Review 2: "Estimating the Potential Impact of Surveillance Test-and-Treat Posts to Reduce Malaria in Border Regions in Sub-Saharan Africa: A Modelling Study"

RR:C19 Evidence Scale rating by reviewer:

• Strong. The main study claims are very well-justified by the data and analytic methods used. There is little room for doubt that the study produced has very similar results and conclusions as compared with the hypothetical ideal study. The study’s main claims should be considered conclusive and actionable without reservation.

***************************************

Review: Human mobility is an important driver of malaria transmission.  So, to achieve national level elimination targets some countries have adopted test-and- treat cross-border control, aimed at reducing spillover from neighboring regions. The efficacy of such strategies, however, is difficult to assess, as it depends on multiple demographic, environmental, disease-specific factors and control interventions.

The authors utilized an expanded version of malaria IBM (malariasimulation) developed in earlier studies, with the detailed account of geospatial, environmental and demographic makeup of sub-Sharan countries, and cross-border mobility (social connectivity matrix between administrative ‘units’). It allowed them to simulate different test-and- treat strategies, to assess their efficacy in terms of prevalence reduction, and case detection.

The paper gives an important contribution to the field of malaria control studies. The exposition is clear and sufficiently detailed. I have a few technical questions/comments that need clarification.

Questions/comments

1. Meta-population ‘8-unit’ system (Supplement) operates with local EIR and FOIM patches linked via social connectivity (mobility) matrix. Are those inputs coupled to ‘disease transmission dynamics’ within units (or refined unit grid points)? Are those dynamics run as individual-based (IBM) or population-based system? What is the role of stochasticity?

2. Re. ‘cluster approach’ to 401 meta-population system (Fig.2), I wonder whether ‘8-cluster’ simulations were run independently (for each cluster), or as a coupled (401x401) system? Also, the model assumes ‘no contribution’ from internal (inland) mobility. Does it mean countries are ‘near elimination’ stage internally, or infection levels within country are assumed ‘equal’ between inland and border regions?

3. In the 2-unit model Fig.4 (A<B), I’m puzzled by asymmetry between 2 units (high/low prevalence) and their outcomes (detected, averted cases). Why U1-high + U2-low is not the same as U1-low+U2-high? Are units different in their makeup, or border posts were asymmetric(?)

Minor comment:  the authors' terminology PfPR2-10 in the Abstract should be explained in words: Pf- prevalence for 2-10  age-group (it was done later in the text).