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denisecammarota/README.md

Denise Cammarota (she/her)

About me ⚡

I am a PhD student in Physics at the Instituto de Física Teórica of UNESP (IFT-UNESP), in Sao Paulo, Brazil. I also hold a BSc and Msc in Physics from Instituto Balseiro, in Argentina. My current PhD research mathematical modeling of infectious diseases - more specifically arboviruses (dengue/chikungunya) and measles - using data analysis, mechanistic models and machine learning. I am particularly interested in climate and susceptibility heterogeneities and their impacts on epidemic dynamics. Furthermore, I also have experience in developing data-driven solutions for epidemiological surveillance of measles and SARI, such as data analysis, dashboards, reports and risk estimation tools. You can find me here on Github sharing code and data for my research work, as well as personal, free-time coding projects, portfolio and visualizations.

Highlighted repositories 🌱

Contact 📫

Don't hesitate to reach out for scientific collaboration, data science work or just for a general chat!

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  1. poo-project poo-project Public

    Final project for Object Oriented Programming subject at Instituto Balseiro. Epidemic simulations with code in C++ and documentation in Spanish.

    C++

  2. covid-corr-paper covid-corr-paper Public

    Data and codes in R for paper in writing on the initials of COVID-19 propagation in Argentina. Work in progress.

    R 1

  3. measles-BR measles-BR Public

    Scripts to download and analyze the data on measles cases in Brazil, as provided by SINAN.

    Jupyter Notebook 1

  4. dengue-AR dengue-AR Public

    Scripts to download and analyze the publicly available data on Dengue and Zika in Argentina, provided by the Argentinian Ministry of Health (work in progress).

    Jupyter Notebook

  5. HIT-dynamics HIT-dynamics Public

    stochastic simulations with the Gillespie Tau Leap algorithm for vaccine preventable diseases near the HIT

    Jupyter Notebook

  6. pinn-infectious-disease pinn-infectious-disease Public

    Examples of Physics Inspired Neural Networks (PINNs) for mathematical modeling of infectious diseases. Solving and parameter inference on the SI and SIR models.

    Jupyter Notebook 2