To prevent drug resistance, treatment needs to be effective and the full prescribed course of treatment needs to be completed. However, this can be difficult to monitor, especially in low to middle income countries where health care access may be limited. For example, perhaps a treatment centre is using low quality drugs, which is known to promote drug resistance occurring and spreading. This model allows these treatment centres to be identified, whilst accounting for uncertainty in the data collection, and varying disease prevalence over the region.
To demonstrate the main model of my project, consider a toy example in Square Land over 20 years (see figures). Within Square Land you have sampled infected people, at different locations and times, and identified whether they carry a resistant strain or not. In addition, suppose you know the location of five treatment access points, such as health care centres or hospitals (however knowledge of the exact locations is not a requirement of the model). There is data on infected people in Square Land for 20 years. Some patients are carrying a sensitive strain, and some are carrying a resistant. We know the location and time that people were tested, and that there is more disease prevalence at the bottom of Square Land.
The model created here identifies which hotspot is introducing more resistant infections into the population. In this example, the model identifies that the top left hotspot (hotspot 1) is contributing the most resistant infection into the population. Therefore, if hotspot 1 was a health care centre, it would be worth investigating the quality of the drugs administered here and/or the adherence of the patients. If this hotspot was a transport hub, it would inform us that drug resistance is entering the population from outside. The middle hotspot (hotspot 3) has more resistant infections in the region (due to higher prevalence of malaria cases here), however because the model explicitly accounts for the higher prevalence of malaria (not necessarily drug resistant malaria) at the bottom of Square Land, it stills successfully identifies hotspot 1. Analysis without a mechanistic component, as presented here, would incorrectly suggest investing hotspot 3, not hotspot 1.
