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A cross-sectional survey of agricultural areas, combined with routinely monitored mosquito larval information, was conducted in urban Dar es Salaam, Tanzania, to investigate how agricultural and geographical features may influence the presence of Anopheles larvae. Data were integrated into a geographical information systems framework, and predictors of the presence of Anopheles larvae in farming areas were assessed using multivariate logistic regression with independent random effects. It was found that more than 5% of the study area (total size 16.8 km2) was used for farming in backyard gardens and larger open spaces. The proportion of habitats containing Anopheles larvae was 1.7 times higher in agricultural areas compared to other areas (95% confidence interval = 1.56-1.92). Significant geographic predictors of the presence of Anopheles larvae in gardens included location in lowland areas, proximity to river, and relatively impermeable soils. Agriculture-related predictors comprised specific seedbed types, mid-sized gardens, irrigation by wells, as well as cultivation of sugar cane or leafy vegetables. Negative predictors included small garden size, irrigation by tap water, rainfed production and cultivation of leguminous crops or fruit trees. Although there was an increased chance of finding Anopheles larvae in agricultural sites, it was found that breeding sites originated by urban agriculture account for less than a fifth of all breeding sites of malaria vectors in Dar es Salaam. It is suggested that strategies comprising an integrated malaria control effort in malaria-endemic African cities include participatory involvement of farmers by planting shade trees near larval habitats.

Background Ethiopia embarked on combating malaria with an aim to eliminate malaria from low transmission districts by 2030. A continuous monitoring of malaria prevalence in areas under elimination settings is important to evaluate the status of malaria transmission and the effectiveness of the currently existing malaria intervention strategies. The aim of this study was to assess the prevalence of malaria and associated risk factors in selected areas of Dembiya district. Methods A cross-sectional parasitological and retrospective survey was conducted in the two localities of Dembiya District, selected based on their long standing history of implementing malaria prevention and elimination strategies. Thin and thick blood smears collected from 735 randomly selected individuals between October and December, 2018 were microscopically examined for malaria parasites. Six years (2012–2017) retrospective malaria data was collected from the medical records of the health centres. Structured questionnaires were prepared to collect information about the socio-economic data of the population. Logistic regression analysis was used to determine a key risk factor explaining the prevalence of malaria. The data were analysed using SPSS version 20 and p ≤ 0.05 were considered statistically significant. Results The 6-year retrospective malaria prevalence trend indicates an overall malaria prevalence of 22.4%, out of which Plasmodium falciparum was the dominant species. From a total of 735 slides examined for the presence of malaria parasites, 3.5% (n = 26) were positive for malaria parasites, in which P. falciparum was more prevalent (n = 17; 2.3%), Plasmodium vivax (n = 5; 0.7%), and mixed infections (n = 4; 0.5%). Males were 2.6 times more likely to be infected with malaria than females (AOR = 2.6; 95% CI 1.0, 6.4), and individuals with frequent outdoor activity were 16.4 times more vulnerable than individuals with limited outdoor activities (AOR = 16.4, 95% CI 1.8, 147.9). Furthermore, awareness about malaria transmission was significantly associated with the prevalence of malaria. Conclusions Malaria is still a public health problem in Dembiya district irrespective of the past and existing vector control interventions. Therefore, the authorities should work on designing alternative intervention strategies targeting outdoor malaria transmission and improving community awareness about malaria transmission and control methods in the study area. For this, continuous monitoring of vectors’ susceptibility, density, and behaviour is very important in such areas.

Nigeria is the most malaria-endemic country in the world. Vegetation and livestock practices have been linked to malaria transmission but little is known about these in Nigeria. The study aimed to evaluate the influence of vegetation and livestock as predictors of malaria transmission in Nigeria. Secondary data obtained from the Nigerian Demographic and Health Survey’s Geospatial Covariate Datasets Manual were used for the analysis. The survey was carried out successfully in 1389 clusters of thirty (30) households each using a two-stage stratified random sampling design. Hierarchical beta regression models were used to model the associations between malaria incidence, enhanced vegetation index (EVI), and livestock practices. The correlation coefficients for vegetation index and livestock-related variables ranged from − 0.063 to 0.074 and varied significantly with the incidence of malaria in Nigeria ( P  < 0.001). The model showed vegetation index, livestock goats, and sheep as positive predictors of malaria transmission. Conversely, livestock chicken and pigs were observed to reduce the risk of malaria. The study recommends the need to take into account local differences in transmission when developing malaria early warning systems that utilize environmental and livestock predictors.

Ranchi, the capital of Jharkhand state is endemic for malaria, particularly the Bundu Primary Health Centre (PHC) is the worst affected. Therefore, a study was initiated during 2009 using remote sensing (RS) and geographical information system (GIS) to identify risk factors responsible for high endemicity in this PHC. Bundu and Angara in Ranchi district were identified as high and low malaria endemic PHCs based on epidemiological data of three years (2007-09). The habitation, streams, other water body, landform, PHC and village boundary thematic maps were prepared using IRS-P6/LISS III-IV imageries and macro level breeding sites were identified. Digital elevation model (DEM) of the PHCs was generated using Cartosat Stereo Pair images and from DEM, slope map was derived to calculate flat area. From slope, aspect map was derived to indicate direction of water flow. Length of perennial streams, area under rocky terrain and buffer zones of 250, 500 and 750 m were constructed around streams. High resolution remote sensing imageries were used to identify micro level breeding sites. Based on macro-micro breeding sites, six villages from each PHC were selected randomly having combination of different parameters representing all ecotypes. Entomological data were collected during 2010-11 in pre- and post-monsoon seasons following standard techniques and analyzed statistically. Differential analysis was attempted to comprehend socioeconomic and other determinants associated with malaria transmission. The study identified eight risk factors responsible for higher malaria endemicity in Bundu in comparison to Angara PHC based on ecological, entomological, socioeconomic and other local parameters. Focused interventions in integrated vector management (IVM) mode are required to be carried out in the district for better management and control of disease.

Background Under-five child malaria is one of the leading causes of morbidity and mortality globally, especially among sub-Saharan African countries like Ghana. In Ghana, malaria is responsible for about 20,000 deaths in children annually of which 25% are those aged < 5 years. To provide opportunities for efficient malaria surveillance and targeted control efforts amidst limited public health resources, the study produced high resolution interactive web-based spatial maps that characterized geographical differences in malaria risk and identified high burden communities. Methods This modelling and web-based mapping study utilized data from the 2019 Malaria Indicators Survey (MIS) of the Demographic and Health Survey Program. A novel and advanced Bayesian geospatial modelling and mapping approaches were utilized to examine predictors and geographical differences in under-five malaria. The model was validated via a cross-validation approach. The study produced an interactive web-based visualization map of the malaria risk by mapping the predicted malaria prevalence at both sampled and unsampled locations. Results In 2019, 718 (25%) of 2867 under-five children surveyed had malaria. Substantial geographical differences in under-five malaria risk were observed. ITN coverage (log-odds 4.5643, 95% credible interval = 2.4086–6.8874), travel time (log-odds 0.0057, 95% credible interval = 0.0017–0.0099) and aridity (log-odds = 0.0600, credible interval = 0.0079–0.1167) were predictive of under-five malaria in the spatial model. The overall predicted national malaria prevalence was 16.3% (standard error (SE) 8.9%) with a range of 0.7% to 51.4% in the spatial model with covariates and prevalence of 28.0% (SE 13.9%) with a range of 2.4 to 67.2% in the spatial model without covariates. Residing in parts of Central and Bono East regions was associated with the highest risk of under-five malaria after adjusting for the selected covariates. Conclusion The high-resolution interactive web-based predictive maps can be used as an effective tool in the identification of communities that require urgent and targeted interventions by programme managers and implementers. This is key as part of an overall strategy in reducing the under-five malaria burden and its associated morbidity and mortality in a country with limited public health resources where universal intervention is practically impossible.

Background Malaria is one of the key research concerns in climate change-health relationships. Numerous risk assessments and modelling studies provide evidence that the transmission range of malaria will expand with rising temperatures, adversely impacting on vulnerable communities in the East African highlands. While there exist multiple lines of evidence for the influence of climate change on malaria transmission, there is insufficient understanding of the complex and interdependent factors that determine the risk and vulnerability of human populations at the community level. Moreover, existing studies have had limited focus on the nature of the impacts on vulnerable communities or how well they are prepared to cope. In order to address these gaps, a systems approach was used to present an integrated risk and vulnerability assessment framework for studies of community level risk and vulnerability to malaria due to climate change. Results Drawing upon published literature on existing frameworks, a systems approach was applied to characterize the factors influencing the interactions between climate change and malaria transmission. This involved structural analysis to determine influential, relay, dependent and autonomous variables in order to construct a detailed causal loop conceptual model that illustrates the relationships among key variables. An integrated assessment framework that considers indicators of both biophysical and social vulnerability was proposed based on the conceptual model. Conclusions A major conclusion was that this integrated assessment framework can be implemented using Bayesian Belief Networks, and applied at a community level using both quantitative and qualitative methods with stakeholder engagement. The approach enables a robust assessment of community level risk and vulnerability to malaria, along with contextually relevant and targeted adaptation strategies for dealing with malaria transmission that incorporate both scientific and community perspectives.

Background Malaria remains a burden globally, with the African region accounting for 94% of the overall disease burden and deaths in 2019. It is the major cause of morbidity and mortality among children in Nigeria. Though different environmental factors have been assessed to influence the distribution and transmission of malaria vectors, there is a shortage of information on how they may influence malaria transmission among under-fives in Nigeria. Methods This study was based on the secondary data analysis of the Nigeria Malaria Indicator Survey 2021. The study sample comprised 10,645 women (aged 15–49) who delivered a child in the 2 years preceding the survey. The study was restricted to under-fives. Logistic regression was used to identify factors associated with the risk of malaria. Results There was a positive association between the risk of malaria and heard/seen malaria messages in the last 6 months (AOR 1.39, 95% CI 1.19–1.62), houses with walls built using rudimentary materials (AOR = 1.38, 95% CI 1.04–1.83), at least 6 children living in the house (AOR 1.22, 95% CI 1.00–1.49), children being 1 or 2 years old was associated with increased odds (AOR 1.89, 95% CI 1.50–2.34 and AOR 1.89, 95% CI 1.52–2.36), children from households with only treated nets (AOR 1.23, 95% CI 1.04–1.46) and those from the North West or South East regions (AOR 1.50, 95% CI 1.10–2.05 and AOR 1.48, 95% CI 1.01–2.16), respectively. All other predictors were not associated with the risk of malaria. Conclusion The factors associated with the risk of malaria in this study included sleeping under treated mosquito nets, the age of the children, residing in the northwest and southeast regions, wall construction material, 6 children and above in the household and hearing/seen malaria messages in the last 6 months. Continuous health education and public health interventions, such as the provision of LLITNs, will reduce the risk of malaria and improve the health and well-being of children under 5 years of age.

Climatic conditions are a key determinant of malaria transmission intensity, through their impacts on both the parasite and its mosquito vectors. Mathematical models relating climatic conditions to malaria transmission can be used to develop spatial maps of climatic suitability for malaria. These maps underpin efforts to quantify the distribution and burden of malaria in humans, enabling improved monitoring and control. Previous work has developed mathematical models and global maps for the suitability of temperature for malaria transmission. In this paper, existing temperature-based models are extended to include two other important bioclimatic factors: humidity and rainfall. This model is combined with fine spatial resolution climatic data to produce a more biologically-realistic global map of climatic suitability for Plasmodium falciparum malaria. The climatic suitability index developed corresponds more closely than previous temperature suitability indices with the global distribution of P. falciparum malaria. There is weak agreement between the Malaria Atlas Project estimates of P. falciparum prevalence in Africa and the estimates of suitability solely based on temperature (Spearman Correlation coefficient of ρ = 0.24 ). The addition of humidity and then rainfall improves the comparison ( ρ = 0.62 when humidity added; ρ = 0.70 when both humidity and rainfall added). By incorporating the impacts of humidity and rainfall, this model identifies arid regions that are not climatically suitable for transmission of P. falciparum malaria. Incorporation of this improved index of climatic suitability into geospatial models can improve global estimates of malaria prevalence and transmission intensity.

Background Vector-borne diseases, such as malaria, pose a significant global threat, and climatological factors greatly influence their intensity. Tropical countries, like India, are particularly vulnerable to such diseases, making accurate estimation of malaria risk crucial. Methods This study utilized the well-known Vector-borne Disease Community Model, VECTRI, developed by the International Centre for Theoretical Physics in Trieste. The model was implemented to estimate malaria’s Entomological Inoculation Rate (EIR). Future climatic prediction datasets, including CMIP 5 and population data sets, were used as inputs for the analysis. Three RCP scenarios are considered (Representative Concentration Pathways are climate change scenarios that project radiative forcing to 2100 due to future greenhouse gas concentrations). The projections covered the period from 1 Jan, 2020, to 31 Dec, 2029. Results The estimated mean EIR for the years 2020–2029 ranged, and a significant decline in malaria risk was observed with all RCP 2.6, 4.5, and 8.5 scenarios. Each year 0.3 to 2.6 [min–max] EIR/person/day decline is observed with a strong decline in man rainfall ranging from 5 to 17 [min–max] mm/year and associated high temperatures ranging from 0.03 to 0.06 [min–max] °C/year. During the post-monsoon period, August to November were identified as highly prone to malaria transmission. Spatial analysis revealed that the east coast of India faced a higher vulnerability to malaria risk, which kept increasing through RCP scenarios. Thus, it is essential to exercise caution, especially in areas with heavy rainfall. Conclusion This research provides valuable insights for policy-makers, highlighting the need to implement future strategies to mitigate malaria risk effectively. By utilizing these findings, appropriate measures can be taken to combat the threat posed by malaria and protect public health.

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.