Leveraging mathematical models of disease dynamics and machine learning to improve development of novel malaria interventions.

BACKGROUND: Substantial research is underway to develop next-generation interventions that address current malaria control challenges. As there is limited testing in their early development, it is difficult to predefine intervention properties such as efficacy that achieve target health goals, and therefore challenging to prioritize selection of novel candidate interventions. Here, we present a quantitative approach to guide intervention development using mathematical models of malaria dynamics coupled with machine learning. Our analysis identifies requirements of efficacy, coverage, and duration of effect for five novel malaria interventions to achieve targeted reductions in malaria prevalence. METHODS: A mathematical model of malaria transmission dynamics is used to simulate deployment and predict potential impact of new malaria interventions by considering operational, health-system, population, and disease characteristics. Our method relies on consultation with product development stakeholders to define the putative space of novel intervention specifications. We couple the disease model with machine learning to search this multi-dimensional space and efficiently identify optimal intervention properties that achieve specified health goals. RESULTS: We apply our approach to five malaria interventions under development. Aiming for malaria prevalence reduction, we identify and quantify key determinants of intervention impact along with their minimal properties required to achieve the desired health goals. While coverage is generally identified as the largest driver of impact, higher efficacy, longer protection duration or multiple deployments per year are needed to increase prevalence reduction. We show that interventions on multiple parasite or vector targets, as well as combinations the new interventions with drug treatment, lead to significant burden reductions and lower efficacy or duration requirements. CONCLUSIONS: Our approach uses disease dynamic models and machine learning to support decision-making and resource investment, facilitating development of new malaria interventions. By evaluating the intervention capabilities in relation to the targeted health goal, our analysis allows prioritization of interventions and of their specifications from an early stage in development, and subsequent investments to be channeled cost-effectively towards impact maximization. This study highlights the role of mathematical models to support intervention development. Although we focus on five malaria interventions, the analysis is generalizable to other new malaria interventions.

Averting a malaria disaster: will insecticide resistance derail malaria control?

World Malaria Day 2015 highlighted the progress made in the development of new methods of prevention (vaccines and insecticides) and treatment (single dose drugs) of the disease. However, increasing drug and insecticide resistance threatens the successes made with existing methods. Insecticide resistance has decreased the efficacy of the most commonly used insecticide class of pyrethroids. This decreased efficacy has increased mosquito survival, which is a prelude to rising incidence of malaria and fatalities. Despite intensive research efforts, new insecticides will not reach the market for at least 5 years. Elimination of malaria is not possible without effective mosquito control. Therefore, to combat the threat of resistance, key stakeholders need to rapidly embrace a multifaceted approach including a reduction in the cost of bringing new resistance management methods to market and the streamlining of associated development, policy, and implementation pathways to counter this looming public health catastrophe.

Disruptive technology for vector control: the Innovative Vector Control Consortium and the US Military join forces to explore transformative insecticide application technology for mosquito control programmes.

Malaria vector control technology has remained largely static for decades and there is a pressing need for innovative control tools and methodology to radically improve the quality and efficiency of current vector control practices. This report summarizes a workshop jointly organized by the Innovative Vector Control Consortium (IVCC) and the Armed Forces Pest Management Board (AFPMB) focused on public health pesticide application technology. Three main topics were discussed: the limitations with current tools and techniques used for indoor residual spraying (IRS), technology innovation to improve efficacy of IRS programmes, and truly disruptive application technology beyond IRS. The group identified several opportunities to improve application technology to include: insuring all IRS programmes are using constant flow valves and erosion resistant tips; introducing compression sprayer improvements that help minimize pesticide waste and human error; and moving beyond IRS by embracing the potential for new larval source management techniques and next generation technology such as unmanned "smart" spray systems. The meeting served to lay the foundation for broader collaboration between the IVCC and AFPMB and partners in industry, the World Health Organization, the Bill and Melinda Gates Foundation and others.

Fit for purpose: do we have the right tools to sustain NTD elimination?

Priorities for NTD control programmes will shift over the next 10-20 years as the elimination phase reaches the 'end game' for some NTDs, and the recognition that the control of other NTDs is much more problematic. The current goal of scaling up programmes based on preventive chemotherapy (PCT) will alter to sustaining NTD prevention, through sensitive surveillance and rapid response to resurgence. A new suite of tools and approaches will be required for both PCT and Intensive Disease Management (IDM) diseases in this timeframe to enable disease endemic countries to: 1. Sensitively and sustainably survey NTD transmission and prevalence in order to identify and respond quickly to resurgence. 2. Set relevant control targets based not only on epidemiological indicators but also entomological and ecological metrics and use decision support technology to help meet those targets. 3. Implement verified and cost-effective tools to prevent transmission throughout the elimination phase. Liverpool School of Tropical Medicine (LSTM) and partners propose to evaluate and implement existing tools from other disease systems as well as new tools in the pipeline in order to support endemic country ownership in NTD decision-making during the elimination phase and beyond.

Framework for rapid assessment and adoption of new vector control tools.

Evidence-informed health policy making is reliant on systematic access to, and appraisal of, the best available research evidence. This review suggests a strategy to improve the speed at which evidence is gathered on new vector control tools (VCTs) using a framework based on measurements of the vectorial capacity of an insect population to transmit disease. We explore links between indicators of VCT efficacy measurable in small-scale experiments that are relevant to entomological and epidemiological parameters measurable only in large-scale proof-of-concept randomised control trials (RCTs). We hypothesise that once RCTs establish links between entomological and epidemiological indicators then rapid evaluation of new products within the same product category may be conducted through smaller scale experiments without repetition of lengthy and expensive RCTs.