Table 5 Continued Population Structure Intervention Method Partnership Formation Outcome of interest Validity Article No S: Fit the model with 20063 Heterosexual 5 sexual activity groups, gender, age Partial HIV prevalence different assumptions; structure differential Uganda, Assortative to equations, with L: Not counting on urban Kenya, HIV status: 5 different disease proportionate parameters concurrent relationship; Zimbabwe stages: susceptible, acute infection, estimated from No sensitivity analysis (at and urban latent infection, pre-AIDS and full data; least not mentioned) Haiti blown AIDS. 1) Reduce HIV prevalence under Runge-Kutta mixing. different intervention 2) Changes in age at 20074 Zimbabwe scenarios; Lifetime first sex. 3) Changes probability of getting in condom use HIV Heterosexual Gender, age, sexual activity level, Cumulative HIV S:Sensitivity analysis, PrEP status, and HIV-1 drug incidence averted Considered drug resistant 20078 Sub-Saharan PrEP ODEs, numerical Assortative to resistance. because of PrEP mutation Africa solution Proportionate HIV status: recent, chronic, AIDS. Effectiveness of L: did not quantify the intervention/PrEP accuracy of model prediction; No impact of mixing pattern on PrEP Sexual activity level which are, S: Include some males who never have sexual uncertainty analysis Heterosexual No Proportionate R0 contacts with FSWs (low) and males equations; L: one infective stage; who have sexual contacts with Analytical 200718 Yunnan, Unrealistic assumption; FSWs; solutions based China size of sex workers on parameters HIV status: susceptible and infective assumed to be static; estimated from data Generic control By gender by population patches, ODES, numerical Contact from one patch The control function S: Fitted with different problem. No specific individual could move between solution, using to another depending which represents the parameters and control intervention strategies Heterosexual patches on certain rate (Each patch cross entropy on the HIV prevalence amount of budget spent strategies, uncertainty have been applied but might keep constant population size; technique within each patch in each population per analysis. Useful in 200812 No Real consider how to with varying population size; or time unit decision-making World Data distribute a fixed (Monte Carlo constant mobility.) budget over time. method) L: Not validated by real HIV status: Susceptible, Infected or world data. AIDS
Table 5 Continued Article Population Structure Intervention Method Partnership Formation Outcome of interest Validity 20063 Heterosexual Uganda, urban Kenya, Zimbabwe and urban Haiti 5 sexual activity groups, gender, age structure HIV status: 5 different disease stages: susceptible, acute infection, latent infection, pre-AIDS and full blown AIDS. No Partial differential equations, with parameters estimated from data; Runge-Kutta Assortative to proportionate HIV prevalence S: Fit the model with different assumptions; L: Not counting on concurrent relationship; No sensitivity analysis (at least not mentioned) 20074 Zimbabwe 1) Reduce mixing. 2) Changes in age at first sex. 3) Changes in condom use HIV prevalence under different intervention scenarios; Lifetime probability of getting HIV 20078 Heterosexual Sub-Saharan Africa Gender, age, sexual activity level, PrEP status, and HIV-1 drug resistance. HIV status: recent, chronic, AIDS. PrEP ODEs, numerical solution Assortative to Proportionate Cumulative HIV incidence averted because of PrEP Effectiveness of intervention/PrEP S:Sensitivity analysis, Considered drug resistant mutation L: did not quantify the accuracy of model prediction; No impact of mixing pattern on PrEP 200718 Heterosexual Yunnan, China Sexual activity level which are, males who never have sexual contacts with FSWs (low) and males who have sexual contacts with FSWs; HIV status: susceptible and infective No equations; Analytical solutions based on parameters estimated from data Proportionate R0 S: Include some uncertainty analysis L: one infective stage; Unrealistic assumption; size of sex workers assumed to be static; 200812 Heterosexual No Real World Data By gender by population patches, individual could move between patches on certain rate (Each patch might keep constant population size; with varying population size; or constant mobility.) HIV status: Susceptible, Infected or AIDS Generic control problem. No specific intervention strategies have been applied but consider how to distribute a fixed budget over time. ODES, numerical solution, using cross entropy technique (Monte Carlo method) Contact from one patch to another depending on the HIV prevalence within each patch The control function which represents the amount of budget spent in each population per time unit S: Fitted with different parameters and control strategies, uncertainty analysis. Useful in decision-making L: Not validated by real world data.