Modelling the effect of bednet coverage on malaria transmission in South Sudan

PLoS One. 2018 Jun 7;13(6):e0198280. doi: 10.1371/journal.pone.0198280. eCollection 2018.


A campaign for malaria control, using Long Lasting Insecticide Nets (LLINs) was launched in South Sudan in 2009. The success of such a campaign often depends upon adequate available resources and reliable surveillance data which help officials understand existing infections. An optimal allocation of resources for malaria control at a sub-national scale is therefore paramount to the success of efforts to reduce malaria prevalence. In this paper, we extend an existing SIR mathematical model to capture the effect of LLINs on malaria transmission. Available data on malaria is utilized to determine realistic parameter values of this model using a Bayesian approach via Markov Chain Monte Carlo (MCMC) methods. Then, we explore the parasite prevalence on a continued rollout of LLINs in three different settings in order to create a sub-national projection of malaria. Further, we calculate the model's basic reproductive number and study its sensitivity to LLINs' coverage and its efficacy. From the numerical simulation results, we notice a basic reproduction number, [Formula: see text], confirming a substantial increase of incidence cases if no form of intervention takes place in the community. This work indicates that an effective use of LLINs may reduce [Formula: see text] and hence malaria transmission. We hope that this study will provide a basis for recommending a scaling-up of the entry point of LLINs' distribution that targets households in areas at risk of malaria.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Humans
  • Insecticide-Treated Bednets
  • Malaria / epidemiology*
  • Malaria / prevention & control
  • Malaria / transmission*
  • Markov Chains
  • Models, Theoretical
  • Monte Carlo Method
  • Mosquito Control / instrumentation*
  • Population Surveillance
  • Prevalence
  • South Sudan / epidemiology

Grants and funding

Abdulaziz Y.A. Mukhtar acknowledges the support of the DST-NRF Centre of Excellence in Mathematical and Statistical Sciences (CoE-MaSS) and DST-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA) towards this research. Rachid Ouifki acknowledges the support of the DST/NRF SARChI Chair M3B2 grant 82770. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.