Group Testing Strategies Research Articles

Adaptive group testing strategy for infectious diseases using social contact graph partitions

Mass testing is essential for identifying infected individuals during an epidemic and allowing healthy individuals to return to normal social activities. However, testing capacity is often insufficient to meet global health needs, especially during newly emerging epidemics. Dorfman’s method, a classic group testing technique, helps reduce the number of tests required by pooling the samples of multiple individuals into a single sample for analysis. Dorfman’s method does not consider the time dynamics or limits on testing capacity involved in infection detection, and it assumes that individuals are infected independently, ignoring community correlations. To address these limitations, we present an adaptive group testing (AGT) strategy based on graph partitioning, which divides a physical contact network into subgraphs (groups of individuals) and assigns testing priorities based on the social contact characteristics of each subgraph. Our AGT aims to maximize the number of infected individuals detected and minimize the number of tests required. After each testing round (perhaps on a daily basis), the testing priority is increased for each neighboring group of known infected individuals. We also present an enhanced infectious disease transmission model that simulates the dynamic spread of a pathogen and evaluate our AGT strategy using the simulation results. When applied to 13 social contact networks, AGT demonstrates significant performance improvements compared to Dorfman’s method and its variations. Our AGT strategy requires fewer tests overall, reduces disease spread, and retains robustness under changes in group size, testing capacity, and other parameters. Testing plays a crucial role in containing and mitigating pandemics by identifying infected individuals and helping to prevent further transmission in families and communities. By identifying infected individuals and helping to prevent further transmission in families and communities, our AGT strategy can have significant implications for public health, providing guidance for policymakers trying to balance economic activity with the need to manage the spread of infection.

Subset Intersection Group Testing Strategy

Many a time, items can be classified as defective or non-defective and the objective is to identify all the defective items, if any, in the population. The concept of group testing deals with identifying all such defective items using a minimum number of tests. This paper proposes probabilistic group testing through a subset intersection group testing strategy. The proposed algorithm 'Subset Intersection Group Testing Strategy' deals with dividing the whole population, if it is positive, into different rows and columns and individually testing all the defective rows and columns. Through this proposed strategy, the number of group tests is either always one when no defective is found or 1+r+c, where r and c denote the number of rows and columns, when at least one defective is found. The proposed algorithms are validated using simulation for different combinations of group size and the incidence probability of an item being defective (p) and implications are drawn. The results indicate that the average number of total tests required is smaller when p is small and considerably increases as p increases. Therefore, for the smaller values of p, this proposed strategy is more effective. Also, an attempt is made to estimate an upper bound for the number of tests through this strategy in various scenarios.

Optimal group testing strategy for the mass screening of SARS-CoV-2

We analyze the group testing strategy that maximizes the efficiency of the SARS-CoV-2 screening test while ensuring its effectiveness, where the effectiveness of group testing guarantees that negative results from pooled samples can be considered presumptive negative. Two aspects of test efficiency are considered, one concerning the maximization of the welfare throughput and the other concerning the maximization of the identification rate (namely, identifying as many infected individuals as possible). We show that compared with individual testing, group testing leads to a higher probability of false negative results but a lower probability of false positive results. To ensure the test effectiveness, both the group size and the prevalence of SARS-CoV-2 must be below certain respective thresholds. To achieve test efficiency that concerns either the welfare throughput maximization or the identification rate maximization, the optimal group size is jointly determined by the test accuracy parameters, the infection prevalence rate, and the relative importance of identifying infected subjects. We also show that the optimal group size that maximizes the welfare throughput is weakly smaller than the one that maximizes the identification rate.

Noisy Adaptive Group Testing via Noisy Binary Search

The group testing problem consists of determining a small set of defective items from a larger set of items based on a number of possibly-noisy tests, and has numerous practical applications. One of the defining features of group testing is whether the tests are adaptive (i.e., a given test can be chosen based on all previous outcomes) or non-adaptive (i.e., all tests must be chosen in advance). In this paper, building on the success of binary splitting techniques in noiseless group testing (Hwang, 1972), we introduce noisy group testing algorithms that apply noisy binary search as a subroutine. We provide three variations of this approach with increasing complexity, culminating in an algorithm that succeeds using a number of tests that matches the best known previously (Scarlett, 2019), while overcoming fundamental practical limitations of the existing approach, and more precisely capturing the dependence of the number of tests on the error probability. We provide numerical experiments demonstrating that adaptive group testing strategies based on noisy binary search can be highly effective in practice, using significantly fewer tests compared to state-of-the-art non-adaptive strategies.

An Expectation Maximization Based Adaptive Group Testing Method for Improving Efficiency and Sensitivity of Large-Scale Screening of COVID-19.

The pathogen of the ongoing coronavirus disease 2019 (COVID-19) pandemic is a newly discovered virus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Testing individuals for SARS-CoV-2 plays a critical role in containing COVID-19. For saving medical personnel and consumables, many countries are implementing group testing against SARS-CoV-2. However, existing group testing methods have the following limitations: (1) The group size is determined without theoretical analysis, and hence is usually not optimal. This adversely impacts the screening efficiency. (2) These methods neglect the fact that mixing samples together usually leads to substantial dilution of the SARS-CoV-2 virus, which seriously impacts the sensitivity of tests. In this paper, we aim to screen individuals infected with COVID-19 with as few tests as possible, under the premise that the sensitivity of tests is high enough. We propose an eXpectation Maximization based Adaptive Group Testing (XMAGT) method. The basic idea is to adaptively adjust its testing strategy between a group testing strategy and an individual testing strategy such that the expected number of samples identified by a single test is larger. During the screening process, the XMAGT method can estimate the ratio of positive samples. With this ratio, the XMAGT method can determine a group size under which the group testing strategy can achieve a maximal expected number of negative samples and the sensitivity of tests is higher than a user-specified threshold. Experimental results show that the XMAGT method outperforms existing methods in terms of both efficiency and sensitivity.

Optimized Solution for Nucleic Acid Detection Based on Group Testing Model

Under the critical situation of recurrent new crown outbreaks, large-scale full-scale nucleic acid testing has become the norm across the country. When the total number of people in a region is large and the infection rate is small, mixed testing can greatly improve testing efficiency and save costs compared with one-person testing. For the optimization of the mixed testing model, the Group Testing strategy is used to analyze the mixed testing method with the minimum number of tests, build a model and calculate the results with the help of Matlab, and finally obtain the optimal ratio of mixed testing in each region.

A network-based group testing strategy for colleges

Group testing has recently become a matter of vital importance for efficiently and rapidly identifying the spread of Covid-19. In particular, we focus on college towns due to their density, observability, and significance for school reopenings. We propose a novel group testing strategy which requires only local information about the underlying transmission network. By using cellphone data from over 190,000 agents, we construct a mobility network and run extensive data-driven simulations to evaluate the efficacy of four different testing strategies. Our results demonstrate that our group testing method is more effective than three other baseline strategies for reducing disease spread with fewer tests.

Two-Stage Hierarchical Group Testing Strategy to Increase SARS-CoV-2 Testing Capacity at an Institution of Higher Education: A Retrospective Analysis

Population testing for severe acute respiratory syndrome coronavirus 2 is necessary because of the potential for viral transmission from asymptomatic cases, yet the scarcity of reagents and equipment has increased the cost-prohibitive implementation of screening campaigns at institutions of higher education. Significant analytical sensitivities of nucleic acid amplification methods permit sample pooling to increase testing capacity. Statistical models compared optimal testing configurations for pools of 3, 5, and 10 samples. Assessment of pooling using the TaqPath COVID-19 Combo Kit multiplex assay (ORF1ab, N, and S gene targets) involved a limit-of-detection study, matrix-effect study, and clinical comparison of neat with pooled samples. A limit of detection of 135.02 (ORF1ab; 95% CI, 117.21–155.52), 373.92 (N; 95% CI, 257.05–437.64), and 1001.32 (S; 95% CI, 896.62–1118.33) gene copy equivalents per milliliter was resolved. Seventy-two randomly selected samples showed slight suppression owing to a negative sample matrix. The resulting mean cycle threshold shifts were 2.09 (ORF1ab), 1.76 (N), and 2.31 (S) for the 3-sample pool, 2.83 (ORF1ab), 2.45 (N), and 3.24 (S) for the 5-sample pool, and 3.99 (ORF1ab), 3.46 (N), and 4.07 (S) for the 10-sample pool. Despite a quantitative sensitivity loss trend, the qualitative result was unaffected in each pool. According to the range of disease prevalence observed at the testing site (0.03% to 7.32%), a pool of five samples was deemed an optimal and cost-effective option for monitoring the Northeastern University community.

Group Testing for SARS-CoV-2 Allows for Up to 10-Fold Efficiency Increase Across Realistic Scenarios and Testing Strategies.

Background: Due to the ongoing COVID-19 pandemic, demand for diagnostic testing has increased drastically, resulting in shortages of necessary materials to conduct the tests and overwhelming the capacity of testing laboratories. The supply scarcity and capacity limits affect test administration: priority must be given to hospitalized patients and symptomatic individuals, which can prevent the identification of asymptomatic and presymptomatic individuals and hence effective tracking and tracing policies. We describe optimized group testing strategies applicable to SARS-CoV-2 tests in scenarios tailored to the current COVID-19 pandemic and assess significant gains compared to individual testing.Methods: We account for biochemically realistic scenarios in the context of dilution effects on SARS-CoV-2 samples and consider evidence on specificity and sensitivity of PCR-based tests for the novel coronavirus. Because of the current uncertainty and the temporal and spatial changes in the prevalence regime, we provide analysis for several realistic scenarios and propose fast and reliable strategies for massive testing procedures.Key Findings: We find significant efficiency gaps between different group testing strategies in realistic scenarios for SARS-CoV-2 testing, highlighting the need for an informed decision of the pooling protocol depending on estimated prevalence, target specificity, and high- vs. low-risk population. For example, using one of the presented methods, all 1.47 million inhabitants of Munich, Germany, could be tested using only around 141 thousand tests if the infection rate is below 0.4% is assumed. Using 1 million tests, the 6.69 million inhabitants from the city of Rio de Janeiro, Brazil, could be tested as long as the infection rate does not exceed 1%. Moreover, we provide an interactive web application, available at www.group-testing.com, for visualizing the different strategies and designing pooling schemes according to specific prevalence scenarios and test configurations.Interpretation: Altogether, this work may help provide a basis for an efficient upscaling of current testing procedures, which takes the population heterogeneity into account and is fine-grained towards the desired study populations, e.g., mild/asymptomatic individuals vs. symptomatic ones but also mixtures thereof.Funding: German Science Foundation (DFG), German Federal Ministry of Education and Research (BMBF), Chan Zuckerberg Initiative DAF, and Austrian Science Fund (FWF).

GROUP TESTING FOR LARGE-SCALE COVID-19 SCREENING

ABSTRACT The demand for test kits needed to identify infected individuals during the COVID-19 pandemic is steadily increasing, and many countries are already in short supply. The limited availability of these test kits makes the testing capacity of a large population very limited. To optimise the use of the available testing capacity, we study two group testing strategies expected to considerably reduce the required number of test kits. For each strategy, two problems are addressed: the minimisation of tests required to investigate a targeted population and the maximisation of the size of the tested population subject to a given testing capacity. We mathematically formulate both problems and provide the optimal pool size formula associated with each strategy. Real and simulated case studies are presented to assess the efficiency of both strategies. We provide evidence that the best strategy to be used during the screening process depends on this pandemic’s prevalence rate.

Pool Strategy for Surveillance Testing of SARS-CoV-2

Due to the great morbidity and mortality in the risk groups of the pandemic COVID-19 caused by the emerging coronavirus SARS-CoV-2 and in the absence of effective therapeutic or preventive measures, quarantines, social distancing and the use of masks were the measures most used by health systems to reduce infections. The social, economic and health impact caused by these measures have begun to be evaluated in the different countries. These analyses lead to underestimations because in general they evaluate disease confirmed by a laboratory test and in some cases by epidemiological link without considering asymptomatic or oligosymptomatic infection. Therefore, mitigating fast circulation of the virus requires continuous tracking, detection, and isolation of cases, for which active surveillance able to address asymptomatic cases can make a valuable contribution over the dynamics of the disease in a given society, and to allocate adequate health resources and evaluate the effectiveness of control measures. Mathematical models such as the Susceptible-Exposed-Infectious-Removed (SEIR) allow not only to improve the estimates of the evolution of the pandemic at the local level, but also to evaluate health strategies. In the context of large testing requirements and the expansion of such testing capacity, it is also essential to develop approaches that improve the efficient use of these resources. Active surveillance undoubtedly contributes to improving estimates of virus circulation and it is of particular importance in vulnerable groups of high population density that have one or more risk factors, difficult access to the health system, and inhabit semi-closed facilities such as residential care homes, mental hospitals, prison houses, police stations housing prisoners, etc. Group testing strategies are especially useful for routine community survey and for monitoring of cohesive groups. While the frequency of infection in a population, who have only some symptoms compatible with the disease or do not have any symptoms, may be low, diagnosing even a single positive person typically requires quarantine of the entire group to prevent further spread in the community. In these surveillance strategies, pooling may allow more routine monitoring and detection of low frequency of carriage, thereby improving estimates, informing policy makers, reducing transmission, and alleviating the strain on healthcare services. By means of molecular tests based on RT-qPCR, the pooling strategy has been assayed with different algorithms also for COVID-19, particularly in the asymptomatic population, since a low prevalence of the disease is expected there. This has increased SARS-CoV-2 testing throughput while maintaining high sensitivity. Here, we discuss the relevance of some active surveillance strategies to determine key facts about COVID-19 pandemics and review different testing strategies that different countries have applied for tracking SARS-CoV-2.

Group testing can improve the cost-efficiency of prospective-retrospective biomarker studies

BackgroundCancer treatment is increasingly dependent on biomarkers for prognostication and treatment selection. Potential biomarkers are frequently evaluated in prospective-retrospective studies in which biomarkers are measured retrospectively on archived specimens after completion of prospective clinical trials. In light of the high costs of some assays, random sampling designs have been proposed that measure biomarkers for a random sub-sample of subjects selected on the basis of observed outcome and possibly other variables. Compared with a standard design that measures biomarkers on all subjects, a random sampling design can be cost-efficient in the sense of reducing the cost of the study substantially while achieving a reasonable level of precision.MethodsFor a biomarker that indicates the presence of some molecular alteration (e.g., mutation in a gene), we explore the use of a group testing strategy, which involves physically pooling specimens across subjects and assaying pooled samples for the presence of the molecular alteration of interest, for further improvement in cost-efficiency beyond random sampling. We propose simple and general approaches to estimating the prognostic and predictive values of biomarkers with group testing, and conduct simulation studies to validate the proposed estimation procedures and to assess the cost-efficiency of the group testing design in comparison to the standard and random sampling designs.ResultsSimulation results show that the proposed estimation procedures perform well in realistic settings and that a group testing design can have considerably higher cost-efficiency than a random sampling design.ConclusionsGroup testing can be used to improve the cost-efficiency of biomarker studies.

A Nonadaptive Combinatorial Group Testing Strategy to Facilitate Health Care Worker Screening during the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) Outbreak

Routine testing for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in health care workers (HCWs) is critical. Group testing strategies to increase capacity facilitate mass population testing but do not prioritize turnaround time, an important consideration for HCW screening. We propose a nonadaptive combinatorial (NAC) group testing strategy to increase throughput while facilitating rapid turnaround. NAC matrices were constructed for sample sizes of 700, 350, and 250. Matrix performance was tested by simulation under different SARS-CoV-2 prevalence scenarios of 0.1% to 10%. NAC matrices were compared versus Dorfman sequential (DS) group testing approaches. NAC matrices performed well at low prevalence levels, with an average of 97% of samples resolved after a single round of testing via the n = 700 matrix at a prevalence of 1%. In simulations of low to medium (0.1% to 3%) prevalence, all NAC matrices were superior to the DS strategy, measured by fewer repeated tests required. At very high prevalence levels (10%), the DS matrix was marginally superior, although both group testing approaches performed poorly at high prevalence levels. This strategy maximizes the proportion of samples resolved after a single round of testing, allowing prompt return of results to HCWs. This methodology may allow laboratories to adapt their testing scheme based on required throughput and the current population prevalence, facilitating a data-driven testing strategy.

Optimizing COVID-19 surveillance in long-term care facilities: a modelling study

BackgroundLong-term care facilities (LTCFs) are vulnerable to outbreaks of coronavirus disease 2019 (COVID-19). Timely epidemiological surveillance is essential for outbreak response, but is complicated by a high proportion of silent (non-symptomatic) infections and limited testing resources.MethodsWe used a stochastic, individual-based model to simulate transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) along detailed inter-individual contact networks describing patient-staff interactions in a real LTCF setting. We simulated distribution of nasopharyngeal swabs and reverse transcriptase polymerase chain reaction (RT-PCR) tests using clinical and demographic indications and evaluated the efficacy and resource-efficiency of a range of surveillance strategies, including group testing (sample pooling) and testing cascades, which couple (i) testing for multiple indications (symptoms, admission) with (ii) random daily testing.ResultsIn the baseline scenario, randomly introducing a silent SARS-CoV-2 infection into a 170-bed LTCF led to large outbreaks, with a cumulative 86 (95% uncertainty interval 6–224) infections after 3 weeks of unmitigated transmission. Efficacy of symptom-based screening was limited by lags to symptom onset and silent asymptomatic and pre-symptomatic transmission. Across scenarios, testing upon admission detected just 34–66% of patients infected upon LTCF entry, and also missed potential introductions from staff. Random daily testing was more effective when targeting patients than staff, but was overall an inefficient use of limited resources. At high testing capacity (> 10 tests/100 beds/day), cascades were most effective, with a 19–36% probability of detecting outbreaks prior to any nosocomial transmission, and 26–46% prior to first onset of COVID-19 symptoms. Conversely, at low capacity (< 2 tests/100 beds/day), group testing strategies detected outbreaks earliest. Pooling randomly selected patients in a daily group test was most likely to detect outbreaks prior to first symptom onset (16–27%), while pooling patients and staff expressing any COVID-like symptoms was the most efficient means to improve surveillance given resource limitations, compared to the reference requiring only 6–9 additional tests and 11–28 additional swabs to detect outbreaks 1–6 days earlier, prior to an additional 11–22 infections.ConclusionsCOVID-19 surveillance is challenged by delayed or absent clinical symptoms and imperfect diagnostic sensitivity of standard RT-PCR tests. In our analysis, group testing was the most effective and efficient COVID-19 surveillance strategy for resource-limited LTCFs. Testing cascades were even more effective given ample testing resources. Increasing testing capacity and updating surveillance protocols accordingly could facilitate earlier detection of emerging outbreaks, informing a need for urgent intervention in settings with ongoing nosocomial transmission.

On optimal randomized group testing with one defective item and a constrained number of positive responses

Consider the group testing problem with an input set of size n and the number of defective items being d=1, that allows at most y positive responses. Let An,y denote the minimum expected number of tests required by a randomized testing strategy for this problem. The testing strategy is required to be Las Vagas, that is, guaranteed to give the correct classification of the items. Based on Yao’s minimax principle, An,y is equal to the minimum average number of tests for a deterministic group testing strategy allowing at most y positive responses, where the average is taken over all the n possible input sets of size n with d=1.The main contribution of our paper is to show that An,y is yy+1(ny!)1y+O(y), for 1≤y<⌈log2n⌉. This solves an open question left in Damaschke (2016).

GTHI: A Heuristic Algorithm to Detect Malicious Users in Smart Grids

With many countries trying to establish their own smart grids, smart meters are massively deployed throughout the world. Although smart meters are manufactured with low tamper-resistant components, malicious users with just a moderate level of computer knowledge are able to launch cyber attacks. By manipulating electricity consumption readings to smaller values, malicious users can steal electricity from utility companies. To reduce the losses incurred by electricity theft, utility companies must provide preventative and detective methods to identify fraudulent behaviors. Our goal is to identify all malicious users in a neighborhood area network within the shortest detection time. To achieve this goal, we propose Group Testing based Heuristic Inspection (GTHI) algorithm, which can estimate the ratio of malicious users on-line, mainly by collecting the information that how many malicious users have been identified during the inspection process. Based upon the ratio of malicious users, the GTHI algorithm adaptively adjusts inspection strategies between an individual inspection strategy and a group testing strategy. This helps shorten the detection time. Furthermore, when applying the group testing strategy, the GTHI algorithm also determines the group size of users to be probed in line with the estimated malicious user ratio. Experiment results show that compared to existing methods, the GTHI algorithm has advantages of conducting fewer inspection steps or being more practical.

A class of asymptotically optimal group testing strategies to identify good items

In many fault detection problems, we want to identify all the d defective items from a set of n items using the minimum number of tests. Group testing is for the scenario where each test is on a subset of items, and tells whether the subset contains at least one defective item or not. In this paper, we study a variant of the classical group testing problem, which we call IGI (Identifying Good Items) problem. Instead of identifying all defective items, problem IGI is to identify good items that occupy no less than a prescribed proportion f<1 in the input set. We investigate group testing strategies for problem IGI using the minimum number of tests in the worst case. A class of testing strategies for problem IGI when d is unknown in advance is proposed, and a lower bound on the worst case number of tests required by any deterministic testing strategy for problem IGI is proved. The lower bound is valid no matter whether d is known in advance or not. When f approaches one, all the testing strategies in the proposed class are asymptotically optimal, and the best ratio between the upper bounds from the proposed class and the above lower bound is 3log23.

Adaptive elastic net for group testing.

For disease screening, group (pooled) testing can be a cost-saving alternative to one-at-a-time testing, with savings realized through assaying pooled biospecimen (eg, urine, blood, saliva). In many group testing settings, practitioners are faced with the task of conducting disease surveillance. That is, it is often of interest to relate individuals' true disease statuses to covariate information via binary regression. Several authors have developed regression methods for group testing data, which is challenging due to the effects of imperfect testing. That is, all testing outcomes (on pools and individuals) are subject to misclassification, and individuals' true statuses are never observed. To further complicate matters, individuals may be involved in several testing outcomes. For analyzing such data, we provide a novel regression methodology which generalizes and extends the aforementioned regression techniques and which incorporates regularization. Specifically, for model fitting and variable selection, we propose an adaptive elastic net estimator under the logistic regression model which can be used to analyze data from any group testing strategy. We provide an efficient algorithm for computing the estimator along with guidance on tuning parameter selection. Moreover, we establish the asymptotic properties of the proposed estimator and show that it possesses "oracle" properties. We evaluate the performance of the estimator through Monte Carlo studies and illustrate the methodology on a chlamydia data set from the State Hygienic Laboratory in Iowa City.

Intelligent Optimization of Na−Mn−W/SiO2 Catalysts for the Oxidative Coupling of Methane

AbstractOptimizing the performance and understanding the mechanism of metal oxide catalysts in the oxidative coupling of methane (OCM) is one of the most important projects in industrial catalysis. Here, we report an intelligent approach to screen Na−Mn−W/SiO2 catalysts for the OCM reaction via ink‐jet printing and multidimensional group testing. The ink‐jet‐printing‐assisted (IJP‐A) synthetic methodology ensured a fast synthesis of 1536 catalysts that were evaluated in 24 tests via a multidimensional group testing strategy (m‐GT). After two‐round synthesis‐and‐screening processes, several excellent groups with different composition space were screened out. Among the excellent catalysts, the OCM performance of Na−Mn−W/SiO2 catalyst with higher Na, Mn and W content (G3 and G7) was less sensitive to its constituent than that of Na−Mn−W/SiO2 catalyst with lower Na, Mn and W content (G1). These results indicated the sensitivity of performance to its Na, Mn and W content decreased in larger Na, Mn and W spaces, consistent with the complex homogeneous/heterogeneous reaction mechanism and multi‐active sites. A series of Na−Mn−W/SiO2 formulas with good performance also provide a reasonable explanation to its excellent stability for the OCM reaction. In addition, this intelligent optimization in total space offers a pathway to study the multicomponent metal oxide catalyst in the OCM reaction.

Fast Optimization of LiMgMnOx/La2O3 Catalysts for the Oxidative Coupling of Methane.

The development of efficient catalyst for oxidative coupling of methane (OCM) reaction represents a grand challenge in direct conversion of methane into other useful products. Here, we reported that a newly developed combinatorial approach can be used for ultrafast optimization of La2O3-based multicomponent metal oxide catalysts in OCM reaction. This new approach integrated inkjet printing assisted synthesis (IJP-A) with multidimensional group testing strategy (m-GT) tactfully takes the place of conventionally high-throughput synthesis-and-screen experiment. Just within a week, 2048 formulated LiMgMnOx-La2O3 catalysts in a 64·8·8·8·8 = 262 144 compositional space were fabricated by IJP-A in a four-round synthesis-and-screen process, and an optimized formulation has been successfully identified through only 4·8 = 32 times of tests via m-GT screening strategy. The screening process identifies the most promising ternary composition region is Li0-0.48Mg0-6.54Mn0-0.62-La100Ox with an external C2 yield of 10.87% at 700 °C. The yield of C2 is two times as high as the pure nano-La2O3. The good performance of the optimized catalyst formulation has been validated by the manual preparation, which further prove the effectiveness of the new combinatorial methodology in fast discovery of heterogeneous catalyst.