Geostatistical analysis of Malawi's changing malaria transmission from 2010 to 2017

Abstract

Background: The prevalence of malaria infection in time and space provides important information on the likely sub-national epidemiology of malaria burdens and how this has changed following intervention. Model-based geostatitics (MBG) allow national malaria control programmes to leverage multiple data sources to provide predictions of malaria prevalance by district over time. These methods are used to explore the possible changes in malaria prevalance in Malawi from 2010 to 2017. Methods: Plasmodium falciparum parasite prevalence ( PfPR) surveys undertaken in Malawi between 2000 and 2017 were assembled. A spatio-temporal geostatistical model was fitted to predict annual malaria risk for children aged 2-10 years ( PfPR _2-10) at 1×1 km spatial resolutions. Parameter estimation was carried out using the Monte Carlo maximum likelihood methods. Population-adjusted prevalence and populations at risk by district were calculated for 2010 and 2017 to inform malaria control program priority setting. Results: 2,237 surveys at 1,834 communities undertaken between 2000 and 2017 were identified, geo-coded and used within the MBG framework to predict district malaria prevalence properties for 2010 and 2017. Nationally, there was a 47.2% reduction in the mean modelled PfPR _2-10 from 29.4% (95% confidence interval (CI) 26.6 to 32.3%) in 2010 to 15.2% (95% CI 13.3 to 18.0%) in 2017. Declining prevalence was not equal across the country, 25 of 27 districts showed a substantial decline ranging from a 3.3% reduction to 79% reduction. By 2017, 16% of Malawi's population still lived in areas that support PfPR _2-10 ≥ 25%. Conclusions: Malawi has made substantial progress in reducing the prevalence of malaria over the last seven years. However, Malawi remains in meso-endemic malaria transmission risk. To sustain the gains made and continue reducing the transmission further, universal control interventions need to be maintained at a national level.

Keywords: Malawi; Model-based geostatistics; Plasmodium falciparum; malaria.

Figure 1.. The Geography, population density, districts and unsuitability for malaria transmission in Malawi.

Population density ranges from zero (yellow) to 37,332 person per 1 km grid (dark blue). Grey areas represent a temperature suitability index (TSI) of zero which indicates a temperature range that cannot support malaria parasite development cycles in the mosquito [ Gething et al., 2011], and all correspond to unpopulated areas in the Nyika Plateau in the north and Mulange Massif range in the south.

Figure 2.. Spatial distribution of PfPR _2-10 surveys in Malawi between 2000 and 2017.

Data assembled from 2,237 surveys at 1,834 unique locations of community parasite prevalence showing the lowest values of PfPR _2-10 on top (left panel) and highest values of PfPR _2-10 on top (right panel) to reflect locations sampled more than once during the period.

Figure 3.. Mean standardized Plasmodium falciparum parasite rate ( PfPR _2–10) for 2010 (left) and 2017 (right).

The predicted posterior mean community PfPR _2–10 is presented at 1×1 km ranging from zero (dark blue) to 93% (dark red) in Malawi. Grey areas represent TSI values of zero, unable to support transmission.

Figure 4.. Validity of the assumed covariance model for the spatial correlation.

The empirical semi-variogram (solid line) falls within the 95% tolerance intervals (dashed lines), indicating that the adopted covariance model was compatible with the data.

Figure 5.. Percentage population of people living under different endemicity classes in 2010 (left) and 2017 (right).

The mean community Plasmodium falciparum parasite rate ( PfPR _2–10) have been grouped into six classes ranging from light red (<10%) to dark red (>50%).

Figure 6.. Non - exceedance and exceedance probabilities map.

Showing areas where predicted P fPR _2–10 is less (non-exceedance probability) than 20% which were > 80% confidently predicted (light green and dark green) or > 90% confidently predicted (dark green); and areas where P fPR _2–10 is greater (exceedance probability) than 30% which were > 80% confidently predicted (light red and dark red) or > 90% confidently predicted (dark red). Areas which do not support malaria transmission are shown in grey (see Figure 1); all other areas where transmission can occur are shown in yellow.

Figure 7.. Percentage change in predicted mean PfPR _2–10 by district, between 2010 and 2017.

The percentage change in mean PfPR _2–10 is shown in shades of green for decreasing malaria risk and shades of red for increasing risk.