Flávio Codeço Coelho

Fundação Getulio Vargas

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Cláudia Torres Codeço

Fundação Oswaldo Cruz

Leonardo Soares Bastos

Fundação Oswaldo Cruz

Raquel Martins Lana

Barcelona Supercomputing Center

Daniel Antunes Maciel Villela

Fundação Oswaldo Cruz

Marcelo Ferreira da Costa Gomes

Fundação Oswaldo Cruz

Oswaldo Gonçalves Cruz

Office of Advanced Scientific Computing Research

Luiz Max Carvalho

Fundação Getulio Vargas

Izabel Cristina dos Reis

Fundação Oswaldo Cruz

Olivia Keiser

University of Geneva

Cláudio J. Struchiner

Fundação Getulio Vargas

Cooperative Institutions

Fundação Oswaldo Cruz

Département de la Santé et de l'Action Sociale

Fundação Getulio Vargas

University of Geneva

Universidade Federal do Rio de Janeiro

Universidade de São Paulo

Escola Nacional de Saúde Pública

Ministério da Saúde

World Health Organization Regional Office for Africa

Walker (United States)

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Resting respiratory behavior in minimally instrumented toads - effects of very long apneas on blood gases and pH

Brazilian Journal of Biology (2003)

Flávio Codeço Coelho Neal J. Smatresk

Resting respiratory behavior of Bufo marinus in minimally instrumented toads is described for a period of 24 hours in which the animals are left undisturbed. Torpor-related long apneas are described and their implications for blood gas levels are investigated. Results show that the resting ventilation rate of Bufo marinus is much lower than that reported so far. Levels of arterial oxygen, carbon dioxide, and pH are monitored during artificial long apneas induced by anesthesia. The toads showed an unexpected ability to unload carbon dioxide by non-respiratory means, even while being kept on dry plastic box with no access to water. Oxygen arterial partial pressure dropped to very low levels after one hour of apnea. This suggests that these animals may endure very well severe hypoxia for long periods of time while in torpor.

Torpor

Arterial blood

Hypoxia

10.1590/s1519-69842003000100006

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Citations (16)

Assessing the spread of COVID-19 in Brazil: Mobility, morbidity and social vulnerability

PLoS ONE (2020)

Flávio Codeço Coelho Raquel Martins Lana Oswaldo Gonçalves Cruz Daniel Antunes Maciel Villela Leonardo Soares Bastos

Brazil detected community transmission of COVID-19 on March 13, 2020. In this study we identified which areas in the country were the most vulnerable for COVID-19, both in terms of the risk of arrival of cases, the risk of sustained transmission and their social vulnerability. Probabilistic models were used to calculate the probability of COVID-19 spread from São Paulo and Rio de Janeiro, the initial hotspots, using mobility data from the pre-epidemic period, while multivariate cluster analysis of socio-economic indices was done to identify areas with similar social vulnerability. The results consist of a series of maps of effective distance, outbreak probability, hospital capacity and social vulnerability. They show areas in the North and Northeast with high risk of COVID-19 outbreak that are also highly socially vulnerable. Later, these areas would be found the most severely affected. The maps produced were sent to health authorities to aid in their efforts to prioritize actions such as resource allocation to mitigate the effects of the pandemic. In the discussion, we address how predictions compared to the observed dynamics of the disease.

Vulnerability

Pandemic

Social vulnerability

10.1371/journal.pone.0238214

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Citations (108)

Study of the impact of climate change on the spatial distribution of dengue cases in Rio de Janeiro

Revue d Épidémiologie et de Santé Publique (2018)

Leon Diniz Alves Flávio Codeço Coelho

Longitude

Geographic coordinate system

10.1016/j.respe.2018.05.517

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A Bayesian Framework for Parameter Estimation in Dynamical Models with Applications to Forecasting

Nature Precedings (2009)

Flávio Codeço Coelho Cláudia Torres Codeço

Abstract Mathematical models in Biology are powerful tools for the study and exploration of complex dynamics. Nevertheless, bringing theoretical results to an agreement with experimental observations involves acknowledging a great deal of uncertainty intrinsic to our theoretical representation of a real system.Proper handling of such uncertainties, is key to the successful usage of models to predict experimental or field observations. This problem has been addressed over the years by many tools for model calibration an parameter estimation. In this article we present a general framework for uncertainty analysis and parameter estimation which is designed to handle uncertainties associated with the modeling of dynamic biological systems while remaining agnostic as to the type of model used. We apply the framework to two Influenza transmission models: one deterministic and the other stochastic. The results show that the framework can be applied without modifications to the two types of models and that it performs equally well on both. We also discuss the application of the framework to calibrate models for forecasting purposes.

Representation

Stochastic modelling

10.1038/npre.2009.4044.1

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Citations (1)

Matrix compression methods

Crysttian Arantes Paixão Flávio Codeço Coelho

The biggest cost of computing with large matrices in any modern computer is related to memory latency and bandwidth. The average latency of modern RAM reads is 150 times greater than a clock step of the processor (Alted, 2010). Throughput is a little better but still 25 times slower than the CPU can consume. The application of bitstring compression allows for larger matrices to be moved entirely to the cache memory of the computer, which has much better latency and bandwidth (average latency of L1 cache is 3 to 4 clock steps). This allows for massive performance gains as well as the ability to simulate much larger models efficiently. In this work, we propose a methodology to compress matrices in such a way that they retain their mathematical properties. Considerable compression of the data is also achieved in the process. Thus allowing for the computation of much larger linear problems within the same memory constraints when compared with the traditional representation of matrices.

CAS latency

Clock rate

10.7287/peerj.preprints.849

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Citations (1)

PyPLN: a Distributed Platform for Natural Language Processing

arXiv (Cornell University) (2013)

Flávio Codeço Coelho Renato Rocha Souza Álvaro Justen Flávio Amieiro Heliana Mello

This paper presents a distributed platform for Natural Language Processing called PyPLN. PyPLN leverages a vast array of NLP and text processing open source tools, managing the distribution of the workload on a variety of configurations: from a single server to a cluster of linux servers. PyPLN is developed using Python 2.7.3 but makes it very easy to incorporate other softwares for specific tasks as long as a linux version is available. PyPLN facilitates analyses both at document and corpus level, simplifying management and publication of corpora and analytical results through an easy to use web interface. In the current (beta) release, it supports English and Portuguese languages with support to other languages planned for future releases. To support the Portuguese language PyPLN uses the PALAVRAS parser\citep{Bick2000}. Currently PyPLN offers the following features: Text extraction with encoding normalization (to UTF-8), part-of-speech tagging, token frequency, semantic annotation, n-gram extraction, word and sentence repertoire, and full-text search across corpora. The platform is licensed as GPL-v3.

Normalization

Python

10.48550/arxiv.1301.7738

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Citations (2)

Faster indicators of chikungunya incidence using Google searches

PLoS neglected tropical diseases (2022)

Sam Miller Tobias Preis Giovanni Mizzi Leonardo Soares Bastos Marcelo Ferreira da Costa Gomes

Chikungunya, a mosquito-borne disease, is a growing threat in Brazil, where over 640,000 cases have been reported since 2017. However, there are often long delays between diagnoses of chikungunya cases and their entry in the national monitoring system, leaving policymakers without the up-to-date case count statistics they need. In contrast, weekly data on Google searches for chikungunya is available with no delay. Here, we analyse whether Google search data can help improve rapid estimates of chikungunya case counts in Rio de Janeiro, Brazil. We build on a Bayesian approach suitable for data that is subject to long and varied delays, and find that including Google search data reduces both model error and uncertainty. These improvements are largest during epidemics, which are particularly important periods for policymakers. Including Google search data in chikungunya surveillance systems may therefore help policymakers respond to future epidemics more quickly.

Chikungunya fever

10.1371/journal.pntd.0010441

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Citations (2)

Zika is not a reason for missing the Olympic Games in Rio de Janeiro: response to the open letter of Dr Attaran and colleagues to Dr Margaret Chan, Director - General, WHO, on the Zika threat to the Olympic and Paralympic Games

Memórias do Instituto Oswaldo Cruz (2016)

Cláudia Torres Codeço Daniel Antunes Maciel Villela Marcelo Ferreira da Costa Gomes Leonardo Soares Bastos Oswaldo G. Cruz

Attaran and colleagues in an open letter to WHO expressed their concern about the upcoming Olympic and Paralympic Games in Rio de Janeiro and the threat posed by the Zika epidemic (Attaran 2016). We agree that Zika virus is of great public health concern and much remains to be known about this disease. Care should be taken to reduce the risk of infection, especially to pregnant women. However, we argue that this is not sufficient reason for changing the original plans for the Games, in particular because of the time of the year when they will take place. The present article outlines several scientific results related to Zika and mosquito-borne infectious diseases dynamics that we believe ratify the current position of WHO in not endorsing the postponing or relocation of the 2016 Olympic and Paralympic Games (WHO 2016).

Zika Virus

Relocation

Position (finance)

10.1590/0074-02760160003

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Citations (19)

Modeling Direct Transmission Diseases Using Parallel Bitstring Agent-Based Models

IEEE Transactions on Computational Social Systems (2018)

Rogério Luís Rizzi Wesley Luciano Kaizer Claudia Brandelero Rizzi Guilherme Galante Flávio Codeço Coelho

Agent-based models (ABMs) are gaining importance over traditional epidemiological modeling due to advances in computing technology and catalyzed by the need for detailed epidemiological analysis of emergent diseases. Unfortunately, the advantages of ABMs are realized at the cost of significantly large execution times and high memory consumption for large-scale simulations. To address the memory issue, we designed and implemented an ABM using an innovative feature: the bitstring approach, in which the attributes of each agent are represented by an array of bits instead using traditional data structures. To cope with the high computational demands, we developed a parallel version of model aiming multicore CPUs and GPUs architectures using Thrust parallel algorithms library. The results of our model were validated comparing them with data of a spread of Influenza A in the Cascavel City, South Brazil, occurred in 2009. The model presented good qualitative results and an excellent performance on GPUs. The application of bitstring technique is proved to be relevant in economy of memory, allowing to store the same attributes using 41% less memory space and improving the data copy time between CPU and GPU up to 52%.

Multi-core processor

Address space

10.1109/tcss.2018.2871625

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Citations (5)

Large-scale Epidemiological modeling: Scanning for Mosquito-Borne Diseases Spatio-temporal Patterns in Brazil

arXiv (Cornell University) (2024)

Eduardo Cruz Araújo Cláudia Torres Codeço Sandro Loch Luã Bida Vacaro Laís Picinini Freitas

The influence of climate on mosquito-borne diseases like dengue and chikungunya is well-established, but comprehensively tracking long-term spatial and temporal trends across large areas has been hindered by fragmented data and limited analysis tools. This study presents an unprecedented analysis, in terms of breadth, estimating the SIR transmission parameters from incidence data in all 5,570 municipalities in Brazil over 14 years (2010-2023) for both dengue and chikungunya. We describe the Episcanner computational pipeline, developed to estimate these parameters, producing a reusable dataset describing all dengue and chikungunya epidemics that have taken place in this period, in Brazil. The analysis reveals new insights into the climate-epidemic nexus: We identify distinct geographical and temporal patterns of arbovirus disease incidence across Brazil, highlighting how climatic factors like temperature and precipitation influence the timing and intensity of dengue and chikungunya epidemics. The innovative Episcanner tool empowers researchers and public health officials to explore these patterns in detail, facilitating targeted interventions and risk assessments. This research offers a new perspective on the long-term dynamics of climate-driven mosquito-borne diseases and their geographical specificities linked to the effects of global temperature fluctuations such as those captured by the ENSO index.

10.48550/arxiv.2407.21286

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