Geo-statistical models can be used to combine individual surveys of disease prevalence to produce maps of estimated prevalence and uncertainty. However, the time needed to obtain and collate survey data means that maps can quickly become out of date. It can also be challenging to include survey data from too long ago, as it may conflict with more recent data, especially if interventions have been applied in the interim. Join this lecture to find out more about how we can overcome these challenges.
While the health economic implications of disease elimination have been discussed before, the combination of uncertainty, cost effectiveness, and elimination has not been tackled before. We propose a modification to the net-benefit framework to explicitly consider the implications of switching from an optimal strategy, in terms of cost-per-burden averted, to a strategy with a higher likelihood of meeting the global target of elimination. The modification proposed yields a methodology to quantify the efficiency of elimination and to aid discussions among stakeholders with different objectives. We apply our method to strategies against human African trypanosomiasis in three settings, but this method is flexible enough that it can be applied directly to any simulation-based studies of disease elimination efforts.
R code and simulation results data have been deposited in Open Science Framework (<https://OSF.IO/FH6CA>) ([49][1]). [1]: #ref-49