Assessment of environmental variability on malaria transmission in a malaria-endemic rural dry zone locality of Sri Lanka: The wavelet approach

Abstract

Malaria is a global public health concern and its dynamic transmission is still a complex process. Malaria transmission largely depends on various factors, including demography, geography, vector dynamics, parasite reservoir, and climate. The dynamic behaviour of malaria transmission has been explained using various statistical and mathematical methods. Of them, wavelet analysis is a powerful mathematical technique used in analysing rapidly changing time-series to understand disease processes in a more holistic way. The current study is aimed at identifying the pattern of malaria transmission and its variability with environmental factors in Kataragama, a malaria-endemic dry zone locality of Sri Lanka, using a wavelet approach. Monthly environmental data including total rainfall and mean water flow of the "Menik Ganga" river; mean temperature, mean minimum and maximum temperatures and mean relative humidity; and malaria cases in the Kataragama Medical Officer of Health (MOH) area were obtained from the

Disentangling the seasonality effects of malaria transmission in the Brazilian Amazon Basin

“…When looking at the phase difference between the time series of these variables, as they are almost out of phase for all cases, this shows that rainfall does influences notification, however, with a considerable time lag, as the wavelet cross-power analysis shows an anti-phase relation. This anti-phase relation is not to be considered a fundamental relation between malaria cases and rainfall, as other works have shown that in some areas this relation is inverted, that is, malaria notification and rainfall have an in-phase relation [12,16,36]. Mean temperature, contrary to rainfall, is mostly shown to have not only a good correlation to notification data on an annual basis, but also on a semiannual basis.…”

“…Here, in this work we applied a technique to understand the seasonality effects of malaria in the Brazilian Amazon basin, the wavelet analysis already used in the literature for malaria and diseases in other locations [12,13,14]. To further understand how the climate variables influence the dynamics of malaria, the coherence analysis through wavelets has also been used regarding malaria notification data [15,16,17,18].…”

Environmental Vulnerability against the Presence of Anopheles spp. with Malaria Cases in North Jakarta

“…There are several factors that influence the occurrence of malaria cases in an area, one of which is the presence of vectors and breeding sites for Anopheles spp. (Mahendran et al, 2020). The results of examining the potential habitat of Anopheles spp.…”

Disentangling the seasonality effects of malaria transmission in the Brazilian Amazon basin

“…This approach has been used by numerous researchers to examine the association between climate variables (e.g. temperature and rainfall) and malaria cases and hospital admissions in different countries [17][18][19][23][24][25][26][27][28]. The wavelet analysis allows us to assess the non-stationary spatial and temporal dynamics of diseases in relation to climate variables, aiding in the identification of epidemic periodic cycles.…”

“…In this study, we use wavelet analysis to better understand the seasonality effects of malaria in the Brazilian Amazon basin, a technique used in prior research on malaria and diseases in other regions [17][18][19]. To deepen our understanding of how climate variables influence malaria dynamics, we also conduct coherence analysis through wavelets concerning malaria notification data [20][21][22][23].…”