Causality Detection Methods Applied to the Investigation of Malaria Epidemics

Teddy Craciunescu; Andrea Murari; Michela Gelfusa

doi:10.3390/e21080784

Causality Detection Methods Applied to the Investigation of Malaria Epidemics

Entropy (Basel). 2019 Aug 11;21(8):784. doi: 10.3390/e21080784.

Authors

Teddy Craciunescu¹, Andrea Murari², Michela Gelfusa³

Affiliations

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.

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.

Keywords: Granger causality; causal decomposition; cross-visibility graphs; dynamic system coupling; malaria epidemics; recurrence plots; transfer entropy.