Remotely Sensed Environmental Conditions and Malaria Mortality in Three Malaria Endemic Regions in Western Kenya

Abstract

BackgroundMalaria is an important cause of morbidity and mortality in malaria endemic countries. The malaria mosquito vectors depend on environmental conditions, such as temperature and rainfall, for reproduction and survival. To investigate the potential for weather driven early warning systems to prevent disease occurrence, the disease relationship to weather conditions need to be carefully investigated. Where meteorological observations are scarce, satellite derived products provide new opportunities to study the disease patterns depending on remotely sensed variables. In this study, we explored the lagged association of Normalized Difference Vegetation Index (NVDI), day Land Surface Temperature (LST) and precipitation on malaria mortality in three areas in Western Kenya.Methodology and FindingsThe lagged effect of each environmental variable on weekly malaria mortality was modeled using a Distributed Lag Non Linear Modeling approach. For each variable we constructed a natural spline basis with 3 degrees of freedom for both the lag dimension and the variable. Lag periods up to 12 weeks were considered. The effect of day LST varied between the areas with longer lags. In all the three areas, malaria mortality was associated with precipitation. The risk increased with increasing weekly total precipitation above 20 mm and peaking at 80 mm. The NDVI threshold for increased mortality risk was between 0.3 and 0.4 at shorter lags.ConclusionThis study identified lag patterns and association of remote- sensing environmental factors and malaria mortality in three malaria endemic regions in Western Kenya. Our results show that rainfall has the most consistent predictive pattern to malaria transmission in the endemic study area. Results highlight a potential for development of locally based early warning forecasts that could potentially reduce the disease burden by enabling timely control actions.

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.…”

“…The same holds for the Aedes genus, as their reproductive cycle can be disrupted by extensive rainfall through flushing out the aquatic stages from breeding sites [62]. Precipitation satellite sensor derived data were mainly acquired from the Tropical Rainfall Measuring Mission (TRMM)(n = 10 [32,35,40,43,50,51,[63][64][65][66][67]) (n = 10), while some studies used the WorldClim (https://www.worldclim.org/) (n = 3 [31,41,61]), ERA-Interim (https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim) (n = 1 [30]) and Meteosat-7 (n = 1 [45]) and other sources such as local ground-stations.…”

“…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,…”

Temporal Variations and Associated Remotely Sensed Environmental Variables of Dengue Fever in Chitwan District, Nepal

“…In this study, five remote sensing based environmental variables-precipitation (TRMM_3B43_7), NDVI (MOD13C25), EVI (MOD13C25), nighttime land surface temperature (nLST) (MYD11C3), and daytime land surface temperature (dLST) (MYD11C3)-from TRMM and MODIS satellites were used to understand temporal patterns and the associations of remotely sensed variables with monthly dengue fever cases in Chitwan district of Nepal. These variables have been used for analysis in many previous studies [20,26].…”

“…The high temporal resolution data from the National Oceanic and Atmospheric Administration (NOAA) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellites provide enormous opportunities for epidemiological time series analysis to understand the temporal epidemiology of dengue and other infectious diseases. NOAA Advanced Very High Resolution Radiometer (AVHRR) sea surface temperature [7], MODIS vegetation Index (NDVI and enhanced vegetation index (EVI)), Land Surface Temperature (LST) [13,19,20], and Tropical Rainfall Measuring Mission (TRMM) precipitation estimation are widely used data to explain temporal patterns of dengue and other climate sensitive diseases [13,20,21].…”