Spatio-temporal analysis of abundances of three malaria vector species in southern Benin using zero-truncated models

Landscape and meteorological determinants of malaria vectors' presence and abundance in the rural health district of Korhogo, Côte d'Ivoire, 2016-2018, and comparison with the less anthropized area of Diébougou, Burkina Faso

BACKGROUND

Understanding how weather and landscape shape the fine-scale distribution and diversity of malaria vectors is crucial for efficient and locally tailored vector control. This study examines the meteorological and landscape determinants of (i) the spatiotemporal distribution (presence and abundance) of the major malaria vectors in the rural region of Korhogo (northern Côte d'Ivoire) and (ii) the differences in vector probability of presence, abundance, and diversity observed between that area and another rural West African region located 300 km away in Diébougou, Burkina Faso.

METHODS

We monitored Anopheles human-biting activity in 28 villages of the Korhogo health district for 18 months (2016 to 2018), and extracted fine-scale environmental variables (meteorological and landscape) from high-resolution satellite imagery. We used a state-of-the-art statistical modeling framework to associate these data and identify environmental determinants of the presence and abundance of malaria vectors in the area. We then compared the results of this analysis with those of a similar, previously published study conducted in the Diébougou area.

RESULTS

The spatiotemporal distribution of malaria vectors in the Korhogo area was highly heterogeneous and appeared to be strongly determined and constrained by meteorological conditions. Rice paddies, temporary sites filled by rainfall, rivers and riparian forests appeared to be the larval habitats of Anopheles mosquitoes. As in Diébougou, meteorological conditions (temperatures, rainfall) appeared to significantly affect all developmental stages of the mosquitoes. Additionally, ligneous savannas were associated with lower abundance of malaria vectors. Anopheles species diversity was lower in Korhogo compared to Diébougou, while biting rates were much higher. Our results suggest that these differences may be due to the more anthropized nature of the Korhogo region in comparison to Diébougou (less forested areas, more agricultural land), supporting the hypothesis of higher malaria vector densities and lower mosquito diversity in more anthropized landscapes in rural West Africa.

CONCLUSION

This study offers valuable insights into the landscape and meteorological determinants of the spatiotemporal distribution of malaria vectors in the Korhogo region and, more broadly, in rural west-Africa. The results emphasize the adverse effects of the ongoing landscape anthropization process in the sub-region, including deforestation and agricultural development, on malaria vector control.

A randomized, double-blind placebo-control study assessing the protective efficacy of an odour-based 'push-pull' malaria vector control strategy in reducing human-vector contact

Novel malaria vector control strategies targeting the odour-orientation of mosquitoes during host-seeking, such as 'attract-and-kill' or 'push-and-pull', have been suggested as complementary tools to indoor residual spraying and long-lasting insecticidal nets. These would be particularly beneficial if they can target vectors in the peri-domestic space where people are unprotected by traditional interventions. A randomized double-blind placebo-control study was implemented in western Kenya to evaluate: a 'push' intervention (spatial repellent) using transfluthrin-treated fabric strips positioned at open eave gaps of houses; a 'pull' intervention placing an odour-baited mosquito trap at a 5 m distance from a house; the combined 'push-pull' package; and the control where houses contained all elements but without active ingredients. Treatments were rotated through 12 houses in a randomized-block design. Outdoor biting was estimated using human landing catches, and indoor mosquito densities using light-traps. None of the interventions provided any protection from outdoor biting malaria vectors. The 'push' reduced indoor vector densities dominated by Anopheles funestus by around two thirds. The 'pull' device did not add any benefit. In the light of the high Anopheles arabiensis biting densities outdoors in the study location, the search for efficient outdoor protection and effective pull components needs to continue.

Bayesian spatial modelling of malaria burden in two contrasted eco-epidemiological facies in Benin (West Africa): call for localized interventions

Background

Despite a global decrease in malaria burden worldwide, malaria remains a major public health concern, especially in Benin children, the most vulnerable group. A better understanding of malaria’s spatial and age-dependent characteristics can help provide durable disease control and elimination. This study aimed to analyze the spatial distribution of Plasmodium falciparum malaria infection and disease among children under five years of age in Benin, West Africa.

Methods

A cross-sectional epidemiological and clinical survey was conducted using parasitological examination and rapid diagnostic tests (RDT) in Benin. Interviews were done with 10,367 children from 72 villages across two health districts in Benin. The prevalence of infection and clinical cases was estimated according to age. A Bayesian spatial binomial model was used to estimate the prevalence of malaria infection, and clinical cases were adjusted for environmental and demographic covariates. It was implemented in R using Integrated Nested Laplace Approximations (INLA) and Stochastic Partial Differentiation Equations (SPDE) techniques.

Results

The prevalence of P. falciparum infection was moderate in the south (34.6%) of Benin and high in the northern region (77.5%). In the south, the prevalence of P. falciparum infection and clinical malaria cases were similar according to age. In northern Benin children under six months of age were less frequently infected than children aged 6–11, 12–23, 24–60 months, (p < 0.0001) and had the lowest risk of malaria cases compared to the other age groups (6–12), (13–23) and (24–60): OR = 3.66 [2.21–6.05], OR = 3.66 [2.21–6.04], and OR = 2.83 [1.77–4.54] respectively (p < 0.0001). Spatial model prediction showed more heterogeneity in the south than in the north but a higher risk of malaria infection and clinical cases in the north than in the south.

Conclusion

Integrated and periodic risk mapping of Plasmodium falciparum infection and clinical cases will make interventions more evidence-based by showing progress or a lack in malaria control.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-14032-9.

Data-driven and interpretable machine-learning modeling to explore the fine-scale environmental determinants of malaria vectors biting rates in rural Burkina Faso

Background

Improving the knowledge and understanding of the environmental determinants of malaria vector abundance at fine spatiotemporal scales is essential to design locally tailored vector control intervention. This work is aimed at exploring the environmental tenets of human-biting activity in the main malaria vectors (Anopheles gambiae s.s., Anopheles coluzzii and Anopheles funestus) in the health district of Diébougou, rural Burkina Faso.

Methods

Anopheles human-biting activity was monitored in 27 villages during 15 months (in 2017–2018), and environmental variables (meteorological and landscape) were extracted from high-resolution satellite imagery. A two-step data-driven modeling study was then carried out. Correlation coefficients between the biting rates of each vector species and the environmental variables taken at various temporal lags and spatial distances from the biting events were first calculated. Then, multivariate machine-learning models were generated and interpreted to (i) pinpoint primary and secondary environmental drivers of variation in the biting rates of each species and (ii) identify complex associations between the environmental conditions and the biting rates.

Results

Meteorological and landscape variables were often significantly correlated with the vectors’ biting rates. Many nonlinear associations and thresholds were unveiled by the multivariate models, for both meteorological and landscape variables. From these results, several aspects of the bio-ecology of the main malaria vectors were identified or hypothesized for the Diébougou area, including breeding site typologies, development and survival rates in relation to weather, flight ranges from breeding sites and dispersal related to landscape openness.

Conclusions

Using high-resolution data in an interpretable machine-learning modeling framework proved to be an efficient way to enhance the knowledge of the complex links between the environment and the malaria vectors at a local scale. More broadly, the emerging field of interpretable machine learning has significant potential to help improve our understanding of the complex processes leading to malaria transmission, and to aid in developing operational tools to support the fight against the disease (e.g. vector control intervention plans, seasonal maps of predicted biting rates, early warning systems).

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13071-021-04851-x.

Complementarity of empirical and process-based approaches to modelling mosquito population dynamics with Aedes albopictus as an example-Application to the development of an operational mapping tool of vector populations

Mosquitoes are responsible for the transmission of major pathogens worldwide. Modelling their population dynamics and mapping their distribution can contribute effectively to disease surveillance and control systems. Two main approaches are classically used to understand and predict mosquito abundance in space and time, namely empirical (or statistical) and process-based models. In this work, we used both approaches to model the population dynamics in Reunion Island of the 'Tiger mosquito', Aedes albopictus, a vector of dengue and chikungunya viruses, using rainfall and temperature data. We aimed to i) evaluate and compare the two types of models, and ii) develop an operational tool that could be used by public health authorities and vector control services. Our results showed that Ae. albopictus dynamics in Reunion Island are driven by both rainfall and temperature with a non-linear relationship. The predictions of the two approaches were consistent with the observed abundances of Ae. albopictus aquatic stages. An operational tool with a user-friendly interface was developed, allowing the creation of maps of Ae. albopictus densities over the whole territory using meteorological data collected from a network of weather stations. It is now routinely used by the services in charge of vector control in Reunion Island.

Is Placental Malaria a Long-term Risk Factor for Mild Malaria Attack in Infancy? Revisiting a Paradigm

Background

Children born to mothers with placental malaria (PM) have been described as more susceptible to the occurrence of a first malaria infection. However, whether or not these children remain more at risk during infancy has never been explored. We aimed to determine if children born to mothers with PM are more susceptible to malaria and remain at higher risk between birth and 18 months.

Methods

Five hundred fifty children were followed up weekly with control of temperature and, if >37.5°C, both a rapid diagnostic test for malaria and a thick blood smear were performed. Taking into account environmental risk of infection, the relationship between occurrences of malaria attacks from birth to 18 months was modeled using Cox models for recurrent events.

Results

PM is not associated with an overall susceptibility to malaria but only with the delay of occurrence of the first malaria attack. Children born from mothers with PM tend to have an increased risk for the first malaria attack (hazard ratio [HR] = 1.33; P = .048) but not for subsequent ones (HR = 0.9; P = .46). Children who experienced 1 malaria attack were strongly at risk to develop subsequent infections independent of placental infection and environmental exposure.

Conclusions

These results are consistent with the existence of an individual susceptibility to malaria unrelated to PM. From a public health point of view, protecting children born to infected placenta remains a priority, but seems insufficient to account for other frail children for whom a biomarker of frailty needs to be found.

Spatial Modeling of Mosquito Vectors for Rift Valley Fever Virus in Northern Senegal: Integrating Satellite-Derived Meteorological Estimates in Population Dynamics Models

Mosquitoes are vectors of major pathogen agents worldwide. Population dynamics models are useful tools to understand and predict mosquito abundances in space and time. To be used as forecasting tools over large areas, such models could benefit from integrating remote sensing data that describe the meteorological and environmental conditions driving mosquito population dynamics. The main objective of this study is to assess a process-based modeling framework for mosquito population dynamics using satellite-derived meteorological estimates as input variables. A generic weather-driven model of mosquito population dynamics was applied to Rift Valley fever vector species in northern Senegal, with rainfall, temperature, and humidity as inputs. The model outputs using meteorological data from ground weather station vs satellite-based estimates are compared, using longitudinal mosquito trapping data for validation at local scale in three different ecosystems. Model predictions were consistent with field entomological data on adult abundance, with a better fit between predicted and observed abundances for the Sahelian Ferlo ecosystem, and for the models using in-situ weather data as input. Based on satellite-derived rainfall and temperature data, dynamic maps of three potential Rift Valley fever vector species were then produced at regional scale on a weekly basis. When direct weather measurements are sparse, these resulting maps should be used to support policy-makers in optimizing surveillance and control interventions of Rift Valley fever in Senegal.

Adaptive deletion in resistance gene duplications in the malaria vector Anopheles gambiae

While gene copy-number variations play major roles in long-term evolution, their early dynamics remains largely unknown. However, examples of their role in short-term adaptation are accumulating: identical repetitions of a locus (homogeneous duplications) can provide a quantitative advantage, while the association of differing alleles (heterogeneous duplications) allows carrying two functions simultaneously. Such duplications often result from rearrangements of sometimes relatively large chromosome fragments, and even when adaptive, they can be associated with deleterious side effects that should, however, be reduced by subsequent evolution. Here, we took advantage of the unique model provided by the malaria mosquito Anopheles gambiae s.l. to investigate the early evolution of several duplications, heterogeneous and homogeneous, segregating in natural populations from West Africa. These duplications encompass ~200 kb and 11 genes, including the adaptive insecticide resistance ace-1 locus. Through the survey of several populations from three countries over 3-4 years, we showed that an internal deletion of all coamplified genes except ace-1 is currently spreading in West Africa and introgressing from An. gambiae s.s. to An. coluzzii. Both observations provide evidences of its selection, most likely due to reducing the gene-dosage disturbances caused by the excessive copies of the nonadaptive genes. Our study thus provides a unique example of the early adaptive trajectory of duplications and underlines the role of the environmental conditions (insecticide treatment practices and species ecology). It also emphasizes the striking diversity of adaptive responses in these mosquitoes and reveals a worrisome process of resistance/cost trade-off evolution that could impact the control of malaria vectors in Africa.

Spatial clustering and risk factors of malaria infections in Bata district, Equatorial Guinea

Background

The transmission of malaria is intense in the majority of the countries of sub-Saharan Africa, particularly in those that are located along the Equatorial strip. The present study aimed to describe the current distribution of malaria prevalence among children and its environment-related factors as well as to detect malaria spatial clusters in the district of Bata, in Equatorial Guinea.

Methods

From June to August 2013 a representative cross-sectional survey using a multistage, stratified, cluster-selected sample was carried out of children in urban and rural areas of Bata District. All children were tested for malaria using rapid diagnostic tests (RDTs). Results were linked to each household by global position system data. Two cluster analysis methods were used: hot spot analysis using the Getis-Ord Gi statistic, and the SaTScan™ spatial statistic estimates, based on the assumption of a Poisson distribution to detect spatial clusters. In addition, univariate associations and Poisson regression model were used to explore the association between malaria prevalence at household level with different environmental factors.

Results

A total of 1416 children aged 2 months to 15 years living in 417 households were included in this study. Malaria prevalence by RDTs was 47.53%, being highest in the age group 6–15 years (63.24%, p < 0.001). Those children living in rural areas were there malaria risk is greater (65.81%) (p < 0.001). Malaria prevalence was higher in those houses located <1 km from a river and <3 km to a forest (IRR: 1.31; 95% CI 1.13–1.51 and IRR: 1.44; 95% CI 1.25–1.66, respectively). Poisson regression analysis also showed a decrease in malaria prevalence with altitude (IRR: 0.73; 95% CI 0.62–0.86). A significant cluster inland of the district, in rural areas has been found.

Conclusions

This study reveals a high prevalence of RDT-based malaria among children in Bata district. Those households situated in inland rural areas, near to a river, a green area and/or at low altitude were a risk factor for malaria. Spatial tools can help policy makers to promote new recommendations for malaria control.

Evolution of the levels of human leukocyte antigen G (HLA-G) in Beninese infant during the first year of life in a malaria endemic area: using latent class analysis

Background

HLA-G, a non-classical HLA class I antigen, is of crucial interest during pregnancy by inhibiting maternal immune response. Its role during infections is discussed, and it has been described that high levels of soluble HLA-G during childhood increase the risk of malaria. To explore more precisely interactions between soluble HLA-G and malaria, latent class analysis was used to test whether distinct sub-populations of children, each with distinctive soluble HLA-G evolutions may suggest the existence of groups presenting variable malaria susceptibility.

Method

A study was conducted in Benin from 2010 to 2013 and 165 children were followed from birth to 12 months. Evolution of soluble HLA-G was studied by the latent class method.

Results

Three groups of children were identified: one with consistently low levels of soluble HLA-G during follow-up, a second with very high levels and a last intermediate group. In all groups, low birth weight, high number of malaria infections and high exposure to malaria transmission were associated with high level of soluble HLA-G. Placental malaria was not. Presence of soluble HLA-G in cord blood increased the probability of belonging to the highest trajectory.

Conclusion

These results, together with previous ones, confirm the important role of HLA-G in the individual susceptibility to malaria. Assaying soluble HLA-G at birth could be a good indicator of newborns more fragile and at risk of infections during childhood.

Seasonal Dynamics, Longevity, and Biting Activity of Anopheline Mosquitoes in Southwestern Ethiopia

Continuous monitoring of vector species composition, abundance, dynamics, feeding pattern, and host finding strategy is the base to determine when, what, and how control should be implemented. Thus, this study was conducted to assess entomological parameters of anopheline mosquitoes in nine villages in Seka district, southwestern Ethiopia, from June to December 2012. Mosquito collection was carried out from selected households in each of the nine study villages using light trap catches from June to December 2012. Differences in mean mosquito density, parity rates before, and after indoor residual spraying (IRS) operation were compared. In total, 1,136 adult female anopheline mosquitoes were collected during the study period. All anopheline mosquitoes collected belong to three species. Anopheles gambiae senso lato Giles was the most predominant (69.7%) followed by Anopheles coustani s.l. Laveran (22.7%) and Anopheles pharoensis Theobald (7.6%). There was significant variation in mean mosquito density among An. gambiae s.l., An. coustani s.l., and An. pharoensis. Parity rate of An. gambiae s.l. before spray operation was significantly higher than after spray operation. The highest peak biting activity of An. gambiae s.l. was between 1800 and 2100 hours. The longevity of An. gambiae s.l. ranged from 3.4 to 12.5 d. The highest vector abundance and parity rate were recorded in July and August. In conclusion, the behavioral plasticity and early biting activity of An. gambiae s.l. could affect current vector control tools (IRS and long lasting insecticidal nets). Hence, it is imperative to explore intervention tools for outdoor malaria vector control in addition to the existing IRS and long-lasting insecticidal nets.

Modeling larval malaria vector habitat locations using landscape features and cumulative precipitation measures

Background

Predictive models of malaria vector larval habitat locations may provide a basis for understanding the spatial determinants of malaria transmission.

Methods

We used four landscape variables (topographic wetness index [TWI], soil type, land use-land cover, and distance to stream) and accumulated precipitation to model larval habitat locations in a region of western Kenya through two methods: logistic regression and random forest. Additionally, we used two separate data sets to account for variation in habitat locations across space and over time.

Results

Larval habitats were more likely to be present in locations with a lower slope to contributing area ratio (i.e. TWI), closer to streams, with agricultural land use relative to nonagricultural land use, and in friable clay/sandy clay loam soil and firm, silty clay/clay soil relative to friable clay soil. The probability of larval habitat presence increased with increasing accumulated precipitation. The random forest models were more accurate than the logistic regression models, especially when accumulated precipitation was included to account for seasonal differences in precipitation. The most accurate models for the two data sets had area under the curve (AUC) values of 0.864 and 0.871, respectively. TWI, distance to the nearest stream, and precipitation had the greatest mean decrease in Gini impurity criteria in these models.

Conclusions

This study demonstrates the usefulness of random forest models for larval malaria vector habitat modeling. TWI and distance to the nearest stream were the two most important landscape variables in these models. Including accumulated precipitation in our models improved the accuracy of larval habitat location predictions by accounting for seasonal variation in the precipitation. Finally, the sampling strategy employed here for model parameterization could serve as a framework for creating predictive larval habitat models to assist in larval control efforts.