https://orcid.org/0000-0001-9755-6280
Figures
Abstract
Malaria remains a significant public health challenge, particularly among vulnerable populations in high-burden countries like Tanzania. Despite progress in reducing malaria incidence, climatic and environmental condition variability has led to uneven reductions, hindering the achievement of the WHO 2030 targets. We assessed the impact of climatic and environmental variables on malaria incidence to better understand spatial and temporal trends and their implications for the WHO targets. We utilized geo-covariate data from the Demographic and Health surveys (DHS) program, applying a Moran’s I test for spatial autocorrelation, a geostatistical Bayesian-based model to predict malaria incidence at an unsampled locations, and calculated the percentage change in predicted incidence over a ten-year interval. The results showed that malaria incidence decreased with greater variance across Tanzania. Mean malaria incidence decreased from 0.347 (95% CI: 0.336, 0.357) in 2000 to 0.118 (95% CI: 0.114, 0.122) in 2020, relative to the increasing insecticide-treated bednets (ITNs) coverage (0.037; 95% CI: 0.036, 0.039 in 2000 to 0.496; 95% CI: 0.476, 0.517 in 2020). Malaria incidence was higher in the Lake, western, eastern and southern zones compared to others, with spatial clustering observed (Moran’s I of 0.93 in 2000, 0.87 in 2010, and 0.74 in 2020). Higher temperatures increased malaria incidence (Odds ratio (OR): 1.06; 95% credible interval (CI):1.04,1.08 and 1.13;95% CI:1.10,1.16) in 2000 and 2010, respectively). Enhanced vegetation index increased the likelihood of malaria incidence (ORs ranging from 5.28; 95% CI: 4.96,5.61) in 2000 to 6.22; 95% CI: 5.91,6.55) in 2020 and higher aridity was associated with higher malaria incidence (ORs: 1.11; 95% CI: 1.10,1.13) in 2010 and 1.07; 95% CI: 1.06,1.07) in 2020). To achieve the WHO 2030 malaria reduction targets, fine-scale and region-specific interventions are essential to mitigate the impact of climate and environmental factors on malaria incidence.
Citation: Lutambi AM, Emidi B, Mbuya FG, Ryoba M, Sagamiko TD, Hugo AK, et al. (2025) Modelling the impact of climatic and environmental variables on malaria incidence in Tanzania: Implications for achieving the WHO’s 2030 Targets. PLOS Glob Public Health 5(8): e0005075. https://doi.org/10.1371/journal.pgph.0005075
Editor: Collins Otieno Asweto, University of Embu, KENYA
Received: April 3, 2025; Accepted: July 29, 2025; Published: August 20, 2025
Copyright: © 2025 Lutambi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The data was obtained from the DHS program. Data may be requested from the DHS program through https://www.dhsprogram.com/data/available-datasets.cfm
Funding: The authors received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Introduction
According to the World Malaria Report 2024, Africa accounted for two-thirds of all malaria cases and deaths worldwide [1]. Moreover, Malaria has remained a significant public health challenge, particularly in vulnerable populations living in high-burden-to-high-impact (HBHI) countries. In 2022, HBHI countries accounted for 67% of global malaria cases, with an increase from 157 million cases in 2019–167 million cases in 2022 [2], while in 2023, the global malaria burden was mostly contributed by 10 HBHI countries within the WHO African Region, accounting for 69% of estimated malaria cases and 70% of deaths [1]. Efforts to reduce malaria have intensified, guided by strategic principles and global targets, with the World Health Organization (WHO) setting ambitious 2030 targets to reduce malaria incidence and mortality by 90% [3]. Yet, malaria remains a challenge in many countries.
Tanzania, being one of the HBHI countries [4], has made significant efforts in malaria control, including the implementation of malaria burden stratification. This stratification aimed to guide control programs by developing targeted intervention packages for different strata [5]. However, the country continues to face challenges due to regional variations in malaria risk, driven primarily by differences in climatic conditions. Studies have demonstrated the critical role of climatic and environmental factors in malaria transmission [6–12], influencing the distribution, abundance, behavior of malaria vectors, and the development of malaria parasites within these mosquito vectors [13–21]. Differences in ecological settings such as highlands, coastal and semi-arid regions, have shown variations in temperature and precipitation which affects malaria incidence patterns [22]. Temperature plays a critical role in mosquito survival, biting rate and the extrinsic incubation period of the malaria parasites, with warmer temperatures generally accelerating parasites development within the mosquito [23], while rising temperatures potentially reducing longevity, overall body size, duration of the gonotrophic cycle, and reproductive capacity of Anopheles mosquitoes [23,24]. Additionally, elevated rearing temperatures have been reported to decrease Anopheles gambiae sensu lato mosquitoes susceptibility to pyrethroids and increased the expression of metabolic enzymes [25], and that future warmer climate could increase resistance of mosquitoes to insecticides and complicate malaria vector control measures [23,25,26]. Rainfall usually influences mosquito vector breeding sites availability. However, excessive rainfall sometimes flush out mosquito larvae and moderate rainfall creates suitable aquatic habitats for larval development [27–29]. Furthermore, higher Enhanced Vegetation Index (EVI) values indicate dense vegetation, which create stable microclimatic conditions that support prolonged larval development and enhance adult mosquito survival [30].
Therefore, achieving global malaria elimination targets requires understanding of theses climatic and environmental factors influencing malaria trends and malaria transmission [4]. The main objective of this study was to assess the impact of climatic and environmental variables on malaria incidence in Tanzania, with the aim of understanding spatial and temporal trends, and to inform the development of fine-scale regional-specific, climate and environmental adaptive malaria control strategies to support the achievement of the WHO 2030 malaria reduction targets.
Materials and methods
Study setting
Tanzania is divided into regions which are further classified into nine geographical zones. These zones group together areas with similar characteristics including weather conditions. The country is tropical and situated on the eastern coast of the Indian ocean with weather and climatic conditions favourable to malaria vectors, making malaria a public health problem. Insecticide treated bed nets is the mostly and widely used intervention against malaria with several other malaria control programs in place to mitigate the burden [31].
Data source and study design
We used secondary geo-covariate data from the spatial data repository of the Demographic and Health surveys (DHS) program for spatial analysis [32]. Geo-covariates are external spatial covariates data that are linked to DHS survey cluster locations for use in further studies [33]. These geo-covariate datasets consist of malaria related variables, climatic and environmental data at each sampled cluster. The DHS survey is a cross-sectional survey which uses a two-stage stratified sampling method. In the first stage, the country is divided into regions, and within each region, the primary sampling units (PSUs) are selected. These PSUs form clusters of households. These cluster locations are sampled so that whatever data is extracted relative to these clusters, the data becomes nationally representative. For this particular study, we used the geo-covariate dataset extracted based on the Tanzania Demographic and Health Survey and Malaria Indicator Survey (TDHS-MIS) 2022 sampling strategy conducted between February and July 2022 [34]. The TDHS-MIS 2022 sampled 629 clusters and a total of 628 clusters were georeferenced at their centroid coordinates. Georeferencing accuracy was less than 15 meters and these clusters were randomly displaced for confidentiality, with urban clusters displacements of up to 2 kilometers and up to 10 kilometers for rural clusters.
Outcome and covariates
The geo-covariate dataset included malaria incidence, climatic, environmental, malaria specific interventions, and other covariates at each cluster sampled during the TDHS-MIS 2022 survey. Here, malaria incidence is the average number of clinical cases of Plasmodium falciparum malaria per person per year at the DHS survey cluster location. These incidence and covariates data for each cluster were extracted from raster and vector data from various sources, reprojected to the same standard-based World Geodetic System 1984 coordinate system, and resampled to a uniform 5 by 5 km spatial resolution [33]. Only geospatial covariates presented in Table 1 for 2000, 2010, and 2020 were extracted for use in this study and are widely recommended in literature for their effect on malaria [6–16,18].
Covariate selection
Collinearity undermines significance of covariates and the Variance Inflation Factor (VIF) provides information on redundant variables [35]. We performed covariate selection by calculating the variance inflation factor to assess for collinearity among covariates. During this process, a stepwise selection approach was used to eliminate covariates with highest VIF until all VIFs were below 5 [35]. This elimination process resulted in a set of six covariates which included rainfall, maximum temperature, land surface temperature, enhanced vegetation index, aridity, and Insecticide-treated bednets (ITNs) coverage (Table 1). Furthermore, in order to identify the potentially highly correlated climate and environmental covariates selected based on VIF [36], these covariate-pairs were compared to the correlation between them using correlation analysis implemented and visualized in R using the GGally package (S1A Fig, S1B Fig and S1C Fig in S1 File). Maximum temperature and land surface temperature had a correlation of 0.61, 0.70, and 0.75 for 2000, 2010, and 2020 respectively. Land surface temperature was therefore removed from our analysis. This process was repeated for each year included in the analysis and the final set of covariates selected for further analysis included only five covariates (Table 1).
Descriptive analysis
We conducted a descriptive analysis of malaria incidence and its associated covariates. The data, which was provided at cluster level and aggregated by year, was summarized using the mean as a primary statistic to represent the average malaria incidence and covariate values within each year. To assess temporal patterns, we calculated and plotted median values over time. Additionally, we mapped the cluster level data to visualize their spatial distribution across the country.
Spatial autocorrelation
We used Moran’s I test to investigate the presence of spatial autocorrelation or clustering on malaria incidence, our outcome variable, a condition necessary for spatial analysis [37]. The Moran’s I test was used to assess whether the spatial distribution of malaria incidence was clustered, dispersed, or randomly distributed across the country. This index quantifies similarity in the variable of interest around neighborhoods and is given by
with being the total number of clusters,
is the observed values of malaria incidence at cluster
and
is the mean of all values.
is the spatial weight between location
and j, with
and i,j=1,2,3,…,n. Under normality assumption and large number of clusters, the z-score statistic was calculated from
, where
and
was used to compare if any spatial pattern deviated from a random pattern. A positive Moran’s statistics close 1 indicates a strong autocorrelation of malaria incidence.
Spatial modelling
To predict malaria incidence at unsampled locations, we applied the stochastic partial differential equation (SPDE) approach [38], implemented using the Integrated Nested Laplace Approximation (INLA) technique within the R-INLA package [39]. This method is particularly suited for modelling spatially structured data and has been widely adopted in public health research to address complex spatial dependency and make predictions [40–43]. In this modelling framework, we assumed that the number of malaria cases were given by under the total population at risk,
at cluster
for a given year. Then, conditional to the risk of malaria, the probability that ana individual experiences malaria,
at location,
, malaria cases
out of
were assumed to follow a binomial distribution
with a logit link function given by
is the intercept,
is a vector of coefficients of covariates
, and
is the malaria risk at location,
. To capture spatial variability and account for spatially correlated random effects, the SPDE approach models the spatial field
as a Gaussian field that can be approximated using a Gaussian Markov random field represented by
where is a covariance matrix determined by a spatial correlation function. We use the Matern’s covariance function given by
Here, is the modified Bessel function and
is the shape parameter defining smoothness of
,
is the spatial variance,
is the range parameter which determines the distance at which the spatial correlation approaches 0.1, and
is the Euclidian distance between two locations.
Given that our data was provided in terms of incidence rates () at each cluster location for each year included in the analysis, we calculated malaria incidence cases (
) from
with
being the total population at risk derived from the UN population count variable from the data [33]. This method was applied to predict malaria incidence at unsampled locations, both with and without incorporating covariates to assess and compare the impact of covariates on predicted malaria incidence results. To identify the best performing models, we evaluated model fit to data using the Watanabe-Akaike Information Criterion (WAIC), a widely used metric for Bayesian model comparison. WAIC balances model complexity with goodness of fit, providing an estimate of predictive accuracy of each model and a small WAIC value indicate best model prediction.
Percentage change in predicted incidence
To monitor progress towards reaching the WHO 2030 malaria targets, we utilized the continuous surface (raster) maps of predicted malaria incidence generate through the modelling methods described above for the years 2000, 2010, and 2020 to calculate percentage change over ten-year intervals. Specifically, the percentage change in predicted malaria incidence was computed using the following formula:
For the interval between 2000 and 2010, we subtracted predicted malaria incidence for 2010 (end year) from that of 2000 (start year), and for the interval between 2010 and 2020, we subtracted the predicted malaria incidence for 2020 (end year) from that of 2010 (start year). These calculations were performed using raster operations, ensuring spatial consistency and precision across all regions. The results were visualized through spatial mapping to highlight variability in malaria incidence change across different regions. This allowed us to identify areas where progress has been slow, enabling targeted interventions and strategic decision making to support malaria control and prevention efforts.
All the statistical analyses and mapping were carried out using R 4.3.3 software.
Results
Descriptive statistics
The study included 628 clusters sampled and geo-referenced during the 2022 TDHS-MIS survey. From 2000 to 2020 (Fig 1), mean malaria incidence decreased significantly from 0.347 (95% CI: 0.336, 0.357) in 2000 to 0.118 (95% CI: 0.114, 0.122) in 2020 and ITN coverage raised from 0.037 (95% CI: 0.036, 0.039) in 2000 to 0.496 (95% CI: 0.476, 0.517) in 2020. Mean maximum temperature and EVI remained relatively stable in all the years, while aridity and rainfall increased from 19.6 (95% CI: 19.3, 20.0) in 2000 to 30.1 (95% CI: 29.6, 30.5) in 2020 and 916.1 (95% CI: 896.0, 936.3) mm in 2000 to 1459.8 (95% CI: 1418.2,1501.4) mm in 2020 respectively. Similarly, the median values of malaria incidence decreased significantly over time (Fig 1). During the same period, median rainfall and aridity also increased, with maximum temperature and EVI remaining relatively unchanged. There was a noticeable increasing trend in median ITN coverage over time, starting from a low level in 2000 and increasing significantly by 2020.
Mean values and their confidence intervals are indicated as dots with error bars.
Malaria incidence varied across space (Fig 2), with the northwestern part of the country, around Lake Victoria recording high malaria incidence over the years. The coastal regions, particularly around Dar es Salaam, also exhibited relatively high malaria incidence. The spatial distribution of malaria incidence further improved over the years, with a significant number of regions showing very low incidence. The highest incidence areas were significantly reduced and were primarily confined to specific areas in the northwest and along the coastal regions. The distribution of maximum temperature, aridity, and EVI related to that of malaria incidence for all the years (S2A Fig and S2B Fig in S2 File) with the low malaria belt showing low maximum temperature, low values of aridity and EVI. There was small variability in rainfall across the country in 2000 and 2010, while in 2020, the Kagera, Tanga and Dar es Salaam regions had high amount of rainfall compared to other regions. ITN coverage was low in 2000 across the country, increased substantially in the high malaria areas compared to the low malaria belt in 2010. In 2020, the coverage of ITNs was lower in the central regions extending to the western and north western part compared to other parts of the country (S2C Fig in S2 File).
Each circle represents a sampled cluster location. Column 1, 2 and 3 represent years 2000, 2010, and 2020 respectively. These maps were generated in R using a basemap shapefile obtained Natural Earth Data (https://www.naturalearthdata.com/) via the rnaturalearth package under a public domain license (https://www.naturalearthdata.com/about/terms-of-use/).
Spatial clustering of malaria risk
The Moran’s I scatterplots (Fig 3) illustrate the spatial autocorrelation of malaria incidence across the three different time periods. Each of these plots show a clear positive linear relationship between the standardized malaria incidence in an area and the average incidence in its neighboring areas with Moran’s I of 0.93 in 2000, 0.87 in 2010, and 0.74 in 2020. Areas with high malaria incidence were surrounded by other high-incidence areas, while areas with low malaria incidence were also surrounded by similarly low-incidence areas. This clustering pattern was reflected in the points distributed in the upper right (high-high) and lower left (low-low) quadrants of each plot, which indicate clusters of high and low incidence, respectively.
Predictors of malaria incidence
We compared which model performed better than others in fitting to data. The best model for each year was then used in generating predictions in the particular year (Table 2). Results indicate that a model that combine rainfall, maximum temperature and EVI (WAIC = 15013.76) for 2000, or rainfall, maximum temperature, EVI and Aridity (WAIC = 13092.26) for 2010, or rainfall, maximum temperature, EVI, aridity and ITN (WAIC = 14477.53) for 2020 produced better fits compared to other models. Following these findings, these covariates were used in predicting malaria incidence at unsampled locations.
Table 3 summarizes results from the best models selected to show the impact of the covariates on malaria incidence 2000, 2010, and 2020. Results show that while rainfall showed no significant effect on malaria incidence in any year, maximum temperature exhibited a variable relationship with malaria. In 2000 and 2010, higher temperatures were positively associated with increased malaria incidence (Odds ratio (OR): 1.06; 95% credible interval (CI):1.04,1.08 and 1.13; 95% CI:1.10,1.16), respectively), but in 2020, higher temperatures were associated with a decreased malaria risk (OR: 0.94; 95% CI: 0.92,0.96). EVI was consistently a strong positive predictor, with high values indicating an increased likelihood of malaria incidence in all years (ORs ranging from 5.28; 95% CI: 4.96,5.61 to 6.22; 95% CI: 5.91,6.55). Similarly, aridity was a significant predictor, with higher aridity linked to greater malaria incidence (ORs: 1.11; 95% CI: 1.10,1.13) in 2010 and 1.07; 95% CI: 1.06,1.07) in 2020). Furthermore, ITN coverage emerged as a strong predictor in 2020, with areas with higher ITN coverage having a significantly greater likelihood of high malaria incidence (ORs: 3.88; 95% CI: 3.22,4.68).
Fig 4 illustrates the spatial distribution of predicted malaria incidence across Tanzania for the years 2000, 2010, and 2020 without covariates (Row A), and with covariates of the best models (Row B). In the year 2000, predicted malaria incidence was high in the western and the eastern regions and that highest incidence areas were more localized or clustered together. Malaria decreased in 2010, with most regions exhibiting moderate to low malaria incidence. The decreasing trend continued in 2020 with many regions exhibiting very low to moderate malaria incidence rates. Although malaria decreased significantly, still, high incidence was primarily confined to specific areas in the northwest and along the coastal and southern regions. Results also show that the inclusion of covariates could explain the differences in the distribution of malaria risk. The model without covariates (Row A) produces broad, smooth and generalized predictions with limited spatial details, while the model with covariates (Row B) captured fine-scale spatial variations, showing greater spatial heterogeneity, reflecting the impact of covariates. High risk areas aligned with areas having favorable climatic conditions for malaria, such as high maximum temperature, EVI, aridity, and rainfall (S2 Fig in S2 File). Over time, the high-risk areas appeared to contrast, reflecting the impact of interventions and varying climatic and environmental factors.
These maps were generated in R using a basemap shapefile obtained Natural Earth Data (https://www.naturalearthdata.com/) via the rnaturalearth package.
Progress in achieving global targets
Fig 5 presents the percentage change in predicted malaria incidence for the two decades, 2000–2010 and 2010–2020. The reduction in predicted malaria incidence was much greater during the first decade (2000–2010) compared to the second decade (2010–2020) with percentage change of about 35% and 7% respectively (Fig 5A). Changes in predicted malaria incidence was heterogeneous across the country during the periods studied (Fig 5B). There was a widespread decrease in incidence between 2000 and 2010 with most areas showing positive percentage change. During the period between 2010 and 2020, the spatial distribution shifted, with large part of the country maintaining or had increased malaria incidence. This variability in percentage change was clearly shown by the histograms (Fig 5C) which presents the distribution of percentage change across the country. In 2000–2010, the distribution was skewed towards positive change while during 2010–2020 period, the distribution was nearly normally distributed.
Positive % change indicates a decrease in predicted malaria incidence and negative % change indicates an increase in predicted malaria incidence. (A) Overall country mean percentage change in predicted malaria incidence; (B) Spatial distribution of percentage change in predicted malaria incidence across the country, and (C) Histograms show the distribution of the percentage change. The maps presented here were generated in R using a basemap shapefile obtained Natural Earth Data (https://www.naturalearthdata.com/) via the rnaturalearth package.
Discussion
This study assessed the impact of climatic and environmental variables on malaria to understand the spatial and temporal trends to inform development of regional-specific, climatic adaptive interventions to support achieving the WHO malaria reduction targets. Between 2000 and 2020, malaria incidence in Tanzania varied spatially, influenced by climatic and environmental factors, and interventions. Enhanced vegetation index, maximum temperature and aridity were significant predictors of malaria incidence, while rainfall, however, had no significant effect on malaria incidence in any year. In addition, ITNs coverage as part of the malaria vector control interventions was significantly associated with presence of malaria. A consistent spatial clustering of malaria incidence was observed where areas with similar values were clustered together, with low incidence areas being along the low temperature belt which originate from the northern zone to southern highlands through the central part of the country. Furthermore, inclusion of climatic variables in predictions captured fine-scale spatial variations, showing greater spatial heterogeneity and high-risk areas aligned with regions having favorable climatic conditions for malaria. Tracking change in malaria indicated that predicted malaria incidence decreased significantly during the 2000–2010 period but slowed down during the 2010–2020 period. This decrease was heterogeneous across the country during the period studied.
The finding that EVI was a significant predictor of malaria across all years aligns with other studies conducted in tropical regions [44–47]. EVI has been linked to malaria risk due to its influence on mosquito habitats. Studies in Vietnam and Gabon reported that higher EVI values were significantly associated with increased malaria risk, as dense vegetation offers favourable conditions for mosquito breeding [48,49]. Areas with higher vegetation cover support larval development and enhance the survival of adult mosquitoes, thereby contributing to increased malaria transmission [30,49]. Similarly, a study in Zambia found that environmental metrics such as EVI, topographic position index, and topographical wetness index remained constant over time and were associated with a higher likelihood of detecting malaria infections during case investigations [50]. The significancy of aridity in predicting malaria incidence is supported by evidence from Nigeria and Ghana, where increased aridity was associated with a higher risk of malaria infection [41,51]. Another study in Gabon included aridity as a significant variable in their final model, indicating its relevance in malaria transmission dynamics [48].
Similar to other studies [8,13,52], maximum temperature was positively associated with malaria risk in 2000 and 2010, as higher temperatures within suitable ranges can accelerate parasite development within mosquitoes [49]. However, a negative association between maximum temperature and malaria observed in 2020 highlighted potential changes in malaria transmission dynamics due to temperature changes. This finding may reflect the complex relationship between temperature and malaria transmission where extremely high temperatures can exceed the optimal range for mosquito survival or parasite development [41]. Malaria incidence was lower in the low-temperature belt, corresponding to higher elevations [5]. This was consistent with other studies which have shown that both temperature and malaria incidence decrease with increased elevation [53]. Our results on the impact of temperature on malaria is supported by available evidence which have shown that temperature influences malaria transmission by affecting the development rate of mosquito larvae and the survival rate of adult mosquitoes [54], and that warmer temperatures provide a favourable environment for adult female mosquitoes to feed more frequently, digest blood more rapidly, and the plasmodium parasite matures more quickly within the female mosquitoes [55]. Despite the fact that rainfall is positively associated with malaria in specific areas of Tanzania [56] and that rainfall play a crucial role in creating the necessary conditions for surface water accumulation, which serves as breeding sites for mosquitoes [14,51], our results that rainfall did not significantly affect malaria incidence, suggests that other factors played a dominant role in shaping malaria patterns in the country.
Similar to another study in Gabon which demonstrated that increased ITN coverage was significantly associated with a reduction in malaria prevalence [48], our descriptive and spatial temporal results also indicated that ITNs were associated with malaria reduction, with their impact becoming more evident in later years. The widespread use of insecticide-treated bednets, along with other vector control interventions, has played a crucial role in reducing malaria incidence [57,58]. The expansion of ITN coverage in Tanzania has been driven by various net distribution and delivery initiatives, including social marketing, subsidized vouchers, mass campaigns targeting children under five, universal coverage campaigns, antenatal care initiatives, the private commercial market, and school net distribution programs [59–62]. All of these initiatives increased ITNs coverage, thus, impacting malaria. Although ITNs are proven to reduce malaria transmission by killing or repelling mosquitoes, our 2020 spatial model showed that high ITN coverage was associated with high malaria incidence. Similar findings have been reported elsewhere. In Ghana, higher ITN coverage correlated with increased malaria prevalence [41], in Nigeria, ITN distribution was positively associated with malaria prevalence, suggesting that this may reflect targeted ITN distribution to high-risk areas [63]. In northwest Tanzania, high community-level ITN coverage did not reduce malaria prevalence due to confounding factors like socioeconomic status and housing conditions [64]. All of this evidence likely reflects a complex interplay of programmatic targeting, behavioral, and environmental factors. Areas with a high malaria burden are often prioritized for ITN distribution as part of targeted national malaria control strategies, thus, increased ITN coverage. This phenomenon where disease drives intervention coverage can create a positive association between ITN coverage and malaria.
Spatial analysis revealed consistent geographic variability in malaria incidence over the study period, with high-incidence regions predominantly located in the northwestern areas, around Lake Victoria, parts of the western regions, as well as coastal regions and around the Rufiji basin. Over time, these high-incidence pockets contracted significantly, becoming more localized by 2020. While this reduction can be attributed to interventions [58,65,66], spatial variability in climatic and environmental factors created heterogeneity in the distribution of mosquito resources, of which theoretical modelling studies has shown to impact the mosquito populations [67]. The Moran’s I analysis confirmed consistent spatial clustering of malaria incidence, where high-incidence areas were surrounded by other high-incidence areas, aligning with studies conducted in Kenya and Rwanda [68,69] which emphasized the importance of geographically targeted interventions to disrupt transmission clusters. Predicted malaria incidence maps highlighted the importance of including covariates in modeling malaria risk. Models incorporating climatic and environmental variables and ITN coverage provided fine-scale spatial details, capturing heterogeneity. High-risk areas were concentrated in regions with favourable climatic and/or environmental conditions for malaria, and their contraction over time aligns with the observed reductions in incidence.
In terms of regional focus, the persistent high-incidence areas in the northwest and some coastal regions highlighted the need for targeted interventions in these regions. Understanding the specific local factors contributing to sustained transmission in these areas, such as environmental conditions, vector species, and human behaviors, is crucial for developing effective strategies [70]. A significant reduction in malaria incidence was observed between 2000 and 2010, likely due to the widespread adoption of rapid diagnostic tests and interventions [57,58,71]. However, progress slowed considerably from 35% between 2000 and 2010 to only a 7% reduction in predicted incidence during 2010 and 2020 period. This slow pace in malaria reduction is attributed to variability in climatic conditions [72] and the rapid increase in insecticide resistance among malaria mosquito vectors [73,74], challenging future malaria control strategies and public health interventions. To achieve the WHO 2030 malaria targets requires data-driven, climate-sensitive interventions. This study shows that using spatial modeling by incorporating climatic and environmental factors could help in identifying fine scale high-risk areas for planning and resource allocation. As evidenced by our results, scaling up ITN coverage coupled with climate informed decisions is critical to achieving further reductions in malaria, especially in areas where climatic conditions favour the transmission potential.
Although the malaria control program has created malaria stratification in the country for targeted interventions [5], the fine-scale malaria risk heterogeneity observed in this study suggest that within the large malaria strata created requires concerted malaria control efforts to achieve the WHO 2030 malaria reduction targets. Importantly, the persistence of within-strata fine-scale heterogeneity suggest that even within the low malaria strata, there are areas that face more significant high malaria incidence. These within-strata disparities indicate that not all areas within the strata have equal risk as some may still have relatively higher risk than others, due to differences in factors such as EVI. These finding highlight the need for more targeted and finely tuned interventions that address the unique fine-scale malaria risk differences. Programs aimed at reducing malaria in the country must therefore consider the diverse needs of subpopulations within broader climatic and environmental aspects, ensuring that most areas receive adequate interventions. The identification of environmental predictors such as EVI and aridity could guide spatial targeting of malaria interventions. Regions with persistent high vegetation and aridity levels can benefit from prioritized intervention, enhanced vector surveillance or tailored community health education campaigns. Integrating model outputs into national malaria control programs could boost efficiency and effectiveness of intervention planning and resource allocation.
The study results showcase several strengths that enhance its robustness and relevance in understanding malaria dynamics at finer spatial scales and informing policy. The use of data spanning two decades (2000–2020) provides a detailed temporal analysis, enabling the identification of trends, the effectiveness of malaria interventions over time, and impact of climatic and environmental conditions. With a large spatial coverage of 628 geo-referenced clusters from the TDHS-MIS 2022, the study ensures a representative dataset that captures local variations in malaria incidence across Tanzania. On the other hand, the study utilized secondary data from the DHS geo-covariate dataset to derive geo-referenced malaria incidence and ITN coverage, alongside remote sensing and meteorological data for climatic and environmental covariates. While these sources provided valuable insights, the aggregation of data at cluster levels may not fully reflect micro-environmental or household-level malaria risk variations. Furthermore, our study did not account for the seasonal variations in the covariates, a critical aspect in malaria transmission dynamics. Seasonal fluctuations in these climatic variables can influence mosquito breeding, survival rates, and parasite development, ultimately impact malaria incidence. By averaging climatic variables, we capture their overall impact over time, but this approach may overlook short-term variations that could provide deeper insights into malaria transmission patterns. Future work incorporating seasonal trends in climate data could offer a more detailed understanding of the relationship between climate and malaria risk. Additionally, our model outputs were not externally validated using independent data sources such as entomological, clinical, or surveillance records, which represents a limitation of this study. Moreover, our analysis did not include potential confounders such as access to health services and human mobility or travel. These factors are known to influence malaria transmission in such a way that limited access to health services may lead to underreporting of malaria cases, while mobility can alter exposure risk to malaria vectors [75–79]. The absence of these variables may introduce residual confounding and should be considered when interpreting our findings. Future studies could incorporate these factors to strengthen the predictive power of the models. Furthermore, the modelling approach used assumes spatial stationarity and Gaussian priors which may not fully capture the complex and potentially non-stationary nature of malaria transmission. Also, while the binomial model was appropriate for modeling incidence as a probability based on a known population at risk, it assumes independence of infection events within clusters and does not account for possible overdispersion or local clustering.
Conclusion
This study has provided valuable insights into the spatial and temporal influence of climatic and environmental factors on malaria incidence in Tanzania. Climatic and environmental variables strongly impacted malaria. There was significant within-country spatial heterogeneity in malaria risks with some areas demonstrating notable progress, while others experienced slow progress in malaria reduction. Following this, areas with lower incidence may still require vigilance to prevent resurgence, particularly in the context of changing climatic and environmental conditions that can influence mosquito breeding grounds and malaria parasite transmission dynamics. Further research is needed to explore the specific local factors contributing to malaria persistence in certain areas and to develop tailored interventions which could integrate climatic and environmental characteristics.
Supporting information
S1 File. S1A Fig. Pairwise correlation of covariates for 2000.
S1B Fig. Pairwise correlation of covariates for 2010. S1C Fig. Pairwise correlation of covariates for 2020.
https://doi.org/10.1371/journal.pgph.0005075.s001
(DOCX)
S2 File. S2A Fig. Spatial distribution of covariates.
Each circle represents a sampled cluster location for Rainfall (Row 1), and Maximum temperature (Row 2). S2B Fig. Spatial distribution of covariates. Each circle represents a sampled cluster location for Aridity (Row 1), and EVI (Row 2). S2C Fig: Spatial distribution of ITN coverage. Each circle represents a sampled cluster location for ITN coverage.
https://doi.org/10.1371/journal.pgph.0005075.s002
(DOCX)
Acknowledgments
We would like to express our appreciation to the DHS program for providing access to the data.
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