Malaria survey data and geospatial suitability mapping for understanding spatial and temporal variations of risk across Kenya

Abstract

Malaria remains a public health concern in Kenya where children and pregnant women are vulnerable groups. The common interventions in place to fight malaria include using insecticide-treated bed nets (ITNs), knowledge and awareness about malaria, and intake of malaria anti-malaria drugs. Despite the availability of these interventions, Kenya still records more than 10,000 clinical cases annually. In this study, we examined how malaria and interventions varied across Kenya for 2015 and 2020. We analyzed the Kenya Malaria Indicator Survey (N = 10,072) for 2015 and, (N = 11,549) for 2020, and climate data with Fuzzy overlay method to examine how malaria and its interventions relate to environmental conditions required for malaria. The study found that 79 % of malaria cases were distributed in lake endemic, 11 % in coastal endemic, 7 % in highland epidemic, and 3 % in seasonal zone. Use of Insecticide-treated bed nets (ITNs) was 77 % in lake endemic, 13 % in coastal endemic, 9 % in highland epidemic, and 1 % in seasonal zone. Knowledge about malaria was 82 % in lake endemic, 9 % in highland epidemic, 6 % in coastal endemic, and 3 % in seasonal zone. Additionally, based on climate data, lake endemic zone was 94 % suitable for malaria transmission compared to other zones. Despite the use of ITNs and awareness about malaria, malaria transmission continues to be a threat especially in counties in the lake endemic zone. Furthermore, place of residence, climate factors, ownership of ITNs may be associated with malaria in the region.

Keywords: Geospatial suitability mapping; Malaria survey data; Risk; Spatial; Temporal; Variations.

Fig. 1

Malaria Indicator Survey locations sampled during 2015 and 2020 in five malaria zones.

Fig. 2

The overall (A), 2015 (B), and 2020 (C) spatial distribution of malaria positive and negative cases in each malaria zone. Graphs showing overall (D), 2015 (E), and 2020 (F) proportion of positive cases in each zone.

Fig. 3

The overall (A), 2015 (B), 2020 (C) spatial distribution of ITNs usage and graphs (D—F) showing proportion of ITNs users in each malaria zones. Overall (G), 2015 (H), 2020 (I) spatial distribution of malaria knowledge and graphs (J-L) showing proportion of mothers with malaria knowledge in each zone.

Fig. 4

Annual total precipitation, 2015 and 2020 (A), annual total precipitation in each malaria zones (B), total monthly precipitation (mm), 2015 (C) and 2020 (D). Red bar represents total positive malaria survey data during July 2015 and November 2020. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 5

Annual mean temperature, 2015 and 2020 (A), annual mean temperature in each malaria zones (B), monthly mean temperature in °C, 2015 (C) and 2020 (D). The red bar represents total positive malaria survey data during July 2015 and November 2020. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 6

The overall risk, 2015 (A) and 2020 (D), and monthly risk maps based on temperature and precipitation 2015 (C) and 2020 (F). The graphs show % of the risk covered in each malaria zone during 2015 (B) and 2020 (E).

Fig. 7

The graphs show malaria incidences (A), ITN usage (B), and malaria knowledge (C) in malaria suitable zones, 2015 and 2020.