ABSTRACT
We study the problem of determining the locations and capacities of COVID-19 specimen collection centers to efficiently improve accessibility to polymerase chain reaction testing during surges in testing demand. We develop a two-echelon multi-period location and capacity allocation model that determines optimal number and locations of pop-up testing centers, capacities of the existing centers as well as assignments of demand regions to these centers, and centers to labs. The objective is to minimize the total number of delayed appointments and specimens subject to budget, capacity, and turnaround time constraints, which will in turn improve the accessibility to testing. We apply our model to a case study for locating COVID-19 testing centers in the Region of Waterloo, Canada using data from the Ontario Ministry of Health, public health databases, and medical literature. We also test the performance of the model under uncertain demand and analyze its outputs under various scenarios. Our analyses provide practical insights to the public health decision-makers on the timing of capacity expansions and the locations for the new pop-up centers. According to our results, the optimal strategy is to dynamically expand the existing specimen collection center capacities and prevent bottlenecks by locating pop-up facilities. The optimal locations of pop-ups are among the densely populated areas that are in proximity to the lab and a subset of those locations are selected with the changes in demand. A comparison with a static approach promises up to 39% cost savings under high demand using the developed multi-period model.
Subject(s)
COVID-19 Testing , COVID-19 , Humans , COVID-19/diagnosis , OntarioABSTRACT
BACKGROUND: Roommates of unrecognized nosocomial Methicillin-Resistant Staphylococcus aureus (MRSA) cases are at higher acquisition risk; however, optimal surveillance strategies are unknown. METHODS: Using simulation, we analyzed surveillance testing and isolation strategies for MRSA among exposed hospital roommates. We compared isolating exposed roommates until conventional culture testing on day six (Cult6) and a nasal polymerase chain reaction (PCR) test on day three (PCR3) with/without day zero culture testing (Cult0). The model represents MRSA transmission in medium-sized hospitals using data and recommended best practices from the literature and Ontario community hospitals. RESULTS: Cult0+PCR3 incurred a slightly lower number of MRSA colonizations and 38.9% lower annual cost in the base case compared to Cult0+Cult6 because the reduced isolation cost compensated for the increased testing cost. The reduction in MRSA colonizations was due to 54.5% drop in MRSA transmissions during isolation as PCR3 reduced exposure of MRSA-free roommates to new MRSA carriers. Removing the day zero culture test from Cult0+PCR3 increased total cost, the number of MRSA colonization, and missed cases by $1,631, 4.3%, and 50.9%, respectively. Improvements were higher under aggressive MRSA transmission scenarios. DISCUSSION AND CONCLUSIONS: Adopting direct nasal PCR testing for determining post-exposure MRSA status reduces transmission risk and costs. Day zero culture would still be beneficial.
ABSTRACT
MOTIVATION: Synthesizing proteins in heterologous hosts is an important tool in biotechnology. However, the genetic code is degenerate and the codon usage is biased in many organisms. Synonymous codon changes that are customized for each host organism may have a significant effect on the level of protein expression. This effect can be measured by using metrics, such as codon adaptation index, codon pair bias, relative codon bias and relative codon pair bias. Codon optimization is designing codons that improve one or more of these objectives. Currently available algorithms and software solutions either rely on heuristics without providing optimality guarantees or are very rigid in modeling different objective functions and restrictions. RESULTS: We develop an effective mixed integer linear programing (MILP) formulation, which considers multiple objectives. Our numerical study shows that this formulation can be effectively used to generate (Pareto) optimal codon designs even for very long amino acid sequences using a standard commercial solver. We also show that one can obtain designs in the efficient frontier in reasonable solution times and incorporate other complex objectives, such as mRNA secondary structures in codon design using MILP formulations. AVAILABILITY AND IMPLEMENTATION: http://alpersen.bilkent.edu.tr/codonoptimization/CodonOptimization.zip.
