Air temperature suitability for Plasmodium falciparum malaria transmission in Africa 2000-2012: a high-resolution spatiotemporal prediction

Abstract

Background: Temperature suitability for malaria transmission is a useful predictor variable for spatial models of malaria infection prevalence. Existing continental or global models, however, are synoptic in nature and so do not characterize inter-annual variability in seasonal patterns of temperature suitability, reducing their utility for predicting malaria risk.

Methods: A malaria Temperature Suitability Index (TSI) was created by first modeling minimum and maximum air temperature with an eight-day temporal resolution from gap-filled MODerate Resolution Imaging Spectroradiometer (MODIS) daytime and night-time Land Surface Temperature (LST) datasets. An improved version of an existing biological model for malaria temperature suitability was then applied to the resulting temperature information for a 13-year data series. The mechanism underlying this biological model is simulation of emergent mosquito cohorts on a two-hour time-step and tracking of each cohort throughout its life to quantify the impact air temperature has on both mosquito survival and sporozoite development.

Results: The results of this research consist of 154 monthly raster surfaces that characterize spatiotemporal patterns in TSI across Africa from April 2000 through December 2012 at a 1 km spatial resolution. Generalized TSI patterns were as expected, with consistently high values in equatorial rain forests, seasonally variable values in tropical savannas (wet and dry) and montane areas, and low values in arid, subtropical regions. Comparisons with synoptic approaches demonstrated the additional information available within the dynamic TSI dataset that is lost in equivalent synoptic products derived from long-term monthly averages.

Conclusions: The dynamic TSI dataset presented here provides a new product with far richer spatial and temporal information than any other presently available for Africa. As spatiotemporal malaria modeling endeavors evolve, dynamic predictor variables such as the malaria temperature suitability data developed here will be essential for the rational assessment of changing patterns of malaria risk.

Figure 1

Comparison of observed versus modelled minimum air temperature (T_max). The model was based on nighttime land-surface temperature (LST_night) from the MODIS sensor.

Figure 2

Comparison of maximum air temperature ( T _max ) observed at ground stations and daytime land-surface temperature ( LST _day ) measured by the MODIS sensor.

Figure 3

Comparison of observed versus modelled maximum air temperature (T_max). The model was based on daytime land-surface temperature (LST_day) and daily temperature range (LST_Δ) from the MODIS sensor, along with the number of daylight hours DAY_length at each location.

Figure 4

Example temperature suitability index predictions for three consecutive Aprils. Shown are (A) April 2000, (B) April 2001 and (C) April 2002. Note the obvious differences in southern Africa and Madagascar, as well more subtle differences visible in the zoomed-in view of East Africa (e.g., southern Central African Republic as well as the Ethiopian highlands).

Figure 5

Comparison points for demonstrating the utility of dynamic TSI over synoptic products. Mean annual TSI is used here as the background dataset.

Figure 6

Longitudinal comparison of dynamic vs. synoptic TSI. Points 1 - 3 are cases where the dynamic TSI data provide limited improvement over synoptic averages. Points 4 - 12 are examples of areas where the magnitude and/or timing of TSI are poorly represented by synoptic data in one or more years.

Figure 7

Maximum monthly TSI standard deviation. The value represented by each cell is the maximum per-month standard deviation, as determined from the value calculated for each month (Jan. – Dec.) from the 13-year longitudinal dataset (2000-2012). The month of greatest standard deviation is typically associated with seasonal transitions such as spring, fall, or the onset or conclusion of the wet season.