Table 3.
Summary of included articles
| Study identifier (Year of publication) | Geographic focus | Interventions or Scenarios | Administrative Level | Population | Time horizon of analysis (years) | Method of estimating health benefits | Species | Constraint | Data sources | Optimization goal | Optimization technique | Equity considerations in resource allocation | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Sherrard-Smith et al. [17] | Tanzania, Uganda | Pyrethroid-only ITNs; Pyrethroid-PBO ITNs; IRS | National | Children under 5 years | 3 | Dynamic mathematical model (MINT tool); Scenario-based | P. falciparum | Minimum budget | Peer-reviewed literature (RCT) | Reducing annual cost per case averted | Stochastic programming | No |
| 2 | Njau et al. [32] | South Africa | Passive case detection; IRS; Active case detection; Proactive case detection; Border surveillance | Subnational (Province) | General population | 11 | Dynamic mathematical transmission model; Scenario-based | P. falciparum | Maximum budget | Local data (DHIS) | Achieve malaria elimination within a 10-year period | Stochastic programming | Yes, Malaria Surveillance Agents (MSAs) |
| 3 | Shretta et al. [28] | Ghana | Passive case detection; LLINs; IRS; Health system strengthening; Social and behavioural change; SMC; IPTp | National | General population | 10 | Dynamic compartmental transmission model; Scenario-based | P. falciparum | Minimum and maximum budget | Local data; World Malaria Reports; Peer-reviewed literature; Expert opinion | Provide economic evidence on risks of withdrawing financing as a strategy for resource mobilization | Metaheuristic method (Particle swarm optimization) | No |
| 4 | Shretta et al. [29] | Asia Pacific (22 countries) | LLINs; IRS; MDA; Treatment; Surveillance | Multi-national; National | General population | 12 | Dynamic compartmental transmission model; Scenario-based (METCAP) | P. falciparum; P. vivax | Minimum budget | World Malaria Reports; Peer-reviewed literature | Malaria elimination by 2030 | Stochastic programming | No |
| 5 | Winskill et al. [25] | Sub-Saharan Africa | Treatment; LLINs; SMC; IPTi (PMC); RTS,S vaccine | Multi-national | General population | Not specified | Individual-based model | P. falciparum | Minimum and maximum budget | Peer-reviewed literature; Country level reports; WHO-CHOICE framework; Global Fund Price Reference Report | Maximise reduction in malaria transmission, case incidence and mortality with the least marginal cost | Non-linear programming | No |
| 6 | Sudathip et al. [36] | Thailand | Treatment; IRS; ITNs | National | General population | 20 | Two epidemiological models. Model A: Log-normal generalised linear regression model; Model B:; Scenario-based | P. falciparum; P. vivax | Minimum and maximum budget | Historical data; Expert opinion; Privately shared data | To measure the cost–benefit of a complete implementation of the NMES and thus assess the justification to invest in malaria elimination in Thailand | Linear programming | No |
| 7 | Drake et al. [10] | Myanmar | ITNs; CHWs | National; Subnational | General population | 1 | Geographically targeted resource allocation framework; Scenario-based | P. falciparum | Minimum budget | Local data; Reports | Using a geographic budget allocation network to maximise health benefits | Linear programming (knapsack) | Yes, Community Health Workers (CHWs) |
| 8 | Scott et al. [16] | Nigeria | LLINs; IRS; IPTp; SMC; Larval source management; MDA; Behavioural change communication | National; Subnational | General population | 5 | Geospatial epidemic (dynamic transmission) model; Optimization algorithm (Optima Malaria model); Scenario-based | P. falciparum | Maximum and minimum budget | Malaria Atlas Project (MAP); UN Population Division | Optimizing the allocation of scarce funding in targeted geographical regions to maximize reductions in malaria morbidity and mortality | Stochastic programming | No |
| 9 | Winskill et al. [33] | Sub-Saharan Africa | LLINs; IRS, SMC; RTS,S vaccine | Multi-national | General population | 10 | Individual-based model | P. falciparum | Cost-effectiveness threshold | Peer-reviewed literature; PMI, CHAI, MSF estimates | To derive the most cost-effective pathways for scaling-up malaria interventions in order to inform decisions about the introduction of the RTS,S malaria vaccine | Non-linear programming (gradient descent) | No |
| 10 | Winskill et al. [35]) | Sub-Saharan Africa (19 countries); Greater Mekong Subregion | LLINs; IRS; ACTs | Multi-national; Subnational | General population | 15 | Individual-based model; Scenario-based | P. falciparum | Maximum and minimum budget | PMI reports; WHO World Malaria Reports; NMCPs; DHS; MICS; Peer-reviewed literature | To estimate the impact of PMI investments to date in reducing malaria burden and to explore the potential negative impact on malaria burden should a proposed 44% reduction in PMI funding occur | Linear programming | No |
| 11 | Patouillard et al. [37] | Global (All 97 malaria endemic countries) | All control interventions recommended by the WHO* | Multi-national; Subnational | General population | 15 | Individual-based model | P. falciparum | Maximum budget | World Malaria Reports; Global Rural–Urban Mapping Project; DHS; Procurement databases; Peer-reviewed literature; National malaria strategic plans; NMCP reports; WHO-CHOICE project; Key informant interviews | To estimate the financing required for malaria control and elimination over the 2016–2030 period | Stochastic programming | No |
| 12 | Walker et al. [18] | Sub-Saharan Africa | LLINs; IRS; SMC; MDA; Mass screen and treatment (MSAT) | Multi-national; Subnational; Pixel (Fine-scale) | General population | 20 | Individual-based model; Scenario-based | P. falciparum | Minimum budget | WHO Pesticide Evaluation Scheme (WHOPES); Peer-reviewed literature; PMI reports; Malaria Atlas Project (MAP) | To estimate the most cost-efficient strategies to achieve goals for reducing burden and transmission | Non-linear programming | No |
| 13 | Dudley et al. [38] | NA | LLINs; IRS; IPT; ACT; RTS,S vaccine | Multi-national; Subnational | General population | 5 | Integer linear program and compartment model; Scenario-based | P. falciparum | Maximum and minimum budget | Peer-reviewed literature; Country specific data; WHO Pesticide Evaluation Scheme (WHOPES) | Minimise person-days of malaria infection | Integer linear programming | No |
| 14 | Drake et al. [11] | Myanmar | ITNs; CHWs | National; Subnational | General population | 1 | Decision tree; Spatially explicit resource allocation model; Scenario-based | P. falciparum | Minimum budget | Three Millenium Development Goal (3MDG); Peer-reviewed literature; Routine health system surveillance records | To maximize impact from investment in ITN use and early diagnosis and treatment through malaria CHWs | Linear programming | Yes, Community Health Workers (CHWs) |
| 15 | Stuckey et al. [34] | Kenya | LLINs; IRS; Intermittent screen and treat (IST) | Subnational | General population | 5 | Microsimulation individual-based model (OpenMalaria); Scenario-based | P. falciparum | Cost-effectiveness threshold | Local survey data (MTC); WHO-CHOICE; Global Fund to Fight AIDS, Tuberculosis and Malaria Price and Quality Reporting Tool; Peer-reviewed literature | To address the cost effectiveness of feasible malaria control interventions | Stochastic programming | No |