Allocation Of Testing Resources Research Articles

An optimization model for planning testing and control strategies to limit the spread of a pandemic – The case of COVID-19

The global health crisis caused by the coronavirus SARS-CoV-2 has highlighted the importance of efficient disease detection and control strategies for minimizing the number of infections and deaths in the population and halting the spread of the pandemic. Countries have shown different preparedness levels for promptly implementing disease detection strategies, via mass testing and isolation of identified cases, which led to a largely varying impact of the outbreak on the populations and health-care systems. In this paper, we propose a new pandemic resource allocation model for allocating limited disease detection and control resources, in particular testing capacities, in order to limit the spread of a pandemic. The proposed model is a novel epidemiological compartmental model formulated as a non-linear programming model that is suitable to address the inherent non-linearity of an infectious disease progression within the population. A number of novel features are implemented in the model to take into account important disease characteristics, such as asymptomatic infection and the distinct risk levels of infection within different segments of the population. Moreover, a method is proposed to estimate the vulnerability level of the different communities impacted by the pandemic and to explicitly consider equity in the resource allocation problem. The model is validated against real data for a case study of COVID-19 outbreak in France and our results provide various insights on the optimal testing intervention time and level, and the impact of the optimal allocation of testing resources on the spread of the disease among regions. The results confirm the significance of the proposed modeling framework for informing policymakers on the best preparedness strategies against future infectious disease outbreaks.

Evaluation of Allocation Schemes of COVID-19 Testing Resources in a Community-Based Door-to-Door Testing Program

Overcoming social barriers to COVID-19 testing is an important issue, especially given the demographic disparities in case incidence rates and testing. Delivering culturally appropriate testing resources using data-driven approaches in partnership with community-based health workers is promising, but little data are available on the design and effect of such interventions. To assess and evaluate a door-to-door COVID-19 testing initiative that allocates visits by community health workers by selecting households in areas with a high number of index cases, by using uncertainty sampling for areas where the positivity rate may be highest, and by relying on local knowledge of the health workers. This cohort study was performed from December 18, 2020, to February 18, 2021. Community health workers visited households in neighborhoods in East San Jose, California, based on index cases or uncertainty sampling while retaining discretion to use local knowledge to administer tests. The health workers, also known as promotores de salud (hereinafter referred to as promotores) spent a mean of 4 days a week conducting door-to-door COVID-19 testing during the 2-month study period. All residents of East San Jose were eligible for COVID-19 testing. The promotores were selected from the META cooperative (Mujeres Empresarias Tomando Acción [Entrepreneurial Women Taking Action]). The promotores observed self-collection of anterior nasal swab samples for SARS-CoV-2 reverse transcriptase-polymerase chain reaction tests. A determination of whether door-to-door COVID-19 testing was associated with an increase in the overall number of tests conducted, the demographic distribution of the door-to-door tests vs local testing sites, and the difference in positivity rates among the 3 door-to-door allocation strategies. A total of 785 residents underwent door-to-door testing, and 756 were included in the analysis. Among the 756 individuals undergoing testing (61.1% female; 28.2% aged 45-64 years), door-to-door COVID-19 testing reached different populations than standard public health surveillance, with 87.6% (95% CI, 85.0%-89.8%) being Latinx individuals. The closest available testing site only reached 49.0% (95% CI, 48.3%-49.8%) Latinx individuals. Uncertainty sampling provided the most effective allocation, with a 10.8% (95% CI, 6.8%-16.0%) positivity rate, followed by 6.4% (95% CI, 4.1%-9.4%) for local knowledge, and 2.6% (95% CI, 0.7%-6.6%) for index area selection. The intervention was also associated with increased overall testing capacity by 60% to 90%, depending on the testing protocol. In this cohort study of 785 participants, uncertainty sampling, which has not been used conventionally in public health, showed promising results for allocating testing resources. Community-based door-to-door interventions and leveraging of community knowledge were associated with reduced demographic disparities in testing.

Geographical variations and factors associated with recent HIV testing prevalence in Ghana: spatial mapping and complex survey analyses of the 2014 demographic and health surveys

ObjectiveTo examine the factors associated with recent HIV testing and to develop an HIV testing prevalence surface map using spatial interpolation techniques to identify geographical areas with low and high...

Geographic and demographic heterogeneity of SARS-CoV-2 diagnostic testing in Illinois, USA, March to December 2020

BackgroundAvailability of SARS-CoV-2 testing in the United States (U.S.) has fluctuated through the course of the COVID-19 pandemic, including in the U.S. state of Illinois. Despite substantial ramp-up in test volume, access to SARS-CoV-2 testing remains limited, heterogeneous, and insufficient to control spread.MethodsWe compared SARS-CoV-2 testing rates across geographic regions, over time, and by demographic characteristics (i.e., age and racial/ethnic groups) in Illinois during March through December 2020. We compared age-matched case fatality ratios and infection fatality ratios through time to estimate the fraction of SARS-CoV-2 infections that have been detected through diagnostic testing.ResultsBy the end of 2020, initial geographic differences in testing rates had closed substantially. Case fatality ratios were higher in non-Hispanic Black and Hispanic/Latino populations in Illinois relative to non-Hispanic White populations, suggesting that tests were insufficient to accurately capture the true burden of COVID-19 disease in the minority populations during the initial epidemic wave. While testing disparities decreased during 2020, Hispanic/Latino populations consistently remained the least tested at 1.87 tests per 1000 population per day compared with 2.58 and 2.87 for non-Hispanic Black and non-Hispanic White populations, respectively, at the end of 2020. Despite a large expansion in testing since the beginning of the first wave of the epidemic, we estimated that over half (50–80%) of all SARS-CoV-2 infections were not detected by diagnostic testing and continued to evade surveillance.ConclusionsSystematic methods for identifying relatively under-tested geographic regions and demographic groups may enable policymakers to regularly monitor and evaluate the shifting landscape of diagnostic testing, allowing officials to prioritize allocation of testing resources to reduce disparities in COVID-19 burden and eventually reduce SARS-CoV-2 transmission.

Entropy based enhanced particle swarm optimization on multi‐objective software reliability modelling for optimal testing resources allocation

SummaryThis paper proposes a generalization of the exponential software reliability model to characterize several factors including fault introduction and time‐varying fault detection rate. The software life cycle is designed based on module structure such as testing effort spent during module testing and detected software faults etc. The resource allocation problem is a critical phase in the testing stage of software reliability modelling. It is required to make decisions for optimal resource allocation among the modules to achieve the desired level of reliability. We formulate a multi‐objective software reliability model of testing resources for a new generalized exponential reliability function to characterizes dynamic allocation of total expected cost and testing effort. An enhanced particle swarm optimization (EPSO) is proposed to maximize software reliability and minimize allocation cost. We perform experiments with randomly generated testing‐resource sets and varying the performance using the entropy function. The multi‐objective model is compared with modules according to weighted cost function and testing effort measures in a typical modular testing environment.

Enhanced Constraint Handling for Reliability-Constrained Multiobjective Testing Resource Allocation

The multiobjective testing resource allocation problem (MOTRAP) is how to efficiently allocate the finite testing time to various modules, with the aim of optimizing system reliability, testing cost, and testing time simultaneously. To deal with this problem, a common approach is to use multiobjective evolutionary algorithms (MOEAs) to seek a set of tradeoff solutions between the three objectives. However, such a tradeoff set may contain a substantial proportion of solutions with very low reliability level, which consume lots of computational resources but may be valueless to the software project manager. In this article, a MOTRAP model with a prespecified reliability is first proposed. Then, new lower bounds on the testing time invested in different modules are theoretically deduced from the necessary condition for the achievement of the given reliability, based on which an exact algorithm for determining the new lower bounds is presented. Moreover, several enhanced constraint-handling techniques (ECHTs) derived from the new bounds are successively developed to be combined with MOEAs to correct and reduce the constraint violation. Finally, the proposed ECHTs are evaluated in comparison with various state-of-the-art constraint-solving approaches. The comparative results demonstrate that the proposed ECHTs can work well with MOEAs, make the search focus on the feasible region of the prespecified reliability, and provide the software project manager with better and more diverse, satisfactory choices in test planning.

Risk mapping for COVID-19 outbreaks in Australia using mobility data.

COVID-19 is highly transmissible and containing outbreaks requires a rapid and effective response. Because infection may be spread by people who are pre-symptomatic or asymptomatic, substantial undetected transmission is likely to occur before clinical cases are diagnosed. Thus, when outbreaks occur there is a need to anticipate which populations and locations are at heightened risk of exposure. In this work, we evaluate the utility of aggregate human mobility data for estimating the geographical distribution of transmission risk. We present a simple procedure for producing spatial transmission risk assessments from near-real-time population mobility data. We validate our estimates against three well-documented COVID-19 outbreaks in Australia. Two of these were well-defined transmission clusters and one was a community transmission scenario. Our results indicate that mobility data can be a good predictor of geographical patterns of exposure risk from transmission centres, particularly in outbreaks involving workplaces or other environments associated with habitual travel patterns. For community transmission scenarios, our results demonstrate that mobility data add the most value to risk predictions when case counts are low and spatially clustered. Our method could assist health systems in the allocation of testing resources, and potentially guide the implementation of geographically targeted restrictions on movement and social interaction.

HOPE: Software Defect Prediction Model Construction Method via Homomorphic Encryption

Software defect prediction can predict the defective modules in the project in advance, which is helpful to optimize the allocation of test resources. Recently, privacy protection for datasets and models has gradually attracted the attention of researchers. In this study, we are the first to apply homomorphic encryption to software defect prediction model construction and propose a novel method HOPE. Specifically, we adopt an algorithm approximation strategy to approximate the sigmoid function and select the Paillier homomorphic encryption algorithm for Logistical regression. In our case study, we choose the MORPH dataset gathered from real-world open-source projects as our experimental subjects. Then we design three control groups to simulate three different scenarios based on whether the client sends the encrypted data to the server and whether the server uses the HOPE method. The final results show that if the server uses the original Logistic regression to construct the model on the encrypted data, the performance of the trained model is similar to random guess, which can guarantee the privacy protection of the data. Moreover, compared with the original Logistical regression method, the method HOPE only needs a small amount of computational cost, but there is no obvious performance decrease. We share our implementation scripts and datasets to encourage researchers to conduct more studies on this research direction.

Research and Analysis of Software Defect Measurement Elements Based on Analytic Hierarchy Process

It is not convenient for the general software quality evaluation system to be directly and quickly applied to the software quality evaluation to be tested. Therefore, this paper proposes a method suitable for the analysis of factors affecting software quality based on analytic hierarchy process. The analytic hierarchy process is used as the main framework, the distribution of the software’s own properties and accumulated test findings as input,the constructed defect weight ratio is used as the judgment matrix, Then do the hierarchical analysis. Through the analytic hierarchy process to analyse the weight of the factors of the software itself, test organizations can quickly make a fuzzy assessment of software quality, and provides the reference basis for the reasonable allocation of test resources.

Simulated Software Testing Process and Its Optimization Considering Heterogeneous Debuggers and Release Time

Most traditional software reliability growth models (SRGMs) assume immediate fault correction upon detection and therefore only consider fault detection process (FDP). In order to be more realistic, some researchers have tried to incorporate fault correction process (FCP) and fault introduction process (FIP) into the software reliability models. However, it is still difficult to incorporate into the analytical software reliability models some other factors, such as the different fault detection and correction capabilities of debuggers. In this paper, a simulation approach is proposed to model FDP, FIP, and FCP together considering debuggers with different contributions to fault detection rate, different fault correction rate and different fault introduction rate. Besides, this paper also constructed a cost calculation method to optimize the testing design including debuggers assignment and software release time. Some numerical examples are provided to illustrate the proposed model. The results show that the trends of FDP, FCP and FIP are consistent with the intuition to the practice of software testing, and the optimal testing resources allocation and the optimal release time can be obtained according to the proposed model.

538. Percent Positivity of COVID-19 Testing in Children of Racial/Ethnic Minority Groups in Mississippi

BackgroundCOVID-19 testing data in children from different backgrounds such as race/ethnicity can guide the strategies for controlling the pandemic. In this study, we aimed to characterize the percent positivity of real time reverse transcriptase polymerase chain reaction (RT-PCR) testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) among children with different race/ethnicity in Mississippi, a state with historic racial health disparity.MethodsWe performed a retrospective study using a deidentified COVID-19 registry abstracted from the electronic health record at the University of Mississippi Medical Center, the only academic institution in the state encompassing the main university hospitals and ambulatory sites statewide. Children aged 18 years or younger who underwent RT-PCR for SARS-CoV-2 between 11 March 2020 and 31 May 2020 were included in the study. The primary outcome of interest was positive SARS-CoV-2 RT-PCR test, and the primary exposure of interest was race/ethnicity. Multivariable analysis was performed by developing a logistic regression model.ResultsOf 1838 children who underwent SARS-CoV-2 RT-PCR testing, 162 tested positive, corresponding to percent positivity of 8.8%. Non-Hispanic white children had percent positivity of 2.4%, which was substantially lower than that of non-white children (9.7%, 28.6%, and 11.5% for non-Hispanic black, Hispanic, and other race/ethnicity children, respectively) (Table). Older children (particularly those aged 10–18 years) also had higher percent positivity (Table, Figure). The risks associated with non-white race/ethnicity remained substantial after adjusting for insurance status, which was used as a surrogate for socioeconomic status (Figure).Table Figure ConclusionWe found a striking difference in percent positivity by race/ethnicity among children, suggesting inadequate allocation of testing resources to this population. Higher percent positivity among school age children may also have particular public health implications, considering their school attendance. We argue that minority children should not be left behind in data analysis, research, targeted testing and transmission reduction measures.Disclosures All Authors: No reported disclosures

Optimal allocation of limited test resources for the quantification of COVID-19 infections.

The systematic identification of infected individuals is critical for the containment of the COVID-19 pandemic. Currently, the spread of the disease is mostly quantified by the reported numbers of infections, hospitalisations, recoveries and deaths; these quantities inform epidemiology models that provide forecasts for the spread of the epidemic and guide policy making. The veracity of these forecasts depends on the discrepancy between the numbers of reported, and unreported yet infectious, individuals. We combine Bayesian experimental design with an epidemiology model and propose a methodology for the optimal allocation of limited testing resources in space and time, which maximises the information gain for such unreported infections. The proposed approach is applicable at the onset and spread of the epidemic and can forewarn of a possible recurrence of the disease after relaxation of interventions. We examine its application in Switzerland; the open source software is, however, readily adaptable to countries around the world. We find that following the proposed methodology can lead to vastly less uncertain predictions for the spread of the disease, thus improving estimates of the effective reproduction number and the future number of unreported infections. This information can provide timely and systematic guidance for the effective identification of infectious individuals and for decision-making regarding lockdown measures and the distribution of vaccines.

COVID-MATCH65-A prospectively derived clinical decision rule for severe acute respiratory syndrome coronavirus 2.

ObjectivesWe report on the key clinical predictors of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and present a clinical decision rule that can risk stratify patients for COVID-19.Design, participants and settingA prospective cohort of patients assessed for COVID-19 at a screening clinic in Melbourne, Australia. The primary outcome was a positive COVID-19 test from nasopharyngeal swab. A backwards stepwise logistic regression was used to derive a model of clinical variables predictive of a positive COVID-19 test. Internal validation of the final model was performed using bootstrapped samples and the model scoring derived from the coefficients, with modelling performed for increasing prevalence.ResultsOf 4226 patients with suspected COVID-19 who were assessed, 2976 patients underwent SARS-CoV-2 testing (n = 108 SARS-CoV-2 positive) and were used to determine factors associated with a positive COVID-19 test. The 7 features associated with a positive COVID-19 test on multivariable analysis were: COVID-19 patient exposure or international travel, Myalgia/malaise, Anosmia or ageusia, Temperature, Coryza/sore throat, Hypoxia–oxygen saturation < 97%, 65 years or older—summarized in the mnemonic COVID-MATCH65. Internal validation showed an AUC of 0.836. A cut-off of ≥ 1.5 points was associated with a 92.6% sensitivity and 99.5% negative predictive value (NPV) for COVID-19.ConclusionsFrom the largest prospective outpatient cohort of suspected COVID-19 we define the clinical factors predictive of a positive SARS-CoV-2 test. The subsequent clinical decision rule, COVID-MATCH65, has a high sensitivity and NPV for SARS-CoV-2 and can be employed in the pandemic, adjusted for disease prevalence, to aid COVID-19 risk-assessment and vital testing resource allocation.

A comprehensive comparative study of clustering-based unsupervised defect prediction models

Software defect prediction recommends the most defect-prone software modules for optimization of the test resource allocation. The limitation of the extensively-studied supervised defect prediction methods is that they require labeled software modules which are not always available. An alternative solution is to apply clustering-based unsupervised models to the unlabeled defect data, called Clustering-based Unsupervised Defect Prediction (CUDP). However, there are few studies to explore the impacts of clustering-based models on defect prediction performance. In this work, we performed a large-scale empirical study on 40 unsupervised models to fill this gap. We chose an open-source dataset including 27 project versions with 3 types of features. The experimental results show that (1) different clustering-based models have significant performance differences and the performance of models in the instance-violation-score-based clustering family is obviously superior to that of models in hierarchy-based, density-based, grid-based, sequence-based, and hybrid-based clustering families; (2) the models in the instance-violation-score-based clustering family achieves competitive performance compared with typical supervised models; (3) the impacts of feature types on the performance of the models are related to the indicators used; and (4)the clustering-based unsupervised models do not always achieve better performance on defect data with the combination of the 3 types of features.

Estimation of the number of people with Down syndrome in Europe.

We aimed to estimate the nonselective live birth prevalence, actual live birth prevalence, reduction percentage because of selective terminations, and population prevalence for Down syndrome (DS) in European countries. The number of people with DS alive in a country was estimated by first modeling the number of live births of children with DS by year of birth. Subsequently, for these different years of birth, survival curves for people with DS were constructed and then applied to these yearly estimates of live births with DS. For Europe, 2011-2015, we estimate 8,031 annual live births of children with DS, which would have been around 17,331 births annually, absent selective terminations. The estimated reduction of live birth prevalence was, on average, 54%, varying between 0% in Malta and 83% in Spain. As of 2015, we estimate 417,000 people with DS are living in Europe; without elective terminations, there would have been about 572,000 people with DS, which corresponds to a population reduction rate of 27%. Such statistics can be important barometers for prenatal testing trends and resource allocation within countries. Disability awareness initiatives and public policy initiatives can also be better grounded with these more precise estimates.

Designing a multi-layered surveillance approach to detecting SARS-CoV-2: A modelling study

Background: Countries achieving control of COVID-19 after an initial outbreak will continue to face the risk of SARS-CoV-2 resurgence. This study explores surveillance strategies for COVID-19 containment based on polymerase chain reaction tests. Methods: Using a dynamic SEIR-type model to simulate the initial dynamics of a COVID-19 introduction, we investigate COVID-19 surveillance strategies among healthcare workers, hospital patients, and community members. We estimate surveillance sensitivity as the probability of COVID-19 detection using a hypergeometric sampling process. We identify test allocation strategies that maximise the probability of COVID-19 detection across different testing capacities. We use Beijing, China as a case study. Results: Surveillance subgroups are more sensitive in detecting COVID-19 transmission when they are defined by more COVID-19-specific symptoms. In this study, fever clinics have the highest surveillance sensitivity, followed by respiratory departments. With a daily testing rate of 0.07/1000 residents, via exclusively testing at fever clinic and respiratory departments, there would have been 598 [95% eCI: 35, 2154] and 1373 [95% eCI: 47, 5230] cases in the population by the time of first case detection, respectively. Outbreak detection can occur earlier by including non-syndromic subgroups, such as younger adults in the community, as more testing capacity becomes available. Conclusions: A multi-layer approach that considers both the surveillance sensitivity and administrative constraints can help identify the optimal allocation of testing resources and thus inform COVID-19 surveillance strategies.

An empirical study to investigate the impact of data resampling techniques on the performance of class maintainability prediction models

With the increasing complexity of the software systems nowadays, the trend has been shifted to object-oriented (OO) development. The classes are the central construct in an OO software that are expected to be of utmost quality and high maintainability. The maintainability of a class is the probability that a class can be effortlessly modifiable in the maintenance phase. Unfortunately, it is very tough to determine the maintainability of a class with confidence before the release of the software. However, maintainability can be predicted with the help of internal quality attributes (viz. complexity, cohesion coupling, inheritance, etc.). The researchers in the literature have studied the relation amongst the internal quality attributes and class maintainability. Many class maintainability prediction models have been developed in the past with the help of internal quality attributes. Effective prediction models are vital to forecast class maintainability accurately. However, various datasets used to build prediction models for class maintainability suffer from imbalanced data problem. In that scenario, a model trained with imbalanced data gives erroneous predictions of class maintainability, which results in the inaccurate allocation of testing and maintenance resources to the misclassified classes. Therefore towards this direction, this study assesses the applicability of techniques to take care of imbalanced data. In this study the imbalanced data is treated with nine oversampling and three undersampling methods. A comprehensive comparison of fourteen machine learning (ML) techniques and fourteen search based (SB) techniques is conducted for class maintainability prediction. The results of the study support the applicability Safe-Level Synthetic Minority Oversampling Technique (Safe-SMOTE) to handle the imbalanced data for class maintainability prediction.

Screening for COVID-19: Patient factors predicting positive PCR test.

To inform the efficient allocation of testing resources, we evaluated the characteristics of those tested for COVID-19 to determine predictors of a positive test. Recent travel and exposure to a confirmed case were both highly predictive of positive testing. Symptom-based screening strategies alone may be inadequate to control the ongoing pandemic.

A joint modeling approach for reliability growth planning considering product life cycle cost performance

Reliability growth planning methods have been traditionally focusing on the product design stage rather than the product whole life cycle including both product development and deployment stages. A joint modeling approach for reliability growth planning considering product life cycle cost performance is proposed. The product life cycle performance consists of the cost of reliability growth test, early release incentive or late release penalty, and after sales maintenance cost. The product early release incentive or late release penalty is a virtual cost, which quantifies the impact of product release timing on the product life cycle cost assessment. The reliability growth test cost depends on the allocated testing units and times, which consequently determines the reliability level at product release time and the subsequent maintenance cost. The proposed joint modeling approach balances and determines the optimal product release time and reliability growth test resource allocation with the objective of minimizing the product life cycle cost. A numerical example is illustrated to demonstrate the advantages of the proposed method over traditional reliability growth planning approaches.

Ensemble Techniques-Based Software Fault Prediction in an Open-Source Project

Software engineering repositories have been attracted by researchers to mine useful information about the different quality attributes of the software. These repositories have been helpful to software professionals to efficiently allocate various resources in the life cycle of software development. Software fault prediction is a quality assurance activity. In fault prediction, software faults are predicted before actual software testing. As exhaustive software testing is impossible, the use of software fault prediction models can help the proper allocation of testing resources. Various machine learning techniques have been applied to create software fault prediction models. In this study, ensemble models are used for software fault prediction. Change metrics-based data are collected for an open-source android project from GIT repository and code-based metrics data are obtained from PROMISE data repository and datasets kc1, kc2, cm1, and pc1 are used for experimental purpose. Results showed that ensemble models performed better compared to machine learning and hybrid search-based algorithms. Bagging ensemble was found to be more effective in the prediction of faults in comparison to soft and hard voting.