Trend of malaria parasites infection in Ethiopia along an international border: a Bayesian spatio-temporal study

Abstract

Background: Malaria is a major worldwide health concern that impacts many individuals worldwide. P. falciparum is Africa's main malaria cause. However, P. vivax share a large number in Ethiopia than any other countries in Africa, followed by the closest countries. This research aims to examine the spatiotemporal trends in the risk of malaria caused by P. falciparum and P. vivax in Ethiopia and other countries that share borders between 2011 and 2020.

Methods: This study was carried-out in seven East African countries in 115 administration level 1 (region) settings. We used secondary data on two plasmodium parasites, P. falciparum, and P. vivax, between 2011 and 2020 from the Malaria Atlas Project. This study used a Bayesian setup with an integrated nested Laplace approximation to adopt spatiotemporal models.

Results: We analyzed P. falciparum and P. vivax malaria incidence data from 2011 to 2020 in 115 regions. Between 2011 and 2020, all of South Sudan's areas, Ethiopia's Gambella region, and Kenya's Homa Bay, Siaya, Busia, Kakamega, and Vihita regions were at a higher risk of contracting P. falciparum malaria than their neighbors in seven East African nations. However, the Southern Nations, nationalities, and people, as well as the Oromia, Harari, Afar, and Amhara areas in Ethiopia, and the Blue Nile in Sudan, are the regions with a higher risk of P. vivax malaria than their bordering regions. For both P. falciparum and P. vivax, the spatially coordinated main effect and the unstructured spatial effect show minimal fluctuation across and within 115 regions during the study period. Through a random walk across 115 regions, the time-structured effect of P. falciparum malaria risk shows linear increases, whereas the temporally structured effect of P. vivax shows increases from 2011 to 2014 and decreases from 2017 to 2020.

Conclusions: The global malaria control and eradication effort should concentrate particularly on the South Sudan and Ethiopia regions to provide more intervention control to lower the risk of malaria incidence in East African countries, as both countries have high levels of P. falciparum and P. vivax, respectively.

Keywords: Bayesian; Ethiopia; Integrated nested Laplace approximation; International border; Malaria; Parasites.

Fig. 1

Map of the study area. Source of shapefile: Database of Global Administrative Areas v.4.1 (www.gadm.org), own map output from ArcGIS (v.10.8)

Fig. 2

Observed value of the Plasmodium falciparum (PF) MIR between 2011 and 2020. Source of shapefile: Database of Global Administrative Areas v.4.1 (www.gadm.org), own map output from ArcGIS (v.10.8). MIR Malaria incidence rate

Fig. 3

Observed value of the Plasmodium vivax (PV) MIR between 2011 and 2020. Source of shapefile: Database of Global Administrative Areas v.4.1 (www.gadm.org), own map output from ArcGIS (v.10.8). MIR Malaria incidence rate

Fig. 4

Temporal trend for observed values of yearly MIR in the study area at each region of 115 regions in 7 East African countries of SSA from 2011 to 2020: (a) Plasmodium falciparum (PF) and (b) Plasmodium vivax (PV) MIR Malaria incidence rate

Fig. 5

Malaria cluster maps by regions of East Africa, SSA, 2011 − 2020. (a) Getis-Ord Gi* statistics and (b) Anselin’s Local Moran’s I for Plasmodium falciparum (PF). c Getis-Ord Gi* statistics and (d) Anselin’s Local Moran’s I for Plasmodium vivax (PV). Source of shapefile: Database of Global Administrative Areas v.4.1 (www.gadm.org), own map output from ArcGIS (v.10.8). SSA Sub-Saharan Africa

Fig. 6

Spatial distribution of the posterior means of area-specific effects $\text{exp}({\mathrm{\xi }}_{\text{i}})$ and spatial structured effects $\text{exp}(u_{\text{i}})$ from models in the Eastern Africa region, SSA, 2011 − 2020. a area-specific effects and (b) spatially random effects of Plasmodium falciparum (PF). c area-specific effects and (d) spatially random effects of Plasmodium vivax (PV). Source of shapefile: Database of Global Administrative Areas v.4.1 (www.gadm.org), own map output from ArcGIS (v.10.8). BYM Besag, York, and Mollie

Fig. 7

Posterior mean of temporal trends for Plasmodium falciparum (PF) and Plasmodium vivax (PV) malaria incidence: Temporal structured effect $\text{exp}({\mathrm{\gamma }}_{\text{t}})$ and unstructured temporal effect $\text{exp}({\mathrm{\varphi }}_{\text{t}})$ in the study area from 2011 to 2020

Fig. 8

Posterior means of the spatial and temporal structured effect interact $\text{exp}({\mathrm{\delta }}_{\text{i}})$ for P. falciparum malaria incidence of 115 regions from 2011 to 2020. Source of shapefile: Database of Global Administrative Areas v.4.1 (www.gadm.org), own map output from ArcGIS (v.10.8)

Fig. 9

Posterior means of the combined temporal structured effect and unstructured spatial effect $\text{exp}({\mathrm{\delta }}_{\text{i}})$ for P. vivax malaria incidence of 115 regions from 2011 to 2020. Source of shapefile: Database of Global Administrative Areas v.4.1 (www.gadm.org), own map output from ArcGIS (v.10.8)