Measuring changes in Plasmodium falciparum census population size in response to sequential malaria control interventions
- Department of Microbiology and Immunology, Bio21 Institute and Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Australia
- School of BioSciences, Bio21 Institute, The University of Melbourne, Australia
- Committee on Genetics, Genomics and Systems Biology, The University of Chicago, United States
- Department of Ecology and Evolution, The University of Chicago, United States
- Bioinformatics Division, Walter and Eliza Hall Institute, Australia
- Department of Parasitology, Noguchi Memorial Institute for Medical Research, University of Ghana, Ghana
- Navrongo Health Research Centre, Ghana Health Service, Ghana
- Epidemiology Department, Noguchi Memorial Institute for Medical Research, University of Ghana, Ghana
- Department of Biology and Department of Environmental Sciences, New York University, United States
- Santa Fe Institute, United States
Figures
Figure 1
Study design and changes in the prevalence of microscopic P. falciparum infection following the indoor residual spraying (IRS) and seasonal malaria chemoprevention (SMC) interventions in Bongo, Ghana.
(A) Four age-stratified cross-sectional surveys of ~2000 participants per survey were conducted in Bongo, Ghana, at the end of the wet seasons in October 2012 (Survey 1, baseline pre-IRS, red), October 2014 (Survey 2, during IRS, orange), October 2015 (Survey 3, post-IRS, green), and October 2017 (Survey 4, SMC, purple) (see Materials and methods, Appendix 1—table 1). The three rounds of IRS (grey areas) were implemented between 2013 and 2015 (Tiedje et al., 2022). SMC was distributed to all children <5 years of age during the wet seasons in 2016 (two rounds between August and September 2016) and 2017 (four rounds between September and December 2017) (Gogue et al., 2020). Both IRS and SMC were implemented against a background of widespread long-lasting insecticidal net (LLIN) usage (Tiedje et al., 2022). This figure was adapted from Tiedje et al., 2022, Figure 1 (CC BY 4.0 licence). The copyright holder has granted permission to publish under a CC BY 4.0 licence. Prevalence of microscopic P. falciparum infections (%) in the (B) study population and (C) for all age groups (years) in each survey (Appendix 1—table 1). Error bars represent the upper and lower limits of the 95% confidence interval (CI) calculated using the Wald interval.
Figure 2 with 1 supplement
Sharing of upsA and non-upsA DBLα types among the DBLα isolate repertoires in 2012 (pre-indoor residual spraying [IRS], red), 2014 (during IRS, orange), 2015 (post-IRS, green), and 2017 (seasonal malaria chemoprevention [SMC], purple).
The overlapping density and violin plots (upper right-hand corners) show the distribution of pairwise type sharing (PTS) scores (i.e. DBLα isolate repertoire similarity) between the (A) upsA and (B) non-upsA DBLα isolate repertoires for those isolates with DBLα sequencing data (Appendix 1—tables 2 and 3) in each survey. The PTS scales in the density plots have been zoomed in to provide better visualisation of the upsA and non-upsA DBLα type PTS distributions. The coloured dashed lines in the density plots indicate the median PTS scores in each survey for the upsA (2012 [red]=0.078, 2014 [orange]=0.063, 2015 [green]=0.054, and 2017 [purple]=0.064) and non-upsA (2012 [red]=0.020, 2014 [orange]=0.013, 2015 [green]=0.013, and 2017 [purple]=0.016) DBLα types. Note: The non-upsA median PTS values in 2014 (orange) and 2015 (green) were both 0.013 and overlap in the figure. In the PTS violin plots, the central box plots indicate the medians (centre line), interquartile range (IQR, upper and lower quartiles), whiskers (1.5x IQR), and outliers (points).
Figure 2—figure supplement 1
Sharing of upsA and non-upsA DBLα types among the DBLα isolate repertoires for those isolates with an estimated MOIvar equal to one (MOIvar=1) in 2012 (pre-indoor residual spraying [IRS], red), 2014 (during IRS, orange), 2015 (post-IRS, green), and 2017 (seasonal malaria chemoprevention [SMC], purple).
The overlapping density and violin plots (upper right-hand corners) show the distribution of pairwise type sharing (PTS) scores (i.e. DBLα isolate repertoire similarity) between the (A) upsA and (B) non-upsA DBLα isolate repertoires with an MOIvar=1 in each survey. The PTS scales in the density plots have been zoomed in to provide better visualisation of the PTS distributions for the upsA and non-upsA DBLα types. The black dashed lines in the density plots indicate the median PTS scores for the upsA and non-upsA DBLα types in each survey, which were all equal to zero. Note: In the PTS violin plots, the central box plots indicate the medians (centre line), interquartile ranges (IQR, upper and lower quartiles), whiskers (1.5x IQR), and outliers (points).
Figure 3 with 2 supplements
MOIvar distributions in 2012 (pre-indoor residual spraying [IRS], red), 2014 (during IRS, orange), 2015 (post-IRS, green), and 2017 (seasonal malaria chemoprevention [SMC], purple) based on pooling the maximum a posteriori multiplicity of infection (MOI) estimates.
Estimated MOIvar distributions for the (A) study population and (B) for all age groups (years) in each survey for those isolates with DBLα sequencing data (Appendix 1—tables 2 and 3). The median MOIvar values are indicated with the black dashed lines and have been provided in the top right corner (median MOIvar value [interquartile range (IQR), upper and lower quartiles]) along with the percentage of P. falciparum infections that were multiclonal (MOIvar>1) in each survey and age group (years).
Figure 3—figure supplement 1
UpsA and non-upsA DBLα isolate repertoire sizes in 2012 (pre-indoor residual spraying [IRS], red), 2014 (during IRS, orange), 2015 (post-IRS, green), and 2017 (seasonal malaria chemoprevention [SMC], purple).
Violin plots showing the frequency distributions of the upsA and non-upsA DBLα isolate repertoire sizes for the (A, C) study population and (B, D) for all age groups (years) in each survey. The width of each violin plot illustrates the relative frequency of the upsA and non-upsA DBLα repertoire sizes in each survey. Note: The y-axis range for the upsA and non-upsA DBLα isolate repertoire sizes is different. The central box plots indicate the median upsA and non-upsA DBLα repertoire sizes (centre line), interquartile range (IQR, upper and lower quartiles), whiskers (1.5x IQR), and outliers (points) for each violin plot.
Figure 3—figure supplement 2
MOIvar distributions in 2012 (pre-indoor residual spraying [IRS], red), 2014 (during IRS, orange), 2015 (post-IRS, green), and 2017 (seasonal malaria chemoprevention [SMC], purple) based on the mixture distribution approach.
Estimated MOIvar distributions for the (A) study population and (B) for all age groups (years) in each survey for those isolates with DBLα sequencing data (Appendix 1—tables 2 and 3). The median MOIvar values are indicated with the black dashed lines and have been provided in the top right corner (median MOIvar value [interquartile range (IQR), upper and lower quartiles]) along with the percentage of P. falciparum infections that were multiclonal (MOIvar>1) in each survey and age group (years).
Figure 4
Estimated number and relative change in the number of P. falciparum var repertoires in 2012 (pre-indoor residual spraying [IRS], red), 2014 (during IRS, orange), 2015 (post-IRS, green), and 2017 (seasonal malaria chemoprevention [SMC], purple).
The estimated number of var repertoires (i.e. census population size) for those isolates with DBLα sequencing data (Appendix 1—tables 2 and 3) in the (A) study population and (B) for all age groups (years). The estimated number of var repertoires vs. P. falciparum prevalence for (C) study population and (D) for all age groups (years) (Appendix 1—table 2). The percentage change in P. falciparum prevalence (black dotted line) and the estimated number of var repertoires (black solid line) in 2014, 2015, and 2017 compared to the 2012 baseline survey (red dashed horizontal line at 0% change) for the (E) study population and (F) for all age groups (years). Error bars in (A–D) represent the upper and lower limits of the 95% confidence intervals (95% CIs). To account for differences in sampling depth across age groups and surveys, we performed subsampling with replacement by selecting the minimum number of individuals in each age group across all surveys. We then calculated the total number of var repertoires from these subsampled individuals within each age group in each survey. This approach ensures consistent sample sizes within each age group across all surveys. Finally, we summed the var repertoires across age groups to obtain the total var repertoire count for each survey. The mean (coloured solid points) and 95% CIs for the number of var repertoires were estimated by repeating the subsampling procedure 10,000 times. The CIs were then derived from the distribution of these repeated subsampling replicates. The 95% CIs for P. falciparum prevalence (%) were calculated using the Wald interval.
Figure 5
UpsA and non-upsA DBLα type richness in 2012 (pre-indoor residual spraying [IRS], red), 2014 (during IRS, orange), 2015 (post-IRS, green), and 2017 (seasonal malaria chemoprevention [SMC], purple).
Number of unique (A) upsA and (B) non-upsA DBLα types (i.e. richness) observed in each survey vs. P. falciparum prevalence based on those isolates with DBLα sequencing data (Appendix 1—tables 2 and 3). Error bars represent the upper and lower limits of the 95% confidence intervals (95% CIs) for the P. falciparum prevalence (%; x-axis) and ±2 standard deviations (±2 SD) for the number of unique upsA and non-upsA DBLα types (y-axis). The 95% CIs for P. falciparum prevalence (%) were calculated using the Wald interval. The ±2 SD for the number of unique upsA and non-upsA DBLα types was calculated based on a bootstrap approach. We resampled 10,000 replicates from the original population-level distribution with replacement. Each resampled replicate has the same size as the original sample. We then derive the standard deviation (SD) based on the distribution of the resampled replicates.
Figure 6 with 1 supplement
UpsA and non-upsA DBLα type frequencies and survival in 2012 (pre-indoor residual spraying [IRS], red), 2014 (during IRS, orange), 2015 (post-IRS, green), and 2017 (seasonal malaria chemoprevention [SMC], purple).
Heatmaps showing the patterns of diversity for the (A) upsA and (B) non-upsA DBLα types. The columns represent all the upsA and non-upsA DBLα types observed in the four surveys, and the rows represent each of the 2802 upsA DBLα types and the 50,436 non-upsA DBLα types (Appendix 1—table 3). White rows are used to denote the absence of a specific DBLα type, while the presence of a DBLα type is indicated in colour and further categorised (colour gradations) based on the frequency or the number of times (i.e. number of isolates) a DBLα type was observed in each survey (frequency categories: 1, 2–10, 11–20, >20 isolates). Note the frequency category cut-offs were chosen based on the frequency distributions in Figure 6—figure supplement 1. The proportions of (C) upsA and (D) non-upsA DBLα types in each survey based on the number of times (i.e. number of isolates) they were observed in each survey. Kaplan-Meier survival curves for the (E) upsA and (F) non-upsA DBLα types across time (2012–2017) categorised based on their frequency at baseline in 2012 (pre-IRS, red). The coloured shaded areas represent the upper and lower limits of the 95% confidence intervals (95% CIs), with the number (N) of upsA and non-upsA DBLα types in each frequency category provided in parenthesis. These survival curves include only those upsA (N=2218) and non-upsA (N=33,159) DBLα types that were seen at baseline in 2012 (pre-IRS) as indicated in red (Appendix 1—table 3). The x-axis indicates time where time ‘0’ denotes 2012 (pre-IRS), ‘1’ denotes 2014 (during IRS), ‘2’ denotes 2015 (post-IRS), and finally ‘3’ denotes 2017 (SMC). Note: In the survival curves, the 11–20 and >20 frequency categories for both the (E) upsA and (F) non-upsA DBLα types overlap in the figure.
Figure 6—figure supplement 1
Frequency distributions for the (A) upsA and (B) non-upsA DBLα types in 2012 (pre-indoor residual spraying [IRS], red), 2014 (during IRS, orange), 2015 (post-IRS, green), and 2017 (seasonal malaria chemoprevention [SMC], purple).
Appendix 1—figure 1
Mean multiplicity of infection (MOI) averaged over all sampled individuals for the subsampling replicates in 2012 (pre-indoor residual spraying [IRS]), 2014 (during IRS), 2015 (post-IRS), and 2017 (seasonal malaria chemoprevention [SMC]).
The mean MOI (pink dots) for the (A) study population and (B) for all age groups (years) in each survey is stable relative to the sampling size or sampling depth, which allows for the extrapolation from the census population size of our population sample to that of the whole population of local hosts. For each sampling depth, we generate 1000 subsampling replicates with replacement. Minimum, 5% quantile, median, 95% quantile, and maximum values are shown in the boxplot.
Appendix 1—figure 2
Schematic diagram of the var genotyping (i.e. varcoding) approach.
For additional details about each step, see Materials and methods and Appendix 1. A schematic insert of the var gene locus and PfEMP1 has been included as defined in Rask et al., 2010, with its N-terminal segment (NTS), Duffy binding-like (DBL) domains, cysteine-rich interdomain regions (CIDR), one transmembrane region (TM), and the acidic terminal segment (ATS). The Illumina MiSeq sequencer stock image was created using BioRender.com.
Appendix 1—figure 3
Bioinformatic sequence data processing flowchart.
The flowchart shows the bioinformatic process to clean the raw de-multiplexed paired reads, with details on the filtering parameters utilised at each step. This customised bioinformatic pipeline is described in detail in He et al., 2018.
Appendix 1—figure 4
Histogram of the number of non-upsA DBLα var gene types sequenced per repertoire for those isolates with monoclonal infections (multiplicity of infection [MOI] =1) (Labbé et al., 2023).
The molecular sequences used to derive this repertoire size distribution were previously sequenced from isolates sampled during six cross-sectional surveys made from 2012 to 2016 in Bongo District, Ghana (He et al., 2018; Pilosof et al., 2019; Ruybal-Pesántez et al., 2022; Tiedje et al., 2022). These isolates were estimated to be monoclonal infections (i.e. human hosts estimated to be infected by a single P. falciparum clone, MOI=1), based on a cut-off value of 45 non-upsA DBLα types. This cut-off was selected based on the median number of non-upsA DBLα types identified for the 3D7 laboratory isolate included as a control during varcoding (Ghansah et al., 2023). A version of this figure was previously published (Labbé et al., 2023, Figure 2; CC BY 4.0 licence). The copyright holder has granted permission to publish under a CC BY 4.0 licence.
Tables
Key resources table
| Reagent type (species) or resource | Designation | Source or reference | Identifiers | Additional information |
|---|---|---|---|---|
| Commercial assay or kit | QIAamp DNA mini kit | QIAGEN | Cat #: 51306 | With modifications as described in Tiedje et al., 2017 |
| Sequence-based reagent | dNTP mix | Promega | Cat #: U1511, U1515 | See Appendix 1 |
| Sequence-based reagent | GoTaq G2 Flexi DNA polymerase | Promega | Cat #: M7805 | See Appendix 1 |
| Sequence-based reagent | DBLaAF-MID | Rask et al., 2016 | Forward PCR primers | See Appendix 1 |
| Sequence-based reagent | DBLaBR-MID | Rask et al., 2016 | Reverse PCR primers | See Appendix 1 |
| Other | AMPure XP Beads for DNA Cleanup | Beckman Coulter | Cat #: A63880, A63881 | See Appendix 1 |
| Commercial assay or kit | Quant-iT PicoGreen dsDNA Assay Kit | Invitrogen | P11496 | See Appendix 1 |
| Commercial assay or kit | KAPA HiFi Taq HotStart Ready Mix | Roche | Cat #: KK2601 | See Appendix 1 |
| Software, algorithm | R 4.3.1 | R Development Core Team, 2018 |
Appendix 1—table 1
Age group (years) breakdown and parasitological characteristics of the participants surveyed in Bongo, Ghana, in each survey (i.e. 2012, 2014, 2015, 2017).
| October 2012(pre-IRS) | October 2014(IRS) | October 2015(post-IRS) | October 2017(SMC) | |
|---|---|---|---|---|
| Number of participants surveyed* | 1923 | 1866 | 2022 | 1915 |
| Age groups (years)† | ||||
| Children: 1–5 years | 356 (18.5) | 216 (11.6) | 405 (20.0) | 354 (18.5) |
| Children: 6–10 years | 395 (20.5) | 421 (22.5) | 409 (20.2) | 358 (18.7) |
| Adolescents: 11–20 years | 413 (21.5) | 468 (25.1) | 467 (23.1) | 489 (25.5) |
| Adults: >20 years | 759 (39.5) | 761 (40.8) | 741 (36.7) | 714 (37.3) |
| Microscopic P. falciparum prevalence‡ | 808 (42.0) | 430 (23.0) | 545 (27.0) | 789 (41.2) |
| Children: 1–5 years | 173 (48.6) | 37 (17.1) | 63 (15.6) | 49 (13.8) |
| Children: 6–10 years | 243 (61.5) | 142 (33.7) | 167 (40.8) | 184 (51.4) |
| Adolescents: 11–20 years | 202 (48.9) | 162 (34.6) | 169 (36.2) | 304 (62.2) |
| Adults: >20 years | 190 (25.0) | 89 (11.7) | 146 (19.7) | 295 (35.3) |
| Microscopic P. falciparum density§ | 520 [160–1640] (40–126,040) | 200 [80–600] (40–73,360) | 320 [120-1800] (40–113,520) | 440 [160-1720] (40–97,520) |
| Children: 1–5 years | 1640 [400–9840] (40–126,040) | 440 [120–1080] (40–15,880) | 1840 [240–19,940] (40–113,520) | 4120 [760–20,800] (40–80,360) |
| Children: 6–10 years | 760 [240–1840] (40–61,560) | 320 [120–1280] (40–73,360) | 520 [200–2720] (40–48,600) | 1020 [320–5100] (40–97,520) |
| Adolescents: 11–20 years | 320 [160–760] (40–27,440) | 180 [80–240] (40–42,120) | 280 [120–1000] (40–42,880) | 440 [160–1210] (40–66,760) |
| Adults: >20 years | 200 [120–680] (40–31,040) | 120 [80–240] (40–41,320) | 120 [40–630] (40–40,280) | 220 [80–730] (40–19,040) |
-
Indoor residual spraying (IRS), seasonal malaria chemoprevention (SMC).
-
*
Number of participants surveyed that were analysed by microscopy.
-
†
Data reflect the number (% (n/N)) of participants surveyed in each age group.
-
‡
Data reflect the number (% (n/N)) of participants surveyed that were microscopically positive for an asymptomatic P. falciparum infection (including mixed P. falciparum infections) relative to the number of participants surveyed in the total population and by the age groups presented.
-
§
Median parasite density (i.e. parasites/µL of blood) (interquartile range [IQR]) (min-max) for the microscopically positive asymptomatic P. falciparum infections (including mixed P. falciparum infections).
Appendix 1—table 2
Microscopic P. falciparum DBLα type sequencing results, number of P. falciparum var repertoires (i.e. census population size), and mean MOIvar by age group (years) in each survey (i.e. 2012, 2014, 2015, 2017).
| October 2012(pre-IRS) | October 2014(IRS) | October 2015(post-IRS) | October 2017(SMC) | |
|---|---|---|---|---|
| Number of microscopic P. falciparum isolates | 808 | 430 | 545 | 789 |
| P. falciparum isolates with DBLα sequencing data (≥20 DBLα types)* § | 685 (84.8) | 301 (70.0) | 413 (75.8) | 700 (88.7) |
| Children: 1–5 years | 158 (91.3) | 28 (75.7) | 51 (81.0) | 44 (89.8) |
| Children: 6–10 years | 217 (89.3) | 116 (81.7) | 146 (87.4) | 170 (92.4) |
| Adolescents: 11–20 years | 167 (82.7) | 112 (69.1) | 129 (76.3) | 284 (93.4) |
| Adults: >20 years | 143 (75.3) | 45 (50.6) | 87 (59.6) | 202 (68.5) |
| Number of P. falciparum var repertoires (i.e. census population size)† § | 2552 (2354–2756) | 731 (628 - 836) | 909 (804–1019) | 2087 (1926–2254) |
| Children: 1–5 years | 495 (402–595) | 80 (43-125) | 62 (39–88) | 90 (54–132) |
| Children: 6–10 years | 1035 (908–1166) | 318 (247–397) | 378 (311–451) | 750 (642–861) |
| Adolescents: 11–20 years | 683 (579–792) | 256 (200–315) | 313 (250–382) | 827 (734–921) |
| Adults: >20 years | 338 (278–401) | 76 (48–111) | 155 (119–195) | 420 (365–478) |
| P. falciparum mean MOIvar ‡ § | 4.38 (4.16–4.61) | 2.74 (2.49–3.02) | 2.57 (2.40–2.77) | 3.28 (3.12–4.45) |
| Children: 1–5 years | 5.16 (4.68–5.67) | 2.86 (1.93–4.04) | 2.27 (1.98–2.59) | 3.34 (2.73–4.05) |
| Children: 6–10 years | 5.26 (4.87–5.67) | 3.22 (2.76–3.72) | 2.96 (2.64–3.32) | 4.41 (4.00–4.84) |
| Adolescents: 11–20 years | 4.09 (3.68–4.52) | 2.59 (2.25–2.96) | 2.74 (2.40–3.12) | 3.45 (3.21–3.70) |
| Adults: >20 years | 2.52 (2.27–2.78) | 1.80 (1.38–2.38) | 1.85 (1.62–2.11) | 2.08 (1.93–2.24) |
-
*
Data reflect the number (% (n/N)) of P. falciparum isolates that had DBLα sequencing data relative to the number of participants sampled that were positive for P. falciparum by microscopy (including mixed P. falciparum infections) in the total population and by the age groups (see Appendix 1—table 1 for total number of participants sampled that were microscopy positive).
-
†
Number of var repertoires (i.e. census population size) (95% confidence interval). To account for differences in sampling depth across age groups and surveys, we performed subsampling with replacement by selecting the minimum number of individuals in each age group across all surveys. We then calculated the total number of var repertoires from these subsampled individuals within each age group in each survey. This approach ensures consistent sample sizes within each age group across all surveys. Finally, we summed the var repertoires across age groups to obtain the total var repertoire count for each survey. The mean and 95% CIs for the number of var repertoires (i.e. census population size) were estimated by repeating the subsampling procedure 10,000 times. The CIs were then derived from the distribution of these repeated subsampling replicates.
-
‡
Mean MOIvar (95% confidence interval) based on pooling the maximum a posteriori MOI estimates. The 95% confidence intervals (95% CIs) were calculated based on a bootstrap approach. We resampled 10,000 replicates from the original population-level MOI distribution with replacement. Each resampled replicate has the same size as the original sample. We then derive the 95% CI based on the distribution of the resampled replicates.
-
§
Note: There is a simple map between census population size and mean MOI, as one can simply divide or multiply by the sample size (i.e. the number of P. falciparum isolates with DBLα sequencing data), respectively, to convert between the two quantities.
Appendix 1—table 3
The DBLα sequence pool sizes (upsA and non-upsA) and the number of unique DBLα types (upsA and non-upsA) observed in each survey (i.e. 2012, 2014, 2015, 2017) for those isolates with DBLα sequencing data.
| Survey | P. falciparum isolates sequenced | P. falciparum isolates withDBLα sequence data(≥1 DBLα type)* | P. falciparum isolates withDBLα sequence data(≥20 DBLα types)* | DBLα sequence pool size | Number of unique DBLα types | ||||
|---|---|---|---|---|---|---|---|---|---|
| DBLα sequences | UpsA DBLα sequences† | Non-upsA DBLα sequences ‡ | DBLα types | UpsADBLα types § | Non-upsA DBLα types ¶ | ||||
| October 2012 (pre-IRS) | 808 | 742 (91.8) | 685 (84.8) | 120,029 | 22,881 (19.1) | 97,148 (80.9) | 35,377 | 2218 (6.3) | 33,159 (93.7) |
| October 2014 (IRS) | 430 | 386 (89.8) | 301 (70.0) | 33,489 | 7,048 (21.0) | 26,441 (79.0) | 16,334 | 1503 (9.2) | 14,831 (90.8) |
| October 2015 (post-IRS) | 545 | 510 (93.6) | 413 (75.8) | 42,774 | 8,942 (20.9) | 33,832 (79.1) | 19,584 | 1673 (8.5) | 17,911 (91.5) |
| October 2017 (SMC) | 789 | 759 (96.2) | 700 (88.7) | 92,757 | 18,625 (20.1) | 74,132 (79.9) | 29,423 | 2074 (7.0) | 27,349 (93.0) |
| TOTAL | 2,572 | 2,397 (93.2) | 2,099 (81.6) | 289,049 | 57,496 (19.9) | 231,553 (80.1) | 53,238 | 2802 (5.3) | 50,436 (94.7) |
-
Indoor residual spraying (IRS), seasonal malaria chemoprevention (SMC), interquartile range (IQR).
-
*
Data reflect the number (% (n/N)) of P. falciparum isolates that had DBLα sequencing data relative to the number of participants sampled that were positive for P. falciparum by microscopy (including mixed P. falciparum infections). For those, a breakdown of those isolates by age group (years) with ≥20 DBLα types included in the analyses, see Appendix 1—table 2.
-
†
Data reflect the upsA DBLα sequence pool size (% (n/N)) relative to the DBLα sequence pool size.
-
‡
Data reflect the non-upsA DBLα sequence pool size (% (n/N)) relative to the DBLα sequence pool size.
-
§
Data reflect the number (% (n/N)) of upsA DBLα types identified relative to the number of DBLα types identified.
-
¶
Data reflect the number (% (n/N)) of non-upsA DBLα types identified relative to the number of DBLα types identified.
Appendix 1—table 4
The Kolmogorov-Smirnov test (KS-test) is applied to compare the estimated multiplicity of infection (MOI) distributions for the population in each survey (i.e. 2012, 2014, 2015, 2017) for the two approaches, namely by pooling the maximum a posteriori MOI estimates or by using the mixture distributions (see Materials and methods) for the uniform prior.
| Survey | Prior | KS-test statistics | p-value | Mean MOI difference* |
|---|---|---|---|---|
| October 2012 (pre-IRS) | Uniform | 0.019592 | 0.955175 | –0.13235 |
| October 2014 (IRS) | Uniform | 0.030432 | 0.943197 | –0.13112 |
| October 2015 (post-IRS) | Uniform | 0.038248 | 0.581476 | –0.1149 |
| October 2017 (SMC) | Uniform | 0.023823 | 0.821812 | –0.1298 |
-
Indoor residual spraying (IRS), seasonal malaria chemoprevention (SMC).
-
*
The mean MOI difference is the difference in the mean value of the population-level MOI estimates from either pooling the maximum a posteriori estimates or the mixture distribution.
Appendix 1—table 5
The Kolmogorov-Smirnov test (KS-test) is applied to compare the estimated multiplicity of infection (MOI) distributions for the population in each survey (i.e. 2012, 2014, 2015, 2017) for the two approaches, namely by pooling the maximum a posteriori MOI estimates or by using the mixture distributions (see Materials and methods) using the negative binomial distribution with parameters in the range typical for low- (corresponding to a mean MOI~1.5), medium- (corresponding to a mean MOI~4.3), and high- (corresponding to a mean MOI~6.7) transmission endemic areas.
| Survey | Negative binomial | KS-test statistics | p-value | Mean MOI difference* |
|---|---|---|---|---|
| October 2012 (pre-IRS) | Low | 0.024189 | 0.817681 | –0.14031 |
| Medium | 0.021339 | 0.914045 | –0.11099 | |
| High | 0.019512 | 0.956709 | –0.11381 | |
| October 2014 (IRS) | Low | 0.027862 | 0.973588 | –0.08387 |
| Medium | 0.032505 | 0.908128 | –0.09739 | |
| High | 0.036612 | 0.814540 | –0.12282 | |
| October 2015 (post-IRS) | Low | 0.037872 | 0.594217 | –0.09361 |
| Medium | 0.032974 | 0.760228 | –0.11319 | |
| High | 0.037138 | 0.619209 | –0.11284 | |
| October 2017 (SMC) | Low | 0.025665 | 0.745796 | –0.10952 |
| Medium | 0.026964 | 0.68884 | –0.12406 | |
| High | 0.030275 | 0.542523 | –0.13645 |
-
Indoor residual spraying (IRS), seasonal malaria chemoprevention (SMC).
-
*
The mean MOI difference is the difference in the mean value of the population-level MOI estimates from either pooling the maximum a posteriori estimates or the mixture distribution for different parameter choices of a zero-truncated negative binomial prior.
Appendix 1—table 6
The Kolmogorov-Smirnov test (KS-test) and the Pearson correlation tests (PC-test) are applied to compare the estimated MOI distributions for the population in each survey (i.e. 2012, 2014, 2015, 2017) based on either a uniform prior or a zero-truncated negative binomial prior with parameters in the ranges typical for low- (corresponding to a mean MOI~1.5), medium- (corresponding to a mean MOI~4.3), and high- (corresponding to a mean MOI~6.7) transmission endemic areas (see Materials and methods).
We present the results of comparison for both approaches which obtain the population-level MOI distribution from individual posterior MOI distributions, namely by pooling the maximum a posteriori MOI estimates (i.e. MAP pool) or by using mixture distribution (i.e. Mixture Dist.).
| Survey | Approach | Comparison(Negative binomial vs. Uniform) | KS-test statistics | KS-test p-value | PC-test statistics | PC-test p-value | Mean MOI difference* |
|---|---|---|---|---|---|---|---|
| October 2012 (pre-IRS) | MAP pool | Low vs. Uniform | 0.054015 | 0.036738 | 0.988473 | 0 | –0.32117 |
| MAP pool | Medium vs. Uniform | 0.018978 | 0.966014 | 0.994902 | 0 | –0.08029 | |
| MAP pool | High vs. Uniform | 0.018978 | 0.966014 | 0.997470 | 0 | –0.00438 | |
| October 2014 (IRS) | MAP pool | Low vs. Uniform | 0.036545 | 0.816283 | 0.990630 | 1.4E-260 | –0.13289 |
| MAP pool | Medium vs. Uniform | 0.016611 | 0.999997 | 0.996893 | 0 | –0.00332 | |
| MAP pool | High vs. Uniform | 0.019934 | 0.999760 | 0.997409 | 0 | 0.01661 | |
| October 2015 (post-IRS) | MAP pool | Low vs. Uniform | 0.046005 | 0.346351 | 0.984101 | 0 | –0.15496 |
| MAP pool | Medium vs. Uniform | 0.009685 | 1 | 0.99500 | 0 | –0.02179 | |
| MAP pool | High vs. Uniform | 0.024213 | 0.968793 | 0.996081 | 0 | 0.01937 | |
| October 2017 (SMC) | MAP pool | Low vs. Uniform | 0.055714 | 0.025924 | 0.985225 | 0 | –0.22714 |
| MAP pool | Medium vs. Uniform | 0.011429 | 0.999989 | 0.994265 | 0 | –0.04714 | |
| MAP pool | High vs. Uniform | 0.011429 | 0.999989 | 0.996798 | 0 | –0.00286 | |
| October 2012 (pre-IRS) | Mixture Dist. | Low vs. Uniform | 0.05387 | 0.037968 | – | – | –0.31321 |
| Mixture Dist. | Medium vs. Uniform | 0.015199 | 0.997437 | – | – | –0.10165 | |
| Mixture Dist. | High vs. Uniform | 0.007393 | 1 | – | – | –0.02292 | |
| October 2014 (IRS) | Mixture Dist. | Low vs. Uniform | 0.040398 | 0.709761 | – | – | –0.18014 |
| Mixture Dist. | Medium vs. Uniform | 0.011440 | 1 | – | – | –0.03705 | |
| Mixture Dist. | High vs. Uniform | 0.010149 | 1 | – | – | 0.00832 | |
| October 2015 (post-IRS) | Mixture Dist. | Low vs. Uniform | 0.049573 | 0.263382 | – | – | –0.17625 |
| Mixture Dist. | Medium vs. Uniform | 0.008205 | 1 | – | – | –0.02350 | |
| Mixture Dist. | High vs. Uniform | 0.010618 | 1 | – | – | 0.01732 | |
| October 2017 (SMC) | Mixture Dist. | Low vs. Uniform | 0.048020 | 0.07961 | – | – | –0.24742 |
| Mixture Dist. | Medium vs. Uniform | 0.009626 | 1 | – | – | –0.05289 | |
| Mixture Dist. | High vs. Uniform | 0.009381 | 1 | – | – | 0.00379 |
-
Indoor residual spraying (IRS), seasonal malaria chemoprevention (SMC), NA (–).
-
*
The mean MOI difference is the difference in the mean value of the population-level MOI estimates from assuming either a zero-truncated negative binomial prior with different parameter choices or a uniform prior. Take the first row of the table as an example, the mean MOI difference value (–0.32117) is equal to the mean MOI for the population based on a zero-truncated negative binomial prior with a low-transmission parameter choice minus that based on a uniform prior. The order of comparison is consistent with the one listed in the Comparison column (Low vs. Uniform).
Appendix 1—table 7
The Kolmogorov-Smirnov test (KS-test) and the Pearson correlation tests (PC-test) are applied to compare the estimated MOI distributions for the population in each survey (i.e. 2012, 2014, 2015, 2017) between the zero-truncated negative binomial priors with parameters in the ranges typical for low- (corresponding to a mean MOI~1.5), medium- (corresponding to a mean MOI~4.3), and high- (corresponding to a mean MOI~6.7) transmission endemic areas (see Materials and methods).
We present the results of comparison for both approaches which obtain the population-level MOI distribution from individual posterior MOI distributions, namely by pooling the maximum a posteriori MOI estimates (i.e. MAP pool) or by using mixture distribution (i.e. Mixture Dist.).
| Survey | Approach | Comparison(Negative binomial) | KS-test statistics | KS-test p-value | PC-test statistics | PC-test p-value | Mean MOI difference* |
|---|---|---|---|---|---|---|---|
| October 2012 (pre-IRS) | MAP pool | Medium vs. Low | 0.051095 | 0.055938 | 0.986680 | 0 | 0.240876 |
| MAP pool | Medium vs. High | 0.014599 | 0.998598 | 0.996125 | 0 | –0.075912 | |
| MAP pool | Low vs. High | 0.055474 | 0.029513 | 0.986516 | 0 | –0.316788 | |
| October 2014 (IRS) | MAP pool | Medium vs. Low | 0.053156 | 0.362791 | 0.989269 | 8.011E-252 | 0.129568 |
| MAP pool | Medium vs. High | 0.003322 | 1 | 0.998276 | 0 | –0.019934 | |
| MAP pool | Low vs. High | 0.056478 | 0.292237 | 0.988379 | 1.112E-246 | –0.149502 | |
| October 2015 (post-IRS) | MAP pool | Medium vs. Low | 0.055690 | 0.154269 | 0.981405 | 9.764E-297 | 0.133172 |
| MAP pool | Medium vs. High | 0.014528 | 0.999994 | 0.994613 | 0 | –0.041162 | |
| MAP pool | Low vs. High | 0.070218 | 0.034065 | 0.979963 | 3.920E-290 | –0.174334 | |
| October 2017 (SMC) | MAP pool | Medium vs. Low | 0.044286 | 0.128371 | 0.983760 | 0 | 0.18 |
| MAP pool | Medium vs. High | 0.011429 | 0.999989 | 0.995738 | 0 | –0.044286 | |
| MAP pool | Low vs. High | 0.055714 | 0.025924 | 0.982745 | 0 | –0.224286 | |
| October 2012 (pre-IRS) | Mixture Dist. | Medium vs. Low | 0.040399 | 0.214223 | – | – | 0.211562 |
| Mixture Dist. | Medium vs. High | 0.011531 | 0.999989 | – | – | –0.078734 | |
| Mixture Dist. | Low vs. High | 0.052049 | 0.049412 | – | – | –0.290295 | |
| October 2014 (IRS) | Mixture Dist. | Medium vs. Low | 0.037816 | 0.785935 | – | – | 0.143090 |
| Mixture Dist. | Medium vs. High | 0.011447 | 1 | – | – | –0.045364 | |
| Mixture Dist. | Low vs. High | 0.050535 | 0.425625 | – | – | –0.188454 | |
| October 2015 (post-IRS) | Mixture Dist. | Medium vs. Low | 0.047779 | 0.303800 | – | – | 0.152752 |
| Mixture Dist. | Medium vs. High | 0.010639 | 1 | – | – | –0.040816 | |
| Mixture Dist. | Low vs. High | 0.061610 | 0.087626 | – | – | –0.193568 | |
| October 2017 (SMC) | Mixture Dist. | Medium vs. Low | 0.042425 | 0.161444 | – | – | 0.194537 |
| Mixture Dist. | Medium vs. High | 0.0096285 | 0.999999 | – | – | –0.056674 | |
| Mixture Dist. | Low vs. High | 0.052140 | 0.044719 | – | – | –0.251211 |
-
Indoor residual spraying (IRS), seasonal malaria chemoprevention (SMC), NA (–).
-
*
The mean MOI difference is the difference in the mean value of the population-level MOI estimates from assuming zero-truncated negative binomial priors with different parameter choices. Take the first row of the table as an example, the mean MOI difference value (0.240876) is equal to the mean MOI for the population based on a zero-truncated negative binomial prior with a medium-transmission parameter choice minus that based on a zero-truncated negative binomial prior with a low-transmission parameter choice. The order of comparison is consistent with the one listed in the Comparison column (Medium vs. Low).
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Measuring changes in Plasmodium falciparum census population size in response to sequential malaria control interventions
eLife 12:RP91411.
https://doi.org/10.7554/eLife.91411.4
