Table 1 Description of the EIR and burden estimation methods A and B including their inputs and outputs

A

Malaria transmission as EIR is estimated using previous published statistical relationship between prevalence and EIR [6, 7]. Burden of clinical disease is determined via the OpenMalaria micro-simulation model with EIR distributions derived by this method as inputs

Prevalence distributions from MAP [7] by pixel (5 km by 5 km). See Fig. 3a and results Additional file 2: Figure S2

Population numbers by pixel from [30]

Input for burden estimation only: coverage of effective treatment is country or geographic area specific (Table 2, [25])

Using the empirical relationship between prevalence and EIR [6, 7] (Eq. 1) and the prevalence distributions per pixel weighted by population demographics, a population weighted distribution for EIR is constructed. Overall EIR distribution for a geographic area is found by aggregation of the pixel EIR distributions (Fig. 3c and results Figs. 4, 5)

Using population weighted EIR distributions from Method A as input and assuming coverage of treatment at country specific levels, clinical incidence is determined using the OpenMalaria micro-simulation (process schematic Fig. 3d and results Fig. 7)

B Assuming country levels of coverage of effective treatment

Malaria transmission as EIR is estimated using a derived functional form of the relationship between prevalence and EIR and level of effective treatment from the OpenMalaria micro-simulation model. Burden of clinical disease is determined via the OpenMalaria microsimulation model with EIR distributions derived by this method as inputs

Prevalence distributions from MAP [7] by pixel (5 km by 5 km). Figure 3a and results Additional file 2: Figure S2

Population numbers by pixel from [30]

Input for both EIR and burden estimation: Coverage of effective treatment is country or geographic area specific (Table 2, [25])

A statistical relationship is fit to predict prevalences from the OpenMalaria micro-simulation model for a range of EIR and different levels of coverage of effective treatment of clinical disease (Fig. 3b). Prevalence distributions weighted by population by pixel are transformed to EIR distributions via this fitted function (Eq. 2) for country levels of coverage of effective treatment resulting in a population weighted distribution of EIRs by pixel and aggregated to country or geographic area. (Fig. 3c and results Figs. 4, 5).

Using population weighted EIR distributions from Method B as input and assuming coverage of treatment at country specific levels, clinical incidence is determined using the OpenMalaria micro-simulation (process schematic Fig. 3d and results Fig. 7)

B Assuming coverage of effective treatment at pre-ACT scale up levels

As above

As above

As above

Input for both EIR and burden estimation: E₁₄ = 15 % capturing the situation before recent scale-up of ACT

As above but assuming coverage of effective treatment is 15 %

As above but EIR distributions and incidence are determined assuming coverage of effective treatment is 15 %