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Causality Detection Methods Applied to the Investigation of Malaria Epidemics

1
National Institute for Laser, Plasma and Radiation Physics, RO-077125 Magurele-Bucharest, Romania
2
Consorzio RFX (CNR, ENEA, INFN, Universita’ di Padova, Acciaierie Venete SpA), 35127 Padova, Italy
3
Department of Industrial Engineering, University of Rome Tor Vergata, 00133 Rome, Italy
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(8), 784; https://doi.org/10.3390/e21080784
Received: 11 July 2019 / Revised: 31 July 2019 / Accepted: 9 August 2019 / Published: 11 August 2019
(This article belongs to the Section Multidisciplinary Applications)
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Abstract

Malaria, a disease with major health and socio-economic impacts, is driven by multiple factors, including a complex interaction with various climatic variables. In this paper, five methods developed for inferring causal relations between dynamic processes based on the information encapsulated in time series are applied on cases previously studied in literature by means of statistical methods. The causality detection techniques investigated in the paper are: a version of the kernel Granger causality, transfer entropy, recurrence plot, causal decomposition and complex networks. The methods provide coherent results giving a quite good confidence in the conclusions. View Full-Text
Keywords: dynamic system coupling; Granger causality; transfer entropy; recurrence plots; causal decomposition; cross-visibility graphs; malaria epidemics dynamic system coupling; Granger causality; transfer entropy; recurrence plots; causal decomposition; cross-visibility graphs; malaria epidemics
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Craciunescu, T.; Murari, A.; Gelfusa, M. Causality Detection Methods Applied to the Investigation of Malaria Epidemics. Entropy 2019, 21, 784.

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