| Grant number: | 19/21858-0 |
| Support type: | Scholarships in Brazil - Doctorate |
| Effective date (Start): | January 01, 2021 |
| Effective date (End): | November 30, 2023 |
| Field of knowledge: | Engineering - Electrical Engineering - Telecommunications |
| Principal researcher: | Vitor Heloiz Nascimento |
| Grantee: | Carlos Augusto Prete Junior |
| Home Institution: | Escola Politécnica (EP). Universidade de São Paulo (USP). São Paulo , SP, Brazil |
Abstract 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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