At there is limited migration into and out of Macha as transportation and mobility are limited. Migration can have a major impact on a local HIV epidemic, and also on a mathematical model attempting to capture HIV dynamics in a population. The population in Macha has, however, remained fairly stable over time. A limitation of our modeling approach is that highly sexually active individuals are difficult to identify. Nonetheless, we found that cost-effectiveness remained the same if only 10 of the high sexual activity groups could be prioritized (2 of the total population). Health care providers could begin with prioritizing those individuals who present with STI symptoms at clinics, or areidentified as the seronegative partner in a serodiscordant relationship. Over a wide spectrum of adherence and PrEP prioritization, we predict that PrEP will reduce HIV incidence and will be cost-effective. Our model does not take into account administrative program costs [34], as they would vary widely depending on the precise intervention used. We have also not included indirect costs, as these are very difficult to quantify. We have instead shown the additional amount that could be spent on those costs and retain cost-effectiveness. The government of Zambia or donors could invest an additional 25,200,000 over 10 years in the implementation of prioritized PrEP, and have it remain very cost-effective. Previous models have shown the potential impact of PrEP. A model by Pretorius et al. evaluated cost-effectiveness in a generalized South African epidemic [35]. When all individuals were assigned to receive PrEP, they showed a decrease in incidence in 2025 of about 40 compared to their baseline. This is approximately in line with our findings, albeit a bit low considering that we assigned PrEP to half of our population. A model by Abbas et al. investigated the factors influencing the emergence and spread of HIV drug MC-LR chemical information resistance arising from PrEP rollout, based on a general mature epidemic in sub-Saharan Africa [36]. In their PrEP scenario analyses, the largest decrease in infections was achieved with a non-prioritized strategy (31 in an optimistic scenario, similar to our “high adherence” scenario; 7 in realistic, similar to our “moderate adherence”) and the smallest decrease with the prioritized-by-activity strategy (8 in optimistic, 3 in realistic). The benefits of PrEP in this model were much lower than estimates from our model. Reasons for this could be their definitions of optimistic and realistic, as well as the level of protection offered from PrEP. In iPrEx, HIV drug resistance due to PrEP was not a major issue [2], likely due to monthly monitoring of participants for seroconversion. The only resistance found was in those with a false negative HIV test at randomization and started PrEP. The study by Abbas et al. has also examined the emergence of drug 
resistance due to PrEP in a heterosexual sub-Saharan epidemic [36]. In agreement with our results, the Abbas model has shown that there is not much difference in the prevalence of drug 16574785 resistance in aCost-Effectiveness of PrEP, BIBS39 supplier Zambianon-prioritized or prioritized PrEP scenario, but that higher PrEP adherence would result in less drug resistance. The total prevalence of resistance in their optimistic scenario was about 1.9?.5 and 9.2?.9 in their realistic scenario. If we had evaluated the same measure of drug resistance, these figures are likely lower than ours. Several prevention strate.At there is limited migration into and out of Macha as transportation and mobility are limited. Migration can have a major impact on a local HIV epidemic, and also on a mathematical model attempting to capture HIV dynamics in a population. The population in Macha has, however, remained fairly stable over time. A limitation of our modeling approach is that highly sexually active individuals are difficult to identify. Nonetheless, we found that cost-effectiveness remained the same if only 10 of the high sexual activity groups could be prioritized (2 of the total population). Health care providers could begin with prioritizing those individuals who present with STI symptoms at clinics, or areidentified as the seronegative partner in a serodiscordant relationship. Over a wide spectrum of adherence and PrEP prioritization, we predict that PrEP will reduce HIV incidence and will be cost-effective. Our model does not take into account administrative program costs [34], as they would vary widely depending on the precise intervention used. We have also not included indirect costs, as these are very difficult to quantify. We have instead shown the additional amount that could be spent on those costs and retain cost-effectiveness. The government of Zambia or donors could invest an additional 25,200,000 over 10 years in the implementation of prioritized PrEP, and have it remain very cost-effective. Previous models have shown the potential impact of PrEP. A model by Pretorius et al. evaluated cost-effectiveness in a generalized South African epidemic [35]. When all individuals were assigned to receive PrEP, they showed a decrease in incidence in 2025 of about 40 compared to their baseline. This is approximately in line with our findings, albeit a bit low considering that we assigned PrEP to half of our population. A model by Abbas et al. investigated the factors influencing the emergence and spread of HIV drug resistance arising from PrEP rollout, based on a general mature epidemic in sub-Saharan Africa [36]. In their PrEP scenario analyses, the largest decrease in infections was achieved with a non-prioritized strategy (31 in an optimistic scenario, similar to our “high adherence” scenario; 7 in realistic, similar to our “moderate adherence”) and the smallest decrease with the prioritized-by-activity strategy (8 in optimistic, 3 in realistic). The benefits of PrEP in this model were much lower than estimates from our model. Reasons for this could be their definitions of optimistic and realistic, as well as the level of protection offered from PrEP. In iPrEx, HIV drug resistance due to PrEP was not a major issue [2], likely due to monthly monitoring of participants for seroconversion. The only resistance found was in those with a false negative HIV test at randomization and started PrEP. The study by Abbas et al. has also examined the emergence of drug resistance due to PrEP in a heterosexual sub-Saharan epidemic [36]. In agreement with our results, the Abbas model has shown that there is not much difference in the prevalence of drug 16574785 resistance in 
aCost-Effectiveness of PrEP, Zambianon-prioritized or prioritized PrEP scenario, but that higher PrEP adherence would result in less drug resistance. The total prevalence of resistance in their optimistic scenario was about 1.9?.5 and 9.2?.9 in their realistic scenario. If we had evaluated the same measure of drug resistance, these figures are likely lower than ours. Several prevention strate.
