Dataset Information

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

ABSTRACT:

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).

SUBMITTER: Taconet P

PROVIDER: S-EPMC8243492 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Malaria is a parasitic disease, endemic in many tropical and sub-tropical countries. Malaria is a well-known disease, familiar to almost all people in endemic regions, as they or their family are regularly confronted with it; everyone in these regions has probably experienced the disease, at least once in their life. To investigate the social perceptions of malaria in Burkina Faso, including its diagnosis-driven treatment, we have conducted a survey in both urban (Saint Camille Hospital, Ouagadougou HOSCO) and rural (Boussé Hospital) areas. Fifty-six individuals, mostly representatives of the society variability, were surveyed by questionnaires and 2 focus groups were organized with traditional healers. In general, populations seem to have grasped the causes, symptoms and means of preventing the disease. However, the majority of interviewees make a marked confusion between malaria and dengue; dengue fever is considered like a severe form of malaria. The care modalities (modern and/or traditional medicine) are plural and the choice of therapeutic practice depends on both the socio-economic conditions and education level of the patient. Whereas some patients mark preferences for one type of medicine, others simultaneously recourse to both; for these, a medicine does not outperform the other and their combination multiplies the chances of a quick recovery. Whether for modern or traditional medicine, the diagnosis is considered very important for effective disease management. Modern medicine uses diagnostic tools based on light microscopy and immunochromatography (rapid diagnostic tests; RDT); traditional medicine has its own diagnostic logic but nevertheless recognizes modern medicine diagnosis to guide its therapy. 90 % of those interviewed first use modern medicine to seek an accurate diagnosis of their disease and thus to receive adequate treatment. Presumptive treatments are still widely prescribed and accepted by most patients who trust the judgment of their caregiver, not perceiving any benefit to an objective diagnosis. In front of a negative diagnosis, patient reactions are diverse, some accepting investigations for other diseases (45 %), others opting for self-medication (15 %), others resorting to traditional medicine (20 %). All are unanimous in the importance of diagnosis and are in favor of in-development diagnostic technologies, provided these obviously meet the features of reliability, ease of use, availability and, of course, economical accessibility.