Topography as a modifier of breeding habitats and concurrent vulnerability to malaria risk in the western Kenya highlands

New directions for malaria vector control using geography and geospatial analysis

As we strive towards the ambitious goal of malaria elimination, we must embrace integrated strategies and interventions. Like many diseases, malaria is heterogeneously distributed. This inherent spatial component means that geography and geospatial data is likely to have an important role in malaria control strategies. For instance, focussing interventions in areas where malaria risk is highest is likely to provide more cost-effective malaria control programmes. Equally, many malaria vector control strategies, particularly interventions like larval source management, would benefit from accurate maps of malaria vector habitats - sources of water that are used for malarial mosquito oviposition and larval development. In many landscapes, particularly in rural areas, the formation and persistence of these habitats is controlled by geographical factors, notably those related to hydrology. This is especially true for malaria vector species like Anopheles funestsus that show a preference for more permanent, often naturally occurring water sources like small rivers and spring-fed ponds. Previous work has embraced geographical concepts, techniques, and geospatial data for studying malaria risk and vector habitats. But there is much to be learnt if we are to fully exploit what the broader geographical discipline can offer in terms of operational malaria control, particularly in the face of a changing climate. This chapter outlines potential new directions related to several geographical concepts, data sources and analytical approaches, including terrain analysis, satellite imagery, drone technology and field-based observations. These directions are discussed within the context of designing new protocols and procedures that could be readily deployed within malaria control programmes, particularly those within sub-Saharan Africa, with a particular focus on experiences in the Kilombero Valley and the Zanzibar Archipelago, United Republic of Tanzania.

Malaria transmission heterogeneity in different eco-epidemiological areas of western Kenya: a region-wide observational and risk classification study for adaptive intervention planning

BACKGROUND

Understanding of malaria ecology is a prerequisite for designing locally adapted control strategies in resource-limited settings. The aim of this study was to utilize the spatial heterogeneity in malaria transmission for the designing of adaptive interventions.

METHODS

Field collections of clinical malaria incidence, asymptomatic Plasmodium infection, and malaria vector data were conducted from 108 randomly selected clusters which covered different landscape settings including irrigated farming, seasonal flooding area, lowland dryland farming, and highlands in western Kenya. Spatial heterogeneity of malaria was analyzed and classified into different eco-epidemiological zones.

RESULTS

There was strong heterogeneity and detected hot/cold spots in clinical malaria incidence, Plasmodium prevalence, and vector abundance. The study area was classified into four zones based on clinical malaria incidence, parasite prevalence, vector density, and altitude. The two irrigated zones have either the highest malaria incidence, parasite prevalence, or the highest malaria vector density; the highlands have the lowest vector density and parasite prevalence; and the dryland and flooding area have the average clinical malaria incidence, parasite prevalence and vector density. Different zones have different vector species, species compositions and predominant species. Both indoor and outdoor transmission may have contributed to the malaria transmission in the area. Anopheles gambiae sensu stricto (s.s.), Anopheles arabiensis, Anopheles funestus s.s., and Anopheles leesoni had similar human blood index and malaria parasite sporozoite rate.

CONCLUSION

The multi-transmission-indicator-based eco-epidemiological zone classifications will be helpful for making decisions on locally adapted malaria interventions.

Sibling species of the major malaria vector Anopheles gambiae display divergent preferences for aquatic breeding sites in southern Nigeria

BACKGROUND

When integrated with insecticide-treated bed nets, larval control of Anopheles mosquitoes could fast-track reductions in the incidence of human malaria. However, larval control interventions may deliver suboptimal outcomes where the preferred breeding places of mosquito vectors are not well known. This study investigated the breeding habitat choices of Anopheles mosquitoes in southern Nigeria. The objective was to identify priority sites for mosquito larval management in selected urban and periurban locations where malaria remains a public health burden.  METHODS: Mosquito larvae were collected in urban and periurban water bodies during the wet-dry season interface in Edo, Delta, and Anambra States. Field-collected larvae were identified based on PCR gel-electrophoresis and amplicon sequencing, while the associations between Anopheles larvae and the properties and locations of water bodies were assessed using a range of statistical methods.

RESULTS

Mosquito breeding sites were either man-made (72.09%) or natural (27.91%) and mostly drainages (48.84%) and puddles (25.58%). Anopheles larvae occurred in drainages, puddles, stream margins, and a concrete well, and were absent in drums, buckets, car tires, and a water-holding iron pan, all of which contained culicine larvae. Wild-caught Anopheles larvae comprised Anopheles coluzzii (80.51%), Anopheles gambiae sensu stricto (s.s.) (11.54%), and Anopheles arabiensis (7.95%); a species-specific PCR confirmed the absence of the invasive urban malaria vector Anopheles stephensi among field-collected larvae. Anopheles arabiensis, An. coluzzii, and An. gambiae s.s. displayed preferences for turbid, lowland, and partially sunlit water bodies, respectively. Furthermore, An. arabiensis preferred breeding sites located outside 500 m of households, whereas An. gambiae s.s. and An. coluzzii had increased detection odds in sites within 500 m of households. Anopheles gambiae s.s. and An. coluzzii were also more likely to be present in natural water bodies; meanwhile, 96.77% of An. arabiensis were in man-made water bodies. Intraspecific genetic variations were little in the dominant vector An. coluzzii, while breeding habitat choices of populations made no statistically significant contributions to these variations.

CONCLUSION

Sibling malaria vectors in the An. gambiae complex display divergent preferences for aquatic breeding habitats in southern Nigeria. The findings are relevant for planning targeted larval control of An. coluzzii whose increasing evolutionary adaptations to urban ecologies are driving the proliferation of the mosquito, and An. arabiensis whose adults typically evade the effects of treated bed nets due to exophilic tendencies.

Modeling the effect of climatic conditions and topography on malaria incidence using Poisson regression: a Retrospective study in Bannu, Khyber Pakhtunkhwa, Pakistan

Background

Malaria has been identified as a crucial vector-borne disease around the globe. The primary aim of this study was to investigate the incidence of malaria in the district of Bannu and its relationship with climatic conditions such as temperature, rainfall, relative humidity, and topography.

Methods

Secondary data were obtained from the metrological office and government hospitals across the district for 5 years (2013-2017). A Poisson regression model was applied for the statistical analysis.

Results and discussion

The number of reported cases of malaria was 175,198. The regression analysis showed that temperature, relative humidity, and rainfall had a significant association (p < 0.05) with malaria incidence. In addition, the topographic variables were significantly associated (p < 0.05) with malaria incidence in the region. The percent variation in the odds ratio of incidence was 4% for every unit increase in temperature and 2% in humidity. In conclusion, this study indicated that the temperature, humidity, rainfall, and topographic variables were significantly associated with the incidence of malaria. Effective malaria control and interventions integrated with climatic factors must be considered to overcome the disease burden.

Malaria attributable fractions with changing transmission intensity: Bayesian latent class vs logistic models

Background

Asymptomatic carriage of malaria parasites is common in high transmission intensity areas and confounds clinical case definitions for research studies. This is important for investigations that aim to identify immune correlates of protection from clinical malaria. The proportion of fevers attributable to malaria parasites is widely used to define different thresholds of parasite density associated with febrile episodes. The varying intensity of malaria transmission was investigated to check whether it had a significant impact on the parasite density thresholds. The same dataset was used to explore an alternative statistical approach, using the probability of developing fevers as a choice over threshold cut-offs. The former has been reported to increase predictive power.

Methods

Data from children monitored longitudinally between 2005 and 2017 from Junju and Chonyi in Kilifi, Kenya were used. Performance comparison of Bayesian-latent class and logistic power models in estimating malaria attributable fractions and probabilities of having fever given a parasite density with changing malaria transmission intensity was done using Junju cohort. Zero-inflated beta regressions were used to assess the impact of using probabilities to evaluate anti-merozoite antibodies as correlates of protection, compared with multilevel binary regression using data from Chonyi and Junju.

Results

Malaria transmission intensity declined from over 49% to 5% between 2006 and 2017, respectively. During this period, malaria attributable fraction varied between 27–59% using logistic regression compared to 10–36% with the Bayesian latent class approach. Both models estimated similar patterns of fevers attributable to malaria with changing transmission intensities. The Bayesian latent class model performed well in estimating the probabilities of having fever, while the latter was efficient in determining the parasite density threshold. However, compared to the logistic power model, the Bayesian algorithm yielded lower estimates for both attributable fractions and probabilities of fever. In modelling the association of merozoite antibodies and clinical malaria, both approaches resulted in comparable estimates, but the utilization of probabilities had a better statistical fit.

Conclusions

Malaria attributable fractions, varied with an overall decline in the malaria transmission intensity in this setting but did not significantly impact the outcomes of analyses aimed at identifying immune correlates of protection. These data confirm the statistical advantage of using probabilities over binary data.

Influence of landscape heterogeneity on entomological and parasitological indices of malaria in Kisumu, Western Kenya

Background

Identification and characterization of larval habitats, documentation of Anopheles spp. composition and abundance, and Plasmodium spp. infection burden are critical components of integrated vector management. The present study aimed to investigate the effect of landscape heterogeneity on entomological and parasitological indices of malaria in western Kenya.

Methods

A cross-sectional entomological and parasitological survey was conducted along an altitudinal transect in three eco-epidemiological zones: lakeshore along the lakeside, hillside, and highland plateau during the wet and dry seasons in 2020 in Kisumu County, Kenya. Larval habitats for Anopheles mosquitoes were identified and characterized. Adult mosquitoes were sampled using pyrethrum spray catches (PSC). Finger prick blood samples were taken from residents and examined for malaria parasites by real-time PCR (RT-PCR).

Results

Increased risk of Plasmodium falciparum infection was associated with residency in the lakeshore zone, school-age children, rainy season, and no ITNs (χ² = 41.201, df = 9, P < 0.0001). Similarly, lakeshore zone and the rainy season significantly increased Anopheles spp. abundance. However, house structures such as wall type and whether the eave spaces were closed or open, as well as the use of ITNs, did not affect Anopheles spp. densities in the homes (χ² = 38.695, df = 7, P < 0.0001). Anopheles funestus (41.8%) and An. arabiensis (29.1%) were the most abundant vectors in all zones. Sporozoite prevalence was 5.6% and 3.2% in the two species respectively. The lakeshore zone had the highest sporozoite prevalence (4.4%, 7/160) and inoculation rates (135.2 infective bites/person/year). High larval densities were significantly associated with lakeshore zone and hillside zones, animal hoof prints and tire truck larval habitats, wetland and pasture land, and the wet season. The larval habitat types differed significantly across the landscape zones and seasonality (χ² = 1453.044, df = 298, P < 0.0001).

Conclusion

The empirical evidence on the impact of landscape heterogeneity and seasonality on vector densities, parasite transmission, and Plasmodium infections in humans emphasizes the importance of tailoring specific adaptive environmental management interventions to specific landscape attributes to have a significant impact on transmission reduction.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13071-022-05447-9.

Risk associations of submicroscopic malaria infection in lakeshore, plateau and highland areas of Kisumu County in western Kenya

BACKGROUND

Persons with submicroscopic malaria infection are a major reservoir of gametocytes that sustain malaria transmission in sub-Saharan Africa. Despite recent decreases in the national malaria burden in Kenya due to vector control interventions, malaria transmission continues to be high in western regions of the country bordering Lake Victoria. The objective of this study was to advance knowledge of the topographical, demographic and behavioral risk factors associated with submicroscopic malaria infection in the Lake Victoria basin in Kisumu County.

METHODS

Cross-sectional community surveys for malaria infection were undertaken in three eco-epidemiologically distinct zones in Nyakach sub-County, Kisumu. Adjacent regions were topologically characterized as lakeshore, hillside and highland plateau. Surveys were conducted during the 2019 and 2020 wet and dry seasons. Finger prick blood smears and dry blood spots (DBS) on filter paper were collected from 1,777 healthy volunteers for microscopic inspection and real time-PCR (RT-PCR) diagnosis of Plasmodium infection. Persons who were PCR positive but blood smear negative were considered to harbor submicroscopic infections. Topographical, demographic and behavioral risk factors were correlated with community prevalence of submicroscopic infections.

RESULTS

Out of a total of 1,777 blood samples collected, 14.2% (253/1,777) were diagnosed as submicroscopic infections. Blood smear microscopy and RT-PCR, respectively, detected 3.7% (66/1,777) and 18% (319/1,777) infections. Blood smears results were exclusively positive for P. falciparum, whereas RT-PCR also detected P. malariae and P. ovale mono- and co-infections. Submicroscopic infection prevalence was associated with topographical variation (χ2 = 39.344, df = 2, p<0.0001). The highest prevalence was observed in the lakeshore zone (20.6%, n = 622) followed by the hillside (13.6%, n = 595) and highland plateau zones (7.9%, n = 560). Infection prevalence varied significantly according to season (χ2 = 17.374, df = 3, p<0.0001). The highest prevalence was observed in residents of the lakeshore zone in the 2019 dry season (29.9%, n = 167) and 2020 and 2019 rainy seasons (21.5%, n = 144 and 18.1%, n = 155, respectively). In both the rainy and dry seasons the likelihood of submicroscopic infection was higher in the lakeshore (AOR: 2.71, 95% CI = 1.85-3.95; p<0.0001) and hillside (AOR: 1.74, 95% CI = 1.17-2.61, p = 0.007) than in the highland plateau zones. Residence in the lakeshore zone (p<0.0001), male sex (p = 0.025), school age (p = 0.002), and living in mud houses (p = 0.044) increased the risk of submicroscopic malaria infection. Bed net use (p = 0.112) and occupation (p = 0.116) were not associated with submicroscopic infection prevalence.

CONCLUSION

Topographic features of the local landscape and seasonality are major correlates of submicroscopic malaria infection in the Lake Victoria area of western Kenya. Diagnostic tests more sensitive than blood smear microscopy will allow for monitoring and targeting geographic sites where additional vector interventions are needed to reduce malaria transmission.

Machine Learning and Its Applications for Protozoal Pathogens and Protozoal Infectious Diseases

In recent years, massive attention has been attracted to the development and application of machine learning (ML) in the field of infectious diseases, not only serving as a catalyst for academic studies but also as a key means of detecting pathogenic microorganisms, implementing public health surveillance, exploring host-pathogen interactions, discovering drug and vaccine candidates, and so forth. These applications also include the management of infectious diseases caused by protozoal pathogens, such as Plasmodium, Trypanosoma, Toxoplasma, Cryptosporidium, and Giardia, a class of fatal or life-threatening causative agents capable of infecting humans and a wide range of animals. With the reduction of computational cost, availability of effective ML algorithms, popularization of ML tools, and accumulation of high-throughput data, it is possible to implement the integration of ML applications into increasing scientific research related to protozoal infection. Here, we will present a brief overview of important concepts in ML serving as background knowledge, with a focus on basic workflows, popular algorithms (e.g., support vector machine, random forest, and neural networks), feature extraction and selection, and model evaluation metrics. We will then review current ML applications and major advances concerning protozoal pathogens and protozoal infectious diseases through combination with correlative biology expertise and provide forward-looking insights for perspectives and opportunities in future advances in ML techniques in this field.

Larval flushing alters malaria endemicity patterns in regions with similar habitat abundance

A model of Anopheles gambiae populations dynamics coupled with Plasmodium falciparum transmission dynamics is extended to include mechanisms of larval flushing which are known to occur. Flushing dynamics are modeled using a simulation that incorporates seasonal, autocorrelated, and random components based on 30 years of rainfall data for the Kakamega District of the western Kenya highlands. The model demonstrates that flushing phenomena can account for differences between regions with the same annual larval habitat pattern, changing the World Health Organization endemicity classification from either hyperendemic or holoendemic to hypoendemic disease patterns. Mesoendemic patterns of infection occur at the boundary of the holoendemic to hypoendemic transition. For some levels of flushing the entomological inoculation rate drops to an insignificant amount and disease disappears, while the annual indoor resting density remains well above zero. In these scenarios, the disease is hypoendemic, yet the model shows that outbreaks can occur when disease is introduced at particular time points.

Adaptive interventions for optimizing malaria control: an implementation study protocol for a block-cluster randomized, sequential multiple assignment trial

Background

In the past two decades, the massive scale-up of long-lasting insecticidal nets (LLINs) and indoor residual spraying (IRS) has led to significant reductions in malaria mortality and morbidity. Nonetheless, the malaria burden remains high, and a dozen countries in Africa show a trend of increasing malaria incidence over the past several years. This underscores the need to improve the effectiveness of interventions by optimizing first-line intervention tools and integrating newly approved products into control programs. Because transmission settings and vector ecologies vary from place to place, malaria interventions should be adapted and readapted over time in response to evolving malaria risks. An adaptive approach based on local malaria epidemiology and vector ecology may lead to significant reductions in malaria incidence and transmission risk.

Methods/design

This study will use a longitudinal block-cluster sequential multiple assignment randomized trial (SMART) design with longitudinal outcome measures for a period of 3 years to develop an adaptive intervention for malaria control in western Kenya, the first adaptive trial for malaria control. The primary outcome is clinical malaria incidence rate. This will be a two-stage trial with 36 clusters for the initial trial. At the beginning of stage 1, all clusters will be randomized with equal probability to either LLIN, piperonyl butoxide-treated LLIN (PBO Nets), or LLIN + IRS by block randomization based on their respective malaria risks. Intervention effectiveness will be evaluated with 12 months of follow-up monitoring. At the end of the 12-month follow-up, clusters will be assessed for “response” versus “non-response” to PBO Nets or LLIN + IRS based on the change in clinical malaria incidence rate and a pre-defined threshold value of cost-effectiveness set by the Ministry of Health. At the beginning of stage 2, if an intervention was effective in stage 1, then the intervention will be continued. Non-responders to stage 1 PBO Net treatment will be randomized equally to either PBO Nets + LSM (larval source management) or an intervention determined by an enhanced reinforcement learning method. Similarly, non-responders to stage 1 LLIN + IRS treatment will be randomized equally to either LLIN + IRS + LSM or PBO Nets + IRS. There will be an 18-month evaluation follow-up period for stage 2 interventions. We will monitor indoor and outdoor vector abundance using light traps. Clinical malaria will be monitored through active case surveillance. Cost-effectiveness of the interventions will be assessed using Q-learning.

Discussion

This novel adaptive intervention strategy will optimize existing malaria vector control tools while allowing for the integration of new control products and approaches in the future to find the most cost-effective malaria control strategies in different settings. Given the urgent global need for optimization of malaria control tools, this study can have far-reaching implications for malaria control and elimination.

Trial registration

US National Institutes of Health, study ID NCT04182126. Registered on 26 November 2019.

Malaria around large dams in Africa: effect of environmental and transmission endemicity factors

Background

The impact of large dams on malaria has received widespread attention. However, understanding how dam topography and transmission endemicity influence malaria incidences is limited.

Methods

Data from the European Commission’s Joint Research Center and Shuttle Radar Topography Mission were used to determine reservoir perimeters and shoreline slope of African dams. Georeferenced data from the Malaria Atlas Project (MAP) were used to estimate malaria incidence rates in communities near reservoir shorelines. Population data from the WorldPop database were used to estimate the population at risk of malaria around dams in stable and unstable areas.

Results

The data showed that people living near (< 5 km) large dams in sub-Saharan Africa grew from 14.4 million in 2000 to 18.7 million in 2015. Overall, across sub-Saharan Africa between 0.7 and 1.6 million malaria cases per year are attributable to large dams. Whilst annual malaria incidence declined markedly in both stable and unstable areas between 2000 and 2015, the malaria impact of dams appeared to increase in unstable areas, but decreased in stable areas. Shoreline slope was found to be the most important malaria risk factor in dam-affected geographies, explaining 41–82% (P < 0.001) of the variation in malaria incidence around reservoirs.

Conclusion

Gentler, more gradual shoreline slopes were associated with much greater malaria risk. Dam-related environmental variables such as dam topography and shoreline slopes are an important factor that should be considered in efforts to predict and control malaria around dams.

Satellite Earth Observation Data in Epidemiological Modeling of Malaria, Dengue and West Nile Virus: A Scoping Review

Earth Observation (EO) data can be leveraged to estimate environmental variables that influence the transmission cycle of the pathogens that lead to mosquito-borne diseases (MBDs). The aim of this scoping review is to examine the state-of-the-art and identify knowledge gaps on the latest methods that used satellite EO data in their epidemiological models focusing on malaria, dengue and West Nile Virus (WNV). In total, 43 scientific papers met the inclusion criteria and were considered in this review. Researchers have examined a wide variety of methodologies ranging from statistical to machine learning algorithms. A number of studies used models and EO data that seemed promising and claimed to be easily replicated in different geographic contexts, enabling the realization of systems on regional and national scales. The need has emerged to leverage furthermore new powerful modeling approaches, like artificial intelligence and ensemble modeling and explore new and enhanced EO sensors towards the analysis of big satellite data, in order to develop accurate epidemiological models and contribute to the reduction of the burden of MBDs.

Epidemiological risk factors for clinical malaria infection in the highlands of Western Kenya

Background

Understanding the complex heterogeneity of risk factors that can contribute to an increased risk of malaria at the individual and household level will enable more effective use of control measures. The objective of this study was to understand individual and household factors that influence clinical malaria infection among individuals in the highlands of Western Kenya.

Methods

This was a matched case–control study undertaken in the Western Kenya highlands. Clinical malaria cases were recruited from health facilities and matched to asymptomatic individuals from the community who served as controls. Each participant was screened for malaria using microscopy. Follow-up surveys were conducted with individual households to collect socio-economic data. The houses were also checked using pyrethrum spray catches to collect mosquitoes.

Results

A total of 302 malaria cases were matched to 604 controls during the surveillance period. Mosquito densities were similar in the houses of both groups. A greater percentage of people in the control group (64.6%) used insecticide-treated bed nets (ITNs) compared to the families of malaria cases (48.3%). Use of ITNs was associated with lower level of clinical malaria episodes (odds ratio 0.51; 95% CI 0.39–0.68; P < 0.0001). Low income was the most important factor associated with higher malaria infections (adj. OR 4.70). Use of malaria prophylaxis was the most important factor associated with less malaria infections (adj OR 0.36). Mother’s (not fathers) employment status (adj OR 0.48) and education level (adj OR 0.54) was important malaria risk factor. Houses with open eaves was an important malaria risk factor (adj OR 1.72).

Conclusion

The identification of risk factors for clinical malaria infection provides information on the local malaria epidemiology and has the potential to lead to a more effective and targeted use of malaria control measures. These risk factors could be used to assess why some individuals acquire clinical malaria whilst others do not and to inform how intervention could be scaled at the local level.

Importance of household-level risk factors in explaining micro-epidemiology of asymptomatic malaria infections in Ratanakiri Province, Cambodia

Heterogeneity in malaria risk is considered a challenge for malaria elimination. A cross-sectional study was conducted to describe and explain micro-epidemiological variation in Plasmodium infection prevalence at household and village level in three villages in Ratanakiri Province, Cambodia. A two-level logistic regression model with a random intercept fitted for each household was used to model the odds of Plasmodium infection, with sequential adjustment for individual-level then household-level risk factors. Individual-level risk factors for Plasmodium infection included hammock net use and frequency of evening outdoor farm gatherings in adults, and older age in children. Household-level risk factors included house wall material, crop types, and satellite dish and farm machine ownership. Individual-level risk factors did not explain differences in odds of Plasmodium infection between households or between villages. In contrast, once household-level risk factors were taken into account, there was no significant difference in odds of Plasmodium infection between households and between villages. This study shows the importance of ongoing indoor and peridomestic transmission in a region where forest workers and mobile populations have previously been the focus of attention. Interventions targeting malaria risk at household level should be further explored.

Topographic mapping of the interfaces between human and aquatic mosquito habitats to enable barrier targeting of interventions against malaria vectors

Geophysical topographic metrics of local water accumulation potential are freely available and have long been known as high-resolution predictors of where aquatic habitats for immature Anopheles mosquitoes are most abundant, resulting in elevated densities of adult malaria vectors and human infection burden. Using existing entomological and epidemiological survey data, here we illustrate how topography can also be used to map out the interfaces between wet, unoccupied valleys and dry, densely populated uplands, where malaria vector densities and infection risk are focally exacerbated. These topographically identifiable geophysical boundaries experience disproportionately high vector densities and malaria transmission risk, because this is where Anopheles mosquitoes first encounter humans when they search for blood after emerging or ovipositing in the valleys. Geophysical topographic indicators accounted for 67% of variance for vector density but for only 43% for infection prevalence, so they could enable very selective targeting of interventions against the former but not the latter (targeting ratios of 5.7 versus 1.5 to 1, respectively). So, in addition to being useful for targeting larval source management to wet valleys, geophysical topographic indicators may also be used to selectively target adult Anopheles mosquitoes with insecticidal residual sprays, fencing, vapour emanators or space sprays to barrier areas along their fringes.

Spatial modelling of malaria cases associated with environmental factors in South Sumatra, Indonesia

Background

Malaria, a parasitic infection, is a life-threatening disease in South Sumatra Province, Indonesia. This study aimed to investigate the spatial association between malaria occurrence and environmental risk factors.

Methods

The number of confirmed malaria cases was analysed for the year 2013 from the routine reporting of the Provincial Health Office of South Sumatra. The cases were spread over 436 out of 1613 villages. Six potential ecological predictors of malaria cases were analysed in the different regions using ordinary least square (OLS) and geographically weighted regression (GWR). The global pattern and spatial variability of associations between malaria cases and the selected potential ecological predictors was explored.

Results

The importance of different environmental and geographic parameters for malaria was shown at global and village-level in South Sumatra, Indonesia. The independent variables altitude, distance from forest, and rainfall in global OLS were significantly associated with malaria cases. However, as shown by GWR model and in line with recent reviews, the relationship between malaria and environmental factors in South Sumatra strongly varied spatially in different regions.

Conclusions

A more in-depth understanding of local ecological factors influencing malaria disease as shown in present study may not only be useful for developing sustainable regional malaria control programmes, but can also benefit malaria elimination efforts at village level.

Defining micro-epidemiology for malaria elimination: systematic review and meta-analysis

Background

Malaria risk can vary markedly between households in the same village, or between villages, but the determinants of this “micro-epidemiological” variation in malaria risk remain poorly understood. This study aimed to identify factors that explain fine-scale variation in malaria risk across settings and improve definitions and methods for malaria micro-epidemiology.

Methods

A systematic review of studies that examined risk factors for variation in malaria infection between individuals, households, clusters, hotspots, or villages in any malaria-endemic setting was conducted. Four databases were searched for studies published up until 6th October 2015. Crude and adjusted effect estimates for risk factors for malaria infection were combined in random effects meta-analyses. Bias was assessed using the Newcastle–Ottawa Quality Assessment Scale.

Results

From 743 retrieved records, 51 studies were selected, representing populations comprising over 160,000 individuals in 21 countries, in high- and low-endemicity settings. Sixty-five risk factors were identified and meta-analyses were conducted for 11 risk factors. Most studies focused on environmental factors, especially increasing distance from a breeding site (OR 0.89, 95% CI 0.86–0.92, 10 studies). Individual bed net use was protective (OR 0.63, 95% CI 0.52–0.77, 12 studies), but not household bed net ownership. Increasing household size (OR 1.08, 95% CI 1.01–1.15, 4 studies) and household crowding (OR 1.79, 95% CI 1.48–2.16, 4 studies) were associated with malaria infection. Health seeking behaviour, medical history and genetic traits were less frequently studied. Only six studies examined whether individual-level risk factors explained differences in malaria risk at village or hotspot level, and five studies reported different risk factors at different levels of analysis. The risk of bias varied from low to high in individual studies. Insufficient reporting and comparability of measurements limited the number of meta-analyses conducted.

Conclusions

Several variables associated with individual-level malaria infection were identified, but there was limited evidence that these factors explain variation in malaria risk at village or hotspot level. Social, population and other factors may confound estimates of environmental risk factors, yet these variables are not included in many studies. A structured framework of malaria risk factors is proposed to improve study design and quality of evidence in future micro-epidemiological studies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12936-017-1792-1) contains supplementary material, which is available to authorized users.

Using low-cost drones to map malaria vector habitats

BACKGROUND

There is a growing awareness that if we are to achieve the ambitious goal of malaria elimination, we must compliment indoor-based vector control interventions (such as bednets and indoor spraying) with outdoor-based interventions such as larval source management (LSM). The effectiveness of LSM is limited by our capacity to identify and map mosquito aquatic habitats. This study provides a proof of concept for the use of a low-cost (< $1000) drone (DJI Phantom) for mapping water bodies in seven sites across Zanzibar including natural water bodies, irrigated and non-irrigated rice paddies, peri-urban and urban locations.

RESULTS

With flying times of less than 30 min for each site, high-resolution (7 cm) georeferenced images were successfully generated for each of the seven sites, covering areas up to 30 ha. Water bodies were readily identifiable in the imagery, as well as ancillary information for planning LSM activities (access routes to water bodies by road and foot) and public health management (e.g. identification of drinking water sources, mapping individual households and the nature of their construction).

CONCLUSION

The drone-based surveys carried out in this study provide a low-cost and flexible solution to mapping water bodies for operational dissemination of LSM initiatives in mosquito vector-borne disease elimination campaigns. Generated orthomosaics can also be used to provide vital information for other public health planning activities.

Impact of Highland Topography Changes on Exposure to Malaria Vectors and Immunity in Western Kenya

Background

It is almost an axiom that in the African highlands (above 1,500 m) transmission of Plasmodium falciparum is limited primarily by low ambient temperature and that small changes in temperature could result in temporary favorable conditions for unstable transmission within populations that have acquired little functional immunity. The pattern of malaria transmission in the highland plateau ecosystems is less distinct due to the flat topography and diffuse hydrology resulting from numerous streams. The non-homogeneous distribution of larval breeding habitats in east African highlands obviously affects Anopheles spatial distribution which, consequently, leads to heterogeneous human exposure to malaria. Another delicate parameter in the fragile transmission risk of malaria in the highlands is the rapid loss of primary forest due to subsistence agriculture. The implication of this change in land cover on malaria transmission is that deforestation can lead to changes in microclimate of both adult and larval habitats hence increase larvae survival, population density, and gametocytes development in adult mosquitoes. Deforestation has been documented to enhancing vectorial capacity of Anopheles gambiae by nearly 100% compared to forested areas.

Method

The study was conducted in five different ecosystems in the western Kenya highlands, two U-shaped valleys (Iguhu, Emutete), two V-shaped valleys (Marani, Fort Ternan), and one plateau (Shikondi) for 16 months among 6- to 15-year-old children. Exposure to malaria was tested using circumsporozoite protein (CSP) and merozoite surface protein immunochromatographic antibody tests. Malaria parasite was examined using different tools, which include microscopy based on blood smears, rapid diagnostic test based on HRP 2 proteins, and serology based on human immune response to parasite and vector antigens have been also examined in the highlands in comparison with different topographical systems of western Kenya.

Results

The results suggested that changes in the topography had implication on transmission in highlands of western Kenya and appropriate diagnosis, treatment, and control tool needed to be considered accordingly. Both plateau and U-shaped valley found to have higher parasite density than V-shaped valley. People in V-valley were less immune than in plateau and U-valley residents.

Conclusion

Topography diversity in western Kenya highlands has a significant impact on exposure rates of human to malaria vectors and parasite. The residents of V-shaped valleys are at risk of having explosive malaria outbreaks during hyper-transmission periods due to low exposure to malaria parasite; hence, they have low immune response to malaria, while the U-shaped valleys have stable malaria transmission, therefore, the human population has developed immunity to malaria due to continuous exposure to malaria.

Individual- and Household-Level Risk Factors Associated with Malaria in Mutasa District, Zimbabwe: A Serial Cross-Sectional Study

Malaria constitutes a major public health problem in Zimbabwe, particularly in the north and east bordering Zambia and Mozambique. In Manicaland Province in eastern Zimbabwe, malaria transmission is seasonal and unstable. Over the past decade, Manicaland Province has reported increased malaria transmission due to limited funding, drug resistance and insecticide resistance. The aim of this study was to identify risk factors at the individual and household levels to better understand the epidemiology of malaria and guide malaria control strategies in eastern Zimbabwe. Between October 2012 and September 2014, individual demographic data and household characteristics were collected from cross-sectional surveys of 1,116 individuals residing in 316 households in Mutasa District, one of the worst affected districts. Factors associated with malaria, measured by rapid diagnostic test (RDT), were identified through multilevel logistic regression models. A total of 74 participants were RDT positive. Sleeping under a bed net had a protective effect against malaria despite pyrethroid resistance in the mosquito vector. Multivariate analysis showed that malaria risk was higher among individuals younger than 25 years, residing in households located at a lower household density and in closer proximity to the Mozambique border. The risk factors identified need to be considered in targeting malaria control interventions to reduce host-vector interactions.

Spatially aggregated clusters and scattered smaller loci of elevated malaria vector density and human infection prevalence in urban Dar es Salaam, Tanzania

Background

Malaria transmission, primarily mediated by Anopheles gambiae, persists in Dar es Salaam (DSM) despite high coverage with bed nets, mosquito-proofed housing and larviciding. New or improved vector control strategies are required to eliminate malaria from DSM, but these will only succeed if they are delivered to the minority of locations where residual transmission actually persists. Hotspots of spatially clustered locations with elevated malaria infection prevalence or vector densities were, therefore, mapped across the city in an attempt to provide a basis for targeting supplementary interventions.

Methods

Two phases of a city-wide population-weighted random sample of cross-sectional household surveys of malaria infections were complemented by two matching phases of geographically overlapping, high-resolution, longitudinal vector density surveys; spanning 2010–2013. Spatial autocorrelations were explored using Moran’s I and hotspots were detected using flexible spatial scan statistics.

Results

Seven hotspots of spatially clustered elevated vector density and eight of malaria infection prevalence were detected over both phases. Only a third of vectors were collected in hotspots in phase 1 (30 %) and phase 2 (33 %). Malaria prevalence hotspots accounted for only half of malaria infections detected in phase 1 (55 %) and phase 2 (47 %). Three quarters (76 % in phase 1 and 74 % in phase 2) of survey locations with detectable vector populations were outside of hotspots. Similarly, more than half of locations with higher infection prevalence (>10 %) occurred outside of hotspots (51 % in phase 1 and 54 % in phase 2). Vector proliferation hazard (exposure to An. gambiae) and malaria infection risk were only very loosely associated with each other (Odds ratio (OR) [95 % Confidence Interval (CI)] = 1.56 [0.89, 1.78], P = 0.52)).

Conclusion

Many small, scattered loci of local malaria transmission were haphazardly scattered across the city, so interventions targeting only currently identifiable spatially aggregated hotspots will have limited impact. Routine, spatially comprehensive, longitudinal entomological and parasitological surveillance systems, with sufficient sensitivity and spatial resolution to detect these scattered loci, are required to eliminate transmission from this typical African city. Intervention packages targeted to both loci and hotspots of transmission will need to suppress local vector proliferation, treat infected residents and provide vulnerable residents with supplementary protective measures against exposure.

Molecular Characterization Reveals Diverse and Unknown Malaria Vectors in the Western Kenyan Highlands

The success of mosquito-based malaria control is dependent upon susceptible bionomic traits in local malaria vectors. It is crucial to have accurate and reliable methods to determine mosquito species composition in areas subject to malaria. An unexpectedly diverse set of Anopheles species was collected in the western Kenyan highlands, including unidentified and potentially new species carrying the malaria parasite Plasmodium falciparum. This study identified 2,340 anopheline specimens using both ribosomal DNA internal transcribed spacer region 2 and mitochondrial DNA cytochrome oxidase subunit 1 loci. Seventeen distinct sequence groups were identified. Of these, only eight could be molecularly identified through comparison to published and voucher sequences. Of the unidentified species, four were found to carry P. falciparum by circumsporozoite enzyme-linked immunosorbent assay and polymerase chain reaction, the most abundant of which had infection rates comparable to a primary vector in the area, Anopheles funestus. High-quality adult specimens of these unidentified species could not be matched to museum voucher specimens or conclusively identified using multiple keys, suggesting that they may have not been previously described. These unidentified vectors were captured outdoors. Diverse and unknown species have been incriminated in malaria transmission in the western Kenya highlands using molecular identification of unusual morphological variants of field specimens. This study demonstrates the value of using molecular methods to compliment vector identifications and highlights the need for accurate characterization of mosquito species and their associated behaviors for effective malaria control.

Assessment of mosquito larval productivity among different land use types for targeted malaria vector control in the western Kenya highlands

Background

Mosquito larval source management (LSM) is likely to be more effective when adequate information such as dominant species, seasonal abundance, type of productive habitat, and land use type are available for targeted sites. LSM has been an effective strategy for reducing malaria morbidity in both urban and rural areas in Africa where sufficient proportions of larval habitats can be targeted. In this study, we conducted longitudinal larval source surveillance in the western Kenya highlands, generating data which can be used to establish cost-effective targeted intervention tools.

Methods

One hundred and twenty-four (124) positive larval habitats were monitored weekly and sampled for mosquito larvae over the 85-week period from 28 July 2009 to 3 March 2011. Two villages in the western Kenya highlands, Mbale and Iguhu, were included in the study.

After preliminary sampling, habitats were classified into four types: hoof prints (n = 21; 17 % of total), swamps (n = 32; 26 %), abandoned goldmines (n = 35; 28 %) and drainage ditches (n = 36; 29 %). Positive habitats occurred in two land use types: farmland (66) and pasture (58). No positive larval habitats occurred in shrub land or forest.

Results

A total of 46,846 larvae were sampled, of which 44.1 % (20,907) were from abandoned goldmines, 30.9 % (14,469) from drainage ditches, 22.4 % (10,499) from swamps and 2.1 % (971) from hoof prints. In terms of land use types, 57.2 % (26,799) of the sampled larvae were from pasture and 42.8 % (20,047) were from farmland. Of the specimens identified morphologically, 24,583 (52.5 %) were Anopheles gambiae s.l., 11,901 (25.4 %) were Culex quinquefasciatus, 5628 (12 %) were An. funestus s.l. and 4734 (10.1 %) were other anopheline species (An. coustani, An. squamosus, An. ziemanni or An. implexus). Malaria vector dynamics varied seasonally, with An.gambiae s.s. dominating during wet season and An.arabiensis during dry season. An increased proportion of An. arabiensis was observed compared to previous studies.

Conclusion

These results suggest that long-term monitoring of larval habitats can establish effective surveillance systems and tools. Additionally, the results suggest that larval control is most effective in the dry season due to habitat restriction, with abandoned goldmines, drainage ditches and swamps being the best habitats to target. Both farmland and pasture should be targeted for effective larval control. An increased proportion of An. arabiensis in the An. gambiae complex was noticed in this study for the very first time in the western Kenya highlands; hence, further control tools should be in place for effective control of An. arabiensis.

Clinical malaria case definition and malaria attributable fraction in the highlands of western Kenya

BACKGROUND

In African highland areas where endemicity of malaria varies greatly according to altitude and topography, parasitaemia accompanied by fever may not be sufficient to define an episode of clinical malaria in endemic areas. To evaluate the effectiveness of malaria interventions, age-specific case definitions of clinical malaria needs to be determined. Cases of clinical malaria through active case surveillance were quantified in a highland area in Kenya and defined clinical malaria for different age groups.

METHODS

A cohort of over 1,800 participants from all age groups was selected randomly from over 350 houses in 10 villages stratified by topography and followed for two-and-a-half years. Participants were visited every two weeks and screened for clinical malaria, defined as an individual with malaria-related symptoms (fever [axillary temperature≥37.5°C], chills, severe malaise, headache or vomiting) at the time of examination or 1-2 days prior to the examination in the presence of a Plasmodium falciparum positive blood smear. Individuals in the same cohort were screened for asymptomatic malaria infection during the low and high malaria transmission seasons. Parasite densities and temperature were used to define clinical malaria by age in the population. The proportion of fevers attributable to malaria was calculated using logistic regression models.

RESULTS

Incidence of clinical malaria was highest in valley bottom population (5.0% cases per 1,000 population per year) compared to mid-hill (2.2% cases per 1,000 population per year) and up-hill (1.1% cases per 1,000 population per year) populations. The optimum cut-off parasite densities through the determination of the sensitivity and specificity showed that in children less than five years of age, 500 parasites per μl of blood could be used to define the malaria attributable fever cases for this age group. In children between the ages of 5-14, a parasite density of 1,000 parasites per μl of blood could be used to define the malaria attributable fever cases. For individuals older than 14 years, the cut-off parasite density was 3,000 parasites per μl of blood.

CONCLUSION

Clinical malaria case definitions are affected by age and endemicity, which needs to be taken into consideration during evaluation of interventions.

Habitat hydrology and geomorphology control the distribution of malaria vector larvae in rural Africa

Background

Larval source management is a promising component of integrated malaria control and elimination. This requires development of a framework to target productive locations through process-based understanding of habitat hydrology and geomorphology.

Methods

We conducted the first catchment scale study of fine resolution spatial and temporal variation in Anopheles habitat and productivity in relation to rainfall, hydrology and geomorphology for a high malaria transmission area of Tanzania.

Results

Monthly aggregates of rainfall, river stage and water table were not significantly related to the abundance of vector larvae. However, these metrics showed strong explanatory power to predict mosquito larval abundances after stratification by water body type, with a clear seasonal trend for each, defined on the basis of its geomorphological setting and origin.

Conclusion

Hydrological and geomorphological processes governing the availability and productivity of Anopheles breeding habitat need to be understood at the local scale for which larval source management is implemented in order to effectively target larval source interventions. Mapping and monitoring these processes is a well-established practice providing a tractable way forward for developing important malaria management tools.

Protein microarray analysis of antibody responses to Plasmodium falciparum in western Kenyan highland sites with differing transmission levels

Malaria represents a major public health problem in Africa. In the East African highlands, the high-altitude areas were previously considered too cold to support vector population and parasite transmission, rendering the region particularly prone to epidemic malaria due to the lack of protective immunity of the population. Since the 1980’s, frequent malaria epidemics have been reported and these successive outbreaks may have generated some immunity against Plasmodium falciparum amongst the highland residents. Serological studies reveal indirect evidence of human exposure to the parasite, and can reliably assess prevalence of exposure and transmission intensity in an endemic area. However, the vast majority of serological studies of malaria have been, hereto, limited to a small number of the parasite’s antigens. We surveyed and compared the antibody response profiles of age-stratified sera from residents of two endemic areas in the western Kenyan highlands with differing malaria transmission intensities, during two distinct seasons, against 854 polypeptides of P. falciparum using high-throughput proteomic microarray technology. We identified 107 proteins as serum antibody targets, which were then characterized for their gene ontology biological process and cellular component of the parasite, and showed significant enrichment for categories related to immune evasion, pathogenesis and expression on the host’s cell and parasite’s surface. Additionally, we calculated age-fitted annual seroconversion rates for the immunogenic proteins, and contrasted the age-dependent antibody acquisition for those antigens between the two sampling sites. We observed highly immunogenic antigens that produce stable antibody responses from early age in both sites, as well as less immunogenic proteins that require repeated exposure for stable responses to develop and produce different seroconversion rates between sites. We propose that a combination of highly and less immunogenic proteins could be used in serological surveys to detect differences in malaria transmission levels, distinguishing sites of unstable and stable transmission.

A first report of Anopheles funestus sibling species in western Kenya highlands

Understanding disease vector composition is of priority in designing effective disease control programs. In integrated vector control management, understanding of disease vector species among species complexes simplifies priorities for effective control tools selection. This study identified members of the Anopheles funestus complex sampled in western Kenya from 2002 to 2011 from different breeding sites. Larval sampling was carried out using the standard dipper (350ml) in larval habitats in western Kenya highlands from January 2002 to December 2012. The morphologically identified An. funestus larvae were preserved in absolute ethanol for molecular identification using polymerase chain reaction (PCR). Among the 184 identified specimens of An. funestus sampled, only 76 specimens were clearly identified after DNA amplification and PCR. Among these, 25 (32.9%) were An. funestus s.s, 22 (28.9%) An. leesoni, 9 (11.8%) An. rivulorum and 20 (26.3%) were An. vaneedeni. None was identified as An. parensis. This study has demonstrated the existence of the siblings species of An. funestus complex in western Kenya highlands. However, there is need for further studies to evaluate the dynamics of the adults and sporozoite infectivity rates throughout the region based on these findings.

Malaria risk factors in Butajira area, south-central Ethiopia: a multilevel analysis

Background

The highlands of Ethiopia, situated between 1,500 and 2,500 m above sea level, experienced severe malaria epidemics. Despite the intensive control attempts, underway since 2005 and followed by an initial decline, the disease remained a major public health concern. The aim of this study was to identify malaria risk factors in highland-fringe south-central Ethiopia.

Methods

This study was conducted in six rural kebeles of Butajira area located 130 km south of Addis Ababa, which are part of demographic surveillance site in Meskan and Mareko Districts, Ethiopia. Using a multistage sampling technique 750 households was sampled to obtain the 3,398 people, the estimated sample size for this study. Six repeated cross-sectional surveys were conducted from October 2008 to June 2010. Multilevel, mixed-effects logistic regression models fitted to Plasmodium infection status (positive or negative) and six variables. Both fixed- and random-effects differences in malaria infection were estimated using median odds ratio and interval odds ratio 80%. The odds ratios and 95% confidence intervals were used to estimate the strength of association.

Results

Overall, 19,207 individuals were sampled in six surveys (median and inter-quartile range value three). Six of the five variables had about two-fold to eight-fold increase in prevalence of malaria. Furthermore, among these variables, October-November survey seasons of both during 2008 and 2009 were strongly associated with increased prevalence of malaria infection. Children aged below five years (adjusted OR= 3.62) and children aged five to nine years (adj. OR= 3.39), low altitude (adj. OR= 5.22), mid-level altitude (adj. OR= 3.80), houses with holes (adj. OR= 1.59), survey seasons such as October-November 2008 (adj. OR= 7.84), January-February 2009 (adj. OR= 2.33), June-July 2009 (adj. OR=3.83), October-November 2009 (adj. OR= 7.71), and January-February 2010 (adj. OR= 3.05) were associated with increased malaria infection.

The estimates of cluster variances revealed differences in malaria infection. The village-level intercept variance for the individual-level predictor (0.71 [95% CI: 0.28-1.82]; SE=0.34) and final (0.034, [95% CI: 0.002-0.615]; SE=0.05) were lower than that of empty (0.80, [95% CI: 0.32-2.01]; SE=0.21).

Conclusion

Malaria control efforts in highland fringes must prioritize children below ten years in designing transmission reduction of malaria elimination strategy.

Topographic models for predicting malaria vector breeding habitats: potential tools for vector control managers

BACKGROUND

Identification of malaria vector breeding sites can enhance control activities. Although associations between malaria vector breeding sites and topography are well recognized, practical models that predict breeding sites from topographic information are lacking. We used topographic variables derived from remotely sensed Digital Elevation Models (DEMs) to model the breeding sites of malaria vectors. We further compared the predictive strength of two different DEMs and evaluated the predictability of various habitat types inhabited by Anopheles larvae.

METHODS

Using GIS techniques, topographic variables were extracted from two DEMs: 1) Shuttle Radar Topography Mission 3 (SRTM3, 90-m resolution) and 2) the Advanced Spaceborne Thermal Emission Reflection Radiometer Global DEM (ASTER, 30-m resolution). We used data on breeding sites from an extensive field survey conducted on an island in western Kenya in 2006. Topographic variables were extracted for 826 breeding sites and for 4520 negative points that were randomly assigned. Logistic regression modelling was applied to characterize topographic features of the malaria vector breeding sites and predict their locations. Model accuracy was evaluated using the area under the receiver operating characteristics curve (AUC).

RESULTS

All topographic variables derived from both DEMs were significantly correlated with breeding habitats except for the aspect of SRTM. The magnitude and direction of correlation for each variable were similar in the two DEMs. Multivariate models for SRTM and ASTER showed similar levels of fit indicated by Akaike information criterion (3959.3 and 3972.7, respectively), though the former was slightly better than the latter. The accuracy of prediction indicated by AUC was also similar in SRTM (0.758) and ASTER (0.755) in the training site. In the testing site, both SRTM and ASTER models showed higher AUC in the testing sites than in the training site (0.829 and 0.799, respectively). The predictability of habitat types varied. Drains, foot-prints, puddles and swamp habitat types were most predictable.

CONCLUSIONS

Both SRTM and ASTER models had similar predictive potentials, which were sufficiently accurate to predict vector habitats. The free availability of these DEMs suggests that topographic predictive models could be widely used by vector control managers in Africa to complement malaria control strategies.

Variation in malaria transmission dynamics in three different sites in Western kenya

The main objective was to investigate malaria transmission dynamics in three different sites, two highland villages (Fort Ternan and Lunyerere) and a lowland peri-urban area (Nyalenda) of Kisumu city. Adult mosquitoes were collected using PSC and CDC light trap while malaria parasite incidence data was collected from a cohort of children on monthly basis. Rainfall, humidity and temperature data were collected by automated weather stations. Negative binomial and Poisson generalized additive models were used to examine the risk of being infected, as well as the association with the weather variables. Anopheles gambiae s.s. was most abundant in Lunyerere, An. arabiensis in Nyalenda and An. funestus in Fort Ternan. The CDC light traps caught a higher proportion of mosquitoes (52.3%) than PSC (47.7%), although not significantly different (P = 0.689). The EIR's were 0, 61.79 and 6.91 bites/person/year for Fort Ternan, Lunyerere and Nyalenda. Site, month and core body temperature were all associated with the risk of having malaria parasites (P < 0.0001). Rainfall was found to be significantly associated with the occurrence of P. falciparum malaria parasites, but not relative humidity and air temperature. The presence of malaria parasite-infected children in all the study sites provides evidence of local malaria transmission.