Table 1 Knowledge gaps addressed by integrating modelling at each development stage.

From: Modelling to inform next-generation medical interventions for malaria prevention and treatment

 

Knowledge gaps

Modelling evidence

Clinical trial translation, planning, and design

• Which intervention properties drive effectiveness?

• When and what type of evidence to generate?

• Key drivers of impact and early clinical data needed

• Parameter range validation and translation to other use-cases

• Candidate selection criteria

Use-case, target population, and setting

• Which use-cases to prioritize given unmet needs and intervention characteristics?

• Which use-cases achieve the highest impact?

• Impact and minimum criteria requirements across a range of settings and age groups

• Account for clinical evidence to re-evaluate use-cases

Deployment factors

• How can deployment frequency and timing be optimized?

• What is the impact of mixed and layered intervention strategies?

• Scenarios of different strategies across use-cases to identify minimum criteria requirements that optimize effectiveness

Public health impact

• Does the novel intervention improve the standard of care?

• Is the intervention cost-effective?

• Endpoint translation

• Direct comparison of different interventions and evaluation periods

  1. Knowledge gaps that can be addressed by population-level transmission modelling for next-generation interventions at different stages of development: before, during, and after early clinical trial studies to inform evidence generation and on-board data to better inform guidance documents with model-informed intervention minimum criteria; evaluating and re-evaluating use-cases iteratively to ensure intervention characteristics are appropriate for different use-cases to ensure high public health impact that addresses unmet health need; considering how deployment strategies and mixed interventions can be optimised to achieve health goals informed by modelling evidence; and directly generating predicted public health impact effectiveness for implementation given intervention efficacy characteristics to explore a large spectrum of possible scenarios and inform decision-making.
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