The Value of Information in Decision‐Analytic Modeling for Malaria Vector Control in East Africa

Abstract

Decision analysis tools and mathematical modeling are increasingly emphasized in malaria control programs worldwide, to improve resource allocation and address ongoing challenges with sustainability. However, such tools require substantial scientific evidence, which is costly to acquire. The value of information (VOI) has been proposed as a metric for gauging the value of reduced model uncertainty. We apply this concept to an evidenced-based Malaria Decision Analysis Support Tool (MDAST) designed for application in East Africa. In developing MDAST, substantial gaps in the scientific evidence base were identified regarding insecticide resistance in malaria vector control and the effectiveness of alternative mosquito control approaches, including larviciding. We identify four entomological parameters in the model (two for insecticide resistance and two for larviciding) which involve high levels of uncertainty and to which outputs in MDAST are sensitive. We estimate and compare a VOI for combinations of these parameters in evaluating three policy alternatives relative to a status quo policy. We find having perfect information on the uncertain parameters could improve program net benefits by up to 5%–21%, with the highest VOI associated with jointly eliminating uncertainty about reproductive speed of malaria-transmitting mosquitoes and initial efficacy of larviciding at reducing the emergence of new adult mosquitoes. Future research on parameter uncertainty in decision analysis of malaria control policy should investigate the VOI with respect to other aspects of malaria transmission (such as antimalarial resistance), the costs of reducing uncertainty in these parameters, and the extent to which imperfect information about these parameters can improve payoffs.

Wicked evolution: Can we address the sociobiological dilemma of pesticide resistance?

“…Fitness costs, dominance, and initial frequencies of resistance genes remain highly uncertain in field settings for many pesticides. However, reducing uncertainty is costly, and better information may be more actionable for some of these factors than others, as has been shown for malaria vectors (32). For example, more certainty about the efficacy of noninsecticidal alternatives may be more valuable than better information about the fitness costs of resistance.…”

Simulating the council-specific impact of anti-malaria interventions: A tool to support malaria strategic planning in Tanzania

“…WHO) [2,35,36]. Mathematical models have been applied in various studies at high resolution in all sub-Saharan African (SSA) countries [29,30,37,38], or more local levels, for example in Kenya [39][40][41][42][43], Nigeria [31,32,44], South Africa [45,46], Ghana [41,47,48], Uganda [41], Mozambique [49], and Tanzania [41], and modelling is also being done in Asia [50][51][52]. Previous country specific model predictions were either generalised based on archetypical settings at regional level (admin 1) [31], at 5x5 km 2 level [29], or applied for a specific sub-area of the country [33,[53][54][55].…”