Optimal capacity sharing for global genomic surveillance.

Recent technological advances and substantial cost reductions have made the genomic surveillance of pathogens during pandemics feasible. Our paper focuses on full genome sequencing as a tool that can serve two goals: the estimation of variant prevalences, and the identification of new variants. Assuming that capacity constraints limit the number of samples that can be sequenced, we solve for the optimal distribution of these capacities among countries. Our results show that if the principal goal of sequencing is prevalence estimation, then the optimal capacity distribution is less than proportional to the weights (e.g., sizes) of countries. If, however, the main aim of sequencing is the detection of new variants, capacities should be allocated to countries or regions that have the most infections. Applying our results to the sequencing of SARS-CoV-2 in 2021, we provide a comparison between the observed and a suggested optimal capacity distribution worldwide and in the EU. We believe that following such quantifiable guidance will increase the efficiency of genomic surveillance for pandemics.

An Optimal Vaccine Allocation Model Considering Vaccine Hesitancy and Efficacy Rates Among Populations

Vaccines need to be urgently allocated in pandemics like the ongoing COVID-19 pandemic. In the literature, vaccines are optimally allocated using various mathematical models, including the extensively used Susceptible-Infected-Recovered epidemic model. However, these models do not account for the time duration concerning multi-dose vaccines, time duration from infection to recovery or death, the vaccine hesitancy (i.e., delay in acceptance or refusal of vaccination), and vaccine efficacy (i.e., the time-varying protection capability of the vaccine). To make the vaccine allocation model more applicable to reality, this paper presents an optimal model considering the above mentioned time duration concerning multi-dose vaccination, time duration from infection to recovery or death, hesitancy rates, efficacy levels, and also breakthrough rates - the rates at which individuals get infected after vaccination. This vaccine allocation model is constructed using a revised Susceptible-Infected-Recovered model. The concept of people∗week infections is introduced to measure the number of infected people within a certain time duration, and in this paper, the amount of people∗week infections is minimized by the proposed vaccine allocation model. Our case study of the New York State 2021 population of 19,840,000 shows that this optimal allocation method can avoid 0.05%2.75% people∗week infections than the baseline allocation method when 2 to 11 million vaccines are optimally allocated. In conclusion, the obtained optimal allocation method can effectively reduce people∗week infections and avoid vaccine waste when more vaccines are available. © 2013 IEEE.

Analysis of Pre- and during COVID-19 Mixed Load Models on Unbalanced Radial Distribution System Using a New Metaphor-Less Rao Optimization

An unbalanced electrical distribution system (DS) with radial construction and passive nature suffers from significant power loss. The unstable load demand and poor voltage profile resulted from insufficient reactive power in the DS. This research implements a unique Rao algorithm without metaphors for the optimal allocation of multiple distributed generation (DG) and distribution static compensators (DSTATCOM). For the appropriate sizing and placement of the device, the active power loss, reactive power loss, minimum value of voltage, and voltage stability index are evaluated as a multiobjective optimization to assess the device's impact on the 25-bus unbalanced radial distribution system. Various load models, including residential, commercial, industrial, battery charging, and other dispersed loads, were integrated to develop a mixed load model for examining electrical distribution systems. The impact of unpredictable loading conditions resulting from the COVID-19 pandemic lockdown on DS is examined. The investigation studied the role of DG and DSTATCOM (DGDST) penetration in the electrical distribution system for variations in different load types and demand oscillations under the critical emergency conditions of COVID-19. The simulation results produced for the mixed load model during the COVID-19 scenario demonstrate the proposed method's efficacy with distinct cases of DG and DSTATCOM allocation by lowering power loss with an enhanced voltage profile to create a robust and flexible distribution network. Copyright © 2023 Jitendra Singh Bhadoriya et al.

Unequal allocation of sample/event sizes with considerations of sampling cost for testing equality, non-inferiority/superiority, and equivalence of two Poisson rates.

For non-inferiority/superiority and equivalence tests of two Poisson rates, the determination of the required number of sample sizes has been studied but the studies for the number of events to be observed are very limited. To fill the gap, the present study first is aimed toward determining the number of events to be observed for testing non-inferiority/superiority and equivalence of two Poisson rates, respectively. Also, considering the cost for each event, the second purpose is to apply an exhaustive search to find the unequal but optimal allocation of events for each group such that the budget is minimal for a user-specified power level, or the statistical power is maximal for a user-specified budget. Four R Shiny apps were developed to obtain the number of events needed for each group. A simulation study showed the proposed approach to be valid in terms of Type I error and statistical power. A comparison of the proposed approach with extant methods from various disciplines was performed, and an illustrative example of comparing the adverse reactions to the COVID-19 vaccines was demonstrated. By applying the proposed approach, researchers also can estimate the most economical number of subjects or time intervals after determining the number of events.

Robust operation of distribution network based on photovoltaic/wind energy resources in condition of COVID-19 pandemic considering deterministic and probabilistic approaches.

In this paper, optimal allocation and planning of wind and photovoltaic energy resources are performed in a distribution network with the objective of reducing losses, improving reliability, and minimizing energy generation cost in terms of changes in load consumption pattern during the COVID-19 pandemic condition. The main goal is identifying the best operating point, ie the optimal location and size of clean energy resources in the worst load change conditions, which ensures the best network operation in all conditions during the COVID-19 condition via the turbulent flow of water-based optimization (TFWO). First, the deterministic approach is implemented in Hybrid and Distributed cases before and during COVID-19 conditions. The probabilistic approach is performed considering generation uncertainty during the COVID-19 conditions. The results showed better performance in the Distributed case with the lowest losses and higher reliability improvement. Moreover, the losses are significantly reduced and the reliability is improved during the COVID-19 pandemic conditions. The findings indicate that the allocation and planning during the COVID-19 conditions is a robust option in network operating point changes. Also, the probabilistic results showed that considering the uncertainty has increased active and reactive losses (4.67% and 5.82%) and weakened the reliability (10.26%) of the deterministic approach.

Algorithmic Waste Reduction

Motivated by a desire for waste reduction through surplus redistribution, we explore the paradox of overproduction of resources that are wasted at several levels of the supply chain and the concurrent lack of access to, in most cases, overproduced basic resources by low income socioeconomic classes to whom resource access is normally only available through donation centers. To that end, we contrast two surplus redistribution solutions to this paradox. (1) Local independent donations between producers and donation centers. (2) Redistribution by way of a global redistributor (what we will call a core redistributor) who collects donations from all available producers and redistributes the surplus to all donation centers respective of their demanded quantities. We mathematically show that an optimal allocation of the surplus that minimizes waste and maximizes social welfare is only possible with a core redistributor. As this is a deeply social and economic problem rather than mathematical, we also qualitatively study two cases;(1) food waste and food insecurity in the UK, and (2) Los Angeles County's project RoomKey: a pandemic effort to house covid-vulnerable unhoused persons in vacant hotels and motels. Both case studies give more support for a core redistribution as a solution to waste from overproduction and lack of access to essential resources. © 2022 Owner/Author.

Facility Location Problem to Identify The Optimal Allocation of Near-Expired COVID-19 Vaccines

Coronavirus 2019, popularly known as COVID-19 and declared a pandemic by the World Health Organization (WHO) in 2020, has affected billions of people and claimed millions of lives. Leaders and corporations worldwide have worked feverishly to develop a vaccine to combat the virus. After numerous tests and trials, COVID-19 vaccines were developed. Given the magnitude of the need for vaccination, these vaccines should not go to waste due to expiration from slow-paced rollouts or oversupply. This study aims to maximize near-expired COVID-19 vaccines in cases of oversupply by distributing them in neighbouring facilities at a low delivery cost and by utilizing P-median modelling. All gathered data were loaded into and run through the AMPL simulation model, with varying P-values or the number of facilities to be located to act as suppliers to the remaining demand nodes. Following the model simulation, it was observed that the P-value is inversely proportional to the cost;therefore, the cost of delivering near-expired COVID-19 vaccines to the demand clusters decreases as the P-value increases. Through the simulation model, the researchers determined which node facilities, if opened, would incur the lowest delivery cost. © 2022 IEEE.

Optimal Allocation of COVID-19 Test Kits Among Accredited Testing Centers in the Philippines.

Testing is crucial for early detection, isolation, and treatment of coronavirus disease (COVID-19)-infected individuals. However, in resource-constrained countries such as the Philippines, test kits have limited availability. As of 11 April 2020, there are 11 testing centers in the country that have been accredited by the Department of Health (DOH) to conduct testing. In this paper, we use nonlinear programming (NLP) to determine the optimal percentage allocation of COVID-19 test kits among accredited testing centers in the Philippines that gives an equitable chance to all infected individuals to be tested. Heterogeneity in testing accessibility, population density of municipalities, and the capacity of testing facilities are included in the model. Our results show that the range of optimal allocation per testing center are as follows: Research Institute for Tropical Medicine (4.17-6.34%), San Lazaro Hospital (14.65-24.03%), University of the Philippines-National Institutes of Health (16.25-44.80%), Lung Center of the Philippines (15.8-26.40%), Baguio General Hospital Medical Center (0.58-0.76%), The Medical City, Pasig City (5.96-25.51%), St. Luke's Medical Center, Quezon City (1.09-6.70%), Bicol Public Health Laboratory (0.06-0.08%), Western Visayas Medical Center (0.71-4.52%), Vicente Sotto Memorial Medical Center (1.02-2.61%), and Southern Philippines Medical Center (≈ 0.01%). Our results can serve as a guide to the authorities in distributing the COVID-19 test kits. These can also be used for proposing additional testing centers and utilizing the available test kits properly and equitably, which helps in "flattening" the epidemic curve.