Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Mukhtar AYA

PROVIDER: S-EPMC5991726 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Mukhtar Abdulaziz Y A AYA Munyakazi Justin B JB Ouifki Rachid R Clark Allan E AE

PloS one 20180607 6

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 cap ...[more]

PMID: 29879166

Similar Datasets

Project description:BackgroundReports of declining incidence of malaria disease burden across several countries in Africa suggest that the epidemiology of malaria across the continent is in transition. Whether this transition is directly related to the scaling of intervention coverage remains a moot point.MethodsPaediatric admission data from eight Kenyan hospitals and their catchments have been assembled across two three-year time periods: September 2003 to August 2006 (pre-scaled intervention) and September 2006 to August 2009 (post-scaled intervention). Interrupted time series (ITS) models were developed adjusting for variations in rainfall and hospital use by surrounding communities to show changes in malaria hospitalization over the two periods. The temporal changes in factors that might explain changes in disease incidence were examined sequentially for each hospital setting, compared between hospital settings and ranked according to plausible explanatory factors.ResultsIn six out of eight sites there was a decline in Malaria admission rates with declines between 18% and 69%. At two sites malaria admissions rates increased by 55% and 35%. Results from the ITS models indicate that before scaled intervention in September 2006, there was a significant month-to-month decline in the mean malaria admission rates at four hospitals (trend P < 0.05). At the point of scaled intervention, the estimated mean admission rates for malaria was significantly less at four sites compared to the pre-scaled period baseline. Following scaled intervention there was a significant change in the month-to-month trend in the mean malaria admission rates in some but not all of the sites. Plausibility assessment of possible drivers of change pre- versus post-scaled intervention showed inconsistent patterns however, allowing for the increase in rainfall in the second period, there is a suggestion that starting transmission intensity and the scale of change in ITN coverage might explain some but not all of the variation in effect size. At most sites where declines between observation periods were documented admission rates were changing before free mass ITN distribution and prior to the implementation of ACT across Kenya.ConclusionThis study provides evidence of significant within and between location heterogeneity in temporal trends of malaria disease burden. Plausible drivers for changing disease incidence suggest a complex combination of mechanisms, not easily measured retrospectively.

Project description:The past two decades were associated with innovation and strengthening of malaria control interventions, which have been increasingly adopted at large scale. Impact evaluations of these interventions were mostly performed in moderate or high malaria transmission areas. This study aimed to evaluate the use and performance of malaria interventions in low transmission areas on malaria infections and anaemia. Data from the 2016 Sudan malaria indicator survey was used. Multi-level logistic regression analysis was used to assess the strength of association between real-life community-level utilization of malaria interventions [diagnosis, artemisinin-based combination therapies (ACTs) and long-lasting insecticidal nets (LLINs)] and the study outcomes: malaria infections and anaemia (both overall and moderate-to-severe anaemia). The study analysis involved 26,469 individuals over 242 clusters. Malaria infection rate was 7.6%, overall anaemia prevalence was 47.5% and moderate-to-severe anaemia prevalence was 4.5%. The average community-level utilization was 31.5% for malaria diagnosis, 29.9% for ACTs and 35.7% for LLINs. The odds of malaria infection was significantly reduced by 14% for each 10% increase in the utilization of malaria diagnosis (adjusted odds ratio (aOR) per 10% utilization 0.86, 95% CI 0.78-0.95, p = 0.004). However, the odds of infection was positively associated with the utilization of LLINs at community-level (aOR per 10% utilization 1.20, 95% CI 1.11-1.29, p < 0.001). No association between malaria infection and utilization of ACTs was identified (aOR per 10% utilization 0.97, 95% CI 0.91-1.04, p = 0.413). None of the interventions was associated with overall anaemia nor moderate-to-severe anaemia. There was strong evidence that utilization of malaria diagnosis at the community level was highly protective against malaria infection. No protective effect was seen for community utilization of ACTs or LLINs. No association was established between any of the interventions and overall anaemia or moderate-to-severe anaemia. This lack of effectiveness could be due to the low utilization of interventions or the low level of malaria transmission in the study area. Identification and response to barriers of access and low utilization of malaria interventions are crucial. It is crucial to ensure that every suspected malaria case is tested in a timely way, notably in low transmission settings.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets