arXiv subjects

Rebecca Mitchell

Publications and source records attributed to Rebecca Mitchell.

2 recordsLinked to original sources

arXiv2020

The impact of COVID-19 on the UK fresh food supply chain

The resilience of the food supply chain is a matter of critical importance, both for national security and broader societal well bring. COVID19 has presented a test to the current system, as well as means by which to explore whether the UK's food supply chain will be resilient to future disruptions. In the face of a growing need to ensure that food supply is more environmentally sustainable and socially just, COVOD19 also represents an opportunity to consider the ability of the system to innovative, and its capacity for change. The purpose of this case based study is to explore the response and resilience of the UK fruit and vegetable food supply chain to COVID19, and to assess this empirical evidence in the context of a resilience framework based on the adaptive cycle. To achieve this we reviewed secondary data associated with changes to retail demand, conducted interviews with 23 organisations associated with supply to this market, and conducted four video workshops with 80 organisations representing half of the UK fresh produce community. The results highlight that, despite significant disruption, the retail dominated fresh food supply chain has demonstrated a high degree of resilience. In the context of the adaptive cycle, the system has shown signs of being stuck in a rigidity trap, as yet unable to exploit more radical innovations that may also assist in addressing other drivers for change. This has highlighted the significant role that innovation and R&D communities will need to play in enabling the supply chain to imagine and implement alternative future states post COVID.

econ.GN↗

arXiv2018

A Bayesian framework for molecular strain identification from mixed diagnostic samples

We provide a mathematical formulation and develop a computational framework for identifying multiple strains of microorganisms from mixed samples of DNA. Our method is applicable in public health domains where efficient identification of pathogens is paramount, e.g., for the monitoring of disease outbreaks. We formulate strain identification as an inverse problem that aims at simultaneously estimating a binary matrix (encoding presence or absence of mutations in each strain) and a real-valued vector (representing the mixture of strains) such that their product is approximately equal to the measured data vector. The problem at hand has a similar structure to blind deconvolution, except for the presence of binary constraints, which we enforce in our approach. Following a Bayesian approach, we derive a posterior density. We present two computational methods for solving the non-convex maximum a posteriori estimation problem. The first one is a local optimization method that is made efficient and scalable by decoupling the problem into smaller independent subproblems, whereas the second one yields a global minimizer by converting the problem into a convex mixed-integer quadratic programming problem. The decoupling approach also provides an efficient way to integrate over the posterior. This provides useful information about the ambiguity of the underdetermined problem and, thus, the uncertainty associated with numerical solutions. We evaluate the potential and limitations of our framework in silico using synthetic and experimental data with available ground truths.

stat.AP↗