Modeling vector borne diseases

• Grant number: 19/26595-7
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: August 01, 2020
• End date: December 31, 2022
• Field of knowledge: Physical Sciences and Mathematics - Physics - General Physics
• Principal Investigator: Francisco Aparecido Rodrigues
• Grantee: Kirstin Roster
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Associated scholarship(s): 21/11608-6 - Geographic and socioeconomic heterogeneity in the incidence and treatments of infectious diseases, BE.EP.DR

Abstract

Models of vector borne diseases are traditionally based on detailed knowledge of the pathogen, host, vector, and transmission process. Widely-used compartmental models, for example, contain several differential equations and parameters for each species of vector and host. Alternative approaches take advantage of big data and machine learning to predict disease incidence, but provide little insight into the causes that need to be addressed to prevent and control epidemics. Where disease drivers have been identified, the literature is focused on correlations of small sets of variables with disease incidence. While correlated data points can help predict disease, they are unreliable in a changing environment, as is produced for example by climate change and technological development. This project aims to examine the causal factors of vector borne diseases in Brazil. We will compile data on known correlates and analyze their causal relationships with disease incidence in municipalities and states, utilizing approaches from complex networks. With these results, we will build a machine learning model for prediction of epidemics. Vector borne diseases such as Yellow Fever, Zika virus, and Dengue pose a public health concern in Brazil. This study aims to increase understanding of disease drivers, which will help better tune existing models, select relevant data sources for more accurate predictions, and inform public health decisions. (AU)

Scientific publications

(6)

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

ALVES, CAROLINE L.; PINEDA, ARUANE M.; ROSTER, KIRSTIN; THIELEMANN, CHRISTIANE; RODRIGUES, FRANCISCO A.. EEG functional connectivity and deep learning for automatic diagnosis of brain disorders: Alzheimer's disease and schizophrenia. JOURNAL OF PHYSICS-COMPLEXITY, v. 3, n. 2, p. 13-pg., 2022-06-01. (19/23293-0, 19/26595-7, 19/22277-0)

ROSTER, K. O.. Data-rich modeling helps answer increasingly complex questions on variant and disease interactions Comment on "Mathematical models for dengue fever epidemiology: A 10-year systematic review" by Aguiar et al.. PHYSICS OF LIFE REVIEWS, v. 44, p. 4-pg., 2023-02-09. (19/26595-7)

ALVES, CAROLINE L.; CURY, RUBENS GISBERT; ROSTER, KIRSTIN; PINEDA, ARUANE M.; RODRIGUES, FRANCISCO A.; THIELEMANN, CHRISTIANE; CIBA, MANUEL. Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments. PLoS One, v. 17, n. 12, p. 26-pg., 2022-12-16. (19/22277-0, 19/23293-0, 19/26595-7)

ROSTER, KIRSTIN; CONNAUGHTON, COLM; RODRIGUES, FRANCISCO A.. Machine-Learning-Based Forecasting of Dengue Fever in Brazilian Cities Using Epidemiologic and Meteorological Variables. AMERICAN JOURNAL OF EPIDEMIOLOGY, v. 191, n. 10, p. 10-pg., 2022-05-18. (19/26595-7)

ROSTER, KIRSTIN; CONNAUGHTON, COLM; RODRIGUES, FRANCISCO A.. Forecasting new diseases in low-data settings using transfer learning. CHAOS SOLITONS & FRACTALS, v. 161, p. 8-pg., 2022-08-01. (19/26595-7)

ALVES, CAROLINE L.; TOUTAIN, THAISE G. L. DE O.; AGUIAR, PATRICIA DE CARVALHO; PINEDA, ARUANE M.; ROSTER, KIRSTIN; THIELEMANN, CHRISTIANE; PORTO, JOEL AUGUSTO MOURA; RODRIGUES, FRANCISCO A.. Diagnosis of autism spectrum disorder based on functional brain networks and machine learning. SCIENTIFIC REPORTS, v. 13, n. 1, p. 20-pg., 2023-05-18. (19/26595-7, 19/23293-0, 19/22277-0)