Spatio-Temporal Dynamic of Malaria Incidence: A Comparison of Two Ecological Zones in Mali

Abstract

<jats:p>Malaria transmission largely depends on environmental, climatic, and hydrological conditions. In Mali, malaria epidemiological patterns are nested within three ecological zones. This study aimed at assessing the relationship between those conditions and the incidence of malaria in Dangassa and Koila, Mali. Malaria data was collected through passive case detection at community health facilities of each study site from June 2015 to January 2017. Climate and environmental data were obtained over the same time period from the Goddard Earth Sciences (Giovanni) platform and hydrological data from Mali hydraulic services. A generalized additive model was used to determine the lagged time between each principal component analysis derived component and the incidence of malaria cases, and also used to analyze the relationship between malaria and the lagged components in a multivariate approach. Malaria transmission patterns were bimodal at both sites, but peak and lull periods were longer lasting for Koila study site. Temperatures were associated with malaria incidence in both sites. In Dangassa, the wind speed (p = 0.005) and river heights (p = 0.010) contributed to increasing malaria incidence, in contrast to Koila, where it was humidity (p &lt; 0.001) and vegetation (p = 0.004). The relationships between environmental factors and malaria incidence differed between the two settings, implying different malaria dynamics and adjustments in the conception and plan of interventions.</jats:p>

Predicting Malaria Transmission Dynamics in Dangassa, Mali: A Novel Approach Using Functional Generalized Additive Models

“…In this paper, we have proposed the use of a functional approach to predict malaria outbreaks based on a rigorous selection of the covariates that contribute the most to the spread of malaria in Dangassa. The use of the distance correlation allowed us to identify, as shown extensively in the literature, some environmental variables that influence malaria incidence [ 3 , 6 , 7 , 8 , 12 ].…”

“…For instance, in the work carried out by Ateba and al. [ 3 ] in the same site of Dangassa, temperature was included in our Generalized Additive Models(GAMs) models, but it was not possible to quantify to what extent temperature contributed to explaining malaria incidence. Here, in our framework, we have been able to determine the order of relevance of the factors (mostly meteorological) that have been included.…”

“…The transmission dynamic is bimodal, with the start and end of the rainy season (in June–August and December–January) accounting for peak transmission. Measures such as Long Lasting Insecticidal Nets (LLINs), Artemisinin-based Combination Therapies (ACTs), and Intermittent Preventive Treatment during pregnancy (IPTp) have been in effect in Dangassa since 2008 [ 3 , 23 ].…”

“…Many disparities in the mortality and morbidity rates have been observed countrywide. Such observed differences in malaria burden have their explanations in both socioeconomic and environmental factors, such as education levels, occupation, use of protective measures, living standards, temperature, air humidity, rain, and wind speed [ 3 , 4 , 5 , 6 , 7 , 8 ]. Since malaria is a vector-borne disease, climate change can affect its transmission [ 9 , 10 , 11 ].…”

“…This technique has been used recently in many modeling applications, such as influenza incidence rate modeling with climate covariates [ 22 ], but has yet to be used in the context of malaria. In this study, we use such methods to identify the underlying factors that shape the patterns of malaria prevalence in Dangassa, a rural Malian village which experiences bimodal malaria transmission dynamics [ 3 , 23 ]. Proper understanding of both past and future influences of environment and meteorological factors on malaria risk will help identify the relevant variables that contribute to the spread of the disease and to prevent outbreaks based on past malaria incidence rate and recognizable environmental climatic patterns.…”