arXiv subjects

Leonid Chindelevitch

Publications and source records attributed to Leonid Chindelevitch.

5 recordsLinked to original sources

arXiv2026

Tournaments determined by three and five voters

The Kemeny median problem asks for a linear order minimizing the total pairwise disagreement with $m$ given rankings of $n$ options; it is NP-hard for every even $m \ge 4$ and every odd $m \ge 7$, while $m = 3$ and $m = 5$ remain open. Weighting each arc of the majority tournament by its margin reduces the problem to minimum-weight feedback arc set (FAS). The fewest voters inducing a tournament is its McGarvey number, and its predictability $\alpha^{*}(T)$ is the largest supermajority threshold at which $T$ is inducible. We refute three conjectures on inducibility. (i) In any tournament, every minimum FAS is a minimal hitting set of the directed 3-cycles, strengthening a theorem of Milosz, Hamel and Pierrot; both of their conjectures fail: the 3-cycle extension for all odd $m \ge 5$, and the equality $\mathrm{FAS} = \mathrm{HS}_3$ at $n = 11$. (ii) The threshold conjecture proposed by Shepardson and Tovey fails for $m = 3$, exactly on the boundary (predictability $= 2/3$). (iii) For $m = 5$ it fails strictly: the Paley tournament on 43 vertices, with predictability $181/301 > 3/5$, is not the majority of any 5 voters, making it the first explicit tournament of modest size beyond the reach of five voters.

cs.DM↗

arXiv2022

SplitStrains, a tool to identify and separate mixed Mycobacterium tuberculosis infections from WGS data

The occurrence of multiple strains of a bacterial pathogen such as M. tuberculosis or C. difficile within a single human host, referred to as a mixed infection, has important implications for both healthcare and public health. However, methods for detecting it, and especially determining the proportion and identities of the underlying strains, from WGS (whole-genome sequencing) data, have been limited. In this paper we introduce SplitStrains, a novel method for addressing these challenges. Grounded in a rigorous statistical model, SplitStrains not only demonstrates superior performance in proportion estimation to other existing methods on both simulated as well as real M. tuberculosis data, but also successfully determines the identity of the underlying strains. We conclude that SplitStrains is a powerful addition to the existing toolkit of analytical methods for data coming from bacterial pathogens and holds the promise of enabling previously inaccessible conclusions to be drawn in the realm of public health microbiology

stat.AP↗

arXiv2022

Applying data technologies to combat AMR: current status, challenges, and opportunities on the way forward

Antimicrobial resistance (AMR) is a growing public health threat, estimated to cause over 10 million deaths per year and cost the global economy 100 trillion USD by 2050 under status quo projections. These losses would mainly result from an increase in the morbidity and mortality from treatment failure, AMR infections during medical procedures, and a loss of quality of life attributed to AMR. Numerous interventions have been proposed to control the development of AMR and mitigate the risks posed by its spread. This paper reviews key aspects of bacterial AMR management and control which make essential use of data technologies such as artificial intelligence, machine learning, and mathematical and statistical modelling, fields that have seen rapid developments in this century. Although data technologies have become an integral part of biomedical research, their impact on AMR management has remained modest. We outline the use of data technologies to combat AMR, detailing recent advancements in four complementary categories: surveillance, prevention, diagnosis, and treatment. We provide an overview on current AMR control approaches using data technologies within biomedical research, clinical practice, and in the "One Health" context. We discuss the potential impact and challenges wider implementation of data technologies is facing in high-income as well as in low- and middle-income countries, and recommend concrete actions needed to allow these technologies to be more readily integrated within the healthcare and public health sectors.

cs.CY↗

arXiv2020

How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19?

To what extent are effectiveness estimates of nonpharmaceutical interventions (NPIs) against COVID-19 influenced by the assumptions our models make? To answer this question, we investigate 2 state-of-the-art NPI effectiveness models and propose 6 variants that make different structural assumptions. In particular, we investigate how well NPI effectiveness estimates generalise to unseen countries, and their sensitivity to unobserved factors. Models that account for noise in disease transmission compare favourably. We further evaluate how robust estimates are to different choices of epidemiological parameters and data. Focusing on models that assume transmission noise, we find that previously published results are remarkably robust across these variables. Finally, we mathematically ground the interpretation of NPI effectiveness estimates when certain common assumptions do not hold.

stat.AP↗

arXiv2016

Relatedness of the Incidence Decay with Exponential Adjustment (IDEA) Model, "Farr's Law" and Compartmental Difference Equation SIR Models

Mathematical models are often regarded as recent innovations in the description and analysis of infectious disease outbreaks and epidemics, but simple models have been in use for projection of epidemic trajectories for more than a century. We recently described a single equation model (the incidence decay with exponential adjustment, or IDEA, model) that can be used for short term forecasting. In the mid-19th century, Dr. William Farr developed a single equation approach (Farr's law) for epidemic forecasting. We show here that the two models are in fact identical, and can be expressed in terms of one another, and also in terms of a susceptible-infectious-removed (SIR) compartmental model with improving control. This demonstrates that the concept of the reproduction number, R0, is implicit to Farr's (pre-microbial era) work, and also suggests that control of epidemics, whether via behavior change or intervention, is as integral to the natural history of epidemics as is the dynamics of disease transmission.

stat.AP↗