Interactions between climatic changes and intervention effects on malaria spatio-temporal dynamics in Uganda

Abstract

BackgroundAlthough malaria burden in Uganda has declined since 2009 following the scale-up of interventions, the disease is still the leading cause of hospitalization and death. Transmission remains high and is driven by suitable weather conditions. There is a real concern that intervention gains may be reversed by climatic changes in the country. In this study, we investigate the effects of climate on the spatio-temporal trends of malaria incidence in Uganda during 2013–2017.MethodsBayesian spatio-temporal negative binomial models were fitted on district-aggregated monthly malaria cases, reported by two age groups, defined by a cut-off age of 5 years. Weather data was obtained from remote sensing sources including rainfall, day land surface temperature (LSTD) and night land surface temperature (LSTN), Normalized Difference Vegetation Index (NDVI), altitude, land cover, and distance to water bodies. Spatial and temporal correlations were taken into account by assuming a conditional autoregressive and a first-order autoregressive process on district and monthly specific random effects, respectively. Fourier trigonometric functions modeled seasonal fluctuations in malaria transmission. The effects of climatic changes on the malaria incidence changes between 2013 and 2017 were estimated by modeling the difference in time varying climatic conditions at the two time points and adjusting for the effects of intervention coverage, socio-economic status and health seeking behavior.ResultsMalaria incidence declined steadily from 2013 to 2015 and then increased in 2016. The decrease was by over 38% and 20% in children <5 years and individuals ≥5 years, respectively. Temporal trends depict a strong bi-annual seasonal pattern with two peaks during April–June and October–December. The annual average of rainfall, LSTD and LSTN increased by 3.7 mm, 2.2 °C and 1.0 °C, respectively, between 2013 and 2017, whereas NDVI decreased by 6.8%. On the one hand, the increase in LSTD and decrease in NDVI were associated with a reduction in the incidence decline. On the other hand, malaria interventions and treatment seeking behavior had reverse effects, that were stronger compared to the effects of climatic changes. Important interactions between interventions with NDVI and LSTD suggest a varying impact of interventions on malaria burden in different climatic conditions.ConclusionClimatic changes in Uganda during the last five years contributed to a favorable environment for malaria transmission, and had a detrimental effect on malaria reduction gains achieved through interventions scale-up efforts. The NMCP should create synergies with the National Meteorological Authority with an ultimate goal of developing a Malaria Early Warning System to mitigate adverse climatic change effects on malaria risk in the country.

Climatic changes and their role in emergence and re-emergence of diseases

“…These numbers are expected to expand in response to global warming. The situation is additionally complicated due to the growing resistance against antiplasmodium drugs and by mosquitoes against insecticides (Gunda et al 2017;Ssempiira et al 2018). Historically, malaria invaded Europe on many occasions.…”

Satellite Earth Observation Data in Epidemiological Modeling of Malaria, Dengue and West Nile Virus: A Scoping Review

“…Refs. [31,[34][35][36][37][38][39] explicitly used laboratory confirmed cases (microscopy/Rapid Diagnostic Tests (RDT)), while Sewe et al utilized the number of deaths caused by malaria [40]. The second category (b) used entomological data providing information on the vectors' density, that is highly dependent on the ambient climatic and environmental conditions and significantly influences the transmission of the pathogen.…”

“…According to Stilianakis et al [30] low wind speed was associated with the existence of infected mosquitoes, while high speed reduced the chances for blood meals, and consequently human infections. Overview of studies that utilized EO derived environmental/climatic variables: LST [32,35,[37][38][39][40]43,45,46,[49][50][51][52][63][64][65][66][67]75,[83][84][85], Air temperature [31,41,44,48,53,59,60,76,81,82,86,87], Temperature Index [61], Soil Temperature [30], SST [59,60], SMT [71], SSTA [32], Precipitaiton (Sat) [32,35,40,43,45,50,51,…”

“…Most of the studies in this review took place in tropical, subtropical, temperate and Sub-Saharan climatic zones as shown in Figure 2. Temperature was one of the most influential parameters affecting the malaria occurrence in tropical regions as shown in [35,37,39,45,46,48,84,88] and only one study [38] located in Tanzania, claimed that temperature performed poorly as a predictor. Lag times and degrees of temperature varied between the studies, which could be explained due to the fact that the studies were located in different climatic zones; Diboulo et al that is located in Burkina Faso, and as such is characterized by a Sub-Saharan climate, has found that the density of the vector An.…”

Malaria Outbreak Detection with Machine Learning Methods

“…They reported 99 % detection accuracy with SVM, 75 % with logistic regression, 64 % with decision tree, and 81 % with KNN. Julius Ssempiira et al [5] applied Bayesian spatio-temporal negative binomial models that were fitted on district-aggregated monthly malaria cases, reported by age groups. Weather data included rainfall, day and night land surface temperatures, normalized vegetation index, altitude, land cover, and distance to water bodies.…”