Sherrard-Smith Ellie, Churcher Thomas S, Upton Leanna M, Sala Katarzyna A, Zakutansky Sara E, Slater Hannah C, Blagborough Andrew M, Betancourt Michael
MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, Norfolk Place, London, W2 1PG, UK.
Department of Life Sciences, Imperial College London, South Kensington, London, SW7 2AZ, UK.
Malar J. 2017 Apr 4;16(1):137. doi: 10.1186/s12936-017-1782-3.
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.
传播阻断干预措施(TBIs)旨在通过减少寄生虫在宿主和无脊椎动物媒介之间的传播来消除疟疾。传播阻断干预措施包括传播阻断药物和疫苗,当给予人类时,会被蚊子摄取并抑制媒介体内的寄生虫发育。准确的方法对于评估传播阻断干预措施的疗效至关重要,以确保只有最有效的候选药物才能进入昂贵且耗时的临床试验。测量干预效果可能存在问题,因为宿主和媒介种群中的寄生虫数量存在很大差异,这即使在实验室环境中也会影响传播。
引入一种统计稳健的实证方法,用于从标准化种群试验实验中估计干预效果。这种方法比简单的汇总统计更可靠,因为它捕捉了不同生命阶段寄生虫密度的变化。它还允许以比以前的方法更高的分辨率估计疗效,从而能够跟踪干预措施对连续几代的影响。新方法的一个主要优点是它对感染的种群动态不做任何假设。这使得宿主到媒介和媒介到宿主的传播都可以是密度依赖性(或其他)过程,并产生易于理解的干预效果估计。
该方法提高了干预效果估计的精度,并表明仅依靠感染率(感染宿主的比例)的变化可能不足以捕捉传播阻断干预措施的影响,这些措施还会抑制继发感染宿主中的寄生虫密度。
该方法表明,尽管能够更快地抑制寄生虫密度,但潜在有用的、部分有效的传播阻断干预措施可能需要多个感染周期才能观察到患病率的大幅降低。准确量化疗效的模型对于理解传播阻断干预候选药物在现场情况下的表现以及它们在临床试验中应如何评估具有重要意义。