Abstract

BACKGROUND

Sustaining malaria elimination requires addressing residual transmission driven by Anopheles sinensis amid rapid environmental change, with a critical knowledge gap in understanding how climate, agricultural practices, and urbanization shape vector ecology. We quantified the One Health drivers of An. sinensis biting activity and population dynamics along urban-rural landscapes in eastern China to inform targeted control.

METHODS

We conducted longitudinal surveillance (2016-2021) in both rural and urban settings in eastern China. Biting rates and adult densities of An. sinensis were monitored using human-baited double net traps and UV-light traps. We employed a One Health concept-based analytical framework integrating generalized linear mixed models combined with distributed lag non-linear models (GLMM-DLNM) to assess lagged climate effects, regression analyses for socio-demographic, urbanization, and livestock effects, and random forests to determine variable importance. Models were validated using cross-correlation functions with the Modified Chelton method to account for temporal autocorrelation.

RESULTS

The six-year surveillance documented 49,970 An. sinensis specimens, confirming its dominance as the primary malaria vector. We found divergent ecological drivers between urban and rural landscapes. Temperature was the dominant predictor, with biting activity and population peaking at 27.45-30.13 °C after a 1-month lag in rural settings. Precipitation exhibited threshold effects, with significant risk peaking elevation observed between 65.57 and 99.66 mm in rural settings. In urban settings, the peak risks associated with temperature and precipitation occurred at the highest levels. In rural settings, a male-biased sex ratio increased biting rates by 12.2-14.0% (IRR = 1.122-1.140). Furthermore, crop cultivation elevated vector risk (IRR = 1.034-1.043), whereas cattle or buffalo presence provided strong zooprophylaxis (IRR = 0.667-0.780). Urbanization rate modestly increased vector density (IRR = 1.053-1.077). We identified a protective urban density paradox, where higher human population density suppressed vector populations (IRR = 0.633-0.682). Cross-correlation function analyses indicated that One Health-based models captured 37.8-90.8% of the spatiotemporal variation across sites.

CONCLUSIONS

Climatic, zooprophylactic, and anthropogenic factors synergistically shape An. sinensis ecology. Sustaining elimination requires strategies harnessing protective urban factors and optimizing livestock management and agricultural planning in high-risk rural regions. Our One Health framework provides a replicable model for mitigating residual transmission globally.

MeSH

Malaria, Ecology, Longitudinal Studies, Climate, Parasites