Figures and data in Modelling the drivers of the spread of Plasmodium falciparum hrp2 gene deletions in sub-Saharan Africa

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Version of Record updated: September 29, 2017 (This version)
Version of Record published: September 18, 2017 (Go to version)
Accepted Manuscript updated: August 29, 2017 (Go to version)
Accepted Manuscript published: August 24, 2017 (Go to version)
Accepted: August 21, 2017
Received: January 9, 2017

Figures
Videos
Tables
Additional files

5 figures, 1 video, 3 tables and 2 additional files

Figures

Figure 1 with 5 supplements

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Predicted increase in *pfhrp2-*deletion upon RDT introduction after 10 years.

Graphs show the time course of *pfhrp2*-deletion emergence under (a) different transmission intensities (10%, 25% and 60% PfPR) and 8% starting frequency of *pfhrp2*-deletion prior to RDT introduction and under (b) different assumed starting frequencies of *pfhrp2*-deletion prior to RDT introduction (2%, 8% and 12% starting frequency) and 25% PfPR. Five years after RDT introduction, the proportion of strains that are *pfhrp2*-deleted (c), and the proportion of the population that are infected with only *pfhrp2*-deleted mutants (d) is recorded. The dark grey dots denote individual simulation runs with a LOESS regression fit shown in blue. Source data for Figure 1 is provided within Figure 1—source data 1.

https://doi.org/10.7554/eLife.25008.004

Figure 1—source data 1 Effect of transmission intensity and pfhrp2-deletion starting upon pfhrp2-deletion emergence.: https://doi.org/10.7554/eLife.25008.010
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Figure 1—figure supplement 1

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Impact of increase *pfhrp2*-deletion upon malaria prevalence.

Graphs show the increase in malaria prevalence over time as a result of increasing *pfhrp2-*deletion upon using only HRP2-based RDTs to guide treatment decisions, with the greatest increase in prevalence observed at the lowest starting prevalence.

https://doi.org/10.7554/eLife.25008.005

Figure 1—figure supplement 2

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Impact of *pfhrp2*-deletion fitness cost.

Graphs show the mean time course of *pfhrp2*-deletion emergence under different assumptions concerning the negative impact of *pfhrp2*-deletion. The fitness cost is incorporated by comparatively reducing the contribution to the human infectious reservoir made by the deletion strains in order to represent an assumed decrease in parasitaemia. The fitness cost is only implemented at the time of RDT introduction to illustrate the sum effect of the opposing selection pressures.

https://doi.org/10.7554/eLife.25008.006

Figure 1—figure supplement 3

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Impact of microscopy use and non-adherence to RDT results.

Graphs show the mean time course of *pfhrp2*-deletion emergence under different assumptions concerning the use of microscopy as an additional diagnostic and the impact of non-adherence to RDT test results, that is an individual receiving treatment despite yielding a negative RDT result. Microscopy use and nonadherence to RDT results are only implemented at the time of RDT introduction to illustrate the sum effect of the opposing selection pressures.

https://doi.org/10.7554/eLife.25008.007

Figure 1—figure supplement 4

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Impact of non-malarial fever.

Graphs show the mean time course of *pfhrp2*-deletion emergence under different assumptions concerning the rate of non-malarial fever (NMF). The introduction of non-malarial fevers increases the selection pressure in favour of *pfhrp2*-deletion, with 125% the observed rate of non-malarial fever yielding the quickest emergence of *pfhrp2-*deletion. Non-malarial fever is only implemented at the time of RDT introduction to illustrate the sum effect of the opposing selection pressures.

https://doi.org/10.7554/eLife.25008.008

Figure 1—figure supplement 5

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Combined impact of model assumptions.

Graphs show the mean time course of *pfhrp2*-deletion emergence under different assumptions concerning any negative fitness cost associated with *pfhrp2*-deletion, use of microscopy-based diagnosis, non-adherence to RDT results and non-malarial fever (NMF). These factors were explored at three different relative rates and compared to the method used within the main investigation (red line). An increase in *pfhrp2*-deletion is observed in all cases, with the method used within the main investigation exhibiting an increase in *pfhrp2*-deletion slightly slower than exhibited by the intermediate level of model assumptions (blue line).

https://doi.org/10.7554/eLife.25008.009

Figure 2 with 1 supplement

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The predicted rate at which the population is only infected with *pfhrp2*-deleted mutants.

The graphs show the time in years after RDT introduction at which 20% of the population are only infected with *pfhrp2*-deleted mutants up to a maximum follow-up time of 20 years post RDT introduction. PfHRP3 epitopes were assumed to cause a positive RDT result in (a) 0% or (b) 25% of individuals only infected with *pfhrp2*-deleted mutants. The plotted years represent the mean time grouped in each prevalence and treatment setting, with black dots representing where 20% was reached in less than five years. Each simulation had a starting *pfhrp2-*deletion frequency of 8% before RDT introduction. Source data for Figure 2 is provided within Figure 2—source data 1.

https://doi.org/10.7554/eLife.25008.011

Figure 2—source data 1 Years after RDT introduction at which 20% of the population are only infected with pfhrp2-deleted parasites, with an assumed PfHRP3 epitope effect equal to 0% and 0.25%.: https://doi.org/10.7554/eLife.25008.013
Download elife-25008-fig2-data1-v4.csv

Figure 2—figure supplement 1

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Frequency of *pfhrp2*-deletion after 20 years.

The graphs show the frequency of *pfhrp2-*deletion within the population 20 years after RDT introduction. PfHRP3 epitopes were assumed to cause a positive RDT result in (a) 0% or (b) 25% of individuals only infected with *pfhrp2*-deleted mutants. Each simulation had a starting *pfhrp2* deletion frequency of 8% before RDT introduction.

https://doi.org/10.7554/eLife.25008.012

Figure 3 with 1 supplement

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Simulated province level burden of *pfhrp2*-deleted mutants within the DRC, with an assumed probability of a clinical case seeking treatment, who is only infected with *pfhrp2*-deleted mutants, producing a positive RDT result (ε) equal to 0.25.

In (a) the mean simulated proportion of children aged 6–59 months who are infected with only *pfhrp2*-deleted mutants is shown in red. Each region had an assumed starting frequency of 6% *pfhrp2*-deletion prior to RDT introduction in 2010 (2007 in North- and South-Kivu). The results in grey represent the recorded burden from the DHS survey (Figure 3—source data 1), with both datasets fitted with a LOESS regression. Error bars show the 95% confidence interval. In (b) the same simulation conditions were used as in (a) however it is assumed that no selection pressure is exerted by the introduction RDTs, i.e. ε = 1. Source data for Figure 3 is provided within Figure 3—source data 1.

https://doi.org/10.7554/eLife.25008.014

Figure 3—source data 1 Estimates of the proportion of pfhrp2-deleted mutants from a national study in DRC. Sourced from Parr JB, Verity R, Doctor SM, Janko M, Carey-Ewend K, Turman BJ, Keeler C, Slater HC, Whitesell AN, Mwandagalirwa K, Ghani AC, Likwela JL, Tshefu AK, Emch M, Juliano JJ, Meshnick SR. 2016. Pfhrp2-deleted Plasmodium falciparum parasites in the Democratic Republic of Congo: A national cross-sectional survey. J Infect Dis: 1–34. doi: 10.1093/infdis/jiw538. Data is provided additionally in an importable format for plotting (Figure 3.csv).: https://doi.org/10.7554/eLife.25008.016
Download elife-25008-fig3-data1-v4.csv
Figure 3—source data 2 Simulated proportion of children aged 6–59 months who are only infected with pfhrp2-deleted parasites within the Democratic Republic of Congo, with an assumed PfHRP3 epitope effect equal to 0.25% and 1%, that is under no selection pressure.: https://doi.org/10.7554/eLife.25008.017
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Figure 3—figure supplement 1

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Simulated province level burden of *pfhrp2*-deleted mutants within DRC, with an assumed probability of a clinical case seeking treatment, who is only infected with *pfhrp2*-deleted mutants, producing a positive RDT result (ε) equal to 0.

In (a) the mean simulated proportion of children aged 6–59 months who are infected with only *pfhrp2*-deleted mutants is shown in red. Each region had an assumed starting frequency of 4.5% *pfhrp2*-deletion prior to RDT introduction in 2010 (2007 in North- and South-Kivu). The results in grey represent the recorded burden from the DHS survey, with both datasets fitted with a LOESS regression. Error bars show the 95% confidence interval. In (b) the graph shows the plot of the residuals when comparing the simulation predicted proportion of children aged 6–59 months only infected with *pfhrp2-*deleted mutants to the recorded DHS data.

https://doi.org/10.7554/eLife.25008.015

Figure 4 with 5 supplements

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Predicted areas of HRP2 concern in comparison to recorded prevalence and treatment seeking rate, with an assumed probability of a clinical case seeking treatment, who is only infected with *pfhrp2*-deleted mutants, producing a positive RDT result (ε) equal to 0.25.

The graphs show (a) the recorded malaria prevalence in children aged 2–10 by microscopy in 2010, (b) the frequency of people seeking treatment in 2010 and (c) the predicted concern for the impact of *pfhrp2*-deleted mutants. In (c), high, moderate and slight risk represent >20% infection due to only *pfhrp2*-deleted mutants by 2016, 2022 and 2030 respectively, and marginal risk represents <20% by 2030. In 2010, each region was assumed to have a starting frequency of 6% *pfhrp2-*deletion. Source data for Figure 4 is provided within Figure 4—source data 1.

https://doi.org/10.7554/eLife.25008.018

Figure 4—source data 1 Recorded malaria prevalence in children aged 2–10 by microscopy in 2010 (sourced from the Malaria Atlas mapping project [see Metadata - Datasets]), the frequency of people seeking treatment in 2010 (sourced from Cohen et al., 2012 [see Metadata – Datasets]) and the simulated predicted concern for the impact of pfhrp2-deleted mutants, with an assumed PfHRP3 epitope effect equal to 0.25%. High, moderate and slight risk represent >20% infection due to only pfhrp2-deleted mutants by 2016, 2022 and 2030 respectively, and marginal risk represents <20% by 2030.: https://doi.org/10.7554/eLife.25008.024
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Figure 4—figure supplement 1

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Model malaria prevalence output against Malaria Atlas Project prevalence 2010 (Bhatt et al., 2015).

The maps show the reported microscopy prevalence in children aged 2–10 from (a) the Malaria Atlas Project and (b) the presented model outputs at the first-administrative unit.

https://doi.org/10.7554/eLife.25008.019

Figure 4—figure supplement 2

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HRP2 Concern heat maps.

The graphs show the time after RDT introduction at which 20% of the population are only infected with *pfhrp2*-deleted mutants with an assumed probability of a clinical case seeking treatment, who is only infected with pfhrp2-deleted mutants, producing a positive RDT result (ε) equal to (a) 0.25 and (b) 0. Areas in grey represent simulation space in which, after 20 years, the proportion of 2–10 year olds only infected with *pfhrp2*-deleted mutants was less than 20%.

https://doi.org/10.7554/eLife.25008.020

Figure 4—figure supplement 3

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Predicted areas of HRP2 concern in comparison to recorded prevalence and treatment coverage with an assumed probability of a clinical case seeking treatment, who is only infected with pfhrp2-deleted mutants, producing a positive RDT result (ε) equal to 0.

The graphs show (a) the recorded malaria prevalence in children aged 2–10 in 2010, (b) the frequency of people seeking treatment in 2010 and (c) the predicted concern for the impact of *pfhrp2*-deleted mutants. In (c), high, moderate and slight risk represent >20% infection due to only *pfhrp2*-deleted mutants by 2016, 2022 and 2030 respectively, and marginal risk represents <20% by 2030. In 2010 each region was assumed to have a starting frequency of 4.5% *pfhrp2-*deletion.

https://doi.org/10.7554/eLife.25008.021

Figure 4—figure supplement 4

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Impact of different assumptions about starting frequency of *pfhrp2-*deletion upon the geographical pattern of selection-driven increase in *pfhp2-*deletion.

Three different starting frequencies of *pfhrp2-*deletion were explored, with an assumed probability of a clinical case seeking treatment, who is only infected with *pfhrp2*-deleted mutants, producing a positive RDT result (ε) equal to 0.25. The frequency of *pfhrp2-*deletion after 20 years was recorded and admin regions ranked accordingly, with the first rank representing the highest frequency of *pfhrp2-*deletion, and tied ranks being represented with the same colour.

https://doi.org/10.7554/eLife.25008.022

Figure 4—figure supplement 5

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Years in which RDTs were used at community level in Sub-Saharan Africa.

The map shows the year that RDTs were reported to be available at the community level within WHO malaria country profiles in 2012 (World Health Organization, 2012b). Countries in grey were not reported to use RDTs at the community level, or there was insufficient data.

https://doi.org/10.7554/eLife.25008.023

Figure 5

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Transmission Model.

Flow diagram for the human component of the transmission model, with dashed arrows indicating superinfection. S, susceptible; T, treated clinical disease; D, untreated clinical disease; P, prophylaxis; A, asymptomatic patent infection; U, asymptomatic sub-patent infection. All parameters are described within Table 2.

https://doi.org/10.7554/eLife.25008.026

Videos

Video 1

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posterframe for video — The projected increase in individuals who are only infected with *pfhrp2*-deleted parasites, from 2010 to 2030, with an assumed starting frequency of 6% *pfhrp2-*deletion, and an assumed PfHRP3 epitope effect equal to 0.25%.

The video relates directly to Figure 4.

https://doi.org/10.7554/eLife.25008.025

Tables

Table 1

Published studies reporting P. falciparum in Africa with deletions or no deletions of the pfhrp2 gene (Cheng et al., 2014).

https://doi.org/10.7554/eLife.25008.003

Origin		Source of samples*	Initial evidence			Gene deletion analysis by PCR			Antigen analysis		Ref	Prevalence (no. of samples, year of collection)
Country	Area		Microscopy	Quality RDT	Species PCR	pfhrp2 (exon 1 and 2)	No. single copy genes	Flanking genes	HRP ELISA	2nd quality RDT
Mali	Bamako	A/S	D	ND	D	D	1	ND	ND	ND	(Koita et al., 2012)	2% (480, 1996)
DRC,Gambia, Kenya, Mozambique, Rwanda, Tanzania, Uganda		S	D	ND	D	Exon 2 only	ND	ND	D	ND	(Ramutton et al., 2012)	0% (77, 2–19 per country, 2005–2010)
Senegal	Dakar	S	D	ND	D	D	1	ND	ND	ND	(Wurtz et al., 2013)	2.4% (136, 2009–2012)
Ghana	Accra and Cape Coast	A	D	D	D	Exon 2 only	2	ND	ND	ND	(Amoah et al., 2016)	29.5% (315, 2015)
Zambia	Choma, South Zambia	A/S	D	D	D	D	1	ND	ND	ND	(Laban et al., 2015)	20% (61, 2009–2012^)†
DRC	Country-wide	A	D	D	D	D	3	D	ND	ND	(Parr et al., 2016)	6.4% (783, 2013–2014)
Rwanda	Busogo, Musanze, Kayonza	S	D	D	D	Exon 2 only	1	ND	ND	D	(Kozycki et al., 2017)	23% (140, 2014–2015)
Eritrea	Anseba, Debub, Gash-Barka, Northern Red-Sea	S	D	D	D	ND	1	ND	ND	D	(Berhane et al., 2017)	80% (51, 2015)

*Source of samples: S = Symptomatic case, A = Asymptomatic case, U = not specified, D = done; ND = not done.

^† Authors suggested that failure to detect pfhrp gene in some samples was more likely to be the result of low parasite density rather than deletion
Note: Quality RDT indicates RDTs that meet the WHO RDT recommended procurement criteria based on WHO Malaria RDT Product Testing.

Table 2

Parameters used within the human transmission and mosquito population models.

https://doi.org/10.7554/eLife.25008.028

Parameter	Symbol	Estimate
Human infection duration (days)
Latent period	$d_{E}$	12
Patent infection	$d_{A}$	200
Clinical disease (treated)	$d_{T}$	5
Clinical disease (untreated)	$d_{D}$	5
Sub-patent infection	$d_{U}$	110
Prophylaxis following treatment	$d_{P}$	25
Treatment Parameters
Probability of seeking treatment if clinically diseased	$f_{T}$	Variable
Probability of a clinical case seeking treatment, who is only infected with pfhrp2-deleted mutants, producing a positive RDT result.	ε	0 or 0.25
Infectiousness to mosquitoes
Lag from parasites to infectious gametocytes	$d_{g}$	12 days
Untreated disease	$c_{D}$	0.0680 day⁻¹
Treated disease	$c_{T}$	0.0219 day⁻¹
Sub-patent infection	$c_{U}$	0.000620 day⁻¹
Parameter for infectiousness of state A	$γ_{1}$	1.824
Age and heterogeneity
Age-dependent biting parameter	$ρ$	0.85
Age-dependent biting parameter	$a_{0}$	8 years
Daily mortality rate of humans	$μ$	0.000130
Variance of the log heterogeneity in biting rates	$σ^{2}$	1.67
Immunity reducing probability of infection
Maximum probability due to no immunity	$b_{0}$	0.590
Maximum relative reduction due to immunity	$b_{1}$	0.5
Inverse of decay rate	$d_{B}$	10 years
Scale parameter	$I_{B 0}$	43.879
Shape parameter	$κ_{B}$	2.155
Duration in which immunity is not boosted	$u_{B}$	7.199
Immunity reducing probability of clinical disease
Maximum probability due to no immunity	ϕ₀	0.791
Maximum relative reduction due to immunity	ϕ₁	0.000737
Inverse of decay rate	$d_{C A}$	30 years
Scale parameter	$I_{C 0}$	18.0237
Shape parameter	$κ_{C}$	2.370
Duration in which immunity is not boosted	$u_{C}$	6.0635
New-born immunity relative to mother’s	$P_{M}$	0.774
Inverse of decay rate of maternal immunity	$d_{M}$	67.695
Immunity reducing probability of detection
Minimum probability due to maximum immunity	$d_{1}$	0.161
Inverse of decay rate	$d_{I D}$	10 years
Scale parameter	$I_{D 0}$	1.578
Shape parameter	$κ_{D}$	0.477
Duration in which immunity is not boosted	$u_{D}$	9.445
Scale parameter relating age to immunity	$a_{D}$	21.9 years
Time-scale at which immunity changes with age	$f_{D 0}$	0.00706
Shape parameter relating age to immunity	$γ_{D}$	4.818
PCR detection probability parameters state A	$α_{A}$	0.757
PCR detection probability parameters state U	$α_{U}$	0.186
Mosquito Population Model
Daily mortality of adults	$μ_{M}$	0.132
Daily biting rate	$α_{k}$	0.307
Extrinsic incubation period	$d_{E M}$	10 days

Table 3

HRP2 classifiers used in sub-Saharan Africa mapping assuming RDT introduction in 2010.

https://doi.org/10.7554/eLife.25008.030

Proportion of population only infected with pfhrp2-deleted mutants	Concern classifier
>20% by 2016	High
>20% by 2022	Moderate
>20% by 2030	Slight
<20% by 2030	Marginal

Additional files

Supplementary file 1 Simulation model pseudocode. Mathematical style pseudocode description of the simulation model.: https://doi.org/10.7554/eLife.25008.029
Download elife-25008-supp1-v4.docx
Transparent reporting form: https://doi.org/10.7554/eLife.25008.031
Download elife-25008-transrepform-v4.pdf

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Oliver J Watson
Hannah C Slater
Robert Verity
Jonathan B Parr
Melchior K Mwandagalirwa
Antoinette Tshefu
Steven R Meshnick
Azra C Ghani

(2017)

Modelling the drivers of the spread of Plasmodium falciparum hrp2 gene deletions in sub-Saharan Africa

eLife 6:e25008.

https://doi.org/10.7554/eLife.25008

Figures

Predicted increase in pfhrp2-deletion upon RDT introduction after 10 years.

Figure 1—source data 1

Impact of increase pfhrp2-deletion upon malaria prevalence.

Impact of pfhrp2-deletion fitness cost.

Impact of microscopy use and non-adherence to RDT results.

Impact of non-malarial fever.

Combined impact of model assumptions.

The predicted rate at which the population is only infected with pfhrp2-deleted mutants.

Figure 2—source data 1

Frequency of pfhrp2-deletion after 20 years.

Simulated province level burden of pfhrp2-deleted mutants within the DRC, with an assumed probability of a clinical case seeking treatment, who is only infected with pfhrp2-deleted mutants, producing a positive RDT result (ε) equal to 0.25.

Figure 3—source data 1

Figure 3—source data 2

Simulated province level burden of pfhrp2-deleted mutants within DRC, with an assumed probability of a clinical case seeking treatment, who is only infected with pfhrp2-deleted mutants, producing a positive RDT result (ε) equal to 0.

Predicted areas of HRP2 concern in comparison to recorded prevalence and treatment seeking rate, with an assumed probability of a clinical case seeking treatment, who is only infected with pfhrp2-deleted mutants, producing a positive RDT result (ε) equal to 0.25.

Figure 4—source data 1

Model malaria prevalence output against Malaria Atlas Project prevalence 2010 (Bhatt et al., 2015).

HRP2 Concern heat maps.

Predicted areas of HRP2 concern in comparison to recorded prevalence and treatment coverage with an assumed probability of a clinical case seeking treatment, who is only infected with pfhrp2-deleted mutants, producing a positive RDT result (ε) equal to 0.

Impact of different assumptions about starting frequency of pfhrp2-deletion upon the geographical pattern of selection-driven increase in pfhp2-deletion.

Years in which RDTs were used at community level in Sub-Saharan Africa.

Transmission Model.

Videos

The projected increase in individuals who are only infected with pfhrp2-deleted parasites, from 2010 to 2030, with an assumed starting frequency of 6% pfhrp2-deletion, and an assumed PfHRP3 epitope effect equal to 0.25%.

Tables

Parameters used within the human transmission and mosquito population models.

HRP2 classifiers used in sub-Saharan Africa mapping assuming RDT introduction in 2010.

Additional files

Supplementary file 1

Transparent reporting form

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Predicted increase in pfhrp2-deletion upon RDT introduction after 10 years.

Figure 1—source data 1

Impact of increase pfhrp2-deletion upon malaria prevalence.

Impact of pfhrp2-deletion fitness cost.

Impact of microscopy use and non-adherence to RDT results.

Impact of non-malarial fever.

Combined impact of model assumptions.

The predicted rate at which the population is only infected with pfhrp2-deleted mutants.

Figure 2—source data 1

Frequency of pfhrp2-deletion after 20 years.

Simulated province level burden of pfhrp2-deleted mutants within the DRC, with an assumed probability of a clinical case seeking treatment, who is only infected with pfhrp2-deleted mutants, producing a positive RDT result (ε) equal to 0.25.

Figure 3—source data 1

Figure 3—source data 2

Simulated province level burden of pfhrp2-deleted mutants within DRC, with an assumed probability of a clinical case seeking treatment, who is only infected with pfhrp2-deleted mutants, producing a positive RDT result (ε) equal to 0.

Predicted areas of HRP2 concern in comparison to recorded prevalence and treatment seeking rate, with an assumed probability of a clinical case seeking treatment, who is only infected with pfhrp2-deleted mutants, producing a positive RDT result (ε) equal to 0.25.

Figure 4—source data 1

Model malaria prevalence output against Malaria Atlas Project prevalence 2010 (Bhatt et al., 2015).

HRP2 Concern heat maps.

Predicted areas of HRP2 concern in comparison to recorded prevalence and treatment coverage with an assumed probability of a clinical case seeking treatment, who is only infected with pfhrp2-deleted mutants, producing a positive RDT result (ε) equal to 0.

Impact of different assumptions about starting frequency of pfhrp2-deletion upon the geographical pattern of selection-driven increase in pfhp2-deletion.

Years in which RDTs were used at community level in Sub-Saharan Africa.

Transmission Model.

The projected increase in individuals who are only infected with pfhrp2-deleted parasites, from 2010 to 2030, with an assumed starting frequency of 6% pfhrp2-deletion, and an assumed PfHRP3 epitope effect equal to 0.25%.

Parameters used within the human transmission and mosquito population models.

HRP2 classifiers used in sub-Saharan Africa mapping assuming RDT introduction in 2010.

Supplementary file 1

Transparent reporting form

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Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)