Abstract
Mosquito-borne diseases such as malaria continue to pose a major global health burden, and the impact of currently-available interventions is stagnating. Consequently, there is interest in novel tools to control these diseases, including gene drive-modified mosquitoes. As these tools continue to be refined, decisions on whether to implement them in the field depend on their alignment with target product profiles (TPPs) that define product characteristics required to achieve desired entomological and epidemiological outcomes. TPPs are increasingly being used for malaria and vector control interventions, such as attractive targeted sugar baits and long-acting injectable drugs, as they progress through the development pipeline. For mosquito gene drive products, reliable predictions from mathematical models are an essential part of these analyses, as field releases could potentially be irreversible. Here, we review the prior use of mathematical models in developing TPPs for malaria and vector control tools and discuss lessons from these analyses that may apply to mosquito gene drives. We recommend that, as gene drive technology gets closer to field release, discussions regarding target outcomes engage a wide range of stakeholders and account for settings of interest and vector species present. Given the relatively large number of parameters that describe gene drive products, machine learning approaches may be useful to explore parameter space, and an emphasis on conservative fitness estimates is advisable, given the difficulty of accurately measuring these parameters prior to field studies. Modeling may also help to inform the risk, remediation and cost dimensions of mosquito gene drive TPPs.
Highlights
Mosquito-borne diseases such as malaria, dengue and yellow fever continue to pose a major public health burden throughout much of the world
In order for gene drive mosquito products to advance from laboratory to field studies, their characteristics will be assessed against target product profiles (TPPs) - planning tools that provide a list of preferred characteristics and minimum criteria products must satisfy as they progress through the development pipeline
Both studies had focused on entomological outcomes, and a mathematical model was used to estimate the expected epidemiological impact based on the randomized controlled trial (RCT) results [12] prior to planning epidemiological RCTs
Summary
Mosquito-borne diseases such as malaria, dengue and yellow fever continue to pose a major public health burden throughout much of the world. In 2017, a randomized controlled trial (RCT) involving 14 villages in Mali demonstrated that ATSBs were capable of significantly reducing An. gambiae populations, including sporozoite-infected females, when LLINs were already present [10] Both studies had focused on entomological outcomes (i.e., mosquito density and sporozoite infection), and a mathematical model was used to estimate the expected epidemiological impact based on the RCT results [12] prior to planning epidemiological RCTs. Predicting outcomes, especially epidemiological ones, based on product parameters embodies the primary role that mathematical models play in informing TPPs. For ATSBs, the key product parameter is the excess daily mosquito mortality rate caused by the intervention, which was estimated to be 0.09 per mosquito per day from the Mali RCT [12]. An excess mortality rate due to ATSBs of ~0.05
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