Defining the Relationship Between Infection Prevalence and Clinical Incidence of Plasmodium falciparum Malaria

September 8, 2015


Background: Despite encouraging recent progress, Plasmodium falciparum continues to impose an enormous burden of disease and death across sub-Saharan Africa. In many countries with the most intense transmission, disease-reporting infrastructures are weak and precise enumeration of the burden on human health arising from malaria is challenging. This, in turn, limits evidence-based disease-control planning, implementation and evaluation. In response, cartographic approaches have been developed that use maps of infection prevalence (termed the P. falciparum parasite rate, PfPR) or other transmission metrics as a basis for estimating the incidence rate of clinical disease in different locations. While maps of PfPR are becoming increasingly robust, in part because of the proliferation of high-quality data on infection prevalence from nation-wide household surveys, the relationship between PfPR and clinical incidence remains relatively poorly understood and informed by a much smaller and less standardized empirical evidence base.

Transmission Models: The three contemporary transmission platforms employed in this study were OpenMalaria (run in a single baseline configuration, rather than as full ensemble itself), the EMOD DTK v1.6  and the Griffin et al. model. Here we employ the publicly available microsimulation codes for the former two and, for the latter, a bespoke code based on the compartmental model described therein (we will refer to this implementation as 'the Griffin IS', that is, Individual Simulation).

Age dependence of the PfPR2-10-incidence relationship: Although fitted against a common data set, our posterior calibrations of the three microsimulation models exhibited a number of subtle differences in their predictions for the PfPR2-10-incidence relationship stratified by age. Figure 1 presents a direct comparison of their posterior envelopes from simulations under a low seasonality profile (here constant EIR) for three key age groups chosen for consistency with the reporting conventions (and prevalence/incidence-to-mortality modelling methodologies) of the Global Burden of Disease project and the World Malaria Report:

Figure 1

Calibrated posterior predictions of the P. falciparum prevalence–incidence relationship under conditions of low historical treatment and low transmission seasonality from the three microsimulation models comprising our ensemble, stratified by age.

(a,d,g) OpenMalaria; (b,e,h) EMOD DTK; (c,f,i) Griffin IS. In each panel the coloured curve and shaded zones illustrate the (pointwise) median and surrounding 68 and 95% credible intervals for incidence detectable with daily ACD supposing no change to treatment, while the dashed black lines illustrate the median prediction corresponding to a study year intervention increasing the effective treatment rate from 35 to 85% (that is, the 'observer effect' of ethical study designs).


Conclusions: Through a novel emulator-based approach we have been able to calibrate three contemporary microsimulation models against a common, purpose-built data set of age-structured prevalence and incidence counts across 30 unique sites in sub-Saharan Africa.