Malaria Risk Research Articles

Geospatial based model for malaria risk prediction in Kilombero valley, South-eastern, Tanzania.

Malaria continues to pose a major public health challenge in tropical regions. Despite significant efforts to control malaria in Tanzania, there are still residual transmission cases. Unfortunately, little is known about where these residual malaria transmission cases occur and how they spread. In Tanzania for example, the transmission is heterogeneously distributed. In order to effectively control and prevent the spread of malaria, it is essential to understand the spatial distribution and transmission patterns of the disease. This study seeks to predict areas that are at high risk of malaria transmission so that intervention measures can be developed to accelerate malaria elimination efforts. This study employs a geospatial based model to predict and map out malaria risk area in Kilombero Valley. Environmental factors related to malaria transmission were considered and assigned valuable weights in the Analytic Hierarchy Process (AHP), an online system using a pairwise comparison technique. The malaria hazard map was generated by a weighted overlay of the altitude, slope, curvature, aspect, rainfall distribution, and distance to streams in Geographic Information Systems (GIS). Finally, the risk map was created by overlaying components of malaria risk including hazards, elements at risk, and vulnerability. The study demonstrates that the majority of the study area falls under moderate risk level (61%), followed by the low risk level (31%), while the high malaria risk area covers a small area, which occupies only 8% of the total area. The findings of this study are crucial for developing spatially targeted interventions against malaria transmission in residual transmission settings. Predicted areas prone to malaria risk provide information that will inform decision-makers and policymakers for proper planning, monitoring, and deployment of interventions.

Complete sources of cluster variation on the risk of under-five malaria in Uganda: a multilevel-weighted mixed effects logistic regression model approach

BackgroundMalaria, a major cause of mortality worldwide is linked to a web of determinants ranging from individual to contextual factors. This calls for examining the magnitude of the effect of clustering within malaria data. Regrettably, researchers usually ignore cluster variation on the risk of malaria and also apply final survey weights in multilevel modelling instead of multilevel weights. This most likely produces biased estimates, misleads inference and lowers study power. The objective of this study was to determine the complete sources of cluster variation on the risk of under-five malaria and risk factors associated with under-five malaria in Uganda.MethodsThis study applied a multilevel-weighted mixed effects logistic regression model to account for both individual and contextual factors.ResultsEvery additional year in a child’s age was positively associated with malaria infection (AOR = 1.42; 95% CI 1.33–1.52). Children whose mothers had at least a secondary school education were less likely to suffer from malaria infection (AOR = 0.53; 95% CI 0.30–0.95) as well as those who dwelled in households in the two highest wealth quintiles (AOR = 0.42; 95% CI 0.27–0.64). An increase in altitude by 1 m was negatively associated with malaria infection (AOR = 0.98; 95% CI 0.97–0.99). About 77% of the total variation in the positive testing for malaria was attributable to differences between enumeration areas (ICC = 0.77; p < 0.001).ConclusionsInterventions towards reducing the burden of under-five malaria should be prioritized to improve individual-level characteristics compared to household-level features. Enumeration area (EA) specific interventions may be more effective compared to household specific interventions.

Gut Bacteroides act in a microbial consortium to cause susceptibility to severe malaria

Malaria is caused by Plasmodium species and remains a significant cause of morbidity and mortality globally. Gut bacteria can influence the severity of malaria, but the contribution of specific bacteria to the risk of severe malaria is unknown. Here, multiomics approaches demonstrate that specific species of Bacteroides are causally linked to the risk of severe malaria. Plasmodium yoelii hyperparasitemia-resistant mice gavaged with murine-isolated Bacteroides fragilis develop P. yoelii hyperparasitemia. Moreover, Bacteroides are significantly more abundant in Ugandan children with severe malarial anemia than with asymptomatic P. falciparum infection. Human isolates of Bacteroides caccae, Bacteroides uniformis, and Bacteroides ovatus were able to cause susceptibility to severe malaria in mice. While monocolonization of germ-free mice with Bacteroides alone is insufficient to cause susceptibility to hyperparasitemia, meta-analysis across multiple studies support a main role for Bacteroides in susceptibility to severe malaria. Approaches that target gut Bacteroides present an opportunity to prevent severe malaria and associated deaths.

Space-time modelling of monthly malaria incidence for seasonal associated drivers and early epidemic detection in Southern Ethiopia

BackgroundAlthough Ethiopia has made great strides in recent years to reduce the threat of malaria, the disease remains a significant issue in most districts of the country. It constantly disappears in parts of the areas before reappearing in others with erratic transmission rates. Thus, developing a malaria epidemic early warning system is important to support the prevention and control of the incidence.MethodsSpace-time malaria risk mapping is essential to monitor and evaluate priority zones, refocus intervention, and enable planning for future health targets. From August 2013 to May 2019, the researcher considered an aggregated count of genus Plasmodium falciparum from 149 districts in Southern Ethiopia. Afterwards, a malaria epidemic early warning system was developed using model-based geostatistics, which helped to chart the disease’s spread and future management.ResultsRisk factors like precipitation, temperature, humidity, and nighttime light are significantly associated with malaria with different rates across the districts. Districts in the southwest, including Selamago, Bero, and Hamer, had higher rates of malaria risk, whereas in the south and centre like Arbaminch and Hawassa had moderate rates. The distribution is inconsistent and varies across time and space with the seasons.ConclusionDespite the importance of spatial correlation in disease risk mapping, it may occasionally be a good idea to generate epidemic early warning independently in each district to get a quick picture of disease risk. A system like this is essential for spotting numerous inconsistencies in lower administrative levels early enough to take corrective action before outbreaks arise.

Understanding the importance of spatial correlation in identifying spatio-temporal variation of disease risk, in the case of malaria risk mapping in southern Ethiopia

Malaria remains a major health problem in developing countries despite a significant reduction in incidence in the last few years. Disease mapping thus helps to understand the spatial pattern and identify areas characterized by unusual risks. Several spatial models have been used to analyze the incidence of malaria. We aim to compare the predictive performance of these models and investigate the effect of ignoring spatial correlation. The reported malaria case counts of genus P.falciparum in 149 districts of southern Ethiopia from January 2016 to May 2019 were analyzed using the spatial time series model (STS) that ignores spatial correlation, Spatio-temporal conditional autoregressive model (STCAR), Spatio-temporal geostatistical model (STG) and Spatio-temporal spatial discrete approximation to log Gaussian cox process (STSDALGCP). We assess the predictive performance of the models using root mean square error, mean absolute error, and coverage probability. We found that monthly average rainfall, temperature, humidity, and EVI are significantly associated with malaria risk. The spatial variation of malaria incidence changes with time, in particular, the high incidence was observed from November to December, months after heavy rainfall, and more pronounced in the southwest of the country. STSDALGCP gives a small prediction error in test set and captures the uncertainties better than other models, while the STS model gives a high prediction error. Accounting for spatial correlation is crucial for disease risk mapping and leads to better prediction of disease risk. Since malaria transmission operates in a spatially continuous manner, a spatially continuous model should be considered when it is computationally feasible.

Bayesian belief network modelling approach for predicting and ranking risk factors for malaria infections among children under 5 years in refugee settlements in Uganda

BackgroundMalaria risk factors at household level are known to be complex, uncertain, stochastic, nonlinear, and multidimensional. The interplay among these factors, makes targeted interventions, and resource allocation for malaria control challenging. However, few studies have demonstrated malaria’s transmission complexity, control, and integrated modelling, with no available evidence on Uganda’s refugee settlements. Using the 2018–2019 Uganda’s Malaria Indicator Survey (UMIS) data, an alternative Bayesian belief network (BBN) modelling approach was used to analyse, predict, rank and illustrate the conceptual reasoning, and complex causal relationships among the risk factors for malaria infections among children under-five in refugee settlements of Uganda.MethodsIn the UMIS, household level information was obtained using standardized questionnaires, and a total of 675 children under 5 years were tested for malaria. From the dataset, a casefile containing malaria test results, demographic, social-economic and environmental information was created. The casefile was divided into a training (80%, n = 540) and testing (20%, n = 135) datasets. The training dataset was used to develop the BBN model following well established guidelines. The testing dataset was used to evaluate model performance.ResultsModel accuracy was 91.11% with an area under the receiver-operating characteristic curve of 0.95. The model’s spherical payoff was 0.91, with the logarithmic, and quadratic losses of 0.36, and 0.16 respectively, indicating a strong predictive, and classification ability of the model. The probability of refugee children testing positive, and negative for malaria was 48.1% and 51.9% respectively. The top ranked malaria risk factors based on the sensitivity analysis included: (1) age of child; (2) roof materials (i.e., thatch roofs); (3) wall materials (i.e., poles with mud and thatch walls); (4) whether children sleep under insecticide-treated nets; 5) type of toilet facility used (i.e., no toilet facility, and pit latrines with slabs); (6) walk time distance to water sources (between 0 and 10 min); (7) drinking water sources (i.e., open water sources, and piped water on premises).ConclusionRanking, rather than the statistical significance of the malaria risk factors, is crucial as an approach to applied research, as it helps stakeholders determine how to allocate resources for targeted malaria interventions within the constraints of limited funding in the refugee settlements.

The public health response to a Plasmodium malariae outbreak in Penampang district, Sabah during a COVID-19 movement control order

BackgroundSince 2018, no indigenous human malaria cases has been reported in Malaysia. However, during the recent COVID-19 pandemic the World Health Organization is concerned that the pandemic might erode the success of malaria control as there are reports of increase malaria cases in resource limited countries. Little is known how the COVID-19 pandemic has impacted malaria in middle-income countries like Malaysia. Here the public health response to a Plasmodium malariae outbreak occurred in a village in Sabah state, Malaysia, during a COVID-19 movement control order is reported.MethodsAn outbreak was declared following the detection of P. malariae in July 2020 and active case detection for malaria was performed by collecting blood samples from residents residing within 2 km radius of Moyog village. Vector prevalence and the efficacy of residual insecticides were determined. Health awareness programmes were implemented to prevent future outbreaks. A survey was conducted among villagers to understand risk behaviour and beliefs concerning malaria.ResultsA total of 5254 blood samples collected from 19 villages. Among them, 19 P. malariae cases were identified, including the index case, which originated from a man who returned from Indonesia. His return from Indonesia and healthcare facilities visit coincided with the movement control order during COVID-19 pandemic when the healthcare facilities stretched its capacity and only serious cases were given priority. Despite the index case being a returnee from a malaria endemic area presenting with mild fever, no malaria test was performed at local healthcare facilities. All cases were symptomatic and uncomplicated except for a pregnant woman with severe malaria. There were no deaths; all patients recovered following treatment with artemether-lumefantrine combination therapy. Anopheles balabacensis and Anopheles barbirostris were detected in ponds, puddles and riverbeds. The survey revealed that fishing and hunting during night, and self-treatment for mild symptoms contributed to the outbreak. Despite the index case being a returnee from a malaria-endemic area presenting with mild fever, no malaria test was performed at local healthcare facilities.ConclusionThe outbreak occurred during a COVID-19 movement control order, which strained healthcare facilities, prioritizing only serious cases. Healthcare workers need to be more aware of the risk of malaria from individuals who return from malaria endemic areas. To achieve malaria elimination and prevention of disease reintroduction, new strategies that include multisectoral agencies and active community participation are essential for a more sustainable malaria control programme.

Spatial Optimization Methods for Malaria Risk Mapping in Sub-Saharan African Cities Using Demographic and Health Surveys.

Vector-borne diseases, such as malaria, are affected by the rapid urban growth and climate change in sub-Saharan Africa (SSA). In this context, intra-urban malaria risk maps act as a key decision-making tool for targeting malaria control interventions, especially in resource-limited settings. The Demographic and Health Surveys (DHS) provide a consistent malaria data source for mapping malaria risk at the national scale, but their use is limited at the intra-urban scale because survey cluster coordinates are randomly displaced for ethical reasons. In this research, we focus on predicting intra-urban malaria risk in SSA cities-Dakar, Dar es Salaam, Kampala and Ouagadougou-and investigate the use of spatial optimization methods to overcome the effect of DHS spatial displacement. We modeled malaria risk using a random forest regressor and remotely sensed covariates depicting the urban climate, the land cover and the land use, and we tested several spatial optimization approaches. The use of spatial optimization mitigated the effects of DHS spatial displacement on predictive performance. However, this comes at a higher computational cost, and the percentage of variance explained in our models remained low (around 30%-40%), which suggests that these methods cannot entirely overcome the limited quality of epidemiological data. Building on our results, we highlight potential adaptations to the DHS sampling strategy that would make them more reliable for predicting malaria risk at the intra-urban scale.

Zoonotic Malaria Risk in Serra Do Mar, Atlantic Forest, Brazil.

Here, the main goal is to assess natural infections of Plasmodium spp. in anophelines in a forest reserve from the same region where we previously found a surprisingly high rate (5.2%) of plasmodia infections (n = 25) in Kerteszia mosquitoes (N = 480) on the slopes of Serra do Mar, Atlantic Forest, Brazil. The mosquito collection sampling was carried out at the Legado das Águas Forest Reserve using CDC light traps and Shannon traps at night (5-10 pm) in 3-day collections in November 2021 and March, April, May, and November 2022. The captured specimens were morphologically identified at the species level and had their genomic DNA extracted in pools of up to 10 mosquitoes/pool. Each pool was tested using 18S qPCR and cytb nested PCR plus sequencing. A total of 5301 mosquitoes, mostly belonging to the genus Kerteszia (99.7%), were sampled and sorted into 773 pools. Eight pools positive for Plasmodium spp. were identified: four for Plasmodium spp., one for P. vivax or P. simium, one for P. malariae or P. brasilianum, and two for the P. falciparum-like parasite. After Sanger sequencing, two results were further confirmed: P. vivax or P. simium and P. malariae or P. brasilianum. The minimum infection rate for Kerteszia mosquitoes was 0.15% (eight positive pools/5285 Kerteszia mosquitoes). The study reveals a lower-than-expected natural infection rate (expected = 5.2% vs. observed = 0.15%). This low rate relates to the absence of Alouatta monkeys as the main simian malaria reservoir in the studied region. Their absence was due to a significant population decline following the reemergence of yellow fever virus outbreaks in the Atlantic Forest from 2016 to 2019. However, this also indicates the existence of alternative reservoirs to infect Kerteszia mosquitoes. The found zoonotic species of Plasmodium, including the P. falciparum-like parasite, may represent a simian malaria risk and thus a challenge for malaria elimination in Brazil.

Epidemiology of malaria in Rohingya refugee camps in Bangladesh within 2017–2020

BackgroundMalaria causes significant morbidity and mortality in tropical and sub-tropical regions, particularly in humanitarian emergencies including refugee camps in malaria endemic areas. An epidemiological investigation was conducted on malaria disease distribution and risk factors in the world’s largest refugee settlement, the Rohingya refugee camps on the south-eastern border area of Bangladesh, within 2017–2020.MethodsFrom February 2017 to March 2020, 30,460 febrile patients were tested for malaria using light microscopy and rapid diagnostic tests. Most were self-presenting symptomatic patients and a minority were from door-to-door malaria screening. Diagnostic tests were done by trained medical technologists upon the advice of the concerned physicians in the camps. Test positivity rate (%) and annual parasite incidence were calculated and compared using chi-squared (χ 2) test or odds ratios.ResultsThe overall average annual test positivity rate (TPR) was 0.05%. TPR was highest in people who had travelled to the forest in the previous 2 months, at 13.60%. Cases were clustered among male adults aged 15–60 years. There were no cases among children under five years or pregnant women and no deaths from malaria.ConclusionThis study found very few malaria cases among Rohingya refugees with the majority of cases being imported from hilly forested areas, which were thus assumed to act as the reservoir for transmission.

Associations between prenatal malaria exposure, maternal antibodies at birth, and malaria susceptibility during the first year of life in Burkina Faso.

In this study, we investigated how different categories of prenatal malaria exposure (PME) influence levels of maternal antibodies in cord blood samples and the subsequent risk of malaria in early childhood in a birth cohort study (N = 661) nested within the COSMIC clinical trial (NCT01941264) in Burkina Faso. Plasmodium falciparum infections during pregnancy and infants' clinical malaria episodes detected during the first year of life were recorded. The levels of maternal IgG and IgG1-4 to 15 P. falciparum antigens were measured in cord blood by quantitative suspension array technology. Results showed a significant variation in the magnitude of maternal antibody levels in cord blood, depending on the PME category, with past placental malaria (PM) more frequently associated with significant increases of IgG and/or subclass levels across three groups of antigens defined as pre-erythrocytic, erythrocytic, and markers of PM, as compared to those from the cord of non-exposed control infants. High levels of antibodies to certain erythrocytic antigens (i.e., IgG to EBA140 and EBA175, IgG1 to EBA175 and MSP142, and IgG3 to EBA140 and MSP5) were independent predictors of protection from clinical malaria during the first year of life. By contrast, high levels of IgG, IgG1, and IgG2 to the VAR2CSA DBL1-2 and IgG4 to DBL3-4 were significantly associated with an increased risk of clinical malaria. These findings indicate that PME categories have different effects on the levels of maternal-derived antibodies to malaria antigens in children at birth, and this might drive heterogeneity to clinical malaria susceptibility in early childhood.

Whole-genome surveillance identifies markers of Plasmodium falciparum drug resistance and novel genomic regions under selection in Mozambique.

Malaria is a devastating disease caused by Plasmodium parasites. The evolution of parasite drug resistance continues to hamper progress toward malaria elimination, and despite extensive efforts to control malaria, it remains a leading cause of death in Mozambique and other countries in the region. The development of successful vaccines and identification of molecular markers to track drug efficacy are essential for managing the disease burden. We present an analysis of the parasite genome in Mozambique, a country with one of the highest malaria burdens globally and limited available genomic data, revealing current selection pressure. We contribute additional evidence to limited prior studies supporting the effectiveness of SWGA in producing reliable genomic data from complex clinical samples. Our results provide the identity of genomic loci that may be associated with current antimalarial drug use, including artemisinin and lumefantrine, and reveal selection pressure predicted to compromise the efficacy of current vaccine candidates.

Association of anthelmintic treatment with malaria prevalence in Rural Sussundenga, Mozambique.

Background: Mozambique has the 4 th highest malaria incidence and mortality globally. Despite the existing malaria control strategies, malaria prevalence remains stagnant. These challenges have increased calls for innovative strategies in areas with the highest disease burden. Community mass treatment with anthelmintic agents have been used as an effective tool for the control of major helminth infections and has emerged as a potential tool for vector control in the fight against malaria. Methods: This was an analysis of data from a cross-sectional community-based survey designed to study malaria risk, prevention, and health seeking behaviors in Sussundenga, Mozambique. Using logistic regression models, we quantified the association between ever receiving anthelmintic treatment and P. falciparum infection. We also fit models to determine the association between recent anthelmintic treatment and malaria infection. Results: Two-hundred, seventy-seven (277) participants from 83 households were included in this analysis. The prevalence of P. falciparum infection measured by rapid diagnostic test (RDT) was 30%. 77% of participants reported having ever received anthelmintics. The prevalence of malaria was slightly higher among participants who reported ever taking anthelmintics. There was no statistically significant association between prior receipt of anthelmintic and P. falciparum malaria infection after adjusting for age, ITN use and head of household full-time employment (OR = 1.37, 95% CI, 0.70-2.70, p = 0.36). However, recent intake of anthelmintics was associated with lower odds of testing positive for in the adjusted models (OR = 0.35, 95% CI, 0.07-1.80, p = 0.21), but this was not statistically significant. Conclusions: Our findings show that the benefit of anthelmintics treatment as a control tool for P. falciparum malaria infection is likely tied to when it is administered rather than if it was ever administered. These findings offer evidence for making decisions in planning mass community deworming in sub-Saharan Africa.

Risk of Malaria Following Untreated Subpatent Plasmodium falciparum Infections: Results Over 4 Years From a Cohort in a High-Transmission Area in Western Kenya.

People with suspected malaria may harbor Plasmodium falciparum undetected by rapid diagnostic test (RDT). The impact of these subpatent infections on the risk of developing clinical malaria is not fully understood. We analyzed subpatent P. falciparum infections using a longitudinal cohort in a high-transmission site in Kenya. Weighted Kaplan-Meier models estimated the risk difference (RD) for clinical malaria during the 60 days following a symptomatic subpatent infection. Stratum-specific estimates by age and transmission season assessed modification. Over 54 months, we observed 1128 symptomatic RDT-negative suspected malaria episodes, of which 400 (35.5%) harbored subpatent P. falciparum. Overall, the 60-day risk of developing clinical malaria was low following all episodes (8.6% [95% confidence interval, 6.7%-10.4%]). In the low-transmission season, the risk of clinical malaria was slightly higher in those with subpatent infection, whereas the opposite was true in the high-transmission season (low-transmission season RD, 2.3% [95% confidence interval, .4%-4.2%]; high-transmission season RD, -4.8% [-9.5% to -.05%]). The risk of developing clinical malaria among people with undetected subpatent infections is low. A slightly elevated risk in the low-transmission season may merit alternate management, but RDTs identify clinically relevant infections in the high-transmission season.

Non-O ABO blood group genotypes differ in their associations with Plasmodium falciparum rosetting and severe malaria.

Blood group O is associated with protection against severe malaria and reduced size and stability of P. falciparum-host red blood cell (RBC) rosettes compared to non-O blood groups. Whether the non-O blood groups encoded by the specific ABO genotypes AO, BO, AA, BB and AB differ in their associations with severe malaria and rosetting is unknown. The A and B antigens are host RBC receptors for rosetting, hence we hypothesized that the higher levels of A and/or B antigen on RBCs from AA, BB and AB genotypes compared to AO/BO genotypes could lead to larger rosettes, increased microvascular obstruction and higher risk of malaria pathology. We used a case-control study of Kenyan children and in vitro adhesion assays to test the hypothesis that "double dose" non-O genotypes (AA, BB, AB) are associated with increased risk of severe malaria and larger rosettes than "single dose" heterozygotes (AO, BO). In the case-control study, compared to OO, the double dose genotypes consistently had higher odds ratios (OR) for severe malaria than single dose genotypes, with AB (OR 1.93) and AO (OR 1.27) showing most marked difference (p = 0.02, Wald test). In vitro experiments with blood group A-preferring P. falciparum parasites showed that significantly larger rosettes were formed with AA and AB host RBCs compared to OO, whereas AO and BO genotypes rosettes were indistinguishable from OO. Overall, the data show that ABO genotype influences P. falciparum rosetting and support the hypothesis that double dose non-O genotypes confer a greater risk of severe malaria than AO/BO heterozygosity.

Human NCR3 gene variants rs2736191 and rs11575837 alter longitudinal risk for development of pediatric malaria episodes and severe malarial anemia

BackgroundPlasmodium falciparum malaria is a leading cause of pediatric morbidity and mortality in holoendemic transmission areas. Severe malarial anemia [SMA, hemoglobin (Hb) < 5.0 g/dL in children] is the most common clinical manifestation of severe malaria in such regions. Although innate immune response genes are known to influence the development of SMA, the role of natural killer (NK) cells in malaria pathogenesis remains largely undefined. As such, we examined the impact of genetic variation in the gene encoding a primary NK cell receptor, natural cytotoxicity-triggering receptor 3 (NCR3), on the occurrence of malaria and SMA episodes over time.MethodsSusceptibility to malaria, SMA, and all-cause mortality was determined in carriers of NCR3 genetic variants (i.e., rs2736191:C > G and rs11575837:C > T) and their haplotypes. The prospective observational study was conducted over a 36 mos. follow-up period in a cohort of children (n = 1,515, aged 1.9–40 mos.) residing in a holoendemic P. falciparum transmission region, Siaya, Kenya.ResultsPoisson regression modeling, controlling for anemia-promoting covariates, revealed a significantly increased risk of malaria in carriers of the homozygous mutant allele genotype (TT) for rs11575837 after multiple test correction [Incidence rate ratio (IRR) = 1.540, 95% CI = 1.114–2.129, P = 0.009]. Increased risk of SMA was observed for rs2736191 in children who inherited the CG genotype (IRR = 1.269, 95% CI = 1.009–1.597, P = 0.041) and in the additive model (presence of 1 or 2 copies) (IRR = 1.198, 95% CI = 1.030–1.393, P = 0.019), but was not significant after multiple test correction. Modeling of the haplotypes revealed that the CC haplotype had a significant additive effect for protection against SMA (i.e., reduced risk for development of SMA) after multiple test correction (IRR = 0.823, 95% CI = 0.711–0.952, P = 0.009). Although increased susceptibility to SMA was present in carriers of the GC haplotype (IRR = 1.276, 95% CI = 1.030–1.581, P = 0.026) with an additive effect (IRR = 1.182, 95% CI = 1.018–1.372, P = 0.029), the results did not remain significant after multiple test correction. None of the NCR3 genotypes or haplotypes were associated with all-cause mortality.ConclusionsVariation in NCR3 alters susceptibility to malaria and SMA during the acquisition of naturally-acquired malarial immunity. These results highlight the importance of NK cells in the innate immune response to malaria.

Assessing the population’s correct knowledge of malaria in Malaysia: a vital component for malaria elimination certification

BackgroundMalaria remains a public health problem in Malaysia despite a decline in the number of cases in recent years. Public knowledge of malaria is essential to achieving and maintaining malaria elimination. Therefore, this survey assessed the prevalence of people who had ever heard of malaria, had correct knowledge of malaria transmission, symptoms, risk activities, and prevention measures in the Malaysian population, and identified the associated factors involved.MethodsThe data were obtained from the National Health and Morbidity Survey (NHMS) which was conducted from September to October 2020. A cross-sectional survey with five structured questionnaires using the method of computer-assisted telephone interviews (CATI) was used to collect data. The socio-demographic characteristics such as age, gender, ethnicity, nationality, marital status, educational level, and occupation were recorded. Data were analysed using STATA SE Version 16. Associations between variables were tested using chi-square and logistic regression, with the level of statistical significance set at p < 0.05.ResultsOut of 3085 respondents who participated in this survey, 2491 had ever heard of malaria with a prevalence of 76.1% (95% CI 70.5, 80.9). The prevalence of correct knowledge of malaria transmission was 53.9% (95% CI 50.2, 57.7), symptoms 24.1% (95% CI 21.2, 27.2), risk activities 34.0% (95% CI 29.3, 39.1) and prevention measures 59.7% (95% CI 55.5, 63.7). Multivariate analysis showed that age, ethnicity, and educational level were significantly associated with correct malaria transmission and risk activities. In addition, age and educational level were significantly associated with correct malaria symptoms. Subsequently, only the variable ethnicity showed a significant association with the correct malaria prevention measures.ConclusionIn general, the prevalence of ever heard of malaria was considered high since the survey was performed based on a non-specific malaria population. However, the prevalence of correct knowledge of malaria symptoms and risk activities were considered low. It is concluded that educational level is one of the major factors associated with correct malaria knowledge, along with age and ethnicity. Therefore, based on these findings, targeted intervention and innovation should be planned by malaria programme managers to sustain malaria elimination in Malaysia.

‘Forest malaria’ in Myanmar? Tracking transmission landscapes in a diversity of environments

BackgroundIn the Greater Mekong Subregion, case–control studies and national-level analyses have shown an association between malaria transmission and forest activities. The term ‘forest malaria’ hides the diversity of ecosystems in the GMS, which likely do not share a uniform malaria risk. To reach malaria elimination goals, it is crucial to document accurately (both spatially and temporally) the influence of environmental factors on malaria to improve resource allocation and policy planning within given areas. The aim of this ecological study is to characterize the association between malaria dynamics and detailed ecological environments determined at village level over a period of several years in Kayin State, Myanmar.MethodsWe characterized malaria incidence profiles at village scale based on intra- and inter-annual variations in amplitude, seasonality, and trend over 4 years (2016–2020). Environment was described independently of village localization by overlaying a 2-km hexagonal grid over the region. Specifically, hierarchical classification on principal components, using remote sensing data of high spatial resolution, was used to assign a landscape and a climate type to each grid cell. We used conditional inference trees and random forests to study the association between the malaria incidence profile of each village, climate and landscape. Finally, we constructed eco-epidemiological zones to stratify and map malaria risk in the region by summarizing incidence and environment association information.ResultsWe identified a high diversity of landscapes (n = 19) corresponding to a gradient from pristine to highly anthropogenically modified landscapes. Within this diversity of landscapes, only three were associated with malaria-affected profiles. These landscapes were composed of a mosaic of dense and sparse forest fragmented by small agricultural patches. A single climate with moderate rainfall and a temperature range suitable for mosquito presence was also associated with malaria-affected profiles. Based on these environmental associations, we identified three eco-epidemiological zones marked by later persistence of Plasmodium falciparum, high Plasmodium vivax incidence after 2018, or a seasonality pattern in the rainy season.ConclusionsThe term forest malaria covers a multitude of contexts of malaria persistence, dynamics and populations at risk. Intervention planning and surveillance could benefit from consideration of the diversity of landscapes to focus on those specifically associated with malaria transmission.Graphical

Outbreak of Locally Acquired Mosquito-Transmitted (Autochthonous) Malaria - Florida and Texas, May-July 2023.

Eight cases of locally acquired, mosquito-transmitted (i.e., autochthonous) Plasmodium vivax malaria, which has not been reported in the United States since 2003, were reported to CDC from state health departments in Florida and Texas during May 18-July 17, 2023. As of August 4, 2023, case surveillance, mosquito surveillance and control activities, and public outreach and education activities continue in both states. U.S. clinicians need to consider a malaria diagnosis in patients with unexplained fever, especially in areas where autochthonous malaria has been recently reported, although the risk for autochthonous malaria in the United States remains very low. Prompt diagnosis and treatment of malaria can prevent severe disease or death and limit ongoing transmission to local Anopheles mosquitoes and other persons. Preventing mosquito bites and controlling mosquitoes at home can prevent mosquitoborne diseases, including malaria. Before traveling internationally to areas with endemic malaria, travelers should consult with a health care provider regarding recommended malaria prevention measures, including potentially taking malaria prophylaxis. Malaria is a nationally notifiable disease; continued reporting of malaria cases to jurisdictional health departments and CDC will also help ensure robust surveillance to detect and prevent autochthonous malaria in the United States.

Spatial analysis and mapping of malaria risk areas using multi-criteria decision making in Ibadan, Oyo state, Nigeria

Malaria is a mosquito-borne infectious disease that affects humans and poses a severe public health problem. Nigeria has the highest number of global cases. Geospatial technology has been widely used to study the risks and factors associated with malaria hazards. The present study is conducted in Ibadan, Oyo State, Nigeria. The objective of this study is to map out areas that are at high risk of the prevalence of malaria by considering a good number of factors as criteria that determine the spread of malaria within Ibadan using open-source and Landsat remote sensing data and further analysis in GIS-based multi-criteria evaluation (MCE). This study considered factors like climate, environmental, socio-economic, and proximity to health centers as criteria for mapping malaria risk. The MCE used a weighted overlay of the factors to produce an element at-risk map, a malaria hazard map, and a vulnerability map. These maps were overlaid to produce the final malaria risk map, which showed that 72% of Ibadan has a risk of malaria prevalence. Identification and delineation of risk areas in Ibadan would help policymakers and decision-makers mitigate the hazards and improve the health status of the state.