Abstract
BackgroundMalaria prevalence, clinical incidence, treatment, and transmission rates are dynamically interrelated. Prevalence is often considered a measure of malaria transmission, but treatment of clinical malaria reduces prevalence, and consequently also infectiousness to the mosquito vector and onward transmission. The impact of the frequency of treatment on prevalence in a population is generally not considered. This can lead to potential underestimation of malaria exposure in settings with good health systems. Furthermore, these dynamical relationships between prevalence, treatment, and transmission have not generally been taken into account in estimates of burden.MethodsUsing prevalence as an input, estimates of disease incidence and transmission [as the distribution of the entomological inoculation rate (EIR)] for Plasmodium falciparum have now been made for 43 countries in Africa using both empirical relationships (that do not allow for treatment) and OpenMalaria dynamic micro-simulation models (that explicitly include the effects of treatment). For each estimate, prevalence inputs were taken from geo-statistical models fitted for the year 2010 by the Malaria Atlas Project to all available observed prevalence data. National level estimates of the effectiveness of case management in treating clinical attacks were used as inputs to the estimation of both EIR and disease incidence by the dynamic models.Results and conclusionsWhen coverage of effective treatment is taken into account, higher country level estimates of average EIR and thus higher disease burden, are obtained for a given prevalence level, especially where access to treatment is high, and prevalence relatively low. These methods provide a unified framework for comparison of both the immediate and longer-term impacts of case management and of preventive interventions.Electronic supplementary materialThe online version of this article (doi:10.1186/s12936-015-0864-3) contains supplementary material, which is available to authorized users.
Highlights
Malaria prevalence, clinical incidence, treatment, and transmission rates are dynamically interrelated
National level prevalence distributions National levels of PfPR2–10 aggregated at country level after extracting from Malaria Atlas Project (MAP) [7] posterior distributions at each 5 by 5 km pixel illustrate high average levels of PfPR2–10 in 2010 in many African countries but for many countries the wide distribution of prevalence levels (values summarized in Modelled relationships between entomological inoculation rate (EIR) and prevalence Where PfPR2–10 is high, the OpenMalaria models predict on average a slightly higher EIR at a given prevalence than does the empirical model, with relatively little influence of effective treatment (E14) (Fig. 2a, b)
The general pattern for Method A is for prevalence to increase steeply with EIR at low transmission levels, but to saturate at higher transmission (Fig. 2a)
Summary
Clinical incidence, treatment, and transmission rates are dynamically interrelated. The impact of the frequency of treatment on prevalence in a population is generally not considered This can lead to potential under‐ estimation of malaria exposure in settings with good health systems. One part of malaria transmission, is best quantified by the entomological inoculation rate (EIR: the number of infectious bites per human host, per unit time), which is more directly related to morbidity and mortality than is prevalence. Measuring this quantity directly requires intensive entomological studies over the whole annual period of malaria transmission. EIR data are relatively sparse, and indirect methods, that ideally account for treatment effects, are needed for estimating EIR from available prevalence data [4, 5]
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