LLIN Evaluation in Uganda Project (LLINEUP): modelling the impact of COVID-19-related disruptions on delivery of long-lasting insecticidal nets on malaria indicators in Uganda.

BACKGROUND: Disruptions in malaria control due to COVID-19 mitigation measures were predicted to increase malaria morbidity and mortality in Africa substantially. In Uganda, long-lasting insecticidal nets (LLINs) are distributed nationwide every 3-4 years, but the 2020-2021 campaign was altered because of COVID-19 restrictions so that the timing of delivery of new nets was different from the original plans made by the National Malaria Control Programme. METHODS: A transmission dynamics modelling exercise was conducted to explore how the altered delivery of LLINs in 2020-2021 impacted malaria burden in Uganda. Data were available on the planned LLIN distribution schedule for 2020-2021, and the actual delivery. The transmission model was used to simulate 100 health sub-districts, and parameterized to match understanding of local mosquito bionomics, net use estimates, and seasonal patterns based on data collected in 2017-2019 during a cluster-randomized trial (LLINEUP). Two scenarios were compared; simulated LLIN distributions matching the actual delivery schedule, and a comparable scenario simulating LLIN distributions as originally planned. Model parameters were otherwise matched between simulations. RESULTS: Approximately 70% of the study population received LLINs later than scheduled in 2020-2021, although some areas received LLINs earlier than planned. The model indicates that malaria incidence in 2020 was substantially higher in areas that received LLINs late. In some areas, early distribution of LLINs appeared less effective than the original distribution schedule, possibly due to attrition of LLINs prior to transmission peaks, and waning LLIN efficacy after distribution. On average, the model simulations predicted broadly similar overall mean malaria incidence in 2021 and 2022. After accounting for differences in cluster population size and LLIN distribution dates, no substantial increase in malaria burden was detected. CONCLUSIONS: The model results suggest that the disruptions in the 2020-2021 LLIN distribution campaign in Uganda did not substantially increase malaria burden in the study areas.

[Blood count abnormalities in the association of sickle cell disease and malaria in clinical hematology at the CNHU-HKM in Cotonou (Bénin)]. / Anomalies de l'hémogramme dans l'association drépanocytose et paludisme chez l'adulte en hématologie clinique au CNHU-HKM de Cotonou (Bénin).

Introduction: Although a protective effect of hemoglobin S has been described, malaria has frequently been associated with increased morbidity and mortality in sickle cell disease patients in Africa. Various cytopenias are frequently found on the haemograms of these patients. In Benin, a malaria-endemic zone with a high prevalence of sickle cell disease, the aim of this study was to establish and compare the blood count profile according to hemoglobin type in the association of sickle cell disease and malaria. Material and method: This was a prospective descriptive study. It covered a 24-month period from October 2020 to October 2022. It included all patients with major sickle cell syndrome seen in clinical haematology and with a positive thick drop/parasite density, whatever the parasitaemia value. For each patient, a blood count was performed on the Sysmex XT 4000i machine, supplemented by a smear study after staining with May-Grunwald Giemsa. Data were analyzed using R 3.6.1 software. Results: Three hundred non-redundant cases with a positive thick smear were identified in sickle cell patients, including 208 SS homozygotes (69.3%) and 92 SC heterozygotes (30.7%). In contrast, there were 181 non-redundant cases with a negative thick smear, including 119 SS homozygotes (65.7%) and 62 SC heterozygotes (34.3%). Among subjects with a positive thick smear, the majority of patients (70%) exhibited clinical symptoms. Severe malaria was observed in 58% of the cases. The proportion of severe malaria was higher in SS homozygote patients than in double heterozygote SC patients (p < 0.0001). The mean parasite density was higher in SS individuals (4 320.7 ± 2 185 trophozoites/pL) compared to SC individuals (1 564.4 ± 1 221 trophozoites/pL; p < 0.0001). Plasmodium falciparum was the only species identified. The mean hemoglobin level in impaludated SS subjects was 6.1 g/dL, significantly lower than that in non-impaludated SS subjects (p < 0.0001). The average white blood cell count in impaludated SS subjects was 16.58 G/L, compared to 13.2 G/L in those with a negative thick smear (p < 0.0001). Twenty cases of thrombocytopenia were found in SS subjects with a positive thick smear, compared to 6 cases in those with a negative thick smear. As for SC subjects with a positive thick smear, the average hemoglobin levels and white blood cell counts were 9.8 g/dL and 10.63 G/L, respectively, compared to 11.27 g/dL and 7.3 G/L in SC subjects with a negative thick smear. Eighteen cases of thrombocytopenia were found in subjects with a positive thick smear, compared to 17 cases in those with a negative thick smear. Discussion: Sickle cell disease and malaria represent two major public health problems. However, contrary to popular belief, sickle cell disease is not immune to malaria infestation. Malaria is recognized as one of the main causes of morbidity and mortality in sickle cell patients, particularly children. In Benin, its association with sickle cell emergencies has already been reported.Our study found that malaria was predominantly associated with the homozygous SS form (p < 0.00001). Severe malaria was the most common clinical form. All malaria infestations in our series were due to Plasmodium falciparum, and parasitaemia was significantly higher in SS patients (p < 0.0001).The hematological profile of the association of sickle cell disease and malaria in homozygous SS individuals in our series showed characteristics of a normocytic normochromic anemia with neutrophil-predominant leukocytosis. Compared to non-malaria-infected SS individuals, there was a significant worsening of anemia, neutrophil-predominant leukocytosis, and a decrease in the average platelet count. In SC individuals, there was rather a microcytic normochromic regenerative anemia associated with neutrophil-predominant leukocytosis. Compared to non-malaria-infected SC individuals, there was a significant decrease in the rate of anemia and neutrophil-predominant leukocytosis. Anemia is a constant feature in homozygous sickle cell disease, and the low values recorded illustrate the hemolytic nature of malaria, especially in SS individuals, and the better tolerance of SC individuals. Furthermore, the low baseline hemoglobin levels make SS individuals more vulnerable to malaria-induced anemia compared to SC individuals. The observed leukocytosis is generally accompanied by reticulocytosis in the case of major sickle cell syndrome, which must be taken into account for result validation. It is the expression of compensatory bone marrow reaction to anemia and inflammatory mechanisms resulting from malaria infestation. Finally, thrombocytopenia was significantly more common in SC patients, even though they were adults living in malaria-endemic areas. Malaria can frequently induce thrombocytopenia through platelet consumption during the "rosetting" phenomenon. In SS patients, the effects of "rosetting" could be compensated for by the bone marrow stimulation induced by anemia. In our series with adult subjects living in an endemic area, thrombocytopenia is not a frequent biological disturbance. In a clinicalbiological context combining a systemic inflammatory response syndrome with anemia and neutrophil-predominant leukocytosis in a SS or SC sickle cell patient, the clinician should be able to consider malaria and confirm or rule out this diagnosis.

Intense malarial transmission during the dry season in irrigated rice-growing areas: a case study in Sakassou, Côte d'Ivoire.

The health district of Sakassou is one of the 83 health districts in Côte d'Ivoire, located in a zone with very high malarial transmission rates, with an incidence rate of ≥40% Therefore, to guide vector control methods more effectively, it was crucial to have a good understanding of the vectors in the area. This study aimed to determine the level of malarial transmission during the dry season in Sakassou, Côte d'Ivoire. Female Anopheles mosquitoes were sampled using human landing catches (HLCs) and pyrethrum spraying catches (PSCs). The larvae were collected using the 'dipping' method. A total of 10,875 adult female mosquitoes of Anopheles gambiae were collected. The PCR analysis revealed that all individuals were Anopheles coluzzii. The geographical distribution of potential breeding sites of Anopheles showed the presence of An. coluzzii in all the wetlands of the city of Sakassou. During the dry season, the human-biting rate of An. coluzzii was 139.1 bites/person/night. An exophagic trend was displayed by an adult female of An. coluzzii. The entomological inoculation rate during the dry season was 1.49 infectious bites/person/night. This study demonstrated that An. coluzzii was the main vector of malarial transmission in Sakassou, and the intensity of transmission remains high throughout the dry season.

Geospatial joint modeling of vector and parasite serology to microstratify malaria transmission.

The World Health Organization identifies a strong surveillance system for malaria and its mosquito vector as an essential pillar of the malaria elimination agenda. Anopheles salivary antibodies are emerging biomarkers of exposure to mosquito bites that potentially overcome sensitivity and logistical constraints of traditional entomological surveys. Using samples collected by a village health volunteer network in 104 villages in Southeast Myanmar during routine surveillance, the present study employs a Bayesian geostatistical modeling framework, incorporating climatic and environmental variables together with Anopheles salivary antigen serology, to generate spatially continuous predictive maps of Anopheles biting exposure. Our maps quantify fine-scale spatial and temporal heterogeneity in Anopheles salivary antibody seroprevalence (ranging from 9 to 99%) that serves as a proxy of exposure to Anopheles bites and advances current static maps of only Anopheles occurrence. We also developed an innovative framework to perform surveillance of malaria transmission. By incorporating antibodies against the vector and the transmissible form of malaria (sporozoite) in a joint Bayesian geostatistical model, we predict several foci of ongoing transmission. In our study, we demonstrate that antibodies specific for Anopheles salivary and sporozoite antigens are a logistically feasible metric with which to quantify and characterize heterogeneity in exposure to vector bites and malaria transmission. These approaches could readily be scaled up into existing village health volunteer surveillance networks to identify foci of residual malaria transmission, which could be targeted with supplementary interventions to accelerate progress toward elimination.

The relationship between anaemia and the use of treated bed nets among pregnant and non-pregnant women in Ghana.

Studies have indicated that the risk of malaria, particularly its association with anaemia in pregnant women, increases when treated bed nets are not used. This paper utilizes a statistical mechanical model to investigate whether there is a statistical relationship between the presence or absence of anaemia in pregnant and non-pregnant women and their decision to sleep under treated bed nets. Data from the Ghana Malaria Indicator Survey (GMIS), which includes both rural and urban malaria-endemic areas in Ghana, were employed in this study. A total of 2,434 women, comprising 215 pregnant and 2,219 non-pregnant participants, were involved. Among these, 4.76% of the pregnant and anaemic women and 45.89% of the non-pregnant and anaemic women slept under treated bed nets, while 0.86% of the pregnant and anaemic and 6.82% of the non-pregnant and anaemic women did not. The findings revealed that, in the absence of social interaction, non-anaemic pregnant women have a lower prevalence of choosing to use bed nets compared to their anaemic counterparts. Additionally, non-pregnant anaemic women showed a positive private incentive (30.87%) to use treated bed nets, implying a positive correlation between anaemia and the choice to sleep in a treated bed net. Furthermore, the study demonstrated that both pregnancy and anaemia status have a relationship with the use of treated bed nets in Ghana, especially when social interactions are considered. The interaction strength between non-pregnant and anaemic women interacting with each other shows a negative estimate (-1.49%), implying that there is no rewarding effect from imitation. These insights are crucial for malaria prevention and control programs, emphasizing the need for targeted interventions to enhance the use of treated bed nets among both pregnant and non-pregnant women in Ghana's malaria-endemic regions.

Modeling the effect of imported malaria on the elimination programme in KwaZulu-Natal province of South Africa.

Introduction: with imported malaria cases in a given population, the question arises as to what extent the local cases are a consequence of the imports or not. We perform a modeling analysis for a specific area, in a region aspiring for malaria-free status. Methods: data on malaria cases over ten years is subjected to a compartmental model which is assumed to be operating close to the equilibrium state. Two of the parameters of the model are fitted to the decadal data. The other parameters in the model are sourced from the literature. The model is utilized to simulate the malaria prevalence with or without imported cases. Results: in any given year the annual average of 460 imported cases, resulted in an end-of-year season malaria prevalence of 257 local active infectious cases, whereas without the imports the malaria prevalence at the end of the season would have been fewer than 10 active infectious cases. We calculate the numerical value of the basic reproduction number for the model, which reveals the extent to which the disease is being eliminated from the population or not. Conclusion: without the imported cases, over the ten seasons of malaria, 2008-2018, the KwaZulu-Natal province would have been malaria-free over at least the last 7 years of the decade indicated. This simple methodology works well even in situations where data is limited.

Prevalence of malaria and associated risk factors among household members in South Ethiopia: a multi-site cross-sectional study.

BACKGROUND: Despite continuous prevention and control strategies in place, malaria remains a major public health problem in sub-Saharan Africa including Ethiopia. Moreover, prevalence of malaria differs in different geographical settings and epidemiological data were inadequate to assure disease status in the study area. This study was aimed to determine the prevalence of malaria and associated risk factors in selected rural kebeles in South Ethiopia. METHODS: A community-based cross-sectional study was conducted between February to June 2019 in eight malaria-endemic kebeles situated in four zones in South Ethiopia. Mult-stage sampling techniques were employed to select the study zones, districts, kebeles and households. Blood sample were collected from 1674 participants in 345 households by finger prick and smears were examined by microscopy. Sociodemographic data as well as risk factors for Plasmodium infection were collected using questionnaires. Bivariate and multivariate logistic regressions were used to analyse the data. RESULTS: The overall prevalence of malaria in the study localities was 4.5% (76/1674). The prevalence was varied among the study localities with high prevalence in Bashilo (14.6%; 33/226) followed by Mehal Korga (12.1%; 26/214). Plasmodium falciparum was the dominant parasite accounted for 65.8% (50/76), while Plasmodium vivax accounted 18.4% (14/76). Co-infection of P. falciparum and P. vivax was 15.8% (12/76). Among the three age groups prevalence was 7.8% (27/346) in age less than 5 years and 7.5% (40/531) in 5-14 years. The age groups > 14years were less likely infected with Plasmodium parasite (AOR = 0.14, 95% CI 0.02-0.82) than under five children. Non-febrile individuals 1638 (97.8%) were more likely to had Plasmodium infection (AOR = 28.4, 95% CI 011.4-70.6) than febrile 36 (2.2%). Individuals living proximity to mosquito breeding sites have higher Plasmodium infection (AOR = 6.17, 95% CI 2.66-14.3) than those at distant of breeding sites. CONCLUSIONS: Malaria remains a public health problem in the study localities. Thus, malaria prevention and control strategies targeting children, non-febrile cases and individuals living proximity to breeding sites are crucial to reduce malaria related morbidity and mortality.

MGDrivE 3: A decoupled vector-human framework for epidemiological simulation of mosquito genetic control tools and their surveillance.

Novel mosquito genetic control tools, such as CRISPR-based gene drives, hold great promise in reducing the global burden of vector-borne diseases. As these technologies advance through the research and development pipeline, there is a growing need for modeling frameworks incorporating increasing levels of entomological and epidemiological detail in order to address questions regarding logistics and biosafety. Epidemiological predictions are becoming increasingly relevant to the development of target product profiles and the design of field trials and interventions, while entomological surveillance is becoming increasingly important to regulation and biosafety. We present MGDrivE 3 (Mosquito Gene Drive Explorer 3), a new version of a previously-developed framework, MGDrivE 2, that investigates the spatial population dynamics of mosquito genetic control systems and their epidemiological implications. The new framework incorporates three major developments: i) a decoupled sampling algorithm allowing the vector portion of the MGDrivE framework to be paired with a more detailed epidemiological framework, ii) a version of the Imperial College London malaria transmission model, which incorporates age structure, various forms of immunity, and human and vector interventions, and iii) a surveillance module that tracks mosquitoes captured by traps throughout the simulation. Example MGDrivE 3 simulations are presented demonstrating the application of the framework to a CRISPR-based homing gene drive linked to dual disease-refractory genes and their potential to interrupt local malaria transmission. Simulations are also presented demonstrating surveillance of such a system by a network of mosquito traps. MGDrivE 3 is freely available as an open-source R package on CRAN (https://cran.r-project.org/package=MGDrivE2) (version 2.1.0), and extensive examples and vignettes are provided. We intend the software to aid in understanding of human health impacts and biosafety of mosquito genetic control tools, and continue to iterate per feedback from the genetic control community.

Modeling the Regional Distribution of International Travelers in Spain to Estimate Imported Cases of Dengue and Malaria: Statistical Inference and Validation Study.

BACKGROUND: Understanding the patterns of disease importation through international travel is paramount for effective public health interventions and global disease surveillance. While global airline network data have been used to assist in outbreak prevention and effective preparedness, accurately estimating how these imported cases disseminate locally in receiving countries remains a challenge. OBJECTIVE: This study aimed to describe and understand the regional distribution of imported cases of dengue and malaria upon arrival in Spain via air travel. METHODS: We have proposed a method to describe the regional distribution of imported cases of dengue and malaria based on the computation of the "travelers' index" from readily available socioeconomic data. We combined indicators representing the main drivers for international travel, including tourism, economy, and visits to friends and relatives, to measure the relative appeal of each region in the importing country for travelers. We validated the resulting estimates by comparing them with the reported cases of malaria and dengue in Spain from 2015 to 2019. We also assessed which motivation provided more accurate estimates for imported cases of both diseases. RESULTS: The estimates provided by the best fitted model showed high correlation with notified cases of malaria (0.94) and dengue (0.87), with economic motivation being the most relevant for imported cases of malaria and visits to friends and relatives being the most relevant for imported cases of dengue. CONCLUSIONS: Factual descriptions of the local movement of international travelers may substantially enhance the design of cost-effective prevention policies and control strategies, and essentially contribute to decision-support systems. Our approach contributes in this direction by providing a reliable estimate of the number of imported cases of nonendemic diseases, which could be generalized to other applications. Realistic risk assessments will be obtained by combining this regional predictor with the observed local distribution of vectors.

Distribution and dynamics of Anopheles gambiae s.l. larval habitats in three Senegalese cities with high urban malaria incidence.

Urban malaria has become a challenge for most African countries due to urbanization, with increasing population sizes, overcrowding, and movement into cities from rural localities. The rapid expansion of cities with inappropriate water drainage systems, abundance of water storage habitats, coupled with recurrent flooding represents a concern for water-associated vector borne diseases, including malaria. This situation could threaten progress made towards malaria elimination in sub-Saharan countries, including Senegal, where urban malaria has presented as a threat to national elimination gains. To assess drivers of urban malaria in Senegal, a 5-month study was carried out from August to December 2019 in three major urban areas and hotspots for malaria incidence (Diourbel, Touba, and Kaolack) including the rainy season (August-October) and partly dry season (November-December). The aim was to characterize malaria vector larval habitats, vector dynamics across both seasons, and to identify the primary eco- environmental entomological factors contributing to observed urban malaria transmission. A total of 145 Anopheles larval habitats were found, mapped, and monitored monthly. This included 32 in Diourbel, 83 in Touba, and 30 in Kaolack. The number of larval habitats fluctuated seasonally, with a decrease during the dry season. In Diourbel, 22 of the 32 monitored larval habitats (68.75%) were dried out by December and considered temporary, while the remaining 10 (31.25%) were classified as permanent. In the city of Touba 28 (33.73%) were temporary habitats, and of those 57%, 71% and 100% dried up respectively by October, November, and December. However, 55 (66.27%) habitats were permanent water storage basins which persisted throughout the study. In Kaolack, 12 (40%) permanent and 18 (60%) temporary Anopheles larval habitats were found and monitored during the study. Three malaria vectors (An. arabiensis, An. pharoensis and An. funestus s.l.) were found across the surveyed larval habitats, and An. arabiensis was found in all three cities and was the only species found in the city of Diourbel, while An. arabiensis, An. pharoensis, and An. funestus s.l. were detected in the cities of Touba and Kaolack. The spatiotemporal observations of immature malaria vectors in Senegal provide evidence of permanent productive malaria vector larval habitats year-round in three major urban centers in Senegal, which may be driving high urban malaria incidence. This study aimed to assess the presence and type of anopheline larvae habitats in urban areas. The preliminary data will better inform subsequent detailed additional studies and seasonally appropriate, cost-effective, and sustainable larval source management (LSM) strategies by the National Malaria Control Programme (NMCP).

Using image segmentation models to analyse high-resolution earth observation data: new tools to monitor disease risks in changing environments.

BACKGROUND: In the near future, the incidence of mosquito-borne diseases may expand to new sites due to changes in temperature and rainfall patterns caused by climate change. Therefore, there is a need to use recent technological advances to improve vector surveillance methodologies. Unoccupied Aerial Vehicles (UAVs), often called drones, have been used to collect high-resolution imagery to map detailed information on mosquito habitats and direct control measures to specific areas. Supervised classification approaches have been largely used to automatically detect vector habitats. However, manual data labelling for model training limits their use for rapid responses. Open-source foundation models such as the Meta AI Segment Anything Model (SAM) can facilitate the manual digitalization of high-resolution images. This pre-trained model can assist in extracting features of interest in a diverse range of images. Here, we evaluated the performance of SAM through the Samgeo package, a Python-based wrapper for geospatial data, as it has not been applied to analyse remote sensing images for epidemiological studies. RESULTS: We tested the identification of two land cover classes of interest: water bodies and human settlements, using different UAV acquired imagery across five malaria-endemic areas in Africa, South America, and Southeast Asia. We employed manually placed point prompts and text prompts associated with specific classes of interest to guide the image segmentation and assessed the performance in the different geographic contexts. An average Dice coefficient value of 0.67 was obtained for buildings segmentation and 0.73 for water bodies using point prompts. Regarding the use of text prompts, the highest Dice coefficient value reached 0.72 for buildings and 0.70 for water bodies. Nevertheless, the performance was closely dependent on each object, landscape characteristics and selected words, resulting in varying performance. CONCLUSIONS: Recent models such as SAM can potentially assist manual digitalization of imagery by vector control programs, quickly identifying key features when surveying an area of interest. However, accurate segmentation still requires user-provided manual prompts and corrections to obtain precise segmentation. Further evaluations are necessary, especially for applications in rural areas.

A systematic review of the factors associated with malaria infection among forest rangers.

INTRODUCTION: Malaria is a vector-borne disease that initially manifests as fever, headache, and chills. The illness could progress to more severe conditions, including lethargy, impaired consciousness, convulsions, shortness of breath, blood in urine, jaundice, and haemorrhage if left untreated. The risk of contracting malaria is considerably heightened in specific occupational settings, particularly among forest rangers, following frequent exposure to natural habitats. Consequently, advancing the understanding of malaria and emphasising how specific occupational environments (including those of forest rangers) contribute to disease risk and management is imperative. OBJECTIVE: The present study aims to determine the factors associated with malaria infection among forest rangers by systematically reviewing electronic articles from three databases (EBSCOhost, ScienceDirect, and ResearchGate). METHODS: The current review was prepared based on the updated preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. First, three independent reviewers screened the titles and abstracts of the data collected. The information was then stored in Endnote20 based on the inclusion and exclusion criteria. The articles were critically appraised with the mixed methods appraisal tool (MMAT) to assess their quality. RESULT: A total of 103, 31, and 51 articles from EBSCOhost, ScienceDirect, and ResearchGate, respectively, were selected, resulting in 185 unique hits. Nevertheless, only 63 full-text publications were assessed following a rigorous selection screening, from which only five were included in the final review. The studies revealed that several factors contribute to malaria infection among forest rangers. The parameters were classified into sociodemographic, individual, and living condition-related. CONCLUSION: A better understanding of malaria progresses and identifying its potential risk factors is essential to impact worker well-being. The findings might be utilised to improve malaria infection prevention programme implementations, hence maximising their success. Pre-employment and regular health screenings could also aid in evaluating and identifying potential risks for malaria infection among forest rangers.

Malaria prevalence and use of control measures in an area with persistent transmission in Senegal.

INTRODUCTION: In Senegal, the widespread use of vector control measures has resulted in a significant reduction in the malaria burden and led the country to consider the possibility of elimination. Given this shift and changing context, it is important to characterize the malaria burden across all age groups to guide decision-making on programmatic interventions to interrupt transmission and ultimately eradicate the disease. In Senegal, there is a lack of information on malaria prevalence among certain populations, particularly among adolescents and adults. This study sought to assess the magnitude of malaria infections in all age groups, as well as malaria associated factors in an area of persistent transmission in Senegal. METHODS: A cross-sectional household survey was conducted in four health posts (Khossanto, Mamakhona, Diakhaling and Sambrambougou), of the health district of Saraya, in November 2021, among individuals over 6 months of age. Households were selected using multistage sampling. Consented participants were screened for malaria parasites by microscopic examination of blood smears, and hemoglobin levels were measured using the Hemocue HB 301TM analyzer. Socio-demographic information of the participants, household heads, household assets, and information on ownership and use of preventive measures were collected using a structured questionnaire. Weighted generalized mixed effects logistic regression model was used to identify factors associated with microscopically confirmed malaria infection. RESULTS: A total of 1759 participants were enrolled in the study. Overall, about 21% of participants were classified as having Plasmodium infection; children aged 5-10 years old (26.6%), adolescents aged 10-19 years old (24.7%), and children under five years of age (20.5%) had higher rates of infection compared to adults (13.5%). Plasmodium falciparum accounted for 99.2% of the malaria infections, and most infections (69%) were asymptomatic. Around one-third of study participants had anemia (hemoglobin level <11.0 g/dl), with under five children bearing the highest burden (67.3%). Multivariate analysis showed that the odds of having a malaria infection were around 2 times higher among participants in Khossanto compared to Diakhaling (aOR = 1.84, 95% CI:1.06-3.20). Participants aged 5-9 years were more likely to have malaria infection compared to under five children (aOR = 1.40, 95% CI:1.02-1.91). Factors associated with anemia were P. falciparum infection (aOR = 1.36, p = 0.027), females (aOR = 2.16, p = 0.000), under-five age group (aOR = 13.01, p = 0.000). CONCLUSION: Malaria burden was considerable among adolescents and under ten children living in an area of persistent transmission, with adolescents more commonly presenting as asymptomatic. Interventions tailored to this specific group of the population are needed to better control the disease and reduce its burden.

Predicting malaria outbreak in The Gambia using machine learning techniques.

Malaria is the most common cause of death among the parasitic diseases. Malaria continues to pose a growing threat to the public health and economic growth of nations in the tropical and subtropical parts of the world. This study aims to address this challenge by developing a predictive model for malaria outbreaks in each district of The Gambia, leveraging historical meteorological data. To achieve this objective, we employ and compare the performance of eight machine learning algorithms, including C5.0 decision trees, artificial neural networks, k-nearest neighbors, support vector machines with linear and radial kernels, logistic regression, extreme gradient boosting, and random forests. The models are evaluated using 10-fold cross-validation during the training phase, repeated five times to ensure robust validation. Our findings reveal that extreme gradient boosting and decision trees exhibit the highest prediction accuracy on the testing set, achieving 93.3% accuracy, followed closely by random forests with 91.5% accuracy. In contrast, the support vector machine with a linear kernel performs less favorably, showing a prediction accuracy of 84.8% and underperforming in specificity analysis. Notably, the integration of both climatic and non-climatic features proves to be a crucial factor in accurately predicting malaria outbreaks in The Gambia.

Landscape and Socioeconomic Factors Determine Malaria Incidence in Tropical Forest Countries.

Deforestation, landscape dynamics, and socioeconomic factors within the tropical Americas, Africa, and Asia may have different impacts on malaria incidence. To evaluate how these drivers affect malaria incidence at the global and regional scale, we collected malaria incidence rates from 2000 to 2019 from 67 tropical countries, along with forest loss, land use change types, and socioeconomic elements. LASSO regression, linear mixed effect modeling, and k-fold cross validation were used to create and evaluate the models. Regionality plays a role in the significance of varying risk factors. The Tropical Americas model had the highest coefficient of determination (marginal R2 = 0.369), while the Africa model showed the highest predictive accuracy with only a 17.4% error rate. Strong associations between tree cover loss (ß = -4037.73, p < 0.001) and percentage forest area (ß = 5373.18, p = 0.012) in Africa, and percent of key biodiversity areas under protection (ß = 496.71, p < 0.001; ß = 1679.20, p < 0.001) in the tropical Americas and Asia with malaria incidence indicates that malaria risk should be considered during conservation policy development, and recommends that individual approaches to policy and investment be considered when implementing malaria interventions on different spatial scales.

Notes from the Field: Increases in Imported Malaria Cases - Three Southern U.S. Border Jurisdictions, 2023.

Malaria is a severe and potentially fatal mosquitoborne disease caused by infection with Plasmodium spp. parasites. Although malaria is no longer endemic in the United States, imported infections are reported annually; the primary risk group has been U.S. residents traveling to areas where malaria is endemic (1). In 2023, sporadic locally acquired mosquito-transmitted malaria cases were reported in several U.S. states (2,3). This report describes increases in imported malaria cases in 2023 compared with 2022 in three public health jurisdictions along the U.S. southern border.

Comparison of malaria diagnostic methods for detection of asymptomatic Plasmodium infections among pregnant women in northwest Ethiopia.

BACKGROUND: Malaria in pregnancy remains a major public health problem in the globe, especially in sub-Saharan Africa. In malaria endemic areas, most pregnant women remain asymptomatic, but malaria could still cause complications on the mother and her offspring; as well as serve as reservoirs to transmit infection. Despite these effects, no attention is given to the diagnosis of asymptomatic Plasmodium infections (APIs) using highly sensitive and specific laboratory diagnostic tools in Ethiopia. Therefore, the goal of this study was to compare the performance of Rapid Diagnostic Test (RDT), microscopy and real-time polymerase chain reaction (RT-PCR) to detect APIs among pregnant women. METHODS: A health facility based cross -sectional study was conducted among pregnant women attending antenatal care at Fendeka town health facilities Jawi district, northwest Ethiopia from February to March, 2019. A total of 166 participants were enrolled by using convenient sampling technique. Socio-demographic features were collected using a semi structured questionnaire. Dried blood spot (DBS) samples were collected for molecular analysis. Asymptomatic Plasmodium infection on pregnant women was diagnosed using RDT, microscopy and RT-PCR. Descriptive statistics were used to determine the prevalence of APIs. Method comparison was performed, and Cohen's kappa coefficient (k) was used to determine the degree of agreement among the diagnostic methods. Parasite densities were also calculated. RESULTS: The prevalence of API was 9.6%, 11.4% and 18.7% using RDT, microscopy and RT-PCR, respectively. The overall proportion of API was 19.3%. Sensitivity of the RDT was 83.3% as compared with microscopy. Rapid Diagnostic Test and microscopy also showed sensitivity of 50% and 60%, respectively, as compared with RT-PCR. The mean parasite density was 3213 parasites/µl for P falciparum and 1140 parasites/µl of blood for P. vivax. CONCLUSION: Prevalence of API in the study area was high. Both RDT and microscopy had lower sensitivity when compared with RT-PCR. Therefore, routine laboratory diagnosis of API among pregnant women should be given attention and done with better sensitive and specific laboratory diagnostic tools.

Gestational glucose intolerance among pregnant women at the Cape Coast Teaching Hospital.

BACKGROUND: Malaria in pregnancy can have adverse outcomes if untreated. Both malaria and pregnancy are associated with insulin resistance and diabetes. Although malaria is treated prophylactically with gestational diabetes mellitus (GDM) screened for in pregnancy as part a routine antenatal care, their impacts have not been examined in terms of other forms of dysglycaemia. This cross-sectional study examined insulin resistance and its relationship with dysglycaemia and malaria among pregnant women in the Cape Coast Teaching Hospital (CCTH). METHODS: Using a structured questionnaire, demographic and clinical information were obtained from 252 pregnant women aged 18-42 years. Weight and height were measured for computation of body mass index (BMI). Measurement of insulin, lipid profile and glucose were taken under fasting conditions followed by oral glucose tolerant test. Insulin resistance and beta-cell function were assessed by the homeostatic model as malaria was diagnosed by microscopy. RESULTS: The respective prevalence of GDM, gestational glucose intolerance (GGI) and insulin resistance were 0.8% (2/252), 19.44% (49/252) and 56.75% (143/252). No malaria parasite or dyslipidaemia was detected in any of the participants. Apart from BMI that increased across trimesters, no other measured parameter differed among the participants. Junior High School (JHS) education compared with no formal education increased the odds (AOR: 2.53; CI: 1.12-5.71; P = 0.03) but 2nd trimester of pregnancy compared to the 1st decreased the odds (AOR: 0.32; CI: 0.12-0.81; P = 0.02) of having insulin resistance in the entire sample. In a sub-group analysis across trimesters, pregnant women with JHS education in their 3rd trimester had increased odds (AOR: 4.41; CI: 1.25-15.62; P = 0.02) of having insulin resistance. CONCLUSION: Prevalence of GDM and GGI were 0.8% and 19.44% respectively. The odds of insulin resistance increased in pregnant women with JHS education in the 3rd trimester. Appropriate measures are needed to assuage the diabetogenic risk posed by GGI in our setting.

Landscape drives zoonotic malaria prevalence in non-human primates.

Zoonotic disease dynamics in wildlife hosts are rarely quantified at macroecological scales due to the lack of systematic surveys. Non-human primates (NHPs) host Plasmodium knowlesi, a zoonotic malaria of public health concern and the main barrier to malaria elimination in Southeast Asia. Understanding of regional P. knowlesi infection dynamics in wildlife is limited. Here, we systematically assemble reports of NHP P. knowlesi and investigate geographic determinants of prevalence in reservoir species. Meta-analysis of 6322 NHPs from 148 sites reveals that prevalence is heterogeneous across Southeast Asia, with low overall prevalence and high estimates for Malaysian Borneo. We find that regions exhibiting higher prevalence in NHPs overlap with human infection hotspots. In wildlife and humans, parasite transmission is linked to land conversion and fragmentation. By assembling remote sensing data and fitting statistical models to prevalence at multiple spatial scales, we identify novel relationships between P. knowlesi in NHPs and forest fragmentation. This suggests that higher prevalence may be contingent on habitat complexity, which would begin to explain observed geographic variation in parasite burden. These findings address critical gaps in understanding regional P. knowlesi epidemiology and indicate that prevalence in simian reservoirs may be a key spatial driver of human spillover risk.

Zoonotic diseases are infectious diseases that are transmitted from animals to humans. For example, the malaria-causing parasite Plasmodium knowlesi can be transmitted from monkeys to humans through mosquitos that have previously fed on infected monkeys. In Malaysia, progress towards eliminating malaria is being undermined by the rise of human incidences of 'monkey malaria', which has been declared a public health threat by The World Health Organisation. In humans, cases of monkey malaria are higher in areas of recent deforestation. Changes in habitat may affect how monkeys, insects and humans interact, making it easier for diseases like malaria to pass between them. Deforestation could also change the behaviour of wildlife, which could lead to an increase in infection rates. For example, reduced living space increases contact between monkeys, or it may prevent behaviours that help animals to avoid parasites. Johnson et al. wanted to investigate how the prevalence of malaria in monkeys varies across Southeast Asia to see whether an increase of Plasmodium knowlesi in primates is linked to changes in the landscape. They merged the results of 23 existing studies, including data from 148 sites and 6322 monkeys to see how environmental factors like deforestation influenced the amount of disease in different places. Many previous studies have assumed that disease prevalence is high across all macaques, monkey species that are considered pests, and in all places. But Johnson et al. found that disease rates vary widely across different regions. Overall disease rates in monkeys are lower than expected (only 12%), but in regions with less forest or more 'fragmented' forest areas, malaria rates are higher. Areas with a high disease rate in monkeys tend to further coincide with infection hotspots for humans. This suggests that deforestation may be driving malaria infection in monkeys, which could be part of the reason for increased human infection rates. Johnsons et al.'s study has provided an important step towards better understanding the link between deforestation and the levels of monkey malaria in humans living nearby. Their study provides important insights into how we might find ways of managing the landscape better to reduce health risks from wildlife infection.

Level of awareness and utilization of insecticide-treated bed nets among medical students as measures for reducing malaria episodes.

This study examined the level of awareness and utilization of insecticide-treated bed nets among medical students as measures for reducing malaria episodes in Delta State University, Abraka. It was a descriptive study with objectives and research questions formulated to achieve the study design. A sample size of 200 male and female students resident in the campus hostels were selected using random sampling technique. A self-structured questionnaire was designed and administered to the study participants, however, only 148 copies of the questionnaires were successfully retrieved and used for the study. Data generated were subjected to quantitative statistical analysis for frequencies, percentages, average mean and Chi-square testing. Findings revealed that the level of awareness was significantly associated with the role of health workers in the distribution of insecticide-treated bed nets in Delta State University, Abraka, although, factors hindering health workers from distributing insecticide-treated bed nets were identified. There was significant difference between perception of medical students and the utilization of insecticide-treated bed nets on risk of malaria spread. In addition, there was significant difference between the benefits of using insecticide-treated bed nets and the prevention and control of malaria. We therefore conclude that regular utilization of insecticide-treated bed nets due to adequate awareness eliminates contact with mosquitoes and prevents transmitting vectors of malaria from having contact with the users of insecticide-treated bed net. Massive health education campaign is recommended to further scale up the awareness and effective utilization of insecticide-treated bed nets towards prevention and control of malaria bites among students in Delta State University, Abraka.