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Intelligent Systems for Dengue, Chikungunya, and Zika Temporal and Spatio-Temporal Forecasting: A Contribution and a Brief Review

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Assessing COVID-19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis

Abstract

Arboviruses are diseases transmitted by viruses which are maintained in the wild through a vertebrate host and a hematophagous arthropod, such as a mosquito. The transmitter vector of an arbovirus is the arthropod which transmits the virus from one vertebrate to the other through a bite. The biological transmission usually occurs when the hematophagous arthropod feeds on a viremic vertebrate and deposits infectious saliva during the feeding of the blood of another vertebrate. However, in some types of arboviruses, the biological transmission occurs directly in the human–mosquito cycle. Moreover, other forms of transmission have been reported, such as transmission from mother to child during pregnancy, blood transfusion, and through sexual intercourse. Demographic changes and the intense migratory flow from rural areas to urban areas have generated disorderly growth in cities. Deficiencies in basic sanitation also contribute to the vector’s proliferation in tropical and subtropical countries. Brazil, which is a tropical country, is very affected by arboviruses, such as dengue, malaria, and yellow fever. With climate change and the increase in the number and frequency of international flights, two new arboviruses transmitted by the Aedes aegypti mosquito appeared in Brazil: the chikungunya and the Zika virus. This situation brings new challenges regarding the control and vector monitoring. The advancement of Digital Epidemiology, together with the development of Data Mining and Machine Learning techniques, provided rapid monitoring, control, and simulation of the spread of diseases. With this in mind, the prediction tools are able to assist public health systems in controlling epidemics and behavioral factors that favor the vector of these diseases. In this sense, in this chapter we present a literature review to identify methods of predicting cases of arboviruses, as well as the prediction of breeding sites.

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Notes

  1. 1.

    Entomological research is understood as research involving insects and their relationship with humans, with other living beings and with the environment.

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Authors and Affiliations

  1. Graduate Program in Computer Engineering, Polytechnique School of the University of Pernambuco, Recife, Brazil

    Clarisse Lins de Lima

  2. Department of Biomedical Engineering, Federal University of Pernambuco, Recife Pernambuco, Brazil

    Ana Clara Gomes da Silva & Wellington P. dos Santos

  3. Center for Informatics, Federal University of Pernambuco, CIn-UFPE, Recife, Brazil

    Cecilia Cordeiro da Silva & Abel Guilhermino da Silva Filho

  4. Department of Atmospheric Sciences, IAG, University of São Paulo, USP, São Paulo, Brazil

    Giselle Machado Magalhães Moreno, Livia Dutra, Tercio Ambrizzi, Iuri Valério Graciano Borges & Patty Kostkova

  5. Institute for Risk and Disaster Reduction, University College London, UCL-IRDR, London, UK

    Anwar Musah & Aisha Aldosery

  6. Bogaziçi University, Institute of Environmental Sciences, Istanbul, Turkey

    Merve Tunali, Selma Basibuyuk & Orhan Yenigün

  7. Department Systems and Computing, Federal University of Campina Grande, Campina Grande, Brazil

    Tiago Lima Massoni

  8. Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, UCL, London, UK

    Kate Jones

  9. Department of Civil Environmental and Geomatic Engineering, University College London, UCL, London, UK

    Luiza Campos

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  1. Clarisse Lins de Lima
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  2. Ana Clara Gomes da Silva
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  18. Wellington P. dos Santos
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Correspondence to Wellington P. dos Santos .

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Editors and Affiliations

  1. School of Computer Science and Engg., Orissa Engineering College, Bhubaneswar-Khordha, Odisha, India

    Subhendu Kumar Pani

  2. Department of Computer Science, Maharaja Sriram Chandra Bhanja Deo University (Erstwhile North Orissa University), Takatpur, Baripada, Odisha, India

    Sujata Dash

  3. Biomedical Engineering, Federal University of Pernambuco, Recife, Pernambuco, Brazil

    Wellington P. dos Santos

  4. Comp Science, Mathematics and Science, St. John’s University, Jamaica, NY, USA

    Syed Ahmad Chan Bukhari

  5. Innovation, Design and Engineering, Mälardalen University, Västerås, Sweden

    Francesco Flammini

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de Lima, C.L. et al. (2022). Intelligent Systems for Dengue, Chikungunya, and Zika Temporal and Spatio-Temporal Forecasting: A Contribution and a Brief Review. In: Pani, S.K., Dash, S., dos Santos, W.P., Chan Bukhari, S.A., Flammini, F. (eds) Assessing COVID-19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis. Springer, Cham. https://doi.org/10.1007/978-3-030-79753-9_17

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