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. 2021 Dec 7;1(12):e0000014. doi: 10.1371/journal.pgph.0000014

Plasmodium falciparum parasite prevalence in East Africa: Updating data for malaria stratification

Victor A Alegana 1,2,*, Peter M Macharia 1,3, Samuel Muchiri 1, Eda Mumo 1, Elvis Oyugi 4, Alice Kamau 1, Frank Chacky 5, Sumaiyya Thawer 5,6,7, Fabrizio Molteni 5,6,7, Damian Rutazanna 8, Catherine Maiteki-Sebuguzi 8,9, Samuel Gonahasa 9, Abdisalan M Noor 10, Robert W Snow 1,10
Editor: Ruth Ashton11
PMCID: PMC7612417  EMSID: EMS143660  PMID: 35211700

Abstract

The High Burden High Impact (HBHI) strategy for malaria encourages countries to use multiple sources of available data to define the sub-national vulnerabilities to malaria risk, including parasite prevalence. Here, a modelled estimate of Plasmodium falciparum from an updated assembly of community parasite survey data in Kenya, mainland Tanzania, and Uganda is presented and used to provide a more contemporary understanding of the sub-national malaria prevalence stratification across the sub-region for 2019. Malaria prevalence data from surveys undertaken between January 2010 and June 2020 were assembled form each of the three countries. Bayesian spatiotemporal model-based approaches were used to interpolate space-time data at fine spatial resolution adjusting for population, environmental and ecological covariates across the three countries. A total of 18,940 time-space age-standardised and microscopy-converted surveys were assembled of which 14,170 (74.8%) were identified after 2017. The estimated national population-adjusted posterior mean parasite prevalence was 4.7% (95% Bayesian Credible Interval 2.6–36.9) in Kenya, 10.6% (3.4–39.2) in mainland Tanzania, and 9.5% (4.0–48.3) in Uganda. In 2019, more than 12.7 million people resided in communities where parasite prevalence was predicted ≥ 30%, including 6.4%, 12.1% and 6.3% of Kenya, mainland Tanzania and Uganda populations, respectively. Conversely, areas that supported very low parasite prevalence (<1%) were inhabited by approximately 46.2 million people across the sub-region, or 52.2%, 26.7% and 10.4% of Kenya, mainland Tanzania and Uganda populations, respectively. In conclusion, parasite prevalence represents one of several data metrics for disease stratification at national and sub-national levels. To increase the use of this metric for decision making, there is a need to integrate other data layers on mortality related to malaria, malaria vector composition, insecticide resistance and bionomic, malaria care-seeking behaviour and current levels of unmet need of malaria interventions.

Introduction

The High Burden High Impact (HBHI) strategy of the World Health Organisations (WHO) Global Malaria Programme (GMP) encourages countries to use multiple sources of available data to provide a platform to understand the sub-national malaria risk. These national epidemiological frameworks should be used to rationalise finite resources and maximise the impact of malaria control [1, 2]. Ideally, data should be layered to provide meaningful stratifications based on the epidemiology of malaria risk and burden, areas of poor intervention coverage, vulnerability, and marginalisation to ensure health equity. Importantly, information must be resolved to administrative areas used by National Malaria Control Programmes (NMCPs) to guide sub-national stratification, policies, and resource allocation [2–4].

Varied data sources are increasingly used by countries across sub-Saharan Africa to measure the quantity of malaria risk at fine spatial resolution and decentralised health units. These include routine health system data on malaria test-positivity, case incidence, and screening for malaria infection among women attending ante-natal clinics [5]. Less common has been the sub-national variability in the composition of dominant vectors, their sibling species and respective Entomological Inoculation Rates (EIR) [6]. Historically, for over 100 years community-based surveys of malaria parasite prevalence have formed an important measure of malaria risk [7, 8]. Recently, these data have been collected from national household surveys undertaken every 3–5 years such as the Demographic and Health Surveys (DHS) or Malaria Indicator Surveys (MIS). However, because they lack adequate power for effective sub-national stratification on their own [9], they have been used in combination with other more opportunistic, assembled research survey data, sub-national monitoring data and school surveys, increasing the power of national data to provide estimates of risk at fine spatial scale. These data are often analysed using model-based geostatistical (MBG) methods [10] to predict risk in areas without data augmented by environmental geospatial covariates of transmission [11]. MBG incorporates measures of uncertainty of disease predictions at the population level. Modelled national parasite prevalence predictions have been used to inform sub-national approaches to malaria control in Kenya [12, 13], Uganda [14], Somalia [15, 16], Namibia [17], Senegal [18], Cote D’Ivoire [19, 20], Malawi [21–23], Angola [24], Madagascar [25], Ghana [26], Rwanda [27, 28], Mozambique [29, 30], Burkina Faso [31], Sudan [32] and Tanzania [33–35]. The use of routine health facility data sources to define malaria incidence has increased in recent years, in combination with parasite prevalence or independently, in Namibia [36], Zambia [37], Malawi [38], Tanzania [34, 35, 39], Madagascar [40–42], Zimbabwe [43], Ghana [44], Burkina Faso [45] and Uganda [46, 47].

Kenya has adopted a stratified, sub-national control response since 2010 [48], while Uganda [49] and Tanzania [50] have begun to use epidemiological data for stratified control following recommendations of the WHO’s HBHI [2]. However, there is no sub-regional epidemiological stratification to tailor malaria control responses.

While community-based parasite prevalence data represent one layer in the sub-national stratification process, they are only as valuable as the quantity of data available spatiotemporally to compute contemporary predictions. Without updated empirical data, predictions are based largely on the relationship between geospatial covariates and historical data. The process of stratification is dynamic, requiring constant revision, and integration into policy. As new data become available, sub-national stratification should be updated. This paper presents an updated assembly of community parasite survey data in Kenya, mainland Tanzania and Uganda using MBG inference to provide a more contemporary predictions of the sub-national malaria prevalence stratification across the sub-region for 2019.

Methods

Geographic scope and context

Kenya, mainland Tanzania and Uganda share national borders and since the launch of the Roll Back Malaria initiative in 2000, implemented simultaneously national malaria policies changes on case management and vector control. During the 1990s national parasite prevalence mapping efforts began in Kenya [51] and was repeated several times [12, 13]. Similar approaches were undertaken in Tanzania [34, 35] and Uganda [52]. In addition, the Information for Malaria (INFORM) project provided support to NMCPs in each country since 2015 in malaria risk mapping for decision making, feeding into National Malaria Strategic Plans (NMSPs) and applications for Global Fund support [53]. Each modelled product of parasite prevalence used community parasite prevalence data from different periods, different methods of spatiotemporal modelling and different spatial resolutions of prediction. The most recent parasite survey data used in developing prevalence maps in Kenya [12, 54, 55], Tanzania [34, 35, 50, 56] and Uganda [49, 57] were assembled between the 1990s and 2017. Each country recognises the changing heterogeneity of malaria transmission within its national borders and with time NMSPs have used the varying epidemiology to encompass a stratified, sub-national control response in Kenya [48, 55] and Tanzania [50].

Parasite survey data assembly

Malaria parasite rate (PR) data from each of the three countries were assembled from existing data resources published in 2017 starting from January 2010 [58] and updated using published and survey data from 2017–2020. Data that precede 2010 are important historical information but are less valuable when informing the contemporary intensity of malaria transmission under the current levels of vector and parasite control. The processes of identifying, geo-coding, and standardizing community and school-based parasite survey data were described in detail elsewhere [8]. In brief, data search strategies included traditional peer-reviewed publication using PubMed, Google Scholar and Scopus using the free text keywords "malaria" and "country-name" and routine malaria publication alerts from Malaria World (http://www.malaria-world.com/). Importantly, the presence of the almost 30-year presence of the KEMRI-Wellcome Trust Programme in the sub-region has fostered a network of malaria scientists and ministry of health collaborators across the sub-region. These personal and institutional connections formed a significant part of the data search to identify higher spatial resolution of information in published and unpublished reports. All those who have contributed to this exercise are acknowledged for data provision for surveys undertaken between 2010 and 2020 in the (S1 Text).

Data comprised of the national survey of malaria infection in school children which started in Kenya in 2009 [59] and Tanzania in 2014 [60], subsequently repeated in Tanzania in 2017 and 2019 and Western Kenya in 2014 and 2019; the national household sample surveys of malaria indicators undertaken as part of DHS or MIS, where malaria infection was documented in children under 5 years (mainland Tanzania and Uganda) or under 15 years (Kenya); the multi-district mini-MIS to evaluate the impact of insecticide-treated nets in Uganda (2017–2019) [61] or baseline RTSS vaccine impact implementation studies (2019) (CDC Kenya); and the continuous annual repeat surveys undertaken in specific sentinel demographic and epidemiological surveillance sites at Kilifi, Kakamega and Siaya (Kenya), Muheza (Tanzania) and Tororo, Jinja and Kanungu (Uganda). Data were reconciled to the smallest possible spatial extent covering a village or community, census enumeration area (EA) or school. Most data were within a 5 km2 radius but were excluded if the spatial coverage exceeded >10 km2.

The first phase of data assembled between 2010 and 2017 comprised of 4,771 space-time surveys in Kenya, Tanzania and Uganda and were published as open access in 2017 [8, 58]. This analysis includes new data assembled post-2017 to June 2020, resulting in the identification of new national household, school and research survey data. Furthermore, since the Open Access data release in 2017, the spatial and temporal consistency has been improved. Geographic coordinates of the individual-level survey and national household survey data were revised using village coordinates from the national statistical agency geo-coded village or EA databases, updated school databases, population settlement shapefiles from the Ministries of Lands, Google Maps and research surveys geolocated using Global Positioning Systems (GPS). For example, where information was available, it was possible to assign school children to their village of residence rather than aggregated to the school and continuous survey data from specific health and demographic surveillance sites. In addition, DHS and MIS data were matched to the EA and the centroid calculated. This enabled the identification of repeatedly sampled clusters over time as part of national household survey rounds and matched to surveys undertaken within sub-national areas by research groups. If a survey location was sampled by more than one group within a three-month time window the largest sample was selected. Where more than one sample cluster was sampled during the same survey within a single village or EA, these were combined as one time-survey location. This revised approach to the spatial aggregation of individual-level data improved the temporal and spatial congruence of survey sampling locations across the three countries.

Data extracted from each survey report included the name of the study location, survey dates (month and year), the upper and lower age of those included in the school/village/cluster sample, the number examined for malaria infection, the number positive, and the methods used to assess malaria infection (microscopy, Rapid Diagnostic Tests (RDTs) or combinations). Where multiple methods of diagnosis had been used, preference was given to microscopy where these were undertaken by research groups, or quality assured laboratories during national school/household surveys.

Given that there was a diversity in the age ranges of sampled populations between studies, it was necessary to standardise the variation in sampled ages to allow for comparison of Plasmodium falciparum parasite prevalence rate (PfPR) in space and time. Algorithms based on catalytic conversion models were used to standardize the ages for each survey to 2–10 years assuming a modelled relationship between the upper and lower age of the sampled population, henceforth referred to as PfPR2-10 [62, 63]. Parasite prevalence in children aged 2–10 years (PfPR2-10) is predictive of other parameters of malaria transmission intensity such as the Entomological Inoculation Rate (EIR) and the Basic Reproduction Rate of Infection (R0) [64]. As such values of PfPR2-10 are widely used to model malaria control transmission reduction and the appropriate combinations of available interventions. They have also been used as a variable to predict probability of elimination [65]. Additionally, surveys varied in diagnostic methods to detect infection; either the visual presence of parasites in red cells or antigenaemia resulting from recent infections [66]. The aim was to standardise to microscopy and therefore RDT-only derived PfPR measurements were converted to microscopy using a functional Bayesian probit regression relationship between RDT and microscopy [67].

Covariates associated with PfPR2-10

Covariates aid in predicting infection prevalence at fine spatial scales [68, 69], but can also be used for explanatory modelling [70, 71]. For predictive modelling, the accuracy is guided by the quantity of the empirical data, the measurement error in the covariates and model specification. Some previous approaches have excluded covariates due to concerns related to the misspecification of the regression relationship between covariates and PfPR2-10, resulting in invalid inferences in areas where empirical data are sparse or absent leading to PfPR2-10 that is entirely driven by the covariates [12, 16, 34]. The inclusion of covariates can reduce the measure of prediction error without distorting the fidelity of regression relationships [71]. Therefore a parsimonious, minimal set of biologically plausible covariates for predictive modelling of PfPR2-10 were selected from candidate lists of covariates guided by previous malaria infection prevalence modelling in Africa [69, 72, 73]. These included: precipitation, the Enhanced Vegetation Index (EVI), Temperature Suitability Index (TSI), night-time light (NTL), and aridity (S2 Text). TSI has also been used independently to define areas where low ambient temperature does not allow the parasite to survive long enough in the mosquito vector to support transmission (unsuitable/non-receptive/malaria-free areas) [74].

The age-adjusted and microscopy-adjusted survey data series were matched to the long-term climatic and ecological covariates and a selection procedure was implemented to select a parsimonious set of covariates that were important predictors of prevalence. This was computed using non-spatial generalised linear regression models implemented in the bestglm package in R [75] (S2 Text). Precipitation, TSI and NTL proved to be the set of covariates with the highest independent predictive power for PfPR2–10 (S2 Text).

Bayesian hierarchical geostatistical model for PfPR210

Bayesian MBG approaches were used to interpolate survey data points at known space-time locations to provide posterior predictions based on parsimonious set of covariates for each period at unsampled locations with associated uncertainty. Since the three East Africa countries share national boundaries, the space-time data analysis was conducted jointly across the three countries using age-adjusted, microscopy-converted data collected between 2010 and 2020. The Bayesian hierarchical space-time model was implemented through a stochastic partial differential equations (SPDE) approach and using the Integrated Nested Laplace Approximations in R (R-INLA) [76, 77], to produce continuous maps of PfPR2–10 for the year 2019. A Binomial likelihood was used with spatial effects introducing a measure of spatial autocorrelation. The spatial and temporal interaction was implemented using a space-time separable Matérn covariance function. The SPDE approach has a computation advantages over traditional Markov Chain Monte Carlo (MCMC) as the continuous domain Gaussian Field (GF) is represented as Gaussian-Markov Random Fields (GMRF) [78–80]. For complete hierarchical Bayesian model specification, more recent developments in R-INLA SPDE methodology allow for the selection of priors through model spatial range (the spatial distance at which the spatial correlation is small, often less than 0.01) and the field standard deviation [81, 82]. The selection of other prior information of the parameters (intercept, covariate distribution and residual error) followed the standard fixed prior specifications [68, 83]. Computation used the full spatial and temporal range of the data and aimed at estimating the continuous posterior mean of PfPR2–10 and the 95% Bayesian credible intervals at 1 × 1 km spatial resolutions for 2019. Details of the Bayesian model specification are provided in S3 Text.

Model validation

A 20% spatially and temporally representative validation set of PfPR2-10 survey data were selected randomly based on equal selection probability. Prediction statistics computed from the validation set included the mean square error, the correlation between the predicted and observed vector densities, and the root mean square error (RMSE). The correlation coefficient provides a simple measure of linear association between the data and prediction sets, and RMSE is a measure of the average accuracy of individual predictions.

Prediction resolutions adjusted for population density

Population and malaria risk are not evenly, or congruently, distributed in space. Thus, population density and PfPR2-10 were combined to provide a population-weighted parasite prevalence (PAPfPR2-10). Pixels, where a zero value of TSI was regarded as zero probability of infection and zero population would not be included in risk attribution. The population density was assembled from the WorldPop gridded data that uses random-forest modelled disaggregation of latest census data projected to 2019 [84–87]. The gridded population data are available for at 100 m x 100 m spatial resolution and raster data were resampled to a matching grid of 1 x 1 km corresponding to the PfPR2-10 surface.

The predicted PAPfPR2-10 was classified into 6 endemicity strata corresponding to classes used currently in Tanzania [50]: areas which were unsuitable for transmission (malaria-free, TSI = 0), areas of very low risk where PAPfPR2–10 <1%; low risk 1% to <5% (low-risk strata 1); 5% to <10% (low-risk strata 2); moderate risk PAPfPR2–10 10% to <30%; and the high endemicity areas of PAPfPR2–10 ≥30% (high risk). The samples from posterior predictive distributions, weighted for the population were aggregated to the 2019 spatial units representing health decision-making units used for sub-national stratification: 47 counties in Kenya, 184 councils in Tanzania, and 135 districts in Uganda. Within each administrative polygon, the aggregated means were summed to provide a risk class for each health unit using strata defined earlier.

Ethics statement

The study involved the assembly of aggregated secondary data, previously published or part of national anonymised surveys. Ethical approvals for all specific survey data assembled was presumed sought by national investigators.

Results

Survey data assembly

Since the release of survey data from Kenya, Tanzania and Uganda in 2017, a total of 14,170 (74.8%) new time-space survey data were identified. One data point was excluded because it was not possible to find a reasonable geo-position, one survey location was excluded because it represented an area >10 km2. The final age-standardised and microscopy-converted data represented 18,940 time-space surveys between January 2010 and June 2020 at 6,866 unique spatial locations (Fig 1; Fig 2A). 7122, 4342 and 7476 surveys were from Kenya, mainland Tanzania and Uganda respectively (Fig 2A). 11,642 (61.5%) surveys were undertaken among samples ≥10 individuals; 4729 (25.0%) were a result of school surveys, 2366 (12.5%) from MIS, 4259 (22.5%) among sub-national MIS, and 682 (3.6%) from malaria modules within DHS (Fig 2B).

Fig 1. Assembled parasite rate surveys.

Fig 1

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(A) Distribution of all assembled survey data (n = 18940) between 2010–2020; (B) the distribution of age-corrected and microscopy-standard parasite prevalence (PfPR2-10) estimates among samples ≥10 individuals with the highest values on top when multiple surveys conducted at the same location. Base shapefiles used in all figures downloaded from: Kenya– https://data.humdata.org/dataset/ken-administrative-boundaries; Uganda– https://data.humdata.org/dataset/uganda-administrative-boundaries-admin-1-admin-3 and Tanzania–https://data.humdata.org/dataset/tanzania-administrative-boundaries-level-1-to-3-regions-districts-and-wards-with-2012-population https://gadm.org/.

Fig 2. Assembled surveys by year and data source.

Fig 2

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(A) Temporal distribution of surveys 2010–2020 by country (B) Temporal distribution of surveys 2010–2020 according to the data source

Malaria parasite prevalence in 2019

Based on the 20% subset data, the mean error in the prediction of PfPR2-10 for 2019 for the covariate-adjusted model revealed low bias with a slight tendency to underpredict by -0.007 (mean prediction error). The average error associated with predictions (absolute error) was 0.02 suggesting a good model precision. The correlation between the actual and predicted values for the hold-out set was 0.88 indicating a strong linear agreement between observed values and predictions.

The continuous maps of PAPfPR2–10 for 2019 is shown in Fig 3A and by each of the six endemicity strata in Fig 3B. At a national level, the PAPfPR2-10 was 4.7% (95% Bayesian Credible Interval 2.6–36.9) in Kenya, 10.6% (3.4–39.2) in Tanzania, and 9.5% (4.0–48.3) in Uganda. For all three countries, spatial heterogeneity in parasite prevalence is evident, ranging from unsuitable for transmission to the highest predictions above 77% (Fig 3A). Malaria prevalence was higher around the Lake Victoria basin, North-Western Tanzania, Northern Uganda and the southern coastal tip of Kenya (Fig 3A). A large swathe of Kenya and Tanzania running from North Eastern Kenya to Lake Malawi in southern Tanzania predicted to have very low malaria transmission (Fig 3A and 3B). In 2019, more than 12.7 million people resided in communities where parasite prevalence was predicted to be ≥ 30%, including 6.4%, 12.1% and 6.3% of Kenya, mainland Tanzania and Uganda population, respectively (Fig 3B; Table 1). Conversely, areas that supported very low parasite prevalence (<1%) but were suitable for transmission (TSI >0), consisted of approximately 46.2 million people across the sub-region, or 52.2%, 26.7% and 10.4% of Kenya, mainland Tanzania and Uganda populations respectively (Fig 3B; Table 1).

Fig 3. Predicted mean PAPfPR2-10 at 1 × 1 km spatial resolution maps in 2019.

Fig 3

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(A) mean prevalence (continuous stretched scale), and (B) Classified mean of the endemicity. The white represents the climatic unsuitability for transmission (TSI = 0). PAPfPR2–10 predictions are shown for areas within the stable limits of transmission.

Table 1. The estimated population at risk (percentage) for all-ages in each malaria endemicity class in 2019.

The population at risk 2019 per endemicity class
Population from the Continuous surface (Fig 3B) Number and Population in the Health units (Fig 4)
Endemicity Classes Kenya Tanzania Uganda Kenya (Counties) Tanzania (Councils) Uganda (Districts)
Malaria Free 7,225,608 (14.0) 1,837,735 (3.3) 1,604,742 (3.7) 0 (0.0) 0 (0.0) 0 (0.0)
0 (0.0) 0 (0.0) 0 (0.0)
Very low (<1%) 26,876,619 (52.2) 14,788,527 (26.7) 4,578,961 (10.4) 28 (59.6) 51 (27.7) 17 (12.6)
30,700,484 (59.6) 13,978,579 (25.3) 3,795,512 (8.7)
Low 1 (1%—<5%) 7,019,389 (13.6) 12,488,093 (22.6) 16,068,838 (36.7) 9 (19.1) 29 (15.8) 44 (32.6)
9,463,322 (18.4) 12,549,065 (22.7) 17,384,567 (39.7)
Low 2 (5%—<10%) 3,399,312 (6.6) 7,383,339 (13.3) 6,653,000 (15.2) 3 (6.4) 34 (18.5) 29 (21.5)
2,576,949 (5.0) 8,780,939 (15.9) 8,028,560 (18.3)
Moderate (10%—<30%) 3,693,499 (7.2) 12,130,029 (21.9) 12,149,932 (27.7) 5 (10.6) 54 (29.3) 38 (28.1)
6,838,613 (13.3) 14,542,473 (26.3) 12,148,512 (27.7)
High (> = 30%) 3,304,422 (6.4) 6,689,688 (12.1) 2,772,542 (6.3) 2 (4.3) 16 (8.7) 7 (5.2)
1,939,482 (3.8) 5,466,361 (9.9) 2,470,861 (5.6)
Total 51,518,850 (100) 55,317,472 (100) 43,828,014 (100) 47 (100) 184 (100) 135 (100)
51,518,850 (100) 55,317,417 (100) 43,828,012 (100)
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The mean PAPfPR2-10 per decentralised health administrative unit in Kenya (47 counties), mainland Tanzania (184 councils) and Uganda (135 districts) are shown in Fig 4, representing the class of parasite prevalence predicted for each unit in 2019 (S1 Table). Overall, 25 health administration units had mean PAPfPR2-10 ≥30%, these high transmission settings were uncommon across the entire sub-region in 2019: two counties in Kenya (Busia and Siaya), seven districts in Uganda (Apac, Busia, Namayingo, Luuka, Jinja, Oyam and Kwania); and sixteen councils in mainland Tanzania (Chato, Biharamulo, Bukombe, Nanyamba, Tarime, Ushetu, Kasulu, Buchosa, Tarime TC, Geita TC, Ukerewe, Geita, Tandahimba, Mbogwe, Nyangwale and Mtwara Rural). None of the health administrative units could be classified as entirely unsuitable for malaria transmission. However, in Kenya Nairobi county was classified as having a mean PAPfPR2-10 of 0.1% in 2019, this urban extent, with high levels of mobility could not be classified uniquely as very low transmission but was regarded as malaria-free [88]. 28 (59.6%), 51 (27.7%) and 17 (12.6%) health units in Kenya, mainland Tanzania and Uganda, respectively, were predicted to be of very low risk (PAPfPR2-10 < 1%) in 2019. For health policy planning purposes, it is notable that across all countries, 244 (66.7%) of the health administrative units were defined as <10% PAPfPR2-10 in 2019. The numbers of health units allocated to each of the five, suitable endemicity classifications, and populations in these averaged risk units are provided in Table 1.

Fig 4. East Africa PfPR2-10 stratification.

Fig 4

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Stratification of health decision-making units based on the level of PAPfPR2-10 (aggregated mean) for 2020. These comprised 47 counties in Kenya, 184 councils in mainland Tanzania, and 135 districts in Uganda (see S1 Table).

Discussion

Over 18,000 space-time empirical parasite survey data observations on community-based malaria prevalence were assembled from multiple sources since 2010. This represented an important data resource, where new information was identified since malaria prevalence maps were previously developed in Kenya, mainland Tanzania and Uganda. This large dataset reflects the importance of data sharing at national levels and harnesses considerable information beyond single national, periodic household surveys. Over 80% of the data included here were generously provided by research groups located in each country (S1 Text).

Combining data across the three countries allows information to be shared for modelling malaria risk across borders. Understanding cross-border risk become increasingly significant as countries aim for sub-national elimination [89]. Border communities represent an important migrant population, for example, nomadic pastoralist groups that transect the low transmission borders of Kenya and Tanzania (Fig 3A) and the communities that share high transmission in all three countries bordering Lake Victoria (Fig 3A).

Using the available data since 2010 within a time-space MBG framework predicted that only 8.5% of East Africa’s population lived in areas that support high transmission (PAPfPR2-10 ≥ 30%) in 2019. This varied between Kenya (6.4%), Tanzania (12.1%) and Uganda (6.3%) (Fig 3A, 3B; Table 1). These high parasite prevalence areas were described during more recent parasite prevalence risk mapping [12, 34, 52] and covered 2 counties, 16 councils, 7 districts in Kenya, mainland Tanzania and Uganda, respectively (Fig 4; Table 1; S1 Table). These areas represent the most vulnerable populations to high disease burdens and intensive malaria control efforts are required to reduce transmission using available vector control, drug based-prevention strategies, and malaria vaccines once approved for wide-scale use. Despite decades of promoting universal LLIN and intermittent presumptive treatment among pregnant women (IPTp) coverage nationally, there is a need to re-design distribution that targets the higher prevalence areas. This was initiated in Kenya in 2010, restricting routine and mass campaigns to high-risk counties in Western and Coastal Kenya [48] and has become part of a revised national strategic planning in Tanzania [50].

The very low transmission belt represented 28 counties, 51 councils, and 17 districts in Kenya, Tanzania and Uganda, respectively (Fig 4; Table 1; S1 Table) covered a population of over 46.2 million people. In an average year, most children and pregnant women may not encounter infections and overall disease burdens will continue to be of low prevalence. New vaccines or presumptive use of drugs to reduce disease burdens in these areas would not be cost-effective. These areas of very low infection prevalence represent sub-national regions for pre-elimination [90, 91]. In Tanzania [50] and Kenya [55], the most recent national malaria strategies have identified these areas of each country as an immediate plan for a pathway to elimination. This will require additional resources to mount early case detection, foci detection, mapping risk at a fine spatial resolution, requiring a robust and reactive health information system [92]. However, currently, routine case detection in these countries is inadequate to serve as either an intervention or a means to characterise transmission in very low-risk areas. Recent data from Kenya [93] and Uganda [94] suggest a poor performance in the test-track-treat policy in areas of low-malaria risk compared to more stable, endemic areas, affecting both case management and reliability of routine data.

Considering sub-national elimination, however, requires confidence in the stratification predictions. The MBG approaches come with statistical uncertainty, which might arise from inadequate survey input data (suggesting further sampling needs) and/or inherent variability in small area prediction. In previous work in Kenya and Somalia [12, 16], a 90% certainty in the predictions of PAPfPR2-10 being <1% (Non-Exceedance Probability, NEP) have been used for statistical confidence in the MBG predictions for programmatic certainty. Applying this approach to the 2019 PAPfPR2-10 predictions across East Africa there is a high congruence with predicted and 90% NEP certainty (Fig 5). Among the 96 health administrative units predicted to be <1% in 2019, 70 had more than 90% of the population living in areas where there was a 90% certainty that PAPfPR2-10 was <1% (S1 Table and population map in S1 Fig).

Fig 5. Non-exceedance probability (NEP) maps for 2019.

Fig 5

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PAPfPR2-10 predictions are 90% certain to be < 1%, shown in green. Derived from the fitted spatiotemporal model, formally expressed as: NEP = (Prob PAPfPR2–10 (x, t) < l|Data); where l is the prevalence threshold. A NEP close to 100% indicates that PAPfPR2–10 is highly likely to be below the threshold l; if close to 0%, PAPfPR2–10, is highly likely to be above the threshold l; if close to 50%, PAfPR2–10, is equally likely to be above or below the threshold l, hence corresponding to a high level of uncertainty.

In the absence of reliable routine data, current sampling strategies for parasite prevalence are poorly suited to characterise areas of very low parasite prevalence and require larger sampling effort to increase precision [95]. Both Microscopy and RDTs are routinely used to assess malaria infection but RDTs in particular could underestimate level of infection in low transmission setting [96]. Reducing the uncertainty and improving confidence in stratification of very low-risk areas should be encouraged and may require only one-off large-scale community and/or school surveys including PCR and serology [97] and could be restricted to areas of highest existing statistical uncertainty.

Importantly, areas that migrate to very low risk from low or moderate risk should be handled carefully in reverting immediately to a different mix of intervention and avoid risk of malaria rebounding, remaining cognisant of recent receptive risks [15, 17]. In Uganda, Apac district achieved a rapid reduction in parasite prevalence through indoor residual house-spraying, which after stopping resulted in an immediate rebound [98]. A more cautious use of PAPfPR2-10 < 1% to transition intervention is to remain in this class for a fixed number of years pre-transition to a different intervention-mix class, an enhanced case-detection is established, of proven fidelity and used to constantly review for a potential resurgence.

In between the two extremes of very low and high parasite prevalence people lived in predominantly low malaria prevalence areas (PAPfPR2-10 1%-<10%; 52.8 million people) and areas of moderate transmission (PAPfPR2-10 10%-<29%; 28.0 million people), covering 17, 117 and 90 health administrative units in Kenya, Tanzania and Uganda respectively (Fig 4; Table 1; S1 Table). Whether combinations of available interventions should be tailored across these 3 risk strata remains uncertain, except for maintaining prompt effective diagnosis, treatment, and referral, which must remain constant across all strata. It is important to recognise that the classification of strata based solely on parasite prevalence is arbitrary, there will be overlapping risks based on statistical confidence in the prediction [16]. A more conservative priority setting would be to include these counties, councils or districts in the accelerated, intensive efforts to reduce parasite burdens. Having multiple risk strata, however, allows a national control programme to review progress in moving decentralised health units down a continuum of risk, to set milestones for progress and constantly assess areas of intractable high prevalence and the possible resurgence of risk.

The modelled data presented here included very little data post-2019 (Fig 2A). Predicting beyond 2019 would have been inappropriate in the absence of data. However, using parasite prevalence as one layer in malaria stratification is a dynamic process, repeated every 2–3 years. Data from the November 2020 MIS in Kenya was not available by June-2021 and nationwide school surveys are planned for 2022 in Kenya and mainland Tanzania accompanying a further MIS in 2021. In Uganda an MIS has been planned for 2023 and national research partners continue to maintain surveillance in specific areas of each country. The exercise of parasite prevalence data assembly and modelling would ideally be repeated in 2023. Future mapping can be improved by including more temporally varying covariate layers rather than static covariates such as TSI. Here only long-term means were considered to resolve risk to the year of prediction. Malaria risk could however vary inter and intra annually requiring additional sampling to characterise seasonality in parasite prevalence.

The use of parasite prevalence has both disadvantages and advantages as a component of risk stratification. Sampled community or school populations represents an infection prevalence estimate on one day. National household surveys (DHS, MIS) are not powered to provide sub-national, health unit precision, are expensive and data are often only available to the national malaria control programmes 12–24 months after completion owing to survey agency and national statistical office agreements, beyond the control of the NMCP. School surveys offer a much cheaper alternative to DHS/MIS approaches to sampling parasite prevalence [19, 59, 60] and NMCPs have more direct control over data collection and access. School surveys have become increasingly important in recent years, over 25% of the point estimates included in the present analysis came from school-based surveys (Fig 2B). Routine data are available 365 days a year, and far more powerful to estimate seasonal intra- and inter annual variations in malaria risk. However, they represent risks among those who seek care, depend on the diagnostic capacities of health facilities and pre-existing acquired immunity, as such these data are not directly comparable to the risks of infection among community members. Both are important, and more work is required in understanding the relationships between community infection and infection among those who seek care [99] and how these might be jointly modelled (hybrid mapping) rather than treated as separate entities [100, 101].

Parasite prevalence is only one metric in the stratification process. To increasing its utility in decision making, it should be combined with routine data alongside other aspects of population vulnerability including the possibilities for urban control. Other data have been less well used in stratification, for example layering information on decentralised, health unit estimations of malaria mortality burdens, vector composition, insecticide resistance and bionomics, access to curative services and unmet needs based on existing vector control coverage. There is a need to build data acquisition processes and capacities within national malaria control programmes and the statistical handling of this information within a multi-layered data platform to make more effective decisions on malaria control efficiencies. National data repositories, improved data sharing and increased use of data nationally is paramount. These data must be country-owned and their use country-driven [102, 103].

Supporting information

S1 Text. Data acknowledgements.

(DOCX)

Click here for additional data file. (18.1KB, docx)
S2 Text. Covariate selection.

(DOCX)

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S3 Text. Spatiotemporal modelling of malaria risk.

(DOCX)

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S1 Table. PAPfPR2-10 estimates.

(DOCX)

Click here for additional data file. (73.6KB, docx)
S1 Fig. Additional maps.

(DOCX)

Click here for additional data file. (2.2MB, docx)

Acknowledgments

The many institutions and individuals who have contributed data are acknowledged in Supplementary Information 1. In addition, the authors wish to acknowledge the additional technical help provided in data location, geocoding and assembling additional GIS surfaces at the KEMRI-Wellcome Trust programme including David Kyalo, Laurissa Suiyanka and Lydia Mwangi. We would like to thank national control programme managers in Kenya (George Githuku, Elvis Oyugi), Uganda (Jimmy Opiyo) and Tanzania (Ally Mohammed) for formative discussions over several years on malaria risk mapping in the sub-region and Philip Bejon for comments on earlier drafts.

Abbreviations

BCI

Bayesian Credible Interval

DHS

Demographic and Health Surveys

DIC

Deviance Information Criterion

EA

Enumeration Area

EVI

Enhanced Vegetation Index

GF

Gaussian Fields

GMP

Global Malaria Programme

GPS

Global Positioning Systems

GMRF

Gaussian Markov Random Fields

HBHI

High Burden High Impact

INFORM

Information for Malaria

IPTp

Intermittent Presumptive Treatment Among Pregnant Women

MBG

Model Based Geo-Statistics

MCMC

Markov Chain Monte Carlo

MIS

Malaria Indicator Surveys

NEP

Non-exceedance probability

NMCP

National Malaria Control Programmes

NTL

Night-Time Light

PfPR2-10

age standardised parasite prevalence 2–10 years

PAPfPR2-10

Population adjusted parasite prevalence 2–10 years

RDT

Rapid Diagnostic Test

R-INLA

Integrated Nested Laplace Approximations in R

RMSE

Root Mean Square Error

SPDE

Stochastic Partial Differential Equations

SSA

sub-Saharan Africa

TSI

Temperature Suitability Index

WHO

World Health Organization

Data Availability

As part of a continued commitment by the KEMRI-Wellcome Trust’s INFORM programme (http://inform-malaria.org/) to support sub-regional National Malaria Control Programs, the final geo-coded prevalence databases have been provided to the respective Ministries of Health to be integrated into national malaria data repositories. Data sharing and access is the responsibility of national governments and data requests can be directed to NMCPs. Kenya (EO – eloyugi@gmail.com and http://www.nmcp.or.ke/index.php/contact-us); Tanzania (FC – chackyfa@gmail.com and https://www.moh.go.tz/en/contact-us); and Uganda (DR – damianamanya@gmail.com and https://www.health.go.ug/contact-us). Kenya (http://www.nmcp.or.ke/index.php/contact-us); Tanzania (https://www.moh.go.tz/en/contact-us); and Uganda (https://www.health.go.ug/contact-us/). In 2017, data assembled since 1900 across Africa was released in the public domain and an updated data release of all additional data will be released in 2025.

Funding Statement

VAA is funded by the Wellcome Trust Training Fellow (# 211208). PMM and AK are supported by the DELTAS Africa Initiative (DEL-15-003). The DELTAS Africa Initiative is an independent funding scheme of the African Academy of Sciences (AAS)'s Alliance for Accelerating Excellence in Science in Africa and supported by the New Partnership for Africa's Development Planning and Coordinating Agency with funding from the Wellcome Trust (# 107769) and the UK government and PMM acknowledges the support of the Royal Society under the Newton International Fellowship Scheme (NIF/R1/201418). RWS is supported as a Wellcome Trust Principal Fellow (# 212176) that also provided support to PMM, AK and EA. VAA, PMM, SM, EM, and RWS are grateful to the support of the Wellcome Trust to the Kenya Major Overseas Programme (# 203077). The authors also acknowledge support provided UK’s Department for International Development for their continued support to a project Strengthening the Use of Data for Malaria Decision Making in Africa (DFID Programme Code 203155) that enabled the INFORM project to support National Malaria Control programmes in the region. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLOS Glob Public Health. doi: 10.1371/journal.pgph.0000014.r001

Decision Letter 0

Ruth Ashton, Julia Robinson

19 Aug 2021

 PGPH-D-21-00103

 Plasmodium falciparum parasite prevalence in East Africa: updating data for malaria stratification.

 PLOS Global Public Health

Dear Dr. Alegana,

Thank you for submitting your manuscript to PLOS Global Public Health. After careful consideration, we feel that it has merit but does not fully meet PLOS Global Public Health’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

  

 There is agreement among the 3 reviewers that the statistical methods presented are sound, however some additional improvements and clarifications have been suggested. Please provide a response to each of the comments, and address the methodological clarifications that have been flagged. Regarding reviewer 2's comment on data availability, please confirm if the survey data since 2017 is expected to be included in your data repository, and the justification for release of this data being postponed until 2025. Note that the PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception.

 

Please submit your revised manuscript by Oct 03 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at globalpubhealth@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pgph/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript: 

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  • A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

  • An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

 

Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

We look forward to receiving your revised manuscript.

Kind regards,

Ruth Ashton, Ph.D.

Academic Editor

PLOS Global Public Health

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments (if provided):

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Does this manuscript meet PLOS Global Public Health's publication criteria? Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe methodologically and ethically rigorous research with conclusions that are appropriately drawn based on the data presented.

Reviewer #1: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available (please refer to the Data Availability Statement at the start of the manuscript PDF file)?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception. The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS Global Public Health does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Overall: This analysis involved a large data collection effort to produce an analysis of programmatic relevance. The outputs are clear and operationally relevant. Some methodological decisions could use some further justification and areas of the results and discussion could be expanded to complete the picture of how this analysis – and others like it to come – may be used by programs for strategy decisions.

Major comments:

1. Clarify the abstract

2. Methods – some additional justification for how the limited set of covariates were selected from the three references cited would be useful. There could be additional land cover, potential evaporation index, etc variables of interest. The covariates with the most positive predictive power were identified, but were methods explored to identify the dominant covariates in different operational units? That type of information would be useful for programs.

More information on how time was used – time of survey and matching to the time of covariates would be useful – were monthly covariates and/or lagged variables used?

3. Results – it would be useful to have more presentation of the certainty of the predictions in the results section. Perhaps the non-exceedance map here and an exceedance map as well.

4. Discussion – it would be interesting to have a bit more information about how these outputs could be used alongside the routine data that programs are familiar with and rely on. In spite of the biases of routine data – as you mention – there are many advantages to it like learning the seasonal signals of transmission.

Minor comments:

1. Abstract – in the introduction, it would be good to introduce that you are providing a modeled output and not simply presenting the PR data available (as you do at the end of the introduction). With your modeled outputs there are stratification values beyond the sampled areas.

In the methods section, the wording of data being “undertaken” is a bit confusing. Perhaps say something like, “Malaria prevalence data from surveys undertaken between January 2010 and June 2020 were assembled from each of the three countries”.

Do you adjust the data by the population, enviro, and eco covariates? Or did you simply use them as covariates in the modeling workflow?

Conclusion – I am curious how some of the additional data types listed would help increase the use of prevalence data? PR data seem independent of some of these (for example, care seeking). Would stratification be more useful of these additional metrics were accounted for alongside PR data?

2. Introduction – in the abstract you mention the Global Technical Strategy and here HBHI. Is the use of multiple metrics for planning quoted in both strategies?

What do you mean by vulnerability in the first paragraph? Like environmental receptivity? Or the impact of resource scarcity/vulnerability on different populations?

Paragraph 2 – are surveys not powered to support sub-national stratification? Or is that they lack complete geographic coverage? If it’s both – then those would be two separate issues worth mentioning.

Not sure ‘interpolation’ is the right to say what MBG was used for

Great to see the list of countries with country-specific prevalence maps. Does the first list use PR data?

3. Methods – the geographic scope and context section seems to belong in the introduction section.

Parasite data survey data assembly – it would be useful to have citations for what national PR survey sources are included in the existing data sources [ref 58]. Is that what paragraph 3 of this section outlines? Or we these included in addition from what is in reference 58? The first sentence of the third paragraph in this section seems like a list – are you saying that these are surveys that were gathered?

Great to list the updates and improvements made to the 2017 open access data. What resolution had the DHS and MIS data been at previously? Not the cluster level? The regional level?

For the study location – lat/longs were given for point observations I assume, but how were areal units like EAs treated? Was the centroid used? Or the whole area?

Were the survey data matched to covariates by month? Year? How were areal units matched?

Prediction resolutions adjusted for population density – was zero risk given to pixels with 0 TSI and 0 population or either or? Was there any consideration for comparing the WorldPop data to the CIESEN population data (https://data.humdata.org/dataset/highresolutionpopulationdensitymaps)?

4. Results – an impressive amount of new data assembled since the 2017 publication! Great to see. For Figure 1 – the reds in the colour scale used for B and C are hard to tell apart. Figure 2 is great. It would be useful to see – maybe in an SI – how the colors shown in Fig 2B would map spatially.

Figure 3 – are the very low PR areas in W Tanzania parks/game reserves? Is that an artifact of the population surface?

Figure 4 and Table 1 are great for programmatic use. Would be great to see what you have in SI 4 as a map as well.

5. Discussion – are the 18,000+ surveys assembled since 2010 for this region specifically?

What about limitations with the methods? Aside from the data only? And data limitations beyond the survey data? Is the TSI surface from 2011? Issues with the population data?

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6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

Do you want your identity to be public for this peer review? If you choose “no”, your identity will remain anonymous but your review may still be made public.

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Reviewer #1: No

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Comments to the Author

1. Does this manuscript meet PLOS Global Public Health’s publication criteria? Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe methodologically and ethically rigorous research with conclusions that are appropriately drawn based on the data presented.

Reviewer #2: Yes

Reviewer #3: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

Reviewer #3: Yes

********** 

3. Have the authors made all data underlying the findings in their manuscript fully available (please refer to the Data Availability Statement at the start of the manuscript PDF file)?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception. The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: No

Reviewer #3: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS Global Public Health does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #2: No

Reviewer #3: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #2: The paper is essentially an update to the previous malaria prevalence maps published by the same group across the East Africa region. The overall goal of this work is to feed into the malaria risk stratification planning that has been highlighted by the WHO as critical piece to help with targeting intervention mixes appropriately and monitor the progress of malaria burden. This paper highlights that prevalence estimates are simply one of the key surfaces in the grand scheme that is stratification, but not the final outcome, and should be used appropriately in conjunction with other burden metrics that the countries collect. Whilst the methodology nor data may not be ground-breaking it is certainly an important piece of work operationally. I don’t have major comments besides one:

1. Whilst I commend that the authors have shared these data back to their respective ministries of health and agree that work should be country led, it strikes me as odd to not update your own published data repository for up to four years – Why? This seems to not be in line with the authors key message of transparency and neither fits with the journal’s ethics of data sharing.

Minor

1. Introduction – authors suggest that there is lack of consistency in epidemiological stratification within the region, however, this paragraph is counterintuitive to their original premise of a ‘one-size-fits-all’ approach defined by the WHO will not be sufficient – consider revising this paragraph

2. Limitation of including averaged environmental covariates for a long temporal band (2010 – 2020) is worth acknowledging in the discussion. Using an averaged of the environment is not representation of the biological system of transmission.

3. The data included here from DHS is GPS displaced in urban areas by 2km and in rural up to 5km which can mean that prediction outputs are mis-specified. Whilst there isn’t much you can do I think it should be acknowledged in the discussion section about the DHS.

Figures

1. Figure 1 – figure 1A seems unnecessary and can be removed (though the legend can be consolidated with the PfPR legend). Figure 1C also doesn’t provide the reader much information and can be moved to the supplements allowing you to expand 1B as the points are difficult to see. Lastly, change the colour palette to something that is colour-blind friendly as I couldn’t distinguish the high from low.

2. Figure 2 – include legend in the figure itself rather than only in the caption. Figure 2B the y-axis title needs to be changed to Proper form.

3. Figure 3 – colour-blind friendly palettes

Reviewer #3: The manuscript “Plasmodium falciparum parasite prevalence in East Africa: updating data for malaria stratification” is an excellent article presenting methods for utilizing national malaria prevalence survey data for sub-national estimation of parasite prevalence. The spatial and statistical methods are sound and the authors’ integration of data across contiguous countries is fantastic, as malaria transmission is widely known to have great impacts in border regions due to divergence in national control programs. I have few major concerns and other minor critiques to be addressed.

Major critiques:

The justification for using large parasite prevalence surveys, conducted infrequently, for this work is a bit weak. Most countries now use dhis2 surveillance systems to monitor malaria incidence from all health facilities. Many have also integrated community health worker reporting into this system. This has a much higher spatial resolution and more detailed data. It is not publicly reported, but accessible through NMCPs. The primary research question is different, as these are clinical cases and do not represent parasite prevalence, but are generally used as a proxy for predicting malaria risk. The authors’ objective was to use the existing prevalence surveys to improve predictive risk maps, while acknowledging the limitations of these surveys. I would like to see a better justification for not using the real time passive surveillance systems to create predictive risk maps, particularly since these surveillance systems typically guide malaria control activities.

The authors also fall short on providing justification for the use of these models. It is unclear if this will lead to tools that can be used to inform control programs. This is of particular importance in areas of high prevalence in border regions. Will these methods be shared with NMCPs and how will it be used to guide the national programs? This is mentioned a bit in the discussion, but is not elaborated on and not presented in the introduction as a purpose of the study and potential public health impacts. The findings are great and would really be impactful if integrated into the programs.

Minor critiques:

Methods:

With the spatial scope there is incredible heterogeneity in malaria prevalence. The authors age standardized the prevalence measure to PfPR2-10. This is appropriate for high transmission settings where this age groups represents the highest risk group for transmission and identification. In lower transmission settings, this assumption does not hold, and infection reservoirs are primarily older adolescents and young adults. This could skew the results to make areas of low prevalence seem lower than they actually are. Justification for using this measure across varied transmission settings would be helpful.

More information about the RDT to microscopy conversion would be helpful. The use of these measures should also be addressed in the discussion. Both of these tools are diagnostic and not screening tools designed for measuring parasite prevalence. The sensitivity is quite poor in low transmission settings for this purpose and underestimate the prevalence. This could impact the areas that are presented as very low or low risk. Given the findings, there could be substantial misclassification just based on the data collection methods.

Results:

It would be helpful to present (in text) the proportion of the total population that is represented in this dataset.

In Figure 1 it is clear that most of the data comes from areas of higher population density. Was population density considered as a covariate in the model?

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6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

Do you want your identity to be public for this peer review? If you choose “no”, your identity will remain anonymous but your review may still be made public.

For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: No

Reviewer #3: No

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PLOS Glob Public Health. doi: 10.1371/journal.pgph.0000014.r003

Decision Letter 1

Ruth Ashton, Julia Robinson

15 Nov 2021

Plasmodium falciparum parasite prevalence in East Africa: updating data for malaria stratification.

PGPH-D-21-00103R1

Dear Dr. Alegana,

We're pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. 

Within one week, you'll receive an e-mail detailing the required amendments. When these have been addressed, you'll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Ruth Ashton, Ph.D.

Academic Editor

PLOS Global Public Health

Additional Editor Comments (optional):

Thank you for your patience during this second round of reviews. Unfortunately only one of the original peer reviewers was able to provide feedback on the revised manuscript, but from their feedback and my own review I'm satisfied that all previous comments have been adequately addressed in this version. 

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

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2. Does this manuscript meet PLOS Global Public Health’s publication criteria? Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe methodologically and ethically rigorous research with conclusions that are appropriately drawn based on the data presented.

Reviewer #2: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available (please refer to the Data Availability Statement at the start of the manuscript PDF file)?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception. The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS Global Public Health does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #2: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #2: The paper is fundamental piece for malaria risk stratification. well done.

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Reviewer #2: Yes: Punam Amratia

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Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Text. Data acknowledgements.

    (DOCX)

    Click here for additional data file. (18.1KB, docx)
    S2 Text. Covariate selection.

    (DOCX)

    Click here for additional data file. (511.7KB, docx)
    S3 Text. Spatiotemporal modelling of malaria risk.

    (DOCX)

    Click here for additional data file. (454.6KB, docx)
    S1 Table. PAPfPR2-10 estimates.

    (DOCX)

    Click here for additional data file. (73.6KB, docx)
    S1 Fig. Additional maps.

    (DOCX)

    Click here for additional data file. (2.2MB, docx)
    Attachment

    Submitted filename: Response_to_reviewers_letter_VAA_010921.docx

    Click here for additional data file. (76.6KB, docx)

    Data Availability Statement

    As part of a continued commitment by the KEMRI-Wellcome Trust’s INFORM programme (http://inform-malaria.org/) to support sub-regional National Malaria Control Programs, the final geo-coded prevalence databases have been provided to the respective Ministries of Health to be integrated into national malaria data repositories. Data sharing and access is the responsibility of national governments and data requests can be directed to NMCPs. Kenya (EO – eloyugi@gmail.com and http://www.nmcp.or.ke/index.php/contact-us); Tanzania (FC – chackyfa@gmail.com and https://www.moh.go.tz/en/contact-us); and Uganda (DR – damianamanya@gmail.com and https://www.health.go.ug/contact-us). Kenya (http://www.nmcp.or.ke/index.php/contact-us); Tanzania (https://www.moh.go.tz/en/contact-us); and Uganda (https://www.health.go.ug/contact-us/). In 2017, data assembled since 1900 across Africa was released in the public domain and an updated data release of all additional data will be released in 2025.

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