# Maximum n-times Coverage for Vaccine Design

arXiv: https://arxiv.org/abs/2101.10902 · HTML: https://mlsanity.com/paper/2101.10902/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 66 references that are in the map.
- First posted on arXiv 24 Jan 2021.

## Builds on (most cited first, 2 of 2)

- [Robust Maximization of Non-Submodular Objectives](https://mlsanity.com/paper/1802.07073/) (2018)
- [A Unified Framework of Constrained Robust Submodular Optimization with Applications](https://mlsanity.com/paper/1906.06393/) (2019)

## Built on by (most cited first, 1 of 1)

- [Constrained Submodular Optimization for Vaccine Design](https://mlsanity.com/paper/2206.08336/) (2022)

## Abstract (arXiv)

We introduce the maximum n-times coverage problem that selects k overlays to maximize the summed coverage of weighted elements, where each element must be covered at least n times. We also define the min-cost n-times coverage problem where the objective is to select the minimum set of overlays such that the sum of the weights of elements that are covered at least n times is at least τ. Maximum n-times coverage is a generalization of the multi-set multi-cover problem, is NP-complete, and is not submodular. We introduce two new practical solutions for n-times coverage based on integer linear programming and sequential greedy optimization. We show that maximum n-times coverage is a natural way to frame peptide vaccine design, and find that it produces a pan-strain COVID-19 vaccine design that is superior to 29 other published designs in predicted population coverage and the expected number of peptides displayed by each individual's HLA molecules.