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Towards malaria elimination in Mpumalanga, South Africa: a population-level mathematical modelling approach.

Silal SP, Little F, Barnes KI, White LJ - Malar. J. (2014)

Bottom Line: Focused mass screen and treat campaigns at border-entry points are predicted to result in a knock-on decrease in local infections through a reduction in the infectious reservoir.This knock-on decrease in local infections was also predicted to be achieved through foreign source reduction.While all strategies (in isolation or combined) contributed to decreasing local infections, none was predicted to decrease local infections to zero.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Statistical Sciences, University of Cape Town, Cape Town, South Africa. sheetal.silal@uct.ac.za.

ABSTRACT

Background: Mpumalanga in South Africa is committed to eliminating malaria by 2018 and efforts are increasing beyond that necessary for malaria control. Differential Equation models may be used to study the incidence and spread of disease with an important benefit being the ability to enact exogenous change on the system to predict impact without committing any real resources. The model is a deterministic non-linear ordinary differential equation representation of the dynamics of the human population. The model is fitted to weekly data of treated cases from 2002 to 2008, and then validated with data from 2009 to 2012. Elimination-focused interventions such as the scale-up of vector control, mass drug administration, a focused mass screen and treat campaign and foreign source reduction are applied to the model to assess their potential impact on transmission.

Results: Scaling up vector control by 10% and 20% resulted in substantial predicted decreases in local infections with little impact on imported infections. Mass drug administration is a high impact but short-lived intervention with predicted decreases in local infections of less that one infection per year. However, transmission reverted to pre-intervention levels within three years. Focused mass screen and treat campaigns at border-entry points are predicted to result in a knock-on decrease in local infections through a reduction in the infectious reservoir. This knock-on decrease in local infections was also predicted to be achieved through foreign source reduction. Elimination was only predicted to be possible under the scenario of zero imported infections in Mpumalanga.

Conclusions: A constant influx of imported infections show that vector control alone will not be able to eliminate local malaria as it is insufficient to interrupt transmission. Both mass interventions have a large and immediate impact. Yet in countries with a large migrant population, these interventions may fail due to the reintroduction of parasites and their impact may be short-lived. While all strategies (in isolation or combined) contributed to decreasing local infections, none was predicted to decrease local infections to zero. The number of imported infections highlights the importance of reducing imported infections at source, and a regional approach to malaria elimination.

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Model fitting and validation to treated case data. (a) Model fitting results to locally sourced (blue) and imported (green) reported treated malaria cases and (b) Model validation results to locally sourced (blue) and imported (green) reported treated malaria cases from 2009 to 2012.
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Figure 4: Model fitting and validation to treated case data. (a) Model fitting results to locally sourced (blue) and imported (green) reported treated malaria cases and (b) Model validation results to locally sourced (blue) and imported (green) reported treated malaria cases from 2009 to 2012.

Mentions: The parameters driving the model and their 95% confidence intervals estimated through data-fitting procedures are presented in Table 1. A parameter that is usually unknown and estimated from the data is the proportion of cases treated (p). Case data usually includes cases that have been treated, comprising patients presenting themselves at a health facility (passive case detection), or those cases that have been detected actively. There is often no indication of the number of infections that have remained untreated, and hence there is no/little data from which to derive p. Castillo-Riquelme et al. conducted household surveys in Mozambique and South Africa between 2001 and 2002 to evaluate treatment-seeking behaviour for malaria-related events [30]. It was found that in the Tonga sub-district of Mpumalanga, all of the 457 people with recent cases of malaria (previous month) sought treatment, with 98.5% of cases being treated at a public health facility. More recently, Hlongwana et al. conducted a study on the knowledge and practices towards malaria in Bushbuckridge Municipality in Mpumalanga in 2008 after South Africa was declared ready for malaria elimination [31]. The study revealed that 99% of respondents would seek malaria treatment (95% Confidence interval: (97.5, 99.5)%) with 82% doing so within 24 hours of the onset of symptoms. Based on these two studies, a probability of treatment of 95% is assumed in the model for the entire modelling period as it is informed by studies conducted in 2002 and 2008.Model fitting was performed using different starting values of the parameters with the optimization routine reaching the global minimum in almost all fits. The narrow confidence intervals of the parameter estimates are indicative of the stability of the estimates. Figure 4(a) shows the fit of the model to the data for the treated cases that were locally and imported while Figure 4(b) shows the application of the model to treated case data from 2009 to 2012. The model captures the timing of the season well. As seen in Figure 4(b), there is a sudden unanticipated rise in the number of imported cases in 2011 and 2012 coinciding with the end of the Lubombo Spatial Development Initiative and the model does not capture this.


Towards malaria elimination in Mpumalanga, South Africa: a population-level mathematical modelling approach.

Silal SP, Little F, Barnes KI, White LJ - Malar. J. (2014)

Model fitting and validation to treated case data. (a) Model fitting results to locally sourced (blue) and imported (green) reported treated malaria cases and (b) Model validation results to locally sourced (blue) and imported (green) reported treated malaria cases from 2009 to 2012.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4127654&req=5

Figure 4: Model fitting and validation to treated case data. (a) Model fitting results to locally sourced (blue) and imported (green) reported treated malaria cases and (b) Model validation results to locally sourced (blue) and imported (green) reported treated malaria cases from 2009 to 2012.
Mentions: The parameters driving the model and their 95% confidence intervals estimated through data-fitting procedures are presented in Table 1. A parameter that is usually unknown and estimated from the data is the proportion of cases treated (p). Case data usually includes cases that have been treated, comprising patients presenting themselves at a health facility (passive case detection), or those cases that have been detected actively. There is often no indication of the number of infections that have remained untreated, and hence there is no/little data from which to derive p. Castillo-Riquelme et al. conducted household surveys in Mozambique and South Africa between 2001 and 2002 to evaluate treatment-seeking behaviour for malaria-related events [30]. It was found that in the Tonga sub-district of Mpumalanga, all of the 457 people with recent cases of malaria (previous month) sought treatment, with 98.5% of cases being treated at a public health facility. More recently, Hlongwana et al. conducted a study on the knowledge and practices towards malaria in Bushbuckridge Municipality in Mpumalanga in 2008 after South Africa was declared ready for malaria elimination [31]. The study revealed that 99% of respondents would seek malaria treatment (95% Confidence interval: (97.5, 99.5)%) with 82% doing so within 24 hours of the onset of symptoms. Based on these two studies, a probability of treatment of 95% is assumed in the model for the entire modelling period as it is informed by studies conducted in 2002 and 2008.Model fitting was performed using different starting values of the parameters with the optimization routine reaching the global minimum in almost all fits. The narrow confidence intervals of the parameter estimates are indicative of the stability of the estimates. Figure 4(a) shows the fit of the model to the data for the treated cases that were locally and imported while Figure 4(b) shows the application of the model to treated case data from 2009 to 2012. The model captures the timing of the season well. As seen in Figure 4(b), there is a sudden unanticipated rise in the number of imported cases in 2011 and 2012 coinciding with the end of the Lubombo Spatial Development Initiative and the model does not capture this.

Bottom Line: Focused mass screen and treat campaigns at border-entry points are predicted to result in a knock-on decrease in local infections through a reduction in the infectious reservoir.This knock-on decrease in local infections was also predicted to be achieved through foreign source reduction.While all strategies (in isolation or combined) contributed to decreasing local infections, none was predicted to decrease local infections to zero.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Statistical Sciences, University of Cape Town, Cape Town, South Africa. sheetal.silal@uct.ac.za.

ABSTRACT

Background: Mpumalanga in South Africa is committed to eliminating malaria by 2018 and efforts are increasing beyond that necessary for malaria control. Differential Equation models may be used to study the incidence and spread of disease with an important benefit being the ability to enact exogenous change on the system to predict impact without committing any real resources. The model is a deterministic non-linear ordinary differential equation representation of the dynamics of the human population. The model is fitted to weekly data of treated cases from 2002 to 2008, and then validated with data from 2009 to 2012. Elimination-focused interventions such as the scale-up of vector control, mass drug administration, a focused mass screen and treat campaign and foreign source reduction are applied to the model to assess their potential impact on transmission.

Results: Scaling up vector control by 10% and 20% resulted in substantial predicted decreases in local infections with little impact on imported infections. Mass drug administration is a high impact but short-lived intervention with predicted decreases in local infections of less that one infection per year. However, transmission reverted to pre-intervention levels within three years. Focused mass screen and treat campaigns at border-entry points are predicted to result in a knock-on decrease in local infections through a reduction in the infectious reservoir. This knock-on decrease in local infections was also predicted to be achieved through foreign source reduction. Elimination was only predicted to be possible under the scenario of zero imported infections in Mpumalanga.

Conclusions: A constant influx of imported infections show that vector control alone will not be able to eliminate local malaria as it is insufficient to interrupt transmission. Both mass interventions have a large and immediate impact. Yet in countries with a large migrant population, these interventions may fail due to the reintroduction of parasites and their impact may be short-lived. While all strategies (in isolation or combined) contributed to decreasing local infections, none was predicted to decrease local infections to zero. The number of imported infections highlights the importance of reducing imported infections at source, and a regional approach to malaria elimination.

Show MeSH
Related in: MedlinePlus