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01.12.2017 | Methodology | Ausgabe 1/2017 Open Access

Malaria Journal 1/2017

A novel model fitted to multiple life stages of malaria for assessing efficacy of transmission-blocking interventions

Malaria Journal > Ausgabe 1/2017
Ellie Sherrard-Smith, Thomas S. Churcher, Leanna M. Upton, Katarzyna A. Sala, Sara E. Zakutansky, Hannah C. Slater, Andrew M. Blagborough, Michael Betancourt
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Electronic supplementary material

The online version of this article (doi:10.​1186/​s12936-017-1782-3) contains supplementary material, which is available to authorized users.



Transmission-blocking interventions (TBIs) aim to eliminate malaria by reducing transmission of the parasite between the host and the invertebrate vector. TBIs include transmission-blocking drugs and vaccines that, when given to humans, are taken up by mosquitoes and inhibit parasitic development within the vector. Accurate methodologies are key to assess TBI efficacy to ensure that only the most potent candidates progress to expensive and time-consuming clinical trials. Measuring intervention efficacy can be problematic because there is substantial variation in the number of parasites in both the host and vector populations, which can impact transmission even in laboratory settings.


A statistically robust empirical method is introduced for estimating intervention efficacy from standardised population assay experiments. This method will be more reliable than simple summary statistics as it captures changes in parasite density in different life-stages. It also allows efficacy estimates at a finer resolution than previous methods enabling the impact of the intervention over successive generations to be tracked. A major advantage of the new methodology is that it makes no assumptions on the population dynamics of infection. This enables both host-to-vector and vector-to-host transmission to be density-dependent (or other) processes and generates easy-to-understand estimates of intervention efficacy.


This method increases the precision of intervention efficacy estimates and demonstrates that relying on changes in infection prevalence (the proportion of infected hosts) alone may be insufficient to capture the impact of TBIs, which also suppress parasite density in secondarily infected hosts.


The method indicates that potentially useful, partially effective TBIs may require multiple infection cycles before substantial reductions in prevalence are observed, despite more rapidly suppressing parasite density. Accurate models to quantify efficacy will have important implications for understanding how TBI candidates might perform in field situations and how they should be evaluated in clinical trials.
Additional file 1. A comprehensive statistical description of the probabilistic Bayesian model.
Additional file 2. A collection of files containing the original data in list format (Additional file 2.1), a ‘how to’ R script (Additional file 2.2) to apply the Bayesian model to DFA multi-generational data in RStan (Additional file 2.1 can be used as an example). The probabilistic Bayesian model code (Additional file 2.3) and the data (Additional file 2.1) used in Blagborough et al. [ 12] are supplied together with functions (Additional file 2.4) to help assess the model output (Additional file 2.5).
Additional file 3. The parameter estimates and the alternative efficacy measure using infection probabilities. Table 2.1 The table of parameter estimates and estimated mean values for the posterior predictive data in the model. Table 2.2 The ‘infection reduction’ efficacy \(E^{R}_{i,m}\) is calculated as the difference between the summed mean π V and mean π P for controls compared to treatment arms for each transmission cycle i and biting rate m. The efficacy impact that can be attributed to π V is shown.
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