Dominant malaria vector species in Nigeria: Modelling potential distribution of Anopheles gambiae sensu lato and its siblings with MaxEnt

Spatial distribution and geospatial modeling of potential spread of secondary malaria vectors species in Nigeria using recently collected empirical data

In Nigeria, most research and malaria vector control efforts have focused on primary vectors within the Anopheles gambiae complex, with less emphasis on other secondary vectors. Consequently, understudied secondary vectors have demonstrated a proportional and increasing role in transmission. This study utilized geospatial models to understand the potential distribution of anopheline species other than An. gambiae complex (non-gambiae species) in Nigeria. Adult mosquitoes were sampled monthly between 2020 and 2022, with concurrent surveys of larval sites in selected Local Government Areas (LGAs) across 20 States resulting in the collection and identification of over 100,000 Anopheline mosquitoes. Utilizing 23 environmental variables, the model produced maps depicting the potential geographical distribution of four secondary vector species under current climatic conditions. An. funestus, An. coustani, An. maculipalpis, and An. rufipes dominated collections, with other species also present. Most species collected exhibited higher occurrences in the Northern parts of the country, albeit with lower numbers, while they seem confined to fewer locations in the southern parts - with higher densities. An. funestus, An. maculipalpis, and An. rufipes demonstrated a higher potential for wide range expansion compared to An. coustani based on the model. Overall, modeling outputs indicate that non-An. gambiae were expected to exhibit a wide-spread across the country, with their distribution primarily influenced by temperature rather than precipitation-related factors. These models provide research scientists and decision-makers with a baseline for research, monitoring towards establishing management plans for future national mosquito surveillance and control programs in Nigeria.

A study on the effectiveness of (+)-usnic acid as oral toxic sugar bait against adult male and female Anopheles gambiae

BACKGROUND

Despite the application of various tools for the control of vectors of Plasmodium falciparum, malaria remains the major killer disease in sub-Saharan Africa accounting for up to 90% of deaths due to the disease. Due to limitations of the useage of chemical insecticides such as resistance, negative impact on the environment and to nontarget organisms, the World Health Organization (WHO) requires that affected countries find alternative vector control tools. This study evaluated the effectiveness of ( +)-usnic acid (UA) as an insecticide through oral administration to male and female Anopheles gambiae as an alternative or additional active ingredient to be used in toxic sugar bait.

METHODS

( +)-usnic acid was diluted using acetone at 5, 10, and 15 mg/ml concentrations in three replicates. A 5 ml mixture of 2% food dye and 10% sugar using chlorine-free water mixed with the dilutions of the ( +)-usnic acid and negative control was made containing 2% food dye and 10% sugar solution. The preparations were soaked on a ball of cotton wool and placed over the net of a cup. 5 male and 5 non-blood-fed female newly hatched starved An. gambiae Kisumu strain were introduced together into a cup and monitored for knockdown and mortalities after 4, 24 48, and 72 h. The data were analysed using a multiple linear regression model using the lm function, a base R function and a posthoc test were conducted on the significant main effects and interaction terms using the emmeans function from the emmeans R package. All analyses were performed in RStudio using base R (version 4.3.3).

RESULTS

There was high mortality of both male and female An. gambiae after ingestion of the toxic sugar bait. 15 mg/ml usnic acid caused the highest mortality (50%) within the first 4 h compared to 5 and 10 mg/ml ( +)-UA. There was a decline in the mortality rate with increased exposure time from 24 to 72 h, however, there was a significant difference in mortality at 5, 10 and 15 mg/ml. Acute toxicity was associated with ingestion of 15 mg/ml after 24 h. 72 h post-mortality was lower in all concentrations than in the control. High mortality was observed among females over the first 4 h (60%) compared to males (40%) due to higher feeding rate of the toxic agent. The proportion of dead males and females was equal after 24 h while after 48 h, the proportion of dead males was high.There was a significantly lower mortality rate after 72 h for both males and females (0 to 13.3%). Compared to all the treatments, high mortality of males was observed.

CONCLUSIONS

The results of this study indicate that ( +)-UA when administered as oral sugar bait to An. gambiae has insecticidal properties and is a suitable ingredient to be used as a toxic agent in the novel attractive toxic sugar bait for the control of malaria vectors. ( +)-UA may be an alternative active ingredient as toxic bait in the effort to reduce and eliminate the transmission of Plasmodium falciparum in Africa.

Spatial analysis for risk assessment of dengue in Spain

INTRODUCTION

The establishment of Aedes albopictus in new areas in Europe has changed the risk of local dengue transmission represented by imported human cases. The risk of transmission is determined by the distribution of travelers arriving from dengue-endemic areas and the distribution of Ae. albopictus as potential vectors of dengue in Spain.

METHODS

Environmental, entomological, epidemiological, demographic, tourism and travel data were analyzed to produce a series of maps to represent: the distribution of Ae. albopictus across municipalities; the risk of expansion of Ae. albopictus based on a species distribution model; the calculated index of travelers from dengue-endemic areas (IDVZE) per province; the percentage contribution of each municipality to the total number of cases in Spain. The maps were then added using map algebra, to profile the spatial risk of autochthonous dengue in Spain at a municipal level from 2016 to 2018.

RESULTS

Ae. albopictus was detected in 983 municipalities. The calculated IDVZE varied from 0.23 to 10.38, with the highest IDVZE observed in Madrid. The overall risk of autochthonous cases oscillated between 0.234 and 115, with the very high risk and high risk areas detected in the Mediterranean region, mainly in the Levantine coast and some parts of the Balearic Islands. Most of the interior of the peninsula was characterized as low risk.

CONCLUSION

Prevention and control measures to mitigate the risk of autochthonous dengue should be prioritized for municipalities in the high risk areas integrating early detection of imported dengue cases and vector control.

Temperature dependence of mosquitoes: Comparing mechanistic and machine learning approaches

Mosquito vectors of pathogens (e.g., Aedes, Anopheles, and Culex spp. which transmit dengue, Zika, chikungunya, West Nile, malaria, and others) are of increasing concern for global public health. These vectors are geographically shifting under climate and other anthropogenic changes. As small-bodied ectotherms, mosquitoes are strongly affected by temperature, which causes unimodal responses in mosquito life history traits (e.g., biting rate, adult mortality rate, mosquito development rate, and probability of egg-to-adult survival) that exhibit upper and lower thermal limits and intermediate thermal optima in laboratory studies. However, it remains unknown how mosquito thermal responses measured in laboratory experiments relate to the realized thermal responses of mosquitoes in the field. To address this gap, we leverage thousands of global mosquito occurrences and geospatial satellite data at high spatial resolution to construct machine-learning based species distribution models, from which vector thermal responses are estimated. We apply methods to restrict models to the relevant mosquito activity season and to conduct ecologically plausible spatial background sampling centered around ecoregions for comparison to mosquito occurrence records. We found that thermal minima estimated from laboratory studies were highly correlated with those from the species distributions (r = 0.87). The thermal optima were less strongly correlated (r = 0.69). For most species, we did not detect thermal maxima from their observed distributions so were unable to compare to laboratory-based estimates. The results suggest that laboratory studies have the potential to be highly transportable to predicting lower thermal limits and thermal optima of mosquitoes in the field. At the same time, lab-based models likely capture physiological limits on mosquito persistence at high temperatures that are not apparent from field-based observational studies but may critically determine mosquito responses to climate warming. Our results indicate that lab-based and field-based studies are highly complementary; performing the analyses in concert can help to more comprehensively understand vector response to climate change.

In silico studies of benzothiazole derivatives as potential inhibitors of Anopheles funestus and Anopheles gambiae trehalase

Introduction

In malaria management, insecticides play a crucial role in targeting disease vectors. Benzothiazole derivatives have also been reported to possess insecticidal properties, among several other properties they exhibit. The female Anopheles mosquito is responsible for transmitting the malaria parasite when infected. Anopheles gambiae (Ag) and Anopheles funestus (Af) are two of the most notable Anopheles species known to spread malaria in Nigeria. Trehalase is an enzyme that breaks down trehalose. Recent research has proposed it as a viable target for inhibition since it aids in flight and stress adaptation.

Methods

This study aimed to investigate benzothiazole derivatives as potential inhibitors of trehalase of Anopheles funestus (AfTre) and Anopheles gambiae (AgTre) using toxicity profiling, molecular docking, and dynamic simulation for future insecticidal intervention. A total of 4,214 benzothiazole-based compounds were obtained from the PubChem database and subjected to screening against the 3D modelled structure of AfTre and AgTre. Compounds with some toxicity levels were optimised, and the obtained lead compounds were further investigated through molecular docking studies. Furthermore, the best hit was subjected to parameters such as RMSD, RMSF, SASA, Rg, and hydrogen bond to confirm its stability when in a complex with AfTre, and these parameters were compared to that of validamycin A (control ligand).

Results and discussion

The post-screening analysis showed binding affinities of -8.7 and -8.2 kcal/mol (compound 1), -8.2 and -7.4 kcal/mol (compound 2), compared to -6.3 and -5.1 kcal/mol (Validamycin A, a known inhibitor) against AfTre and AgTre, respectively. The molecular dynamics simulation showed that compound 1 (the best hit) had good stability when in complex with AfTre. These findings suggest that these best hits can serve as potential inhibitors for the development of novel insecticides in the control of malaria vectors.

Range dynamics of Anopheles mosquitoes in Africa suggest a significant increase in the malaria transmission risk

Despite a more than 100-year effort to combat malaria, it remains one of the most malignant infectious diseases globally, especially in Africa. Malaria is transmitted by several Anopheles mosquitoes. However, until now few studies have investigated future range dynamics of major An. mosquitoes in Africa through a unified scheme. Through a unified scheme, we developed 21 species distribution models to predict the range dynamics of 21 major An. species in Africa under future scenarios and also examined their overall range dynamic patterns mainly through suitability overlap index and range overlap index. Although future range dynamics varied substantially among the 21 An. species, we predicted large future range expansions for all 21 An. species, and increases in suitability overlap index were detected in more than 90% of the African continent for all future scenarios. Additionally, we predicted high range overlap index in West Africa, East Africa, South Sudan, Angola, and the Democratic Republic of the Congo under future scenarios. Although the relative impacts of land use, topography and climate variables on the range dynamics depended on species and spatial scale, climate played the strongest roles in the range dynamics of most species. Africa might face an increasing risk of malaria transmissions in the future, and better strategies are required to address this problem. Mitigating climate change and human disturbance of natural ecosystems might be essential to reduce the proliferation of An. species and the risk of malaria transmissions in Africa in the future. Our strategies against their impacts should be species-specific.

Potential distribution of malaria vectors in Central Vietnam: A MaxEnt modeling approach

Background and Aim

In Central Vietnam, Anopheles dirus and Anopheles minimus are the primary malaria vector species. These Anopheles spp.' distribution and prevalence are determined by environmental, climatic, and socioeconomic conditions. This study aimed to predict the potential distribution of these two Anopheles spp. in this region.

Materials and Methods

This study was conducted in 15 Central Vietnamese provinces. From 2014 to 2018, we utilized An. dirus and An. minimus presence records. Proxy data from the Google Earth Engine platform for the study area, encompassing environmental, climatic, and socioeconomic factors. MaxEnt software predicted the potential environmental, climatic, and socioeconomic suitability of these two Anopheles spp. in Central Vietnam.

Results

The test area under the curve values for An. dirus and An. minimus MaxEnt models averaged 0.801 and 0.806, respectively, showing excellent performance. Minimum air temperature had the greatest impact on the distribution of both species. A negative correlation between precipitation and normalized difference water index influences the occurrence of An. dirus. In the temperature range of 13-19.5°C, An. minimus is most likely to be present, with nighttime light detrimentally influencing its distribution. The Central Highlands region is inhabited by both species, with some presence in North-Central and South-Central Coastal areas.

Conclusion

The importance of temperature in determining the presence of both species is emphasized by our findings, with subtle differences in the temperature-related factors shaping their distributions. The results highlight the need for focused malaria vector control and surveillance initiatives in the study area.

Spatiotemporal pattern and suitable areas analysis of equine influenza in global scale (2005-2022)

Equine influenza (EI) is a severe infectious disease that causes huge economic losses to the horse industry. Spatial epidemiology technology can explore the spatiotemporal distribution characteristics and occurrence risks of infectious diseases, it has played an important role in the prevention and control of major infectious diseases in humans and animals. For the first time, this study conducted a systematic analysis of the spatiotemporal distribution of EI using SaTScan software and investigated the important environmental variables and suitable areas for EI occurrence using the Maxent model. A total of 517 occurrences of EI from 2005 to 2022 were evaluated, and 14 significant spatiotemporal clusters were identified. Furthermore, a Maxent model was successfully established with high prediction accuracy (AUC = 0.920 ± 0.008). The results indicated that annual average ultraviolet radiation, horse density, and precipitation of the coldest quarter were the three most important environmental variables affecting EI occurrence. The suitable areas for EI occurrence are widely distributed across all continents, especially in Asia (India, Mongolia, and China) and the Americas (Brazil, Uruguay, USA, and Mexico). In the future, these suitable areas will expand and move eastward. The largest expansion is predicted under SSP126 scenarios, while the opposite trend will be observed under SSP585 scenarios. This study presents the spatial epidemiological characteristics of EI for the first time. The results could provide valuable scientific insights that can effectively inform prevention and control strategies in regions at risk of EI worldwide.

Impacts of Deforestation on Childhood Malaria Depend on Wealth and Vector Biology

Ecosystem change can profoundly affect human well-being and health, including through changes in exposure to vector-borne diseases. Deforestation has increased human exposure to mosquito vectors and malaria risk in Africa, but there is little understanding of how socioeconomic and ecological factors influence the relationship between deforestation and malaria risk. We examined these interrelationships in six sub-Saharan African countries using demographic and health survey data linked to remotely sensed environmental variables for 11,746 children under 5 years old. We found that the relationship between deforestation and malaria prevalence varies by wealth levels. Deforestation is associated with increased malaria prevalence in the poorest households, but there was not significantly increased malaria prevalence in the richest households, suggesting that deforestation has disproportionate negative health impacts on the poor. In poorer households, malaria prevalence was 27%-33% larger for one standard deviation increase in deforestation across urban and rural populations. Deforestation is also associated with increased malaria prevalence in regions where Anopheles gambiae and Anopheles funestus are dominant vectors, but not in areas of Anopheles arabiensis. These findings indicate that deforestation is an important driver of malaria risk among the world's most vulnerable children, and its impact depends critically on often-overlooked social and biological factors. An in-depth understanding of the links between ecosystems and human health is crucial in designing conservation policies that benefit people and the environment.

Socioeconomic, Demographic, and Environmental Factors May Inform Malaria Intervention Prioritization in Urban Nigeria

Urban population growth in Nigeria may exceed the availability of affordable housing and basic services, resulting in living conditions conducive to vector breeding and heterogeneous malaria transmission. Understanding the link between community-level factors and urban malaria transmission informs targeted interventions. We analyzed Demographic and Health Survey Program cluster-level data, alongside geospatial covariates, to describe variations in malaria prevalence in children under 5 years of age. Univariate and multivariable models explored the relationship between malaria test positivity rates at the cluster level and community-level factors. Generally, malaria test positivity rates in urban areas are low and declining. The factors that best predicted malaria test positivity rates within a multivariable model were post-primary education, wealth quintiles, population density, access to improved housing, child fever treatment-seeking, precipitation, and enhanced vegetation index. Malaria transmission in urban areas will likely be reduced by addressing socioeconomic and environmental factors that promote exposure to disease vectors. Enhanced regional surveillance systems in Nigeria can provide detailed data to further refine our understanding of these factors in relation to malaria transmission.

Temperature dependence of mosquitoes: comparing mechanistic and machine learning approaches

Mosquito vectors of pathogens (e.g., Aedes , Anopheles , and Culex spp. which transmit dengue, Zika, chikungunya, West Nile, malaria, and others) are of increasing concern for global public health. These vectors are geographically shifting under climate and other anthropogenic changes. As small-bodied ectotherms, mosquitoes are strongly affected by temperature, which causes unimodal responses in mosquito life history traits (e.g., biting rate, adult mortality rate, mosquito development rate, and probability of egg-to-adult survival) that exhibit upper and lower thermal limits and intermediate thermal optima in laboratory studies. However, it remains unknown how mosquito thermal responses measured in laboratory experiments relate to the realized thermal responses of mosquitoes in the field. To address this gap, we leverage thousands of global mosquito occurrences and geospatial satellite data at high spatial resolution to construct machine-learning based species distribution models, from which vector thermal responses are estimated. We apply methods to restrict models to the relevant mosquito activity season and to conduct ecologically-plausible spatial background sampling centered around ecoregions for comparison to mosquito occurrence records. We found that thermal minima estimated from laboratory studies were highly correlated with those from the species distributions (r = 0.90). The thermal optima were less strongly correlated (r = 0.69). For most species, we did not detect thermal maxima from their observed distributions so were unable to compare to laboratory-based estimates. The results suggest that laboratory studies have the potential to be highly transportable to predicting lower thermal limits and thermal optima of mosquitoes in the field. At the same time, lab-based models likely capture physiological limits on mosquito persistence at high temperatures that are not apparent from field-based observational studies but may critically determine mosquito responses to climate warming.

Machine learning to predict risk for community-onset Staphylococcus aureus infections in children living in southeastern United States

Staphylococcus aureus (S. aureus) is known to cause human infections and since the late 1990s, community-onset antibiotic resistant infections (methicillin resistant S. aureus (MRSA)) continue to cause significant infections in the United States. Skin and soft tissue infections (SSTIs) still account for the majority of these in the outpatient setting. Machine learning can predict the location-based risks for community-level S. aureus infections. Multi-year (2002-2016) electronic health records of children <19 years old with S. aureus infections were queried for patient level data for demographic, clinical, and laboratory information. Area level data (Block group) was abstracted from U.S. Census data. A machine learning ecological niche model, maximum entropy (MaxEnt), was applied to assess model performance of specific place-based factors (determined a priori) associated with S. aureus infections; analyses were structured to compare methicillin resistant (MRSA) against methicillin sensitive S. aureus (MSSA) infections. Differences in rates of MRSA and MSSA infections were determined by comparing those which occurred in the early phase (2002-2005) and those in the later phase (2006-2016). Multi-level modeling was applied to identify risks factors for S. aureus infections. Among 16,124 unique patients with community-onset MRSA and MSSA, majority occurred in the most densely populated neighborhoods of Atlanta's metropolitan area. MaxEnt model performance showed the training AUC ranged from 0.771 to 0.824, while the testing AUC ranged from 0.769 to 0.839. Population density was the area variable which contributed the most in predicting S. aureus disease (stratified by CO-MRSA and CO-MSSA) across early and late periods. Race contributed more to CO-MRSA prediction models during the early and late periods than for CO-MSSA. Machine learning accurately predicts which densely populated areas are at highest and lowest risk for community-onset S. aureus infections over a 14-year time span.

Trends in mosquito species distribution modeling: insights for vector surveillance and disease control

Species distribution modeling (SDM) has become an increasingly common approach to explore questions about ecology, geography, outbreak risk, and global change as they relate to infectious disease vectors. Here, we conducted a systematic review of the scientific literature, screening 563 abstracts and identifying 204 studies that used SDMs to produce distribution estimates for mosquito species. While the number of studies employing SDM methods has increased markedly over the past decade, the overwhelming majority used a single method (maximum entropy modeling; MaxEnt) and focused on human infectious disease vectors or their close relatives. The majority of regional models were developed for areas in Africa and Asia, while more localized modeling efforts were most common for North America and Europe. Findings from this study highlight gaps in taxonomic, geographic, and methodological foci of current SDM literature for mosquitoes that can guide future efforts to study the geography of mosquito-borne disease risk.

Spatial distribution and ecological niche modeling of geographical spread of Anopheles gambiae complex in Nigeria using real time data

The need for evidence-based data, to inform policy decisions on malaria vector control interventions in Nigeria, necessitated the establishment of mosquito surveillance sites in a few States in Nigeria. In order to make evidence-based-decisions, predictive studies using available data becomes imperative. We therefore predict the distribution of the major members of the Anopheles gambiae s.l. in Nigeria. Immature stages of Anopheles were collected from 72 study locations which span throughout the year 2020 resulted in the identification of over 60,000 Anopheline mosquitoes. Of these, 716 breeding sites were identified with the presence of one or more vector species from the An. gambiae complex and were subsequently used for modelling the potential geographical distribution of these important malaria vectors. Maximum Entropy (MaxEnt) distribution modeling was used to predict their potentially suitable vector habitats across Nigeria. A total of 23 environmental variables (19 bioclimatic and four topographic) were used in the model resulting in maps of the potential geographical distribution of three dominant vector species under current climatic conditions. Members of the An. gambiae complex dominated the collections (98%) with Anopheles stephensi, Anopheles coustani, Anopheles funestus, Anopheles moucheti, Anopheles nilli also present. An almost equal distribution of the two efficient vectors of malaria, An. gambiae and Anopheles coluzzii, were observed across the 12 states included in the survey. Anopheles gambiae and Anopheles coluzzii had almost equal, well distributed habitat suitability patterns with the latter having a slight range expansion. However, the central part of Nigeria (Abuja) and some highly elevated areas (Jos) in the savannah appear not suitable for the proliferation of these species. The most suitable habitat for Anopheles arabiensis was mainly in the South-west and North-east. The results of this study provide a baseline allowing decision makers to monitor the distribution of these species and establish a management plan for future national mosquito surveillance and control programs in Nigeria.

Utilization of insecticide-treated nets and associated factors among childbearing women in Northern Nigeria

BACKGROUND

Studies have explored the correlates of insecticide-treated nets in Nigeria. The few studies that focused on Northern Nigeria mostly examined individual correlates, but largely ignored the community correlates. Also, the persistence of armed insurgencies in the region calls for more research attention. This study examines the utilization and the associated individual and community factors of insecticide-treated nets in Northern Nigeria.

METHODS

The study adopted a cross-sectional design. Data were extracted from the 2021 Nigeria Malaria Indicator Survey (NMIS). A weighted sample size of 6873 women was analysed. The outcome variable was the utilization of insecticide-treated nets. The explanatory variables selected at the individual/household level were maternal age, maternal education, parity, religion, sex of head of household, household wealth, and household size. The variables selected at the community level were the type of place of residence, geo-political zone of residence, the proportion of children under five who slept under a bed net, the proportion of women aged 15-49 who heard malaria media messages, and the community literacy level. Two variables, namely, the number of mosquito bed nets in the household, and the number of rooms used for sleeping were included for statistical control. Three multilevel mixed-effect regression models were fitted.

RESULTS

The majority of childbearing women (71.8%) utilized insecticide-treated nets. Parity and household size were the significant individual/household characteristics associated with the utilization of insecticide-treated nets. The proportion of under-five children in the community who slept under mosquito bed nets, and the geopolitical zone of residence were significant community correlates of the use of insecticide-treated nets. In addition, the number of rooms for sleeping, and the number of mosquito bed nets in the households were significantly associated with the utilization of insecticide-treated nets.

CONCLUSION

Parity, household size, number of sleeping rooms, number of treated bed nets, geo-political zone of residence, and proportion of under-five children sleeping under bed nets are important associated factors of the utilization of insecticide-treated nets in Northern Nigeria. Existing malaria preventive initiatives should be strengthened to target these characteristics.

Fine-scale mapping of urban malaria exposure under data scarcity: an approach centred on vector ecology

BACKGROUND

Although malaria transmission has experienced an overall decline in sub-Saharan Africa, urban malaria is now considered an emerging health issue due to rapid and uncontrolled urbanization and the adaptation of vectors to urban environments. Fine-scale hazard and exposure maps are required to support evidence-based policies and targeted interventions, but data-driven predictive spatial modelling is hindered by gaps in epidemiological and entomological data. A knowledge-based geospatial framework is proposed for mapping the heterogeneity of urban malaria hazard and exposure under data scarcity. It builds on proven geospatial methods, implements open-source algorithms, and relies heavily on vector ecology knowledge and the involvement of local experts.

METHODS

A workflow for producing fine-scale maps was systematized, and most processing steps were automated. The method was evaluated through its application to the metropolitan area of Dakar, Senegal, where urban transmission has long been confirmed. Urban malaria exposure was defined as the contact risk between adult Anopheles vectors (the hazard) and urban population and accounted for socioeconomic vulnerability by including the dimension of urban deprivation that is reflected in the morphology of the built-up fabric. Larval habitat suitability was mapped through a deductive geospatial approach involving the participation of experts with a strong background in vector ecology and validated with existing geolocated entomological data. Adult vector habitat suitability was derived through a similar process, based on dispersal from suitable breeding site locations. The resulting hazard map was combined with a population density map to generate a gridded urban malaria exposure map at a spatial resolution of 100 m.

RESULTS

The identification of key criteria influencing vector habitat suitability, their translation into geospatial layers, and the assessment of their relative importance are major outcomes of the study that can serve as a basis for replication in other sub-Saharan African cities. Quantitative validation of the larval habitat suitability map demonstrates the reliable performance of the deductive approach, and the added value of including local vector ecology experts in the process. The patterns displayed in the hazard and exposure maps reflect the high degree of heterogeneity that exists throughout the city of Dakar and its suburbs, due not only to the influence of environmental factors, but also to urban deprivation.

CONCLUSIONS

This study is an effort to bring geospatial research output closer to effective support tools for local stakeholders and decision makers. Its major contributions are the identification of a broad set of criteria related to vector ecology and the systematization of the workflow for producing fine-scale maps. In a context of epidemiological and entomological data scarcity, vector ecology knowledge is key for mapping urban malaria exposure. An application of the framework to Dakar showed its potential in this regard. Fine-grained heterogeneity was revealed by the output maps, and besides the influence of environmental factors, the strong links between urban malaria and deprivation were also highlighted.

Understanding work-related travel and its relation to malaria occurrence in Thailand using geospatial maximum entropy modelling

BACKGROUND

Estimating malaria risk associated with work locations and travel across a region provides local health officials with information useful to mitigate possible transmission paths of malaria as well as understand the risk of exposure for local populations. This study investigates malaria exposure risk by analysing the spatial pattern of malaria cases (primarily Plasmodium vivax) in Ubon Ratchathani and Sisaket provinces of Thailand, using an ecological niche model and machine learning to estimate the species distribution of P. vivax malaria and compare the resulting niche areas with occupation type, work locations, and work-related travel routes.

METHODS

A maximum entropy model was trained to estimate the distribution of P. vivax malaria for a period between January 2019 and April 2020, capturing estimated malaria occurrence for these provinces. A random simulation workflow was developed to make region-based case data usable for the machine learning approach. This workflow was used to generate a probability surface for the ecological niche regions. The resulting niche regions were analysed by occupation type, home and work locations, and work-related travel routes to determine the relationship between these variables and malaria occurrence. A one-way analysis of variance (ANOVA) test was used to understand the relationship between predicted malaria occurrence and occupation type.

RESULTS

The MaxEnt (full name) model indicated a higher occurrence of P. vivax malaria in forested areas especially along the Thailand-Cambodia border. The ANOVA results showed a statistically significant difference between average malaria risk values predicted from the ecological niche model for rubber plantation workers and farmers, the two main occupation groups in the study. The rubber plantation workers were found to be at higher risk of exposure to malaria than farmers in Ubon Ratchathani and Sisaket provinces of Thailand.

CONCLUSION

The results from this study point to occupation-related factors such as work location and the routes travelled to work, being risk factors in malaria occurrence and possible contributors to transmission among local populations.

Predicting Plasmodium knowlesi transmission risk across Peninsular Malaysia using machine learning-based ecological niche modeling approaches

The emergence of potentially life-threatening zoonotic malaria caused by Plasmodium knowlesi nearly two decades ago has continued to challenge Malaysia healthcare. With a total of 376 P. knowlesi infections notified in 2008, the number increased to 2,609 cases in 2020 nationwide. Numerous studies have been conducted in Malaysian Borneo to determine the association between environmental factors and knowlesi malaria transmission. However, there is still a lack of understanding of the environmental influence on knowlesi malaria transmission in Peninsular Malaysia. Therefore, our study aimed to investigate the ecological distribution of human P. knowlesi malaria in relation to environmental factors in Peninsular Malaysia. A total of 2,873 records of human P. knowlesi infections in Peninsular Malaysia from 1st January 2011 to 31st December 2019 were collated from the Ministry of Health Malaysia and geolocated. Three machine learning-based models, maximum entropy (MaxEnt), extreme gradient boosting (XGBoost), and ensemble modeling approach, were applied to predict the spatial variation of P. knowlesi disease risk. Multiple environmental parameters including climate factors, landscape characteristics, and anthropogenic factors were included as predictors in both predictive models. Subsequently, an ensemble model was developed based on the output of both MaxEnt and XGBoost. Comparison between models indicated that the XGBoost has higher performance as compared to MaxEnt and ensemble model, with AUC_ROC values of 0.933 ± 0.002 and 0.854 ± 0.007 for train and test datasets, respectively. Key environmental covariates affecting human P. knowlesi occurrence were distance to the coastline, elevation, tree cover, annual precipitation, tree loss, and distance to the forest. Our models indicated that the disease risk areas were mainly distributed in low elevation (75-345 m above mean sea level) areas along the Titiwangsa mountain range and inland central-northern region of Peninsular Malaysia. The high-resolution risk map of human knowlesi malaria constructed in this study can be further utilized for multi-pronged interventions targeting community at-risk, macaque populations, and mosquito vectors.

Climate Change and Cascading Risks from Infectious Disease

Climate change is adversely affecting the burden of infectious disease throughout the world, which is a health security threat. Climate-sensitive infectious disease includes vector-borne diseases such as malaria, whose transmission potential is expected to increase because of enhanced climatic suitability for the mosquito vector in Asia, sub-Saharan Africa, and South America. Climatic suitability for the mosquitoes that can carry dengue, Zika, and chikungunya is also likely to increase, facilitating further increases in the geographic range and longer transmission seasons, and raising concern for expansion of these diseases into temperate zones, particularly under higher greenhouse gas emission scenarios. Early spring temperatures in 2018 seem to have contributed to the early onset and extensive West Nile virus outbreak in Europe, a pathogen expected to expand further beyond its current distribution, due to a warming climate. As for tick-borne diseases, climate change is projected to continue to contribute to the spread of Lyme disease and tick-borne encephalitis, particularly in North America and Europe. Schistosomiasis is a water-borne disease and public health concern in Africa, Latin America, the Middle East, and Southeast Asia; climate change is anticipated to change its distribution, with both expansions and contractions expected. Other water-borne diseases that cause diarrheal diseases have declined significantly over the last decades owing to socioeconomic development and public health measures but changes in climate can reverse some of these positive developments. Weather and climate events, population movement, land use changes, urbanization, global trade, and other drivers can catalyze a succession of secondary events that can lead to a range of health impacts, including infectious disease outbreaks. These cascading risk pathways of causally connected events can result in large-scale outbreaks and affect society at large. We review climatic and other cascading drivers of infectious disease with projections under different climate change scenarios. Supplementary file1 (MP4 328467 KB).

Ecological Predictors of Human Malaria Risk During Different Phases of the Elimination: An Analysis of Historical Data

To understand the evolutionary ecology of disease dynamics, it is crucial to identify the environmental factors that mediate the spread and abundance of parasites and their vectors. However, human-mediated changes in the biotic and abiotic environment and intervention programs are intensifying in the past 30-40 years at a rate that masks the causal effect of the original ecological predictors. In this study, we used archived epidemiological data spanning over 100 years on malaria risk in Hungary to demonstrate that different associations exist between infection risk and environmental predictors during different phases of the elimination program. In the early 20th century, when malaria was quite common in the country and no defense program was operating, as predicted, there was a positive relationship between the area of flooded habitats and the intensity of malaria infection. In contrast, this relationship was absent during middle of the century, when an effective elimination program was already in effect. Furthermore, malaria morbidity in a given year was predicted by the degree of stagnant water cover of the previous year when considering the period before the launch of a drastic mosquito control program by dichloro-diphenyl-trichloroethane (DDT), whereas such relationship could not be revealed for a latter period. Our results highlight that human-induced alterations of the socioecological environment considerably reorganizes the ecological landscape of pathogens and their vectors.

Climate change impacts on Anopheles (K.) cruzii in urban areas of Atlantic Forest of Brazil: Challenges for malaria diseases

Around 27% of South Americans live in central and southern Brazil. Of 19,400 human malaria cases in Brazil in 2018, some were from the southern and southeastern states. High abundance of malaria vectors is generally positively associated with malaria incidence. Expanding geographic distributions of Anopheles vector mosquito species (e.g. A. cruzii) in the face of climate change processes would increase risk of such malaria transmission; such risk is of particular concern in regions that hold human population concentrations near present limits of vector species' geographic distributions. We modeled effects of likely climate changes on the distribution of A. cruzii, evaluating two scenarios of future greenhouse gas emissions for 2050, as simulated in 21 general circulation models and two greenhouse gas scenarios (RCP 4.5 and RCP 8.5) for 2050. We tested 1305 candidate models, and chose among them based on statistical significance, predictive performance, and complexity. The models closely approximated the known geographic distribution of the species under current conditions. Under scenarios of future climate change, we noted increases in suitable area for the mosquito vector species in São Paulo and Rio de Janeiro states, including areas close to 30 densely populated cities. Under RCP 8.5, our models anticipate areal increases of >75% for this important malaria vector in the vicinity of 20 large Brazilian cities. We developed models that anticipate increased suitability for the mosquito species; around 50% of Brazilians reside in these areas, and ∼89% of foreign tourists visit coastal areas in this region. Under climate change thereefore, the risk and vulnerability of human populations to malaria transmission appears bound to increase.

Prevalence, Parasite Density and Determinants of Falciparum Malaria Among Febrile Children in Some Peri-Urban Communities in Southwestern Nigeria: A Cross-Sectional Study

Background

Malaria remains a serious public health problem worldwide, particularly in tropical and subtropical regions, including Nigeria. This study investigates the prevalence, parasite density and determinants of malaria among symptomatic children in some peri-urban communities in southwestern Nigeria.

Methods

This was a randomized cross-sectional and hospital-based study. The standard method of microscopy was employed. Thick and thin films were prepared and viewed under a light microscope to identify and quantify malaria parasites. A well-structured and pre-tested questionnaire was used to obtain the subject’s information on the demographic, socio-economic and environmental variables.

Results

A total of 380 (71.7%) participants were infected with Plasmodium falciparum with a mean parasite density of 1857.11 parasite/µL of blood. Malaria prevalence and mean parasite density were significantly higher among male compared to their female counterparts [80.3% vs 61.4% and 2026.46 vs 1619.63 parasite/µL of blood]. Similarly, age group ≤5 years had the highest malaria prevalence (92.2%) and mean parasite density (2031.66 parasite/µL of blood) than other age groups (AOR 2.281, 95% CI: 1.187–4.384, P < 0.05). The multivariate logistic analysis showed that malaria disease is significantly associated with having mother with no formal education (AOR 12.235, 95% CI: 3.253–46.021, P < 0.05), having well and river as a major source of household water supply (AOR 13.810, 95% CI: 3.012–63.314, P < 0.05 vs AOR 5.639, 95% CI: 1.455–21.853, P < 0.05) and presence of stagnant water around home (AOR 5.22, 95% CI: 2.921–9.332, P < 0.05). Furthermore, protective factors observed include ownership of mosquito bed net (AOR 0.474, 95% CI: 0.223–1.008, P < 0.05) and distance of home to hospital (AOR 0.279, 95% CI: 0.158–0.493, P < 0.05).

Conclusion

Malaria remains a serious public health problem in the study area. Adopting integrated malaria control measures including educating parents on malaria prevention and control strategies, distributing mosquito bed nets, and establishing larvae source management program is highly imperative.

High pyrethroid/DDT resistance in major malaria vector Anopheles coluzzii from Niger-Delta of Nigeria is probably driven by metabolic resistance mechanisms

Entomological surveillance of local malaria vector populations is an important component of vector control and resistance management. In this study, the resistance profile and its possible mechanisms was characterised in a field population of the major malaria vector Anopheles coluzzii from Port Harcourt, the capital of Rivers state, in the Niger-Delta Region of Nigeria. Larvae collected in Port-Harcourt, were reared to adulthood and used for WHO bioassays. The population exhibited high resistance to permethrin, deltamethrin and DDT with mortalities of 6.7% ± 2.4, 37.5% ± 3.2 and 6.3% ± 4.1, respectively, but were fully susceptible to bendiocarb and malathion. Synergist bioassays with piperonylbutoxide (PBO) partially recovered susceptibility, with mortalities increasing to 53% ± 4, indicating probable role of CYP450s in permethrin resistance (χ2 = 29.48, P < 0.0001). Transcriptional profiling revealed five major resistance-associated genes overexpressed in the field samples compared to the fully susceptible laboratory colony, Ngoussou. Highest fold change (FC) was observed with GSTe2 (FC = 3.3 in permethrin exposed and 6.2 in unexposed) and CYP6Z3 (FC = 1.4 in exposed and 4.6 in unexposed). TaqMan genotyping of 32 F0 females detected the 1014F and 1575Y knockdown resistance (kdr) mutations with frequencies of 0.84 and 0.1, respectively, while 1014S mutation was not detected. Sequencing of a fragment of the voltage-gated sodium channel, spanning exon 20 from 13 deltamethrin-resistant and 9 susceptible females revealed only 2 distinct haplotypes with a low haplotype diversity of 0.33. The findings of high pyrethroid resistance but with a significant degree of recovery after PBO synergist assay suggests the need to move to PBO-based nets. This could be complemented with carbamate- or organophosphate-based indoor residual spraying in this area.

Molecular detection and species identification of Plasmodium spp. infection in adults in the Democratic Republic of Congo: A population-based study

Background

In efforts to control malaria infection, the Democratic Republic of Congo has implemented several strategies. Studies assessing their efficiency mainly involved at-risk groups, especially children under five years of age. This study aimed to determine the prevalence and identify the risk factors associated with Plasmodium spp. infection.

Methods

From October 2014 to March 2015, individuals aged at least 15 years were selected randomly and enrolled in a cross-sectional study conducted throughout the country. Microscopy and polymerase chain reaction (PCR) analysis were used for the detection of Plasmodium ssp.

Results

From 2286 individuals recruited, 1870 with valid laboratory results were included in the study for further analysis. The prevalence of Plasmodium spp. infection assessed by microscopy (355/ 1870 (19%) was lower than that estimated by PCR (580/1870 (31%). In addition, the difference between the two results was statistically significant (P < 0.0001). The most prevalent Plasmodium species was P. falciparum, either as mono-infection (96.3%; 95% C.I. 93.9–98.1) or combined with P. malariae (3.7%; 95% C.I. 2.8–5.9). The mean parasite density was 3272739 trophozoites/μL of blood. Women had higher risks of being infected than men (OR 2.03, 95% C.I.: 1.96. 2.62, P = 0.041)].

Conclusion

In this study, the molecular detection and species identification of Plasmodium spp. showed that, despite all efforts for malaria control, malaria remains a public health problem in the Democratic Republic of Congo. The high prevalence and parasite density of Plasmodium spp. in adults make this age group a potential parasitic infectious reservoir for the at-risk groups and supports the need to include this age group in further programs for malaria control.

Seroprevalence and Parasite Rates of Plasmodium malariae in a High Malaria Transmission Setting of Southern Nigeria

Although Plasmodium falciparum continues to be the main target for malaria elimination, other Plasmodium species persist in Africa. Their clinical diagnosis is uncommon, whereas rapid diagnostic tests (RDTs), the most widely used malaria diagnostic tools, are only able to distinguish between P. falciparum and non-falciparum species, the latter as “pan-species.” Blood samples from health facilities were collected in southern Nigeria (Lagos and Calabar) in 2017 (October–December) and Calabar only in 2018 (October–November), and analyzed by several methods, namely, microscopy, quantitative real-time PCR (qPCR), and peptide serology targeting candidate antigens (Plasmodium malariae apical membrane antigen, P. malariae lactose dehydrogenase, and P. malariae circumsporozoite surface protein). Both microscopy and qPCR diagnostic approaches detected comparable proportions (∼80%) of all RDT-positive samples infected with the dominant P. falciparum malaria parasite. However, higher proportions of non-falciparum species were detected by qPCR than microscopy, 10% against 3% infections for P. malariae and 3% against 0% for Plasmodium ovale, respectively. No Plasmodium vivax infection was detected. Infection rates for P. malariae varied between age-groups, with the highest rates in individuals aged > 5 years. Plasmodium malariae–specific seroprevalence rates fluctuated in those aged < 10 years but generally reached the peak around 20 years of age for all peptides. The heterogeneity and rates of these non-falciparum species call for increased specific diagnosis and targeting by elimination strategies.

Statistical Modelling of the Effects of Weather Factors on Malaria Occurrence in Abuja, Nigeria

Background: despite the increase in malaria control and elimination efforts, weather patterns and ecological factors continue to serve as important drivers of malaria transmission dynamics. This study examined the statistical relationship between weather variables and malaria incidence in Abuja, Nigeria. Methodology/Principal Findings: monthly data on malaria incidence and weather variables were collected in Abuja from the year 2000 to 2013. The analysis of count outcomes was based on generalized linear models, while Pearson correlation analysis was undertaken at the bivariate level. The results showed more malaria incidence in the months with the highest rainfall recorded (June–August). Based on the negative binomial model, every unit increase in humidity corresponds to about 1.010 (95% confidence interval (CI), 1.005–1.015) times increase in malaria cases while the odds of having malaria decreases by 5.8% for every extra unit increase in temperature: 0.942 (95% CI, 0.928–0.956). At lag 1 month, there was a significant positive effect of rainfall on malaria incidence while at lag 4, temperature and humidity had significant influences. Conclusions: malaria remains a widespread infectious disease among the local subjects in the study area. Relative humidity was identified as one of the factors that influence a malaria epidemic at lag 0 while the biggest significant influence of temperature was observed at lag 4. Therefore, emphasis should be given to vector control activities and to create public health awareness on the proper usage of intervention measures such as indoor residual sprays to reduce the epidemic especially during peak periods with suitable weather conditions.

Climate Change and the Risk of Malaria Transmission in Iran

Climate change is an important factor affecting the dynamics of the vectors population and, hence, the risk of vector-borne diseases. This study aimed to predict the environmental suitability for malaria vectors in Iran under climate change scenarios in 2030s and 2050s. Literature search was performed to find documents on the spatial distribution of Anopheles stephensi Liston, 1901, Anopheles culicifacies s.l. Giles, 1901, Anopheles fluviatilis s.l. James, 1902, Anopheles superpictus s.l. Grassi, 1899, Anopheles dthali Patton, 1905, Anopheles maculipennis s.l. Meigen, 1818, and Anopheles sacharovi Favre, 1903 (Diptera: Culicidae) published between 1970 and 2017. The bioclimatic data under three climate change scenarios (representative concentration pathway 2.6 [RCP2.6], RCP4.5, and RCP8.5) and MaxEnt model were used to predict the ecological niches for each species. Comparison between the two study periods under the three scenarios for each species revealed that RCP8.5 would reduce the area at risk for An. culicifacies s.l., An. dthali and An. superpictus s.l. in the 2050s compared to the 2030s, but the reverse will be induced by RCP2.6 and RCP4.5 scenarios. For An. fluviatilis s.l., RCP2.6 will reduce the risk areas in the 2050s, whereas an increase is expected under the two other scenarios. Moreover, all scenarios would decrease the high-risk areas of An. maculipennis s.l. in the 2050s. For An. sacharovi, RCP2.6 would increase its high-risk areas, whereas RCP4.5 and RCP8.5 would decrease its exposure. The high-risk area of An. stephensi is expected to increase under RCP8.5 in the 2030s and RCP4.5 in 2050s, but it will be almost unchanged or reduced under other scenarios.

Ecological niche modelling to estimate the distribution of Culicoides, potential vectors of bluetongue virus in Senegal

BACKGROUND

Vector-borne diseases are among the leading causes of morbidity and mortality in humans and animals. In the Afrotropical region, some are transmitted by Culicoides, such as Akabane, bluetongue, epizootic haemorrhagic fever and African horse sickness viruses. Bluetongue virus infection has an enormous impact on ruminant production, due to its high morbidity and mortality rates.

METHODS

A nationwide Culicoides trapping campaign was organized at the end of the 2012 rainy season in Senegal. A Maximum Entropy approach (MaxEnt), Boosted Regression Tree (BRT) method and Ecological Niche Factor Analysis (ENFA) were used to develop a predictive spatial model for the distribution of Culicoides, using bio-climatic variables, livestock densities and altitude.

RESULTS

The altitude, maximum temperature of the warmest month, precipitation of the warmest quarter, mean temperature of the wettest quarter, temperature seasonality, precipitation of the wettest quarter and livestock density were among the most important factors to predict suitable habitats of Culicoides. Culicoides occurrences were, in most of the cases, positively correlated to precipitation variables and livestock densities; and negatively correlated to the altitude and temperature indices. The Niayes area and the Groundnut basin were the most suitable habitats predicted.

CONCLUSION

We present ecological niche models for different Culicoides species, namely C. imicola, C. oxystoma, C. enderleini and C. miombo, potential vectors of bluetongue virus, on a nationwide scale in Senegal. Through our modelling approach, we were able to determine the effect of bioclimatic variables on Culicoides habitats and were able to generate maps for the occurrence of Culicoides species. This information will be helpful in developing risk maps for disease outbreaks.

Potential distribution of dominant malaria vector species in tropical region under climate change scenarios

Risk assessment regarding the distribution of malaria vectors and environmental variables underpinning their distribution under changing climates is crucial towards malaria control and eradication. On this basis, we used Maximum Entropy (MaxEnt) Model to estimate the potential future distribution of major transmitters of malaria in Nigeria-Anopheles gambiae sensu lato and its siblings: Anopheles gambiae sensu stricto, and Anopheles arabiensis under low and high emissions scenarios. In the model, we used mosquito occurrence data sampled from 1900 to 2010 alongside land use and terrain variables, and bioclimatic variables for baseline climate 1960-1990 and future climates of 2050s (2041-2060) and 2070s (2061-2080) that follow RCP2.6 and RCP8.5 scenarios. The Anopheles gambiae species are projected to experience large shift in potential range and population with increased distribution density, higher under high emissions scenario (RCP8.5) and 2070s than low emission scenario (RCP2.6) and 2050s. Anopheles gambiae sensu stricto and Anopheles arabiensis are projected to have highest invasion with 47-70% and 10-14% percentage increase, respectively in Sahel and Sudan savannas within northern states in 2041-2080 under RCP8.5. Highest prevalence is predicted for Humid forest and Derived savanna in southern and North Central states in 2041-2080; 91-96% and 97-99% for Anopheles gambiae sensu stricto, and 67-71% and 72-75% for Anopheles arabiensis under RCP2.6 and RCP8.5, respectively. The higher magnitude of change in species prevalence predicted for the later part of the 21st century under high emission scenario, driven mainly by increasing and fluctuating temperature, alongside longer seasonal tropical rainfall accompanied by drier phases and inherent influence of rapid land use change, may lead to more significant increase in malaria burden when compared with other periods and scenarios during the century; especially in Humid forest, Derived savanna, Sahel and Sudan savannas.