Mapping and Modelling Malaria Risk Areas Using Climate, Socio-Demographic and Clinical Variables in Chimoio, Mozambique

Abstract

Background: Malaria continues to be a major public health concern in Africa. Approximately 3.2 billion people worldwide are still at risk of contracting malaria, and 80% of deaths caused by malaria are concentrated in only 15 countries, most of which are in Africa. These high-burden countries have achieved a lower than average reduction of malaria incidence and mortality, and Mozambique is among these countries. Malaria eradication is therefore one of Mozambique’s main priorities. Few studies on malaria have been carried out in Chimoio, and there is no malaria map risk of the area. This map is important to identify areas at risk for application of Public Precision Health approaches. By using GIS-based spatial modelling techniques, the research goal of this article was to map and model malaria risk areas using climate, socio-demographic and clinical variables in Chimoio, Mozambique. Methods: A 30 m × 30 m Landsat image, ArcGIS 10.2 and BioclimData were used. A conceptual model for spatial problems was used to create the final risk map. The risks factors used were: the mean temperature, precipitation, altitude, slope, distance to water bodies, distance to roads, NDVI, land use and land cover, malaria prevalence and population density. Layers were created in a raster dataset. For class value comparisons between layers, numeric values were assigned to classes within each map layer, giving them the same importance. The input dataset were ranked, with different weights according to their suitability. The reclassified outputs of the data were combined. Results: Chimoio presented 96% moderate risk and 4% high-risk areas. The map showed that the central and south-west “Residential areas”, namely, Centro Hipico, Trangapsso, Bairro 5 and 1° de Maio, had a high risk of malaria, while the rest of the residential areas had a moderate risk. Conclusions: The entire Chimoio population is at risk of contracting malaria, and the precise estimation of malaria risk, therefore, has important precision public health implications and for the planning of effective control measures, such as the proper time and place to spray to combat vectors, distribution of bed nets and other control measures.

Prevalence and Predictors of Malaria in Human Immunodeficiency Virus Infected Patients in Beira, Mozambique

“…However, the number of studies on prevalence and clinical manifestations of HIV- malaria co-infection in Mozambique is limited [ 7 , 8 ]. The aims of our study were: (i) to verify the prevalence of malaria in HIV patients and (ii) to identify predictors of positivity to malaria test in HIV patients admitted to the health center of São Lucas of Beira, the second largest city of Mozambique.…”

Improving the efficiency of reactive case detection for malaria elimination in southern Zambia: A cross-sectional study

“…For example, in Chimoio, Mozambique a GIS-based spatial model was designed to estimate areas of risk using temperature, precipitation, altitude, slope, distance to water bodies, distance to roads, normalized difference vegetation index (NDVI), land use, and land cover. 40 The model identified that 4% of Chimoio was at high risk for contracting malaria, with precipitation as a key risk factor for the entire area studied. 40 Another study in south Sumatra, Indonesia used ordinary least square and geographically weighted regression to show that altitude, distance to forest, and rainfall determined overall malaria incidence with considerable heterogeneity at the village level.…”

“…40 The model identified that 4% of Chimoio was at high risk for contracting malaria, with precipitation as a key risk factor for the entire area studied. 40 Another study in south Sumatra, Indonesia used ordinary least square and geographically weighted regression to show that altitude, distance to forest, and rainfall determined overall malaria incidence with considerable heterogeneity at the village level. 41 These findings were consistent with other studies in Cambodia, Addis Ababa, Ethiopia, and Rondôia, Brazil.…”

Spatial and spatio-temporal methods for mapping malaria risk: a systematic review

“…35 In this review proxies of; temperature (land surface temperature, temperature suitability index, mean/min/max/weekly) and rainfall/precipitation (weekly/monthly/annual) were the most widely used environmental factors related to malaria transmission in SSA. Vegetative indices such as elevation, surface moisture, land use and land cover were included in malaria risk maps, primarily due to their association with temperature and precipitation which indirectly influences 90 the distribution of malaria. Remote sensing will continue to feature prominently as a cost-effective tool for mapping malaria risk in SSA and an important source of environmental and climatic covariates.…”