Spatial spillovers of violent conflict amplify the impacts of climate variability on malaria risk in sub-Saharan Africa.

Africa carries a disproportionately high share of the global malaria burden, accounting for 94% of malaria cases and deaths worldwide in 2019. It is also a politically unstable region and the most vulnerable continent to climate change in recent decades. Knowledge about the modifying impacts of violent conflict on climate-malaria relationships remains limited. Here, we quantify the associations between violent conflict, climate variability, and malaria risk in sub-Saharan Africa using health surveys from 128,326 individuals, historical climate data, and 17,429 recorded violent conflicts from 2006 to 2017. We observe that spatial spillovers of violent conflict (SSVCs) have spatially distant effects on malaria risk. Malaria risk induced by SSVCs within 50 to 100 km from the households gradually increases from 0.1% (not significant, P>0.05) to 6.5% (95% CI: 0 to 13.0%). SSVCs significantly promote malaria risk within the average 20.1 to 26.9 °C range. At the 12-mo mean temperature of 22.5 °C, conflict deaths have the largest impact on malaria risk, with an approximately 5.8% increase (95% CI: 1.0 to 11.0%). Additionally, a pronounced association between SSVCs and malaria risk exists in the regions with 9.2 wet days per month. The results reveal that SSVCs increase population exposure to harsh environments, amplifying the effect of warm temperature and persistent precipitation on malaria transmission. Violent conflict therefore poses a substantial barrier to mosquito control and malaria elimination efforts in sub-Saharan Africa. Our findings support effective targeting of treatment programs and vector control activities in conflict-affected regions with a high malaria risk.

Molecular Research on Plasmodium Infection and Immunity.

The WHO's global strategy for malaria targets a reduction of at least 90% of both incidence and mortality rates for 2030 [...].

Cooperation for malaria control and elimination in the Guiana Shield.

The Guiana Shield, a small region of South America, is currently one of the main hotspots of malaria transmission on the continent. This Amazonian area is characterised by remarkable socioeconomic, cultural, health, and political heterogeneity and a high degree of regional and cross-border population mobility, which has contributed to the increase of malaria in the region in the past few years. In this context, regional cooperation to control malaria represents both a challenge and an indispensable initiative. This Viewpoint advocates for the creation of a regional cooperative mechanism for the elimination of malaria in the Guiana Shield. This strategy would help address operational and political obstacles to successful technical cooperation in the region and could contribute to reversing the regional upsurge in malaria incidence through creating a functional international control and elimination partnership.

A formative cross-sectional study to assess caregiver's health-seeking behaviour and knowledge surrounding malaria, and understand the burden of malaria among children under-five in conflict-affected communities of Cameroon.

BACKGROUND: Malaria remains a major global health problem often worsened by political instability and armed conflict. The purpose of the study was to explore community knowledge, attitudes and practices on malaria prevention, and to understand the burden of malaria and health-seeking behaviours of caregivers of children under-five in conflict-affected communities of the South West and Littoral Regions of Cameroon. METHODS: A cross-sectional survey involving internally displaced persons (IDPS), host population, and their children under-five was conducted across 80 communities. The survey was conducted from May to June 2021. Participants were interviewed using a structured questionnaire. Malaria prevalence for children under-five was determined using rapid diagnostic tests (RDT) on blood samples. Association between variables and displacement status was measured using chi square test and multivariate logistic regression model was fitted to identify factors associated with adequate knowledge on malaria prevention. RESULTS: A total of 2386 adults participated in the study and 1543 RDTs were conducted for children under-five. Adequate levels of knowledge and attitudes on malaria prevention was recorded among 1258 (52.9%) of the participants, with very strong evidence to suggest the level to be higher among the host (59.5%) compared to the IDPs (49.5%) and returnees (39.7%) (p < 0.001). Good practices towards malaria prevention was 43.3%, with very strong evidence indicating lower levels among IDPs (42.8%) and returnees (28.5%) compared to the host (49.4%) (p < 0.001). Malaria prevalence for children under-five was 54.0% and adequate health-seeking for suspected episodes of malaria was 53.0%, without any difference among IDPs (51.78%) and returnees (48.7%) compared to host populations (55.4%) (p = 0.154). Multivariate logistic regression model showed that there was quite strong evidence to suggest primary and secondary levels of education have higher odds of having correct knowledge of malaria prevention (adjusted odds ratio (AOR) 1.71, 95% confidence interval (CI): 1.11-2.64, p = 0.015 and AOR 1.80, 95% CI 1.15-2.82, p = 0.010 respectively). There was very strong evidence to suggest that owning a radio or a television was associated with greater odds of having a higher knowledge on malaria prevention (AOR 1.49, 95% CI 1.233-1.81, p = 0.000 and AOR 1.47, 95% CI 1.18-1.84, p = 0.001). CONCLUSION: Over half of the population have correct knowledge and attitudes towards malaria prevention but gaps in complete knowledge remained. Some of the caregivers know the correct malaria preventive practices coupled with largely unsatisfactory treatment approaches and reflected by the high prevalence of malaria among their children. In order to effectively treat malaria, innovative strategies should target community participation.

Insights and challenges of insecticide resistance modelling in malaria vectors: a review.

BACKGROUND: Malaria is one of the most devastating tropical diseases, resulting in loss of lives each year, especially in children under the age of 5 years. Malaria burden, related deaths and stall in the progress against malaria transmission is evident, particularly in countries that have moderate or high malaria transmission. Hence, mitigating malaria spread requires information on the distribution of vectors and the drivers of insecticide resistance (IR). However, owing to the impracticality in establishing the critical need for real-world information at every location, modelling provides an informed best guess for such information. Therefore, this review examines the various methodologies used to model spatial, temporal and spatio-temporal patterns of IR within populations of malaria vectors, incorporating pest-biology parameters, adopted ecological principles, and the associated modelling challenges. METHODS: The review focused on the period ending March 2023 without imposing restrictions on the initial year of publication, and included articles sourced from PubMed, Web of Science, and Scopus. It was also limited to publications that deal with modelling of IR distribution across spatial and temporal dimensions and excluded articles solely focusing on insecticide susceptibility tests or articles not published in English. After rigorous selection, 33 articles met the review's elibility criteria and were subjected to full-text screening. RESULTS: Results show the popularity of Bayesian geostatistical approaches, and logistic and static models, with limited adoption of dynamic modelling approaches for spatial and temporal IR modelling. Furthermore, our review identifies the availability of surveillance data and scarcity of comprehensive information on the potential drivers of IR as major impediments to developing holistic models of IR evolution. CONCLUSIONS: The review notes that incorporating pest-biology parameters, and ecological principles into IR models, in tandem with fundamental ecological concepts, potentially offers crucial insights into the evolution of IR. The results extend our knowledge of IR models that provide potentially accurate results, which can be translated into policy recommendations to combat the challenge of IR in malaria control.

Malaria risk mapping among children under five in Togo.

Malaria is a major health threat in sub-Sahara Africa, especially for children under five. However, there is considerable heterogeneity between areas in malaria risk reported, associated with environmental and climatic. We used data from Togo to explore spatial patterns of malaria incidence. Geospatial covariate datasets, including climatic and environmental variables from the 2017 Malaria Indicator Survey in Togo, were used for this study. The association between malaria incidence and ecological predictors was assessed using three regression techniques, namely the Ordinary Least Squares (OLS), spatial lag model (SLM), and spatial error model (SEM). A total of 171 clusters were included in the survey and provided data on environmental and climate variables. Spatial autocorrelation showed that the distribution of malaria incidence was not random and revealed significant spatial clustering. Mean temperature, precipitation, aridity and proximity to water bodies showed a significant and direct association with malaria incidence rate in the SLM model, which best fitted the data according to AIC. Five malaria incidence hotspots were identified. Malaria incidence is spatially clustered in Togo associated with climatic and environmental factors. The results can contribute to the development of specific malaria control plans taking geographical variation into consideration and targeting transmission hotspots.

Modelling spatiotemporal variation in under-five malaria risk in Ghana in 2016-2021.

BACKGROUND: Ghana is among the top 10 highest malaria burden countries, with about 20,000 children dying annually, 25% of which were under five years. This study aimed to produce interactive web-based disease spatial maps and identify the high-burden malaria districts in Ghana. METHODS: The study used 2016-2021 data extracted from the routine health service nationally representative and comprehensive District Health Information Management System II (DHIMS2) implemented by the Ghana Health Service. Bayesian geospatial modelling and interactive web-based spatial disease mapping methods were employed to quantify spatial variations and clustering in malaria risk across 260 districts. For each district, the study simultaneously mapped the observed malaria counts, district name, standardized incidence rate, and predicted relative risk and their associated standard errors using interactive web-based visualization methods. RESULTS: A total of 32,659,240 malaria cases were reported among children < 5 years from 2016 to 2021. For every 10% increase in the number of children, malaria risk increased by 0.039 (log-mean 0.95, 95% credible interval = - 13.82-15.73) and for every 10% increase in the number of males, malaria risk decreased by 0.075, albeit not statistically significant (log-mean - 1.82, 95% credible interval = - 16.59-12.95). The study found substantial spatial and temporal differences in malaria risk across the 260 districts. The predicted national relative risk was 1.25 (95% credible interval = 1.23, 1.27). The malaria risk is relatively the same over the entire year. However, a slightly higher relative risk was recorded in 2019 while in 2021, residing in Keta, Abuakwa South, Jomoro, Ahafo Ano South East, Tain, Nanumba North, and Tatale Sanguli districts was associated with the highest malaria risk ranging from a relative risk of 3.00 to 4.83. The district-level spatial patterns of malaria risks changed over time. CONCLUSION: This study identified high malaria risk districts in Ghana where urgent and targeted control efforts are required. Noticeable changes were also observed in malaria risk for certain districts over some periods in the study. The findings provide an effective, actionable tool to arm policymakers and programme managers in their efforts to reduce malaria risk and its associated morbidity and mortality in line with the Sustainable Development Goals (SDG) 3.2 for limited public health resource settings, where universal intervention across all districts is practically impossible.

Design of a novel intelligent computing framework for predictive solutions of malaria propagation model.

The paper presents an innovative computational framework for predictive solutions for simulating the spread of malaria. The structure incorporates sophisticated computing methods to improve the reliability of predicting malaria outbreaks. The study strives to provide a strong and effective tool for forecasting the propagation of malaria via the use of an AI-based recurrent neural network (RNN). The model is classified into two groups, consisting of humans and mosquitoes. To develop the model, the traditional Ross-Macdonald model is expanded upon, allowing for a more comprehensive analysis of the intricate dynamics at play. To gain a deeper understanding of the extended Ross model, we employ RNN, treating it as an initial value problem involving a system of first-order ordinary differential equations, each representing one of the seven profiles. This method enables us to obtain valuable insights and elucidate the complexities inherent in the propagation of malaria. Mosquitoes and humans constitute the two cohorts encompassed within the exposition of the mathematical dynamical model. Human dynamics are comprised of individuals who are susceptible, exposed, infectious, and in recovery. The mosquito population, on the other hand, is divided into three categories: susceptible, exposed, and infected. For RNN, we used the input of 0 to 300 days with an interval length of 3 days. The evaluation of the precision and accuracy of the methodology is conducted by superimposing the estimated solution onto the numerical solution. In addition, the outcomes obtained from the RNN are examined, including regression analysis, assessment of error autocorrelation, examination of time series response plots, mean square error, error histogram, and absolute error. A reduced mean square error signifies that the model's estimates are more accurate. The result is consistent with acquiring an approximate absolute error close to zero, revealing the efficacy of the suggested strategy. This research presents a novel approach to solving the malaria propagation model using recurrent neural networks. Additionally, it examines the behavior of various profiles under varying initial conditions of the malaria propagation model, which consists of a system of ordinary differential equations.

Effect of spatiotemporal variables on abundance, biting activity and parity of Nyssorhynchus darlingi (Diptera: Culicidae) in peri-Iquitos, Peru.

BACKGROUND: In malaria endemic regions of the Peruvian Amazon, rainfall together with river level and breeding site availability drive fluctuating vector mosquito abundance and human malaria cases, leading to temporal heterogeneity. The main variables influencing spatial transmission include location of communities, mosquito behaviour, land use/land cover, and human ecology/behaviour. The main objective was to evaluate seasonal and microgeographic biting behaviour of the malaria vector Nyssorhynchus (or Anopheles) darlingi in Amazonian Peru and to investigate effects of seasonality on malaria transmission. METHODS: We captured mosquitoes from 18:00 to 06:00 h using Human Landing Catch in two riverine (Lupuna, Santa Emilia) and two highway (El Triunfo, Nuevo Horizonte) communities indoors and outdoors from 8 houses per community, during the dry and rainy seasons from February 2016 to January 2017. We then estimated parity rate, daily survival and age of a portion of each collection of Ny. darlingi. All collected specimens of Ny. darlingi were tested for the presence of Plasmodium vivax or Plasmodium falciparum sporozoites using real-time PCR targeting the small subunit of the 18S rRNA. RESULTS: Abundance of Ny. darlingi varied across village, season, and biting behaviour (indoor vs outdoor), and was highly significant between rainy and dry seasons (p < 0.0001). Biting patterns differed, although not significantly, and persisted regardless of season, with peaks in highway communities at ~ 20:00 h in contrast to biting throughout the night (i.e., 18:00-06:00) in riverine communities. Of 3721 Ny. darlingi tested for Plasmodium, 23 (0.62%) were infected. We detected Plasmodium-infected Ny. darlingi in both community types and most (20/23) were captured outdoors during the rainy season; 17/23 before midnight. Seventeen Ny. darlingi were infected with P. vivax, and 6 with P. falciparum. No infected Ny. darlingi were captured during the dry season. Significantly higher rates of parity were detected in Ny. darlingi during the rainy season (average 64.69%) versus the dry season (average 36.91%) and by community, Lupuna, a riverine village, had the highest proportion of parous to nulliparous females during the rainy season. CONCLUSIONS: These data add a seasonal dimension to malaria transmission in peri-Iquitos, providing more evidence that, at least locally, the greatest risk of malaria transmission is outdoors during the rainy season mainly before midnight, irrespective of whether the community was located adjacent to the highway or along the river.

Molecular tools are crucial for malaria elimination.

The eradication of Plasmodium parasites, responsible for malaria, is a daunting global public health task. It requires a comprehensive approach that addresses symptomatic, asymptomatic, and submicroscopic cases. Overcoming this challenge relies on harnessing the power of molecular diagnostic tools, as traditional methods like microscopy and rapid diagnostic tests fall short in detecting low parasitaemia, contributing to the persistence of malaria transmission. By precisely identifying patients of all types and effectively characterizing malaria parasites, molecular tools may emerge as indispensable allies in the pursuit of malaria elimination. Furthermore, molecular tools can also provide valuable insights into parasite diversity, drug resistance patterns, and transmission dynamics, aiding in the implementation of targeted interventions and surveillance strategies. In this review, we explore the significance of molecular tools in the pursuit of malaria elimination, shedding light on their key contributions and potential impact on public health.

Predicting malaria outbreaks using earth observation measurements and spatiotemporal deep learning modelling: a South Asian case study from 2000 to 2017.

BACKGROUND: Malaria remains one the leading communicable causes of death. Approximately half of the world's population is considered at risk of infection, predominantly in African and South Asian countries. Although malaria is preventable, heterogeneity in sociodemographic and environmental risk factors over time and across diverse geographical and climatological regions make outbreak prediction challenging. Data-driven approaches accounting for spatiotemporal variability could offer potential for location-specific early warning tools for malaria. METHODS: In this case study, we developed and internally validated a data fusion approach to predict malaria incidence in Pakistan, India, and Bangladesh using geo-referenced environmental factors. For 2000-17, district-level malaria incidence rates for each country were obtained from the US Agency for International Development's Demographic and Health Survey datasets. Environmental factors included average annual temperature, rainfall, and normalised difference vegetation index, obtained from the Advancing Research on Nutrition and Agriculture (known as AReNA) project conducted by the International Food Policy Research Institute in 2020. Data on night-time light intensity was derived from two satellites of the National Oceanic and Atmospheric Administration Defense Meteorological Satellite Program-Operational Linescan System: Nighttime Lights Time Series Version 4, and VIIRS Nighttime Day/Night Band Composites version 1. A multi-dimensional spatiotemporal long short-term memory (M-LSTM) model was developed using data from 2000-16 and internally validated for the year 2017. The M-LSTM model consisted of four hidden layers, each with 100 LSTM units; a fully connected layer was used, followed by linear regression, to predict the malaria incidence rate for 2017 using spatiotemporal partitioning. Model performance was measured using accuracy and root mean squared error. Country-specific models were produced for Bangladesh, India, and Pakistan. Bivariate geospatial heatmaps were produced for a qualitative comparison of univariate environmental factors with malaria rates. FINDINGS: Malaria incidence was predicted with 80·6% accuracy in districts across Pakistan, 76·7% in districts across India, and 99·1% in districts across Bangladesh. The root mean squared error was 7 × 10-4 for Pakistan, 4·86 × 10-6 for India, and 1·32 × 10-5 for Bangladesh. Bivariate maps showed an inverse relationship between night-time lights and malaria rates; whereas high malaria rates were found in areas with high temperature, rainfall, and vegetation. INTERPRETATION: Malaria outbreaks could be forecasted using remotely measured environmental factors. Modelling techniques that enable simultaneously forecasting ahead in time as well as across large geographical areas might potentially empower regional decision makers to manage outbreaks early. FUNDING: NIHR Oxford Biomedical Research Centre Programme and The Higher Education Commission of Pakistan.

Study protocol: improving response to malaria in the Amazon through identification of inter-community networks and human mobility in border regions of Ecuador, Peru and Brazil.

INTRODUCTION: Understanding human mobility's role in malaria transmission is critical to successful control and elimination. However, common approaches to measuring mobility are ill-equipped for remote regions such as the Amazon. This study develops a network survey to quantify the effect of community connectivity and mobility on malaria transmission. METHODS: We measure community connectivity across the study area using a respondent driven sampling design among key informants who are at least 18 years of age. 45 initial communities will be selected: 10 in Brazil, 10 in Ecuador and 25 in Peru. Participants will be recruited in each initial node and administered a survey to obtain data on each community's mobility patterns. Survey responses will be ranked and the 2-3 most connected communities will then be selected and surveyed. This process will be repeated for a third round of data collection. Community network matrices will be linked with each country's malaria surveillance system to test the effects of mobility on disease risk. ETHICS AND DISSEMINATION: This study protocol has been approved by the institutional review boards of Duke University (USA), Universidad San Francisco de Quito (Ecuador), Universidad Peruana Cayetano Heredia (Peru) and Universidade Federal Minas Gerais (Brazil). Results will be disseminated in communities by the end of the study.

Effect of the COVID-19 pandemic on HIV, malaria and tuberculosis indicators in Togo: an interrupted time series analysis.

BACKGROUND: Limited data are available on the effects of the COVID-19 pandemic on health-related indicators in sub-Saharan Africa. This study aimed to estimate the effect of the COVID-19 pandemic on nine indicators of HIV, malaria and tuberculosis (TB) in Togo. METHODS: For this interrupted time series analysis, national health information system data from January 2019 to December 2021 and TB programmatic data from the first quarter of 2018 to the fourth quarter of 2022 were analysed. Nine indicators were included. We used Poisson segmented regression to estimate the immediate impact of the pandemic and per-pandemic period trends through incidence rate ratios (IRRs) with 95% CIs. RESULTS: Overall, there was a decrease in six of the nine indicators, ranging from 19.3% (IRR 0.807, 95% CI 0.682 to 0.955, p=0.024) for the hospitalisation of patients for malaria to 36.9% (IRR 0.631, 95% CI 0.457 to 0.871, p=0.013) for TB diagnosis by Mycobacterium tuberculosis Xpert immediately after the declaration of the COVID-19 pandemic. A comparison of the observed and predicted trends showed that the trend remained constant between the prepandemic and pandemic periods of COVID-19 for all malaria indicators. A significant downward monthly trend was observed in antiretroviral therapy initiation (IRR 0.909, 95% CI 0.892 to 0.926, p<0.001) and positive TB microscopy (IRR 0.919, 95% CI 0.880 to 0.960, p=0.002). CONCLUSION: HIV, malaria and TB services were generally maintained over time in Togo despite the COVID-19 pandemic. However, given the decline in levels immediately after the onset of the pandemic, there is an urgent need to improve the preparedness of the healthcare system.

Anopheles sacharovi in Italy: first record of the historical malaria vector after over 50 years.

BACKGROUND: Anopheles sacharovi, a member of the Anopheles maculipennis complex, was a historical malaria vector in Italy, no longer found since the last report at the end of 1960s. In September 2022, within the Surveillance Project for the residual anophelism, a single specimen of An. maculipennis sensu lato collected in Lecce municipality (Apulia region) was molecularly identified as An. sacharovi. This record led to implement a targeted entomological survey in September 2023. METHODS: Investigation was conducted in the areas around the first discovery, focusing on animal farms, riding stables and potential breeding sites. Adult and immature mosquitoes were collected, using active search or traps, in several natural and rural sites. Mosquitoes belonging to An. maculipennis complex were identified morphologically and molecularly by a home-made routine quantitative polymerase chain reaction (qPCR) assay, developed specifically for the rapid identification of An. labranchiae, and, when necessary, by amplification and sequencing of the ITS-2 molecular marker. RESULTS: Out of the 11 sites investigated, 6 were positive for Anopheles presence. All 20 An. maculipennis s.l. (7 adults, 10 larvae and 3 pupae) collected in the areas were identified as An. sacharovi by ITS-2 sequencing. CONCLUSIONS: The discovery of An. sacharovi, considered to have disappeared from Italy for over 50 years, has a strong health relevance and impact, highlighting an increase in the receptivity of the southern areas. As imported malaria cases in European countries are reported every year, the risk of Plasmodium introduction by gametocyte carriers among travellers from endemic countries should be taken into greater consideration. Our findings allow rethinking and building new models for the prediction and expansion of introduced malaria. Furthermore, to prevent the risk of reintroduction of the disease, the need to strengthen the surveillance of residual anophelism throughout the South should be considered.

The effect of explicit convection on simulated malaria transmission across Africa.

Malaria transmission across sub-Saharan Africa is sensitive to rainfall and temperature. Whilst different malaria modelling techniques and climate simulations have been used to predict malaria transmission risk, most of these studies use coarse-resolution climate models. In these models convection, atmospheric vertical motion driven by instability gradients and responsible for heavy rainfall, is parameterised. Over the past decade enhanced computational capabilities have enabled the simulation of high-resolution continental-scale climates with an explicit representation of convection. In this study we use two malaria models, the Liverpool Malaria Model (LMM) and Vector-Borne Disease Community Model of the International Centre for Theoretical Physics (VECTRI), to investigate the effect of explicitly representing convection on simulated malaria transmission. The concluded impact of explicitly representing convection on simulated malaria transmission depends on the chosen malaria model and local climatic conditions. For instance, in the East African highlands, cooler temperatures when explicitly representing convection decreases LMM-predicted malaria transmission risk by approximately 55%, but has a negligible effect in VECTRI simulations. Even though explicitly representing convection improves rainfall characteristics, concluding that explicit convection improves simulated malaria transmission depends on the chosen metric and malaria model. For example, whilst we conclude improvements of 45% and 23% in root mean squared differences of the annual-mean reproduction number and entomological inoculation rate for VECTRI and the LMM respectively, bias-correcting mean climate conditions minimises these improvements. The projected impact of anthropogenic climate change on malaria incidence is also sensitive to the chosen malaria model and representation of convection. The LMM is relatively insensitive to future changes in precipitation intensity, whilst VECTRI predicts increased risk across the Sahel due to enhanced rainfall. We postulate that VECTRI's enhanced sensitivity to precipitation changes compared to the LMM is due to the inclusion of surface hydrology. Future research should continue assessing the effect of high-resolution climate modelling in impact-based forecasting.

Spatiotemporal distribution and bionomics of Anopheles stephensi in different eco-epidemiological settings in Ethiopia.

BACKGROUND: Malaria is a major public health concern in Ethiopia, and its incidence could worsen with the spread of the invasive mosquito species Anopheles stephensi in the country. This study aimed to provide updates on the distribution of An. stephensi and likely household exposure in Ethiopia. METHODS: Entomological surveillance was performed in 26 urban settings in Ethiopia from 2021 to 2023. A kilometer-by-kilometer quadrant was established per town, and approximately 20 structures per quadrant were surveyed every 3 months. Additional extensive sampling was conducted in 50 randomly selected structures in four urban centers in 2022 and 2023 to assess households' exposure to An. stephensi. Prokopack aspirators and CDC light traps were used to collect adult mosquitoes, and standard dippers were used to collect immature stages. The collected mosquitoes were identified to species level by morphological keys and molecular methods. PCR assays were used to assess Plasmodium infection and mosquito blood meal source. RESULTS: Catches of adult An. stephensi were generally low (mean: 0.15 per trap), with eight positive sites among the 26 surveyed. This mosquito species was reported for the first time in Assosa, western Ethiopia. Anopheles stephensi was the predominant species in four of the eight positive sites, accounting for 75-100% relative abundance of the adult Anopheles catches. Household-level exposure, defined as the percentage of households with a peridomestic presence of An. stephensi, ranged from 18% in Metehara to 30% in Danan. Anopheles arabiensis was the predominant species in 20 of the 26 sites, accounting for 42.9-100% of the Anopheles catches. Bovine blood index, ovine blood index and human blood index values were 69.2%, 32.3% and 24.6%, respectively, for An. stephensi, and 65.4%, 46.7% and 35.8%, respectively, for An. arabiensis. None of the 197 An. stephensi mosquitoes assayed tested positive for Plasmodium sporozoite, while of the 1434 An. arabiensis mosquitoes assayed, 62 were positive for Plasmodium (10 for P. falciparum and 52 for P. vivax). CONCLUSIONS: This study shows that the geographical range of An. stephensi has expanded to western Ethiopia. Strongly zoophagic behavior coupled with low adult catches might explain the absence of Plasmodium infection. The level of household exposure to An. stephensi in this study varied across positive sites. Further research is needed to better understand the bionomics and contribution of An. stephensi to malaria transmission.

Low long-lasting insecticidal net use in malaria elimination areas in Southern Ethiopia: results from community based cross-sectional study.

BACKGROUND: Despite remarkable progress in malaria burden reduction, malaria continues to be a major public health problem globally. Ethiopia has been distributing long-lasting insecticidal nets (LLINs) for free and nationwide distribution was completed in 2016. However, evidence suggests that the utilization of LLINs varies from setting to setting and from time to time due to different factors, and up-to-date evidence is required for LLIN related decision-making. Hence, this study was designed to assess LLIN utilization and its determinants in the Southern Nations, Nationalities, and People's Region (SNNPR) of Ethiopia. METHODS: A community-based cross-sectional study was conducted in Southern Ethiopia in 2019. Using multi-stage sampling, a total of 2466 households were included. The region was stratified based on the annual malaria index as high, moderate, low, and free strata. Cluster sampling was then applied to select households from high, moderate, and low strata. Data on LLIN ownership, utilization and different determinant factors were collected using household questionnaire. SurveyCTO was used to collect data and data was managed using Stata 15. Descriptive statistics and multilevel mixed-effects logistic regression were performed to identify the determinants of utilization of LLINs. Effect measures were reported using adjusted odds ratio (AOR) with 95% CI. RESULTS: From a total of 2466 households, 48.7% of households had at least one LLIN. LLIN adequacy based on family size was 23% while it was15.7% based on universal access and 29.2% based on sleeping space. From 1202 households that possessed LLIN(s), 66.0% of households reported that they slept under LLIN the night preceding the survey. However, when the total population in all surveyed households were considered, only 22.9% of household members slept under LLIN the night preceding the survey. Malaria endemicity, educational status, wealth status, and knowledge about malaria were associated with LLINs utilization. In addition, reasons for non-use included perceived absence of malaria, side effects of LLIN, conditions of LLINs, inconvenient space and low awareness. CONCLUSION: Low LLIN coverage and low utilization were noted. A low level of utilization was associated with malaria endemicity, wealth status and level of awareness. Distribution of LLIN and continuous follow-up with community awareness creation activities are vital to improve coverage and utilization of LLINs, and to ensure the country's malaria elimination goal.

Concurrent management of HIV and malaria: A comprehensive review of strategies to enhance quality of life.

The co-occurrence of human immunodeficiency virus and malaria presents a complex medical scenario, significantly impacting the quality of life for affected individuals. This comprehensive review synthesizes current knowledge, challenges, and strategies concerning the concurrent management of these infections to improve overall well-being. Epidemiological insights reveal the prevalence and demographic trends, highlighting geographical areas of concern and socioeconomic factors contributing to the burden of co-infection. Pathophysiological interactions elucidate the compounding effects, altering disease progression and treatment outcomes. Healthcare challenges underscore the necessity for integrated care models, evaluating existing healthcare frameworks and their efficacy in addressing dual infections. In-depth analysis of interventions explores pharmacological, behavioral, and preventive measures, evaluating their efficacy and safety in co-infected individuals. Additionally, the review assesses psychosocial support mechanisms, emphasizing community-based interventions and peer networks in enhancing holistic care. Consideration is given to the role of antiretroviral therapy, malaria prevention strategies, and the evolving landscape of healthcare delivery in optimizing outcomes for this vulnerable population. The paper concludes by emphasizing the significance of multidisciplinary approaches and integrated care models, stressing the need for continued research and collaborative efforts to advance interventions and improve the quality of life for those navigating the complexities of human immunodeficiency virus and malaria co-infection.

Possible potential spread of Anopheles stephensi, the Asian malaria vector.

BACKGROUND: Anopheles stephensi is native to Southeast Asia and the Arabian Peninsula and has emerged as an effective and invasive malaria vector. Since invasion was reported in Djibouti in 2012, the global invasion range of An. stephensi has been expanding, and its high adaptability to the environment and the ongoing development of drug resistance have created new challenges for malaria control. Climate change is an important factor affecting the distribution and transfer of species, and understanding the distribution of An. stephensi is an important part of malaria control measures, including vector control. METHODS: In this study, we collected existing distribution data for An. stephensi, and based on the SSP1-2.6 future climate data, we used the Biomod2 package in R Studio through the use of multiple different model methods such as maximum entropy models (MAXENT) and random forest (RF) in this study to map the predicted global An. stephensi climatically suitable areas. RESULTS: According to the predictions of this study, some areas where there are no current records of An. stephensi, showed significant areas of climatically suitable for An. stephensi. In addition, the global climatically suitability areas for An. stephensi are expanding with global climate change, with some areas changing from unsuitable to suitable, suggesting a greater risk of invasion of An. stephensi in these areas, with the attendant possibility of a resurgence of malaria, as has been the case in Djibouti. CONCLUSIONS: This study provides evidence for the possible invasion and expansion of An. stephensi and serves as a reference for the optimization of targeted monitoring and control strategies for this malaria vector in potential invasion risk areas.

Risk assessment of imported malaria in China: a machine learning perspective.

BACKGROUND: Following China's official designation as malaria-free country by WHO, the imported malaria has emerged as a significant determinant impacting the malaria reestablishment within China. The objective of this study is to explore the application prospects of machine learning algorithms in imported malaria risk assessment of China. METHODS: The data of imported malaria cases in China from 2011 to 2019 was provided by China CDC; historical epidemic data of malaria endemic country was obtained from World Malaria Report, and the other data used in this study are open access data. All the data processing and model construction based on R, and map visualization used ArcGIS software. RESULTS: A total of 27,088 malaria cases imported into China from 85 countries between 2011 and 2019. After data preprocessing and classification, clean dataset has 765 rows (85 * 9) and 11 cols. Six machine learning models was constructed based on the training set, and Random Forest model demonstrated the best performance in model evaluation. According to RF, the highest feature importance were the number of malaria deaths and Indigenous malaria cases. The RF model demonstrated high accuracy in forecasting risk for the year 2019, achieving commendable accuracy rate of 95.3%. This result aligns well with the observed outcomes, indicating the model's reliability in predicting risk levels. CONCLUSIONS: Machine learning algorithms have reliable application prospects in risk assessment of imported malaria in China. This study provides a new methodological reference for the risk assessment and control strategies adjusting of imported malaria in China.