range. D elenda is implemented in JAX, which makes the full simulation differentiable. This enables efficient Bayesian inference and continuous constrained optimization over intervention coverage.

We apply the framework to an illustrative decision problem: a highly seasonal transmission setting where coverage is optimized for ITNs, SMC, IRS, and pediatric malaria vaccination across EIR, budget, objective, and uncertainty grids. Three findings are decision-relevant. First, intervention rankings are more robust than projected impact: posterior, vector-biology, and intervention-efficacy uncertainty change optimized coverage modestly but substantially widen the distribution of cases averted. Second, the objective matters: under-five optimization brings child-targeted SMC and vaccination in earlier, whereas all-age optimization delays vaccination and favors broader population protection through IRS. Third, cost uncertainty is mainly a constraint-side problem: expected-cost optima have material budget-overrun probability, while tail-risk budget rules sharply reduce overrun risk at the cost of lower effective coverage and fewer expected cases averted. D elenda therefore demonstrates an uncertainty-first approach to subnational tailoring: differentiable model structure exposes the biological parameter space to posterior calibration and carries biological and operational uncertainty into constrained decision optimization, tasks that are difficult with the non-differentiable models currently central to SNT workflows.