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Bayesian models for estimating the attack rate of epidemics

Grant number: 19/21858-0
Support Opportunities:Scholarships in Brazil - Doctorate
Effective date (Start): January 01, 2021
Field of knowledge:Engineering - Electrical Engineering - Telecommunications
Principal Investigator:Vitor Heloiz Nascimento
Grantee:Carlos Augusto Prete Junior
Host Institution: Escola Politécnica (EP). Universidade de São Paulo (USP). São Paulo , SP, Brazil
Associated scholarship(s):22/15985-1 - Leveraging genomic data to improve effective reproduction number estimates, BE.EP.DR


Inferring the fraction of the population infected by an infeccious agent (attack rate) in real time is essential for monitoring emerging infectious diseases and predicting the trajectory of future epidemics. Estimating the attack rate is hindered by the high cost of mass testing, the delay in reporting test results in hospitalised patients, and the large proportion of non-reported asymptomatic or mild infections for diseases as COVID-19. Estimating the seroprevalence using blood donation samples is a low-cost alternative to infer the attack rate in real time. However, the reactivity of many assays wanes over time. This process is called seroreversion and leads to a decaying sensitivity, increasing the difficulty to obtain reliable seroprevalence estimates. In this project, we propose to develop a Bayesian model to obtain seroprevalence estimates accounting for seroreversion. We also intend to study the practical identifiability of these models, allowing users to compute the number of samples required for an acceptable uncertainty level. Finally, we propose to study the sensitivity of the estimated parameters to prior distributions. Since Bayesian models are employed in several areas and identifiability issues arise in several problemas, we expect the developed algorithms can be used in several applications. (AU)

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Scientific publications (4)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
FARIA, NUNO R.; MELLAN, THOMAS A.; WHITTAKER, CHARLES; CLARO, INGRA M.; CANDIDO, DARLAN DA S.; MISHRA, SWAPNIL; CRISPIM, MYUKI A. E.; SALES, FLAVIA C.; HAWRYLUK, IWONA; MCCRONE, JOHN T.; et al. Genomics and epidemiology of the P.1 SARS-CoV-2 lineage in Manaus, Brazil. Science, v. 372, n. 6544, p. 815+, . (20/04558-0, 19/21568-1, 19/21301-5, 18/14389-0, 17/13981-0, 20/04272-9, 18/25468-9, 16/18445-7, 19/21858-0, 18/17176-8, 19/12000-1, 19/07544-2, 18/12579-7, 19/24251-9)
PRETE JR, CARLOS A.; NASCIMENTO, VITOR H.; LOPES, CASSIO G.. Optimal Passive Source Localization for Acoustic Emissions. Entropy, v. 23, n. 12, . (19/21858-0, 18/12579-7)
PRETE, JR., CARLOS A.; BUSS, LEWIS F.; BUCCHERI, RENATA; ABRAHIM, CLAUDIA M. M.; SALOMON, TASSILA; CRISPIM, MYUKI A. E.; OIKAWA, MARCIO K.; GREBE, EDUARD; DA COSTA, ALLYSON G.; FRAIJI, NELSON A.; et al. einfection by the SARS-CoV-2 Gamma variant in blood donors in Manaus, Brazi. BMC INFECTIOUS DISEASES, v. 22, n. 1, . (19/21858-0, 18/14389-0)
VERNAL, SEBASTIAN; NAHAS, ANDRESSA K.; NETO, FRANCISCO CHIARAVALLOTI; PRETE JUNIOR, CARLOS A.; CORTEZ, ANDRE L.; SABINO, ESTER CERDEIRA; LUNA, EXPEDITO JOSE DE ALBUQUERQUE. Geoclimatic, demographic and socioeconomic characteristics related to dengue outbreaks in Southeastern Brazil: an annual spatial and spatiotemporal risk model over a 12-year period. Revista do Instituto de Medicina Tropical de São Paulo, v. 63, . (18/14389-0, 19/21858-0)

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