Predicting the impact of border control on malaria transmission: a simulated focal screen and treat campaign

Abstract

Background: South Africa is one of many countries committed to malaria elimination with a target of 2018 and all malaria-endemic provinces, including Mpumalanga, are increasing efforts towards this ambitious goal. The reduction of imported infections is a vital element of an elimination strategy, particularly if a country is already experiencing high levels of imported infections. Border control of malaria is one tool that may be considered.

Methods: A metapopulation, non-linear stochastic ordinary differential equation model is used to simulate malaria transmission in Mpumalanga and Maputo province, Mozambique (the source of the majority of imported infections) to predict the impact of a focal screen and treat campaign at the Mpumalanga-Maputo border. This campaign is simulated by nesting an individual-based model for the focal screen and treat campaign within the metapopulation transmission model.

Results: The model predicts that such a campaign, simulated for different levels of resources, coverage and take-up rates with a variety of screening tools, will not eliminate malaria on its own, but will reduce transmission substantially. Making the campaign mandatory decreases transmission further though sub-patent infections are likely to remain undetected if the diagnostic tool is not adequately sensitive. Replacing screening and treating with mass drug administration results in substantially larger decreases as all (including sub-patent) infections are treated before movement into Mpumalanga.

Conclusions: The reduction of imported cases will be vital to any future malaria control or elimination strategy. This simulation predicts that FSAT at the Mpumalanga-Maputo border will be unable to eliminate local malaria on its own, but may still play a key role in detecting and treating imported infections before they enter the country. Thus FSAT may form part of an integrated elimination strategy where a variety of interventions are employed together to achieve malaria elimination.

Figure 1

A map of Mpumalanga Province in relation to Mozambique and Swaziland [source: Mpumalanga Malaria Elimination Programme (unpublished)].

Figure 2

Hybrid Metapopulation DE-IBM Model flow. a Compartment transmission model for each patch i (1–6) with sub-patch j (1–3) at time step t with compartments S susceptible, I infectious and treated (tr), C infectious, symptomatic and untreated (u), A infectious, asymptomatic and untreated, M Infectious, sub-patent and untreated and P susceptible with prior asymptomatic infection. b) Metapopulation structure highlighting human movement between each local patch $i\mathit{ϵ}\{1,2,3,4,5\}$ and foreign patch 6. Other parameters are described in Table 1 and Additional file 1.

Figure 3

FSAT IBM algorithm.

Figure 4

Predicted weekly treated cases (blue 2002–2008; red 2009–2012) fitted to and validated with data (black). The 95% uncertainty range for weekly case predictions is shown.

Figure 5

Predicted impact due to FSAT between 2014 and 2018 using the following diagnostic tools: microscopy (red), qPCR (orange), RDT (green), LAMP (blue) and a hypothetical RDT (purple). a Shows the percentage decrease in local infections due to the FSAT and b shows the impact of FSAT on local infections in Ehlanzeni district through time compared to the base case of no interventions (black).

Figure 6

Predicted impact due to FSAT between 2014 and 2018. a Shows the percentage decrease in local infections due to the FSAT and b shows the impact of FSAT on local infections in Ehlanzeni district through time compared to the base case of no interventions (black). The impact of FSAT is predicted for different (1) coverage proportions, (2) thresholds of detections for the diagnostic tool used (parasites/µL), (3) take-up proportions, (4) coverage proportions for mass drug administration and (5) weekly targets (capacity) keeping all other variables constant. 95% confidence intervals are depicted for the average percentage decrease. The colours of the bars in (a) correspond to the level of local infections depicted in (b).