ESTIMATING PREVALENCE FROM THE RESULTS OF A SCREENING TEST

A serological prevalence study of important infectious diseases of cattle in rural areas of Kwa Zulu Natal, South Africa

Hesterberg, U. W. (2008) · UpSpace Institutional Repository (University of Pretoria) · Cited by 2

Analysis of COVID-19 case numbers: adjustment for diagnostic misclassification on the example of German case reporting dataPreprint

Günther, F., Berger, U., Höhle, M., Bender, A., Wildner, M., Heid, I. M., & Küchenhoff, H. (2020) · medRxiv · doi:10.1101/2020.09.29.20203877 · Cited by 2

Impacts of an urban sanitation intervention on fecal indicators and the prevalence of human fecal contamination in MozambiquePreprint

Holcomb, D., Knee, J., Capone, D., Sumner, T., Adriano, Z., Nalá, R., Cumming, O., Brown, J. M., & Stewart, J. R. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.02.19.432000 · Cited by 3

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On the limits and opportunities of AI reviewers: Reviewing the reviews of Nature-family papers with 45 expert scientists

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Classical swine fever in Victorian domestic pigs: evidence of disease freedom

Hunnam, J., Moore, K., Daniel, P., Stevenson, M. A., & Salmon, S. (2019) · Australian Veterinary Journal · doi:10.1111/avj.12871 · Cited by 1

Prevalence and correlates of anxiety disorders among school children aged 13-15 years in Colombo District, Sri Lanka

Silva, S. D., Senaviratne, S., & Samaranayake, D. (2024) · Journal of the College of Community Physicians of Sri Lanka · doi:10.4038/jccpsl.v30i2.8675 · Cited by 1

Statistical software skills for master’s-level jobs in epidemiology: an analysis of 15 years of job posting data

Goldmann, E., Barnard‐Mayers, R., Glymour, M. M., & Healey, M. A. (2025) · American Journal of Epidemiology · doi:10.1093/aje/kwaf243 · Cited by 1

Chronic Disease Monitoring: Methodology for Classification Error and Self-Selection Bias Correction in Clinical Laboratory Data

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Nationwide seroprevalence of severe fever with thrombocytopenia syndrome virus in South Korea: Regional patterns and public health implications

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Rethinking navel size thresholds for omphalitis diagnosis in newborn dairy calves using machine learning

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Spatial dynamics and hidden spread of banana bunchy top disease in Benin

Savi, M. K., Ahanhanzo, C., Tiendrébéogo, F., Eni, A. O., & Pita, J. S. (2026) · Frontiers in Agronomy · doi:10.3389/fagro.2026.1754220 · Cited by 1

Low-level endemic circulation of porcine reproductive and respiratory syndrome virus in slaughtered pigs from Meghalaya, Northeast India: A cross-sectional seroepidemiological study

Milton, A. A. P., Srinivas, K., Khan, S., Patil, S. S., Puro, K., Ghatak, S., Joshi, V., & Das, S. (2026) · Veterinary World · doi:10.14202/vetworld.2026.1043-1051 · Cited by 1

Overcoming limitations of the Rogan–Gladen correction: A closed-form solution to a simplified Bayesian method for true prevalence estimation

Kopacka, I., & Fuchs, K. (2026) · Preventive Veterinary Medicine · doi:10.1016/j.prevetmed.2026.106891 · Cited by 1

Integrated slaughterhouse and diagnostic surveillance to characterize bovine anaplasmosis in Tennessee cattle (USA)

Okafor, C. C., Andrews, A. A., Beever, J. E., Reif, K. E., Daniel, J. A., Coetzee, J. F., & Whitlock, B. K. (2026) · Preventive Veterinary Medicine · doi:10.1016/j.prevetmed.2026.106907 · Cited by 1

Using Electronic Health Records to Enhance Lyme Disease Surveillance: Protocol for the SubLyme Network

Hirsch, A. G., Schwartz, B. S., Poulsen, M. N., Schotthoefer, A. M., Sundaram, M. E., Lemieux, J. E., Hu, L. T., Smith, R. P., Linz, A. M., Battan-Wraith, S., et al. (2026) · JMIR Research Protocols · doi:10.2196/94921 · Cited by 1

First serological and molecular investigation of hepatitis E virus infection in dromedary camels in Algeria

Agabou, A., Benaissa, M. H., Bouasla, I., Sabato, L. D., Hireche, S., Ianiro, G., Monini, M., & Bartolo, I. D. (2023) · Frontiers in Veterinary Science · doi:10.3389/fvets.2023.1272250 · Cited by 1

Flexible control of the median of the false discovery proportion

Hemerik, J., Solari, A., & Goeman, J. J. (2024) · Biometrika · doi:10.1093/biomet/asae018 · Cited by 1

Utilizing a capture–recapture strategy to accelerate infectious disease surveillance

Ge, L., Zhang, Y., Waller, L. A., & Lyles, R. H. (2024) · The Annals of Applied Statistics · doi:10.1214/24-aoas1927 · Cited by 1

Estimating Pretest Probabilities

Balayla, J. (2024) · Springer eBooks · doi:10.1007/978-3-031-71452-8_16 · Cited by 1

A Hierarchical Bayesian Model for Estimating Age‐Specific COVID‐19 Infection Fatality Rates in Developing Countries

Pugh, S., Levin, A., Meyerowitz‐Katz, G., Soman, S., Owusu‐Boaitey, N., Zwi, A. B., Malani, A., Wilson, A., & Fosdick, B. K. (2024) · Statistics in Medicine · doi:10.1002/sim.10259 · Cited by 1

True prevalence and spatial distribution of antibodies to Brucella spp. in goat populations in Hubei Province, People’s Republic of China: Progress toward disease elimination

Pei, J., Wang, Y., Zhou, Y., Huang, T., Xie, J. H., Guo, A., Chen, Y., & Robertson, I. (2024) · Preventive Veterinary Medicine · doi:10.1016/j.prevetmed.2024.106414 · Cited by 1

Using Supervised Machine Learning Algorithms to Predict Bovine Leukemia Virus Seropositivity in Florida Beef Cattle: A 10-Year Retrospective Study

Megahed, A., Bommineni, Y. R., Short, M., & Bittar, J. H. J. (2025) · Journal of Veterinary Internal Medicine · doi:10.1111/jvim.70070 · Cited by 2

Bovine Leptospirosis: Serology, Isolation, and Risk Factors in Dairy Farms of La Laguna, Mexico

Pescador-Gutiérrez, A. M., Chávez-Sánchez, J. F., Galavíz-Silva, L., Zárate-Ramos, J. J., Villarreal, J. P. V., Bernal-García, S. E., Castillo-Velázquez, U., Cervantes-Vega, R., & Ávalos-Ramírez, R. (2025) · Life · doi:10.3390/life15081224 · Cited by 1

Seroprevalence of Peste des Petits Ruminants (PPR) and associated risk factors in selected sub-counties of Lyantonde district, South Western Uganda

Musiime, E., Bwambale, K., Babirye, P., Lumu, P., Mutebi, F., Mugizi, D. R., & Onyuth, H. (2026) · BMC Veterinary Research · doi:10.1186/s12917-026-05432-9 · Cited by 1

Factors influencing SARS-CoV-2 IgG test sensitivity: A Bayesian analysis of seroconversion and seroreversion by time since infection, test, age and disease severity

Braeye, T., Abrams, S., & Hens, N. (2026) · PLoS ONE · doi:10.1371/journal.pone.0328144 · Cited by 1

A Machine Learning Approach for Identifying Coronal Mass Ejection–associated Stellar Flares in TESS Observations

Shi, Y., Lu, H.-P., Zhang, L.-Y., Su, T., & Tan, C. (2026) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/ae33c2 · Cited by 1

Cross-sectional seroprevalence study of measles antibodies among children to identify gaps in population immunity, Ireland, 2024

Brady, M., Brouwer, L., Brien, K. O., Carton, M., Whitton, L., Dillon, C., Finucane, J., Culkin, F., Perry, E., Brabazon, E., et al. (2026) · Eurosurveillance · doi:10.2807/1560-7917.es.2026.31.9.2500313 · Cited by 1

Estimating the seroprevalence of Mycobacterium bovis infection in a wild deer population in southwest England

Jinks, R., Hollingdale, A., Avigad, R., Velarde, J., Pugsley, C., de la Rúa-Domènech, R., Pritchard, C., Roberts, T., Pastor, J., Robinson, N., et al. (2026) · Veterinary Record · doi:10.1002/vetr.70547 · Cited by 1

Using forensic autopsy data to estimate the age-specific infection fatality risk of COVID-19

Amemiya, Y., & Nishiura, H. (2026) · Epidemiology and Infection · doi:10.1017/s0950268826101873 · Cited by 1

Seroprevalence and Associated Risk Factors of Peste des Petits Ruminants in Small Ruminants in North Shewa, Ethiopia: Implications for Eradication Efforts

Alemnew, E., Cherenet, T., Sibhatu, D., Aklilu, F., Ibrahim, S. M., Ayele, F., Aydefruhim, D., Bisrat, A., Goshme, S., Alemayehu, Y., et al. (2026) · Veterinary Medicine International · doi:10.1155/vmi/3393476 · Cited by 1

Detection of fecal coronavirus antigen in diarrheic calves of high and average producing Holstein dairy cows

Badiei, K. (2013) · TURKISH JOURNAL OF VETERINARY AND ANIMAL SCIENCES · doi:10.3906/vet-1201-3 · Cited by 1

The Use of Agreement Measures and Latent Class Models to Assess the Reliability of Thermally-marked Otolith Classifications

Blick, J., & Hagen, P. (1998) · Cited by 1

An efficient confidence interval estimation for prevalence calculated from misclassified data

Thomas, A., Shaheen, N. A., & Hussein, M. A. (2022) · Biostatistics & Epidemiology · doi:10.1080/24709360.2022.2076530 · Cited by 1

Household Transmission of SARS-CoV-2 and Long-term Immunity in Children: A Prospective Study in Northern Spain

García-García, E., Calle-Miguel, L., Solís, D. P., Carvajal‐Urueña, I., Merino, Á. G., Gómez, H., Poladura, M. M., & Rodríguez-Pérez, M. (2022) · The Pediatric Infectious Disease Journal · doi:10.1097/inf.0000000000003790 · Cited by 1

Confidence limits for risk differences and risk ratios adjusted for estimated sensitivity and specificity

Hársfalvi, P., & Singer, J. (2023) · Biostatistics & Epidemiology · doi:10.1080/24709360.2023.2183614 · Cited by 1

The impact of inaccurate assumptions about antibody test accuracy on the parametrisation and results of infectious disease models of epidemics

Chaturvedi, M., Köster, D., Rübsamen, N., Jaeger, V. K., Zapf, A., & Karch, A. (2024) · Epidemics · doi:10.1016/j.epidem.2024.100741 · Cited by 1

Comparing population-level humoral and cellular immunity to SARS-Cov-2 in Bangalore, India

Malani, A., Aiyar, J., Sant, A. J., Kamran, N., Mohanan, M., Taneja, S., Woda, B., Zhao, W., & Acharya, A. (2024) · Scientific Reports · doi:10.1038/s41598-024-54922-z · Cited by 1

Combined multiplex panel test results are a poor estimate of disease prevalence without adjustment for test error

Challen, R., Chatzilena, A., Qian, G., Oben, G., Kwiatkowska, R. M., Hyams, C. J., Finn, A. H. R., Tsaneva-Atanasova, K. T., & Danon, L. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012062 · Cited by 1

An assessment of the Chilean COVID-19 surveillance program through the comparison between reported and true SARS-CoV-2 infection prevalence: A case study of three urban centers in southern Chile

Verdugo, C., Verdugo, C., Fíca, A., Hernández, F., Ramírez‐Reveco, A., Plaza, A., Castro, N., Hernández-Riquelme, M., & Acosta‐Jamett, G. (2025) · Public Health · doi:10.1016/j.puhe.2024.12.033 · Cited by 1

Estandarización de la espectrofotometría para la medición de la concentración seminal en el perro doméstico

Díaz, J. D., Valiente, C., Corrada, Y., & Gobello, C. (2011) · Americanae (AECID Library) · Cited by 1

MÉTODOS ESTADÍSTICOS DESARROLLADOS PARA LA ESTIMACIÓN EN LA PREVALENCIA Y PARÁMETROS DE DESEMPEÑO DE TESTS PARA DIAGNÓSTICO CLÍNICO: UNA REVISIÓN DE LITERATURA

Cuevas, J. R. T., & Rafael, J. (2011) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · doi:10.33132/01248146.270 · Cited by 1

Seroprevalence of porcine proliferative enteropathy before initiating vaccine marketing in Korea

Yeh, J. (2015) · Korean Journal of Veterinary Research · doi:10.14405/kjvr.2015.55.1.61 · Cited by 1

Assessing the Seroprevalence Against Avian Pneumovirus and Ornithobacterium rhinotracheale in Broilers in Uruguay

Suzuki, K., Petruccelli, M. Á., Rodríguez, G., Trenchi, G., Giossa, G., & Trenchi, H. (2010) · International Journal of Poultry Science · doi:10.3923/ijps.2010.490.494 · Cited by 1

Evaluation of the association between the lactation stage and serum and milk ELISA results in the diagnosis of ovine Paratuberculosis

Ngwa, V. N., Cuteri, V., Pacifici, L., Preziuso, S., Moriconi, M., & Attili, A. (2016) · Small Ruminant Research · doi:10.1016/j.smallrumres.2016.04.002 · Cited by 1

Item pre-knowledge true prevalence in clinical anatomy - application of gated item response theory model

Severo, M., Silva‐Pereira, F., Ferreira, M. A., Monteiro, M., & Pereira, I. (2019) · BMC Medical Education · doi:10.1186/s12909-019-1710-z · Cited by 1

Sparing the control arm using well-characterized diagnostic approaches – the Gart and Buck prevalence estimator for efficacy estimation in single-arm trials

Hahn, A., Loderstädt, U., Frickmann, H., & Schwarz, N. G. (2019) · Journal of Laboratory Medicine · doi:10.1515/labmed-2019-0091 · Cited by 1

Modelling the probability and impact of false‐positive serology for Borrelia burgdorferi sensu lato: A case study

Houben, R. M. A. C., Meersschaert, C., Hendrickx, G., Pitel, P., & Amory, H. (2020) · Equine Veterinary Journal · doi:10.1111/evj.13277 · Cited by 1

Binary surrogate endpoints in clinical trials from the perspective of case definitions

Hahn, A., Podbielski, A., Heimesaat, M. M., Frickmann, H., & Warnke, P. (2021) · European Journal of Microbiology and Immunology · doi:10.1556/1886.2020.00031 · Cited by 1

Optimization of Case Definitions for Sensitivity as a Preventive Strategy—A Modelling Exemplified with Rapid Diagnostic Test-Based Prevention of Sexual HIV Transmission

Hahn, A., Frickmann, H., & Loderstädt, U. (2021) · Diagnostics · doi:10.3390/diagnostics11112079 · Cited by 1

Assessment of Distress, Disorder, Impairment, and Need in the Population

Eaton, W. W., Mojtabai, R., Stuart, E. A., Leoutsakos, J., & Myllyluoma, J. (2019) · Oxford University Press eBooks · doi:10.1093/oso/9780190916602.003.0005 · Cited by 1

Seroprevalence and risk factors of Q fever in small ruminant flocks in selected States of Peninsular Malaysia

Abdullah, F. F. J., Paul, B. T., Hashi, H. A., Chung, E. L. T., Abdurrahim, N. A., & Lila, M. A. M. (2020) · The Thai Journal of Veterinary Medicine · doi:10.56808/2985-1130.3056 · Cited by 1

SCREENING FOR MENTAL ILLNESS

Goldberg, D. P., & Burns, A. S. (1984) · The Lancet · doi:10.1016/s0140-6736(84)92145-7 · Cited by 1

Food consumption patterns by urban elders in Korea

Kye, S., Kim, C., Lee, H., Yoon, J., Smiciklas‐Wright, H., Mitchell, D. C., & Kim, J. (2000) · Ecology of Food and Nutrition · doi:10.1080/03670244.2000.9991620 · Cited by 1

An Exact Confidence Interval for the Ratio of Two Related Proportions

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Decision analysis, theJournal of General Internal Medicine, and the general internist

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Estimating Sensitivity and Specificity in Order to Correct for Misclassification

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Assessing Binary Measurement Systems

Danila, O. (2012) · UWSpace (University of Waterloo) · Cited by 1

Measurement issues in the comparative manifesto project data set and effectiveness of representative democracy

Mikhaylov, V. S. (2009) · Arrow@dit (Dublin Institute of Technology) · Cited by 1

Bovine lumpy skin disease: epidemiology, economic impact and control opportunities in Ethiopia

Molla, W. A. (2018) · doi:10.18174/430314 · Cited by 1

Development of diagnostic tests for the detection of Neospora caninum infected cattle

Guido, S. (2018) · ERA · Cited by 1

Epidemiologie

Robra, B.-P. (1980) · Zusammenarbeit von Klinik und klinischer Chemie · doi:10.1007/978-3-642-81447-1_10 · Cited by 1

Kommentare

(2014) · SUCHT - Zeitschrift für Wissenschaft und Praxis / Journal of Addiction Research and Practice · doi:10.1024/0939-5911.a000303 · Cited by 1

Potential for bias in (sero)prevalence estimates when not accounting for test sensitivity and specificityPreprint

Haile, S. R., & Kronthaler, D. (2022) · medRxiv · doi:10.1101/2022.11.24.22282720 · Cited by 1

Epidémiologie de la brucellose et de la tuberculose animales dans les milieux urbain, périurbain et rural au Niger

Boukary, A. R. (2013) · ORBi (University of Liège) · Cited by 1

A systematic review with Bayesian modelling of the prevalence of pain with neuropathic characteristicsPreprint

Kamerman, P. R., Hoosen, T., Mnguni, N., & Chikezie, P. C. (2026) · medRxiv · doi:10.64898/2026.01.14.26344090 · Cited by 1

Med-HEAL: Analyzing and Mitigating Hallucinations in Medical LLMs with Hallucination-Aware In-Context Learning

Liao, Y., Franco, Z., Lizarraga Mazaba, J. E., & Chen, K. (2026) · Proceedings of the 17th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics · doi:10.1145/3807503.3819453 · Cited by 1

Herd-level predictive values and sensitivity of mastitis base on individual-animal mastitis characteristics and sample size.

Signorini, M. L., Canavesio, V. R., Neder, V. E., Molineri, A. I., Vitulich, C. A., Tarabla, H. D., & Calvinho, L. F. (2008) · Repositorio Digital Institucional de la Universidad de Buenos Aires (Universidad de Buenos Aires)

Validación de la historia clínica informatizada de atención primaria y su aplicación para la investigación clínico-epidemiológica de la hipertensión arterial en una cohorte de pacientes con diabetes tipo 2

Lunar, B., & de, C. (2012) · Tesis Doctorals en Xarxa (Consorci de Serveis Universitaris de Catalunya)

Extended Rank Analysis of Covariance as a More Efficient Matched Analysis Considering Trend Information

Chen, C. (2001) · Biometrical Journal · doi:10.1002/1521-4036(200111)43:7 · Cited by 1

Epidemiología básica para dermatólogos

Santos‐Juanes, J., Galache, C., Salas, A. M., del Río, J. S., de Delás, J. S., & Cueto, A. (2004) · Actas Dermo-Sifiliográficas · doi:10.1016/s0001-7310(04)76773-3

Modelagem da dependência entre testes para diagnóstico clínico usando funções cópula

Cuevas, J. R. T. (2011) · doi:10.47749/t/unicamp.2011.795157

The Adequacy of Sample Type/Weight and Incubation Period on Detection of Salmonella Spp. in Slaughter Cattle

Vimont, A., Kiermeier, A., Padula, D. J., Holds, G., & Pointon, A. M. (2005) · Journal of Veterinary Diagnostic Investigation · doi:10.1177/104063870501700504

Evaluating the Performance of a Genetic Test Using Information From Previous Studies in Place of a Gold Standard

Fijal, B. A., & Witte, J. S. (1998) · Medical Care · doi:10.1097/00005650-199808001-00006

Der Mythos einer rationalen Effektivitätsforschung

Uhl, A. (2015) · Springer eBooks · doi:10.1007/978-3-662-45152-6_10

Prevalence of Disease Cluster in Particular Age Group in Srinagar

BHAT, M. Y. (2016) · INTERNATIONAL JOURNALS OF MEDICAL SCIENCES · doi:10.15740/has/ijms/9.2/62-67

Statistical Evaluation of the Achievements by Member States of the EU Salmonella Reduction Targets in Animal Populations

Sotto, C., LitiϨre, S., & Aerts, M. (2011) · EFSA Supporting Publications · doi:10.2903/sp.efsa.2011.en-91

Addressing Limitations in Foodborne Outbreak Investigation: Recall Bias and the Feasibility of New Surveillance Strategies

Seitzinger, P. (2017) · University Library - University of Saskatchewan (University of Saskatchewan)

Data collection, analysis and development of a peri-harvest quantitative microbial risk assessment (QMRA) for Shiga toxin-producing Escherichia coli (STEC) in beef production

Ekong, P. S. (2017) · K-State Research Exchange (Kansas State University)

Design and Estimation for the Population Prevalence of Infectious DiseasesPreprint

Oh, E. J., Mikytuck, A. M., Lancaster, V., Goldstein, J., & Keller‐McNulty, S. (2021) · medRxiv · doi:10.1101/2021.02.05.21251231

Rapid antigen test for SARS-CoV-2 and primary health care

Fernández‐Villa, T., Casares, A. M. V., Rivero-Rodriguez, A., Carvajal, A., & Martín, V. (2021) · Journal of Infection · doi:10.1016/j.jinf.2021.05.001

Comment on “Is Group Testing Ready for Prime Time in Disease Identification?”

Johnson, W. O. (2021) · Statistics in Medicine · doi:10.1002/sim.9078

Seroprevalence and risk factors of Mycoplasma capricolum subsp. capricolum among small ruminants in northern Jordan

Momani, W., Nicholas, R. A. J., & Abo‐Shehada, M. N. (2021) · Current Research in Microbiology and Infection · doi:10.31559/crmi2021.2.1.3

Seroprevalence of IgM and IgG Antibodies against SARS-CoV-2 in Asymptomatic People in Wuhan: Data from a General Hospital Near South China Seafood Wholesale Market during March to April in 2020.

Ling, R. J., Yu, Y., He, J. Y., Zhang, J. X., Xu, S., Sun, R. R., Wang, Z., Chen, M. F., Li, T., Ji, H. L., et al. (2021) · Europe PMC (PubMed Central) · doi:10.3967/bes2021.103

Dios en un poema inédito de Juan Ramón Jiménez

del Villar, A. (1996) · Letras de Deusto

Prevalence and risk factors of brucellosis seropositivity of meat goats in Chainat province.

Suddee, W., Opaschaitat, P., Sontiphun, S., Boonyo, K., Kasemsuwan, S., & Rukkwamsuk, T. (2011)

Limited Reliability of the Molecular Detection of Plasmodium spp. from Incubated Blood Culture Samples for Forensic Purposes

Weinreich, F., Hagen, R. M., Loag, W., Maïga‐Ascofaré, O., Dekker, D. M., Frickmann, H., & Loderstädt, U. (2022) · Microorganisms · doi:10.3390/microorganisms10020406

Reply

Jafarzadeh, S. R., & Felson, D. T. (2018) · Arthritis & Rheumatology · doi:10.1002/art.40732

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