Random Sample Tests Research Articles

A Scene Unmixing Deep Learning Network for Local Climate Zone Mapping and Analysis Using Very High Resolution Remote Sensing Imagery

Abstract. With the acceleration of urbanization, the environment and climate necessary for our survival have gradually deteriorated, leading to the increasing prominence of the Urban Heat Island (UHI) effect. Local Climate Zone (LCZ) classification, as a standard of urban morphology, has become an essential tool for monitoring the UHI effect and conducting temperature studies. Deep Learning (DL) models have the ability to represent high-level semantic features. Therefore, this paper proposes amixed scene unmixing DL framework for LCZ mapping and analysis using Very High Resolution (VHR) remote sensing images. This framework consists of a two-stream deep network, including a pure scene classification network (PS-Net) and a mixed scene unmixing network (MSU-Net). We conducted random sampling tests in Wuhan, China in the experiment A. The results show that this model achieved a satisfactory accuracy with the Overall Accuracies (OAs) is 96.78% and a mixed scene unmixing Mean Absolute Error (MAE) of 0.0495. Furthermore, we applied the proposed model to generate LCZ map for five districts in Wuhan in the experiment B. The test accuracy between two experiments differs very slightly. These results demonstrate the applicability and potential of our model for LCZ mapping and urban climate analysis.

Assessment of compaction quality of highway subgrade via roller-integrated positive maximum velocity detection technique

ABSTRACT Quality control of compaction is vital for the long-term performance of highways. Specifically, the assessment of subgrade compaction quality is a key factor. Currently, compaction quality is primarily controlled by parameters set during compaction and subsequent random sampling tests post-construction. However, the impact of human factors and limitations of these sampling tests fall short of meeting the standards of large-scale precision construction. This study introduces a real-time monitoring index to characterise the compaction quality of silt subgrades: the maximum velocity compaction value (MVCV). This index is derived using a combination of the roller-integrated positive maximum velocity detection system and real-time kinematic Beidou positioning system (RTK-BDS). Furthermore, the Green spline interpolation method allows for estimating compaction quality values at any point. A case study from the Hengyong Highway project in China reveals a strong linear relationship between compaction parameters and compactness. This suggests the suitability of this method for characterising the compaction quality of such silt. This assessment approach facilitates a detailed evaluation of compaction quality across the entire construction zone. Hence, it aids in effectively minimising compaction defects, reducing unnecessary rework, and enhancing the overall compaction quality and efficiency of highway construction.

Multilevel Intervention Program Goes to School Camba’ Rokok for School Based Smoking Prevention

Background: The earlier age people start smoking will have the potential to become long-term smokers. One way that can be done to overcome this problem is through program intervention such as a promotive preventive program that is focused on addressing the problem of smoking is urgently needed. In the City of Palopoin 2017 smokers of elementary school children reached 15.27%, people who broke the rules by smoking in schools reached 44.27% and 36.64% of students had been exposed to outdoor cigarette advertisements. Aims: This study aims to assess the program Goes to school camba' rokok in multilevel interventions at the student and school level to increase self-efficacy and skills in school-based smoking prevention. Methods: The population in this study was state junior high school and senior high school in Palopo city. This research is a quasi-experimental study, with a Solomon four-group design. The samples in this study were 131 students who were randomly determined at SMAN 2 Palopo, SMAN 4 Palopo, SMPN 5 Palopo, and SMPN 8 Palopo using simple cluster random sampling tests. Data analysis using STATA for statistical analysis using statistical tests with Wilcoxon, Mann-Whitney, and Kruskal-Wallis at the significance level p-value <0.05. Results: Posttest scores in the four groups, there was a significant difference in the effect of program interventions going to school camba' rokok on self-efficacy with a p-value of 0.0001, skills with a p-value of 0.0012. Conclusion: There is an increase in students 'self-efficacy and skills through a multilevel intervention program going to school camba' rokok in Palopo City.

Effectiveness of E-Module STEM Biotechnology to Empower Metacognitive Skills and Science Process Skills of High School Students with Low Academic Ability in Industrial Agriculture Areas

Metacognitive skills are vital for empowering students, especially in subjects like biology that are intertwined with scientific activities. Science process skills are crucial for enhancing the learning process in science. This study aimed to assess the impact of STEM e-modules in Biotechnology on metacognitive and science process skills among students in the industrial agricultural sector, particularly those with low academic proficiency. The research followed a quasi-experimental, non-equivalent control group design with a pretest-posttest research structure. The study population comprised public high schools in Jember, with class XII students from SMAN 1 Jember selected as the sample through cluster random sampling and class equivalence testing using ANOVA. Data collection involved essay tests, and prerequisite tests ensured homogeneity and normality. Hypothesis testing employed a two-way ANCOVA at a 0.5% significance level. The findings revealed that significant differences in metacognitive and science process skills when STEM e-modules were used in Biotechnology material, variations in metacognitive and science process skills based on students' academic abilities, and an interaction between STEM e-modules and academic ability on metacognitive and science process skills. This research suggests exploring the effects of STEM Biotechnology e-modules and academic ability on other dependent variables.

EFL Students’ Critical Thinking and Their Reading Comprehension Ability: A Study Case Of Students In SMP Negeri 2 Baubau

The purpose of this study was to determine whether there was a relationship between students' critical thinking skills and their capacity for reading comprehension. This study employed the ex-post facto research design as a quantitative approach, and the population of this study consisted of 345 eighth graders from SMP Negeri 2 Baubau, the sample consisted of 50 EFL students from the overall population, which was drawn using a straightforward random sampling procedure, tests and questionnaires served as the research's instruments. The findings of the investigation can be concluded that there was a strong relationship between the capacity of EFL students to think critically and their comprehension of reading. It is suggested that EFL students develop their critical thinking skills because they are necessary for reading comprehension as well as other language skills in language learning.

Discrete Wavelet Transform—Based Metal Material Analysis Model by Constant Phase Angle Pulse Eddy Current Method

Traditional eddy current technology identifies metal information with information of single frequency of limited frequency spectrum. To solve existing problems, this paper proposes a discrete wavelet transform-based metal material analysis model by using a constant phase angle pulse eddy current (CPA-PEC) sensor which collects and depicts metal feature information from multiple dimensions; then, the quantification calculation model of metal material by CPA-PEC feature is presented; finally, an experimental platform is built to collect the CPA-PEC features of various metal samples and verify recognition accuracy of the proposed metal material analysis model. In the investigation, 1000 eddy current signals from four standard metals (Cu, Fe, Al, St) and three types of metallic irons (Fe-K162, Fe-K163, Fe-K240) are measured and the features are identified by discrete wavelet transform. The feature correlation and significance are determined by regression analysis. Finally, the calculation model of feature evaluation index is present. The experimental analysis indicates that the stability of the quantitative evaluation index of eddy current features reaches 97.1%, the comprehensive accuracy error is less than 0.32% and the average measurement speed is about 50 ms for 1000 random sampling tests on standard metals.

The Impact of Foreign Direct Investment on Urban Green Total Factor Productivity and the Mechanism Test.

This paper employs the slack-based model directional distance function to measure the green total factor productivity of each city, using the panel data of 284 prefecture-level cities in China from 2004 to 2019 and considering the unexpected output. The results are as follows: ① Foreign direct investment significantly suppresses the improvement of urban green total factor productivity, and the negative impact on the green technology progress index is the main reason to inhibit the increase of the green total factor productivity. The results are still significant through a series of robustness tests such as replacing variables and eliminating outliers; the positive intermediary effect of scientific and technological innovation exists, and the Sobel test and bootstrap random sampling test are passed. The upgrading of industrial structure has a positive regulating effect on the improvement of urban green total factor productivity. ② The impact of foreign direct investment on urban green total factor productivity has regional heterogeneity. The inhibitory effect of foreign direct investment on resource-based cities and non-coastal cities is greater than that on non-resource-based cities and coastal cities, and the negative impact on China-Europe train opening cities is greater than that on non-opening cities. Accordingly, the paper puts forward policy suggestions from the aspects of improving the quality of foreign direct investment and implementing differentiated management.

Optimization of Samples for Remote Sensing Estimation of Forest Aboveground Biomass at the Regional Scale

Accurately estimating forest aboveground biomass (AGB) based on remote sensing (RS) images at the regional level is challenging due to the uncertainty of the modeling sample size. In this study, a new optimizing method for the samples was suggested by integrating variance function in Geostatistics and value coefficient (VC) in Value Engineering. In order to evaluate the influence of the sample size for RS models, the random forest regression (RFR), nearest neighbor (K-NN) method, and partial least squares regression (PLSR) were conducted by combining Landsat8/OLI imagery in 2016 and 91 Pinus densata sample plots in Shangri-La City of China. The mean of the root mean square error (RMSE) of 200 random sampling tests was adopted as the accuracy evaluation index of the RS models and VC as a relative cost index of the modeling samples. The research results showed that: (1) the statistical values (mean, standard deviation, and coefficient of variation) for each group of samples based on 200 experiments were not significantly different from the sampling population (91 samples) by t-test (p = 0.01), and the sampling results were reliable for establishing RS models; (2) The reliable analysis on the RFR, K-NN, and PLSR models with sample groups showed that the VC decreases with increasing samples, and the decreasing trend of VC is consistent. The number of optimal samples for RFR, K-NN, and PLSR was 55, 54, and 56 based on the spherical model of variance function, respectively, and the optimal results were consistent. (3) Among the established models based on the optimal samples, the RFR model with the determination coefficient R2 = 0.8485, RMSE = 12.25 Mg/hm2, and the estimation accuracy P = 81.125% was better than K-NN and PLSR. Therefore, they could be used as models for estimating the aboveground biomass of Pinus densata in the study area. For the optimal sample size and sampling population, the RFR model of Pinus densata AGB was established, combining 26 variable factors in the study area. The total AGB with the optimal samples was 1.22 × 107 Mg, and the estimation result with the sampling population was 1.24 × 107 Mg based on Landsat8/OLI images. Respectively, the average AGB was 66.42 Mg/hm2 and 67.51 Mg/hm2, with a relative precision of 98.39%. The estimation results of the two sample groups were consistent.

Detection of Psychotropic Compound in Coprophilous Fungi in District of Baturraden Banyumas Regency

Coprophilous fungi or dung loving fungi are a group of fungi adapted to life on dung and fecal pellets of herbivorous animals. Coprophilous fungi contain psychotropic compounds. Psychotropics compounds are substances or drugs, both natural and synthetic, not narcotics, which have psychoactive properties through a selective influence on the central nervous system which causes distinctive changes in mental activity and behavior. Chemical Spot Test still remain an important tool for the preliminary identification of illicit drugs and other psychotropic compound in spite of developments in instrumental technology and the increased portability of this technology which enables its use in the field. Banyumas Regency is a very potential area as a habitat for coprophilous fungi, specifically in Baturraden District because there are many cattle farms where the dung is where the fungi grow, also the climate is suitable for fungal growth. make an inventory and identify the coprophilous fungi found in District of Baturraden Banyumas Regency and to detect the presence of psychotropic compound in the fungi. The research will be conducted using purposive random sampling and Color Test or Chemical Spot Test analysis. The obtained data is analyzed descriptively by comparing with Atlas of The Munsell Color System. This research obtained seven genera of coprophilous fungi i.e: Coprinellus sp., Coprinopsis sp., Entoloma sp., Gymnopus sp., Lepiota sp., Parasola sp. and Stropharia sp. that discovered in two cattle farms in Baturraden District.

Effect of Advertising, Price, and Trend in Fashion Product on the Consumerism Level of Overseas Students in the Management Study Program, Faculty of Economics, Universitas Prima Indonesia

The purpose of this research is to find out and analyze effect of advertising, price, and trend in fashion product on the consumerism level of overseas students in the Management Study Program, Faculty of Economics, Universitas Prima Indonesia. This research uses descriptive quantitative. The population in this study was 2,489. In this research, sampling is using simple random sampling. Based on the slovin formula, the sample is 344. Due to the large number of samples, we will conduct a random sample test of 100 overseas students against the existing population. This study uses multiple linear regression analysis. The results show that advertising has a positive and significant effect on consumerism level. Price has a positive and significant effect on consumerism level. Trend has a positive and significant effect on consumerism level. Keywords: Advertising, Price, Trend, Consumerism Level.

Successive Wave Analysis to Assess Nonresponse Bias in a Statewide Random Sample Testing Study for SARS-CoV-2.

Introduction:Nonresponse bias occurs when participants in a study differ from eligible nonparticipants in ways that can distort study conclusions. The current study uses successive wave analysis, an established but underutilized approach, to assess nonresponse bias in a large-scale SARS-CoV-2 prevalence study. Such an approach makes use of reminders to induce participation among individuals. Based on the response continuum theory, those requiring several reminders to participate are more like nonrespondents than those who participate in a study upon first invitation, thus allowing for an examination of factors affecting participation. Methods:Study participants from the Indiana Population Prevalence SARS-CoV-2 Study were divided into 3 groups (eg, waves) based upon the number of reminders that were needed to induce participation. Independent variables were then used to determine whether key demographic characteristics as well as other variables hypothesized to influence study participation differed by wave using chi-square analyses. Specifically, we examined whether race, age, gender, education level, health status, tobacco behaviors, COVID-19–related symptoms, reasons for participating in the study, and SARS-CoV-2 positivity rates differed by wave. Results:Respondents included 3658 individuals, including 1495 in wave 1 (40.9%), 1246 in wave 2 (34.1%), and 917 in wave 3 (25%), for an overall participation rate of 23.6%. No significant differences in any examined variables were observed across waves, suggesting similar characteristics among those needing additional reminders compared with early participants. Conclusions:Using established techniques, we found no evidence of nonresponse bias in a random sample with a relatively low response rate. A hypothetical additional wave of participants would be unlikely to change original study conclusions. Successive wave analysis is an effective and easy tool that can allow public health researchers to assess, and possibly adjust for, nonresponse in any epidemiological survey that uses reminders to encourage participation.

Comparison of Infants with Low Birth Weight and Normal Weight Regarding Anemia at the Age of 12 Months in Yazd

Introduction: Anemia is common in both age groups of 9-24 months and adolescence. Considering the fact that anemia in children is associated with various complications such as stunted growth, activity intolerance, behavioral changes, inattention, irritability and changes in the immune response, the aim of this study was to compare the frequency of anemia in infants with normal birth weight and LBW at 12 months of age. Methods: This was an analytical study using the historical cohort method with a parallel design from February 2017 to July 2016. The study population was all the infants referred to health centers of Yazd at the age of 12 months for vaccination, who were monitored. 100 infants were selected and divided into two groups of 50 with normal birth weight (more than 2500 g) and the birth weight less than 2500 g by simple random sampling, and CBC and ferritin tests were run for them. In this study, the hemoglobin less than 11 was considered anemia, and finally, the data were analyzed by SPSS software. Results: The results showed that the two groups of the LBW and the normal weight were not different in terms of sex, but the type of feeding was different, and infants of LBW group used more powdered milk. 22% of LBW infants and 38% of normal weight infants were anemic. Conclusion: According to the results of the study, it seems that the rate of anemia is due to the use of powdered milk, iron supplements with higher doses for LBW infants early on, and better and constant care compared to infants with normal weight. However, more comprehensive studies with larger sample sizes can be effective in explaining these anemia-related factors in LBW infants.

Sample Reduction for Physiological Data Analysis Using Principal Component Analysis in Artificial Neural Network

With its potential, extensive data analysis is a vital part of biomedical applications and of medical practitioner interpretations, as data analysis ensures the integrity of multidimensional datasets and improves classification accuracy; however, with machine learning, the integrity of the sources is compromised when the acquired data pose a significant threat in diagnosing and analysing such information, such as by including noisy and biased samples in the multidimensional datasets. Removing noisy samples in dirty datasets is integral to and crucial in biomedical applications, such as the classification and prediction problems using artificial neural networks (ANNs) in the body’s physiological signal analysis. In this study, we developed a methodology to identify and remove noisy data from a dataset before addressing the classification problem of an artificial neural network (ANN) by proposing the use of the principal component analysis–sample reduction process (PCA–SRP) to improve its performance as a data-cleaning agent. We first discuss the theoretical background to this data-cleansing methodology in the classification problem of an artificial neural network (ANN). Then, we discuss how the PCA is used in data-cleansing techniques through a sample reduction process (SRP) using various publicly available biomedical datasets with different samples and feature sizes. Lastly, the cleaned datasets were tested through the following: PCA–SRP in ANN accuracy comparison testing, sensitivity vs. specificity testing, receiver operating characteristic (ROC) curve testing, and accuracy vs. additional random sample testing. The results show a significant improvement in the classification of ANNs using the developed methodology and suggested a recommended range of selectivity (Sc) factors for typical cleaning and ANN applications. Our approach successfully cleaned the noisy biomedical multidimensional datasets and yielded up to an 8% increase in accuracy with the aid of the Python language.

Estimating SARS-CoV-2 infections from deaths, confirmed cases, tests, and random surveys

There are multiple sources of data giving information about the number of SARS-CoV-2 infections in the population, but all have major drawbacks, including biases and delayed reporting. For example, the number of confirmed cases largely underestimates the number of infections, and deaths lag infections substantially, while test positivity rates tend to greatly overestimate prevalence. Representative random prevalence surveys, the only putatively unbiased source, are sparse in time and space, and the results can come with big delays. Reliable estimates of population prevalence are necessary for understanding the spread of the virus and the effectiveness of mitigation strategies. We develop a simple Bayesian framework to estimate viral prevalence by combining several of the main available data sources. It is based on a discrete-time Susceptible-Infected-Removed (SIR) model with time-varying reproductive parameter. Our model includes likelihood components that incorporate data on deaths due to the virus, confirmed cases, and the number of tests administered on each day. We anchor our inference with data from random-sample testing surveys in Indiana and Ohio. We use the results from these two states to calibrate the model on positive test counts and proceed to estimate the infection fatality rate and the number of new infections on each day in each state in the United States. We estimate the extent to which reported COVID cases have underestimated true infection counts, which was large, especially in the first months of the pandemic. We explore the implications of our results for progress toward herd immunity.

Seroprevalence Study and Cross-Sectional Survey on COVID-19 for a Plan to Reopen the University of Alicante (Spain).

The implementation of strategies to mitigate possible cases of COVID-19 were addressed at the University of Alicante for the safe reopening of the 2020/2021 academic year. To discover the prevalence of immunity against SARS-CoV-2, a study was designed using a rapid immunoassay test (carried out between 6 and 22 July 2020), and in addition a cross-sectional survey was conducted on risk factors, symptoms, predisposition for becoming vaccinated, and sources of information about COVID-19. A random sample, stratified by students, faculty, and administrative staff, was selected. The seroprevalence found was 2.64% (39/1479; 95% CI 1.8–3.4), and the adjusted seroprevalence was 2.89% (95% CI 2.1–3.7). The average age of the students was 23.2 years old, and 47.6 years old for staff. In relation to COVID-19, the following was found: 17.7% pauci-symptomatic, 1.3% symptomatic, 5.5% contact with cases, 4.9% confined, and 0.3% PCR positive. More than 90% complied with preventive measures. The proportion willing to receive the COVID-19 vaccine was 91%. Their sources of information were the Internet (74%) and television (70.1%). They requested that the university offer information (45.1%), training (27%), and provide Personal Protective Equipment (PPE) (26.3%). Lastly, 87.9% would repeat the test. A plan was established that included the follow-up of cases and contacts, random sample testing, training courses, bimodal teaching, a specific website, and the distribution of PPE.

Evaluation of COVID-19 Intuitively Appealing Vaccine Protocols and R0 Increase Fears, Strategic Lessons

Background: Late phase COVID-19 strategic decisions are being made for vaccine protocols such as selective vs. fast. Quantitative trade studies have not appeared. A new faster spreading strain has developed. Quantitative analysis of how it will affect vaccination schedules and final unlock protocol are needed. Lessons learned papers so far deal with prevention of spreading and practice of care issues, leaving major strategy questions about vaccination approach and final unlock open. Methods: We use an SIR-based model tuned for COVID-19 and accounting for seasonality, immunity decline, and vaccination. We add the capability to handle a changing fundamental replication rate (R0) and declining ratio of total to known cases (determined by data fitting). Results: For the present R0 and vaccination schedule a full internal U.S. unlock in April or May 2021 is supportable, however infections reintroduced would propagate in the fall if immunity wears off in 240 days. If the increased R0 becomes established then the infection is never completely extinguished and recurs. If immunity lasts 240 days, re-vaccination is not needed until 2022 even with increased R0 making doses available for use in other countries. Likely 77,000 lives were lost by not meeting the initially planned vaccination targets. Conclusions: The usefulness of trade studies in saving lives is established. Intuitively appealing strategies are not necessarily optimal. Lack of inexpensive random sample testing hampers ability to do trade studies. Phase 3 vaccine trial protocols for epidemics with heavy loss of life should be revisited.

Effects of an immersed tube in deep and shallow slugging fluidized beds

The effects of an immersed tube on slugging fluidization and particle behaviors in slugging fluidized beds were investigated using computational fluid dynamics. Both shallow and deep slugging fluidized beds with and without an immersed tube were simulated using the two-fluid model. The presence of an immersed tube gave rise to regions of high voidage below the tube and regions of low voidage above the tube. Formation of the high voidage region occurred by particle deflection away from the immersed tube, while that of the low voidage region resulted from particle vortices above the tube. The presence of an immersed tube was observed to facilitate bubble formation and bubble coalescence in both shallow and deep slugging fluidized beds. In shallow beds, an immersed tube regularized pressure fluctuations but increased the amplitude of fluctuations. In deep beds, an immersed tube reduced the frequency of pressure fluctuations by facilitating wall slug formation. Based on random sampling and statistical testing (Z-test) of mean slug lengths, it was concluded that mean slug lengths increased when U was increased from 0.90 m/s to 1.0 m/s in the deep free column (p-value = 0.0422). Based on an F-test of the slug length variance, it was concluded that there was larger variability of slug lengths for U of 1.0 m/s than for U of 0.90 m/s in the deep free column (p-value = 0.0202). However, the results of these statistical tests on mean slug lengths and slug length variance for the deep single tube column showed that U had no significant effect on mean slug length or slug length variance at the 5% significance level. Thus, one of the effects of an immersed tube in a deep slugging fluidized bed was regulation of the size of slugs formed and prevention of any significant increase in mean slug size or slug size variability.

ANALISIS KESIAPSIAGAAN SISWA DALAM MENGHADAPI RISIKO BENCANA GEMPA BUMI DAN TSUNAMI

This study aimed to determine the preparedness of students in facing the risk of earthquake and tsunami disasters at MTsN 1 Kota Pariaman. This study is motivated by the lack of understanding of students in dealing with disaster risks, so that there are still many students who are not ready. This study is descriptive quantitative. The population in this study were students of class vii and viii MTsN 1 Kota Pariaman in the 2018/2019 academic year, totaling 489 students. Sampling in this study amounted to 220 students with an error rate of 5% and the sampling technique used the Random Sampling Test. The data was collected using a questionnaire with a Guttman scale measurement. The results of the study in general the readiness of students were in the very ready category, namely as many as 118 respondents or 53.6%. This proves that there has been an increase in student preparedness.

Validating, verifying and testing timed data-flow reactive systems in Coq from controlled natural-language requirements

• Coq framework for validating, verifying and testing timed data-flow reactive systems. • Formal specification derived from controlled natural-language requirements. • System validation (verification) supported by bounded exploration (proof scripts). • Test generation supported by random sampling and user-defined test purposes. • Empirical analyses considering examples from the literature and industry. Data-flow reactive systems (DFRSs) form a class of embedded systems whose inputs and outputs are always available as signals. Input signals can be seen as data provided by sensors, whereas the output data are provided to system actuators. In previous works, verifying well-formedness properties of DFRS models was accomplished in a programmatic way, with no formal guarantees, and test cases were generated by translating these models into other notations. Here, we use Coq as a single framework to specify, validate and verify DFRS models. Moreover, the specification of DFRSs in Coq is automatically derived from controlled natural-language requirements, and well-formedness properties are formally verified with no user intervention. System validation is supported by bounded exploration of models; general and domain-specific system property verification is supported by the development of proof scripts, and test generation is achieved with the aid of the QuickChick tool. Considering examples from the literature, but also from the aerospace (Embraer) and the automotive (Mercedes) industries, our automatic testing strategy was evaluated in terms of performance and the ability to detect defects generated by mutation. Within seconds, test cases were generated automatically from the requirements, achieving an average mutation score of about 75%.

Population Point Prevalence of SARS-CoV-2 Infection Based on a Statewide Random Sample - Indiana, April 25-29, 2020.

Population prevalence of persons infected with SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19), varies by subpopulation and locality. U.S. studies of SARS-CoV-2 infection have examined infections in nonrandom samples (1) or seroprevalence in specific populations* (2), which are limited in their generalizability and cannot be used to accurately calculate infection-fatality rates. During April 25-29, 2020, Indiana conducted statewide random sample testing of persons aged ≥12 years to assess prevalence of active infection and presence of antibodies to SARS-CoV-2; additional nonrandom sampling was conducted in racial and ethnic minority communities to better understand the impact of the virus in certain racial and ethnic minority populations. Estimates were adjusted for nonresponse to reflect state demographics using an iterative proportional fitting method. Among 3,658 noninstitutionalized participants in the random sample survey, the estimated statewide point prevalence of active SARS-CoV-2 infection confirmed by reverse transcription-polymerase chain reaction (RT-PCR) testing was 1.74% (95% confidence interval [CI]=1.10-2.54); 44.2% of these persons reported no symptoms during the 2 weeks before testing. The prevalence of immunoglobulin G (IgG) seropositivity, indicating past infection, was 1.09% (95% CI = 0.76-1.45). The overall prevalence of current and previous infections of SARS-CoV-2 in Indiana was 2.79% (95% CI = 2.02-3.70). In the random sample, higher overall prevalences were observed among Hispanics and those who reported having a household contact who had previously been told by a health care provider that they had COVID-19. By late April, an estimated 187,802 Indiana residents were currently or previously infected with SARS-CoV-2 (9.6 times higher than the number of confirmed cases [17,792]) (3), and 1,099 residents died (infection-fatality ratio = 0.58%). The number of reported cases represents only a fraction of the estimated total number of infections. Given the large number of persons who remain susceptible in Indiana, adherence to evidence-based public health mitigation and containment measures (e.g., social distancing, consistent and correct use of face coverings, and hand hygiene) is needed to reduce surge in hospitalizations and prevent morbidity and mortality from COVID-19.