Scientific Advisor: Maria do Rosário De Oliveira Silva (ist12954)
Co-advisor(s): Maria do Rosário De Oliveira Silva (ist12954), CEMAT e Departamento de Matemática, Instituto Superior
Técnico, Universidade de Lisboa; Jorge Filipe Duarte Tiago (ist90590), CEMAT e Departamento de Matemática, Instituto
Superior Técnico, Universidade de Lisboa; Maria da Conceição Esperança Amado (ist13493), CEMAT e Departamento de
Matemática, Instituto Superior Técnico, Universidade de Lisboa.
Organic Unit: Centre for Computational and Stochastic Mathematics
Scholarship Theme: Pandemic Intelligence: Modelling and Monitoring COVID-19 to Support Public Health Decisions
Duration: 6 months
Maximum Duration Including Renewals: 6 months

Objectives
To develop and validate a time-dependent modelling and forecasting framework that captures the evolving dynamics of
the COVID-19 pandemic and predicts the impact of public health interventions, including vaccination strategies, nonpharmaceutical
measures, and the emergence of new variants. The framework is intended to support adaptive resource
planning (e.g., hospital and ICU bed allocation) and to monitor the effectiveness of interventions over time.
The work is organized around three specific objectives:
(i) Data preparation: Integrate, harmonize, and clean historical and current COVID-19 epidemiological and vaccination
data, ensuring suitable temporal structure for time-dependent modelling.
(ii) Modelling: Design and calibrate epidemiological models with time-varying parameters to represent transmission
dynamics, vaccination effects, and intervention scenarios.
(iii) Validation: Benchmark model outputs against historical and current data using time-aware validation protocols and
appropriate error metrics to assess predictive accuracy, robustness, sustainability, and generalizability of the fitted
models.
Work Plan
The work is organized into four main tasks, aligned with the project's specific objectives:
Task 1: Literature review and data collection. Review the state of the art on time-dependent COVID-19 modelling and
forecasting, and identify and collect relevant epidemiological, vaccination, and intervention data, and parametric choices
from national and international cases.
Task 2: Data preparation. Integrate, harmonize, and clean the available data, ensuring a temporal structure suitable for
time-dependent modelling.
Task 3: Model development and fitting. Design, implement, and fit epidemiological models with time-varyingparameters to the data, combining mechanistic and data-driven approaches as appropriate, and apply them to relevantintervention scenarios.Task 4: Validation and dissemination. Evaluate model performance using time-aware validation protocols andappropriate error metrics and prepare the resulting outputs for scientific dissemination and scientific paper writing.

Contest Procedure
Applications must be exclusively submitted on the
admissions platform
of the
Instituto Superior Técnico
at
https://fenix.tecnico.ulisboa.pt/fenixedu-admissions
and requires registration and validation of the candidate's identity.
Applications are only accepted when the form available in the platform is correctly filled, submitted and locked withoutany validation errors. The mandatory documentation to submit in the scholarship aplication includes:
Curriculum Vitae
Proof of Qualifications (or declaration of honor in case you do not yet have the certificate)
Proof of Registration/Enrolment
The application submission deadlines can be viewed in the admissions platform.
The results of the contest will be made available in the same admissions platform.