Bio

Trained as a physicist until my MSc, in which I worked with neutrino detection of core collapse supernovae, I recently switched fields to apply mathematical and computational models to ecological and social systems :)

On the ecological side, I like working with individual based models for cooperation and foraging strategies from an evolutionary perspective. On the social side, I am currently interested in the evolution of cultural values on groups of humans and polarization of opinions on networks of contacts.

Education & Certifications

  • MSc, University of Campinas, Astroparticle physics (2023)
  • BSc, University of Brasilia, Physics (2020)

Contact

  • Academic
    pedrohpc@stanford.edu
    University - Student Department: Environmental Social Sciences Position: Graduate

Additional Info

Links

All Publications

  • Sequential time-window learning with approximate Bayesian computation: an application to epidemic forecasting. Nonlinear dynamics Valeriano, J. P., Cintra, P. H., Libotte, G., Reis, I., Fontinele, F., Silva, R., Malta, S. 2023; 111 (1): 549-558

    Abstract

    The long duration of the COVID-19 pandemic allowed for multiple bursts in the infection and death rates, the so-called epidemic waves. This complex behavior is no longer tractable by simple compartmental model and requires more sophisticated mathematical techniques for analyzing epidemic data and generating reliable forecasts. In this work, we propose a framework for analyzing complex dynamical systems by dividing the data in consecutive time-windows to be separately analyzed. We fit parameters for each time-window through an approximate Bayesian computation (ABC) algorithm, and the posterior distribution of parameters obtained for one window is used as the prior distribution for the next window. This Bayesian learning approach is tested with data on COVID-19 cases in multiple countries and is shown to improve ABC performance and to produce good short-term forecasting.The online version contains supplementary material available at 10.1007/s11071-022-07865-x.

    View details for DOI 10.1007/s11071-022-07865-x

    View details for PubMedID 36188164

    View details for PubMedCentralID PMC9510304

  • Uncanny valley hypothesis and hierarchy of facial features in the human likeness continua: An eye-tracking approach Psychology & Neuroscience Grebot, I. B., Cintra, P. P., de Lima, E. F., de Castro, M. V., de Moraes Jr., R. 2022; 15 (1)

    View details for DOI 10.1037/pne0000281

  • Estimative of real number of infections by COVID-19 in Brazil and possible scenarios. Infectious Disease Modelling Cintra, H. P., Fontinele, F. N. 2020; 5: 720-736

    Abstract

    This paper attempts to provide methods to estimate the real scenario of the novel coronavirus pandemic in Brazil, specifically in the states of Sao Paulo, Pernambuco, Espirito Santo, Amazonas and the Federal District. By the use of a SEIRD mathematical model with age division, we predict the infection and death curves, stating the peak date for Brazil and above states. We also carry out a prediction for the ICU demand in these states and for how severe possible collapse in the local health system would be. Finally, we establish some future scenarios including the relaxation on social isolation and the introduction of vaccines and other efficient therapeutic treatments against the virus.

    View details for DOI 10.1016/j.idm.2020.09.004

    View details for PubMedID 32995682

    View details for PubMedCentralID PMC7513932

https://orcid.org/0000-0001-6604-3597