In 2003, Plasmodium vivax malaria has re-emerged in central eastern China including Yongcheng prefecture, Henan Province, where no case has been reported for eleven years. Our goals were to detect the space-time distribution pattern of malaria and to determine significant environmental variables contributing to malaria incidence in Yongcheng from 2006 to 2010, thus providing scientific basis for further optimizing current malaria surveillance and control programs.
This study examined the spatial and temporal heterogeneities in the risk of malaria and the influencing factors on malaria incidence using geographical information system (GIS) and time series analysis. Univariate analysis was conducted to estimate the crude correlations between malaria incidence and environmental variables, such as mosquito abundance and climatic factors. Multivariate analysis was implemented to construct predictive models to explore the principal environmental determinants on malaria epidemic using a Generalized Estimating Equation (GEE) approach.
Annual malaria incidence at town-level decreased from the north to south, and monthly incidence at prefecture-level demonstrated a strong seasonal pattern with a peak from July to November. Yearly malaria incidence had a visual spatial association with yearly average temperature. Moreover, the best-fit temporal model (model 2) (QIC=16.934, P<0.001, R2=0.818) indicated that significant factors contributing to malaria incidence were maximum temperature at one month lag, average humidity at one month lag, and malaria incidence of the previous month.
Findings supported the effects of environment factors on malaria incidence and indicated that malaria control targets should vary with intensity of malaria incidence, with more public resource allocated to control the source of infections instead of large scale An. sinensis control when malaria incidence was at a low level, which would benefit for optimizing the malaria surveillance project in China and some other countries with unstable or low malaria transmission.
Keywords: Malaria, Anopheles, Weather, Geographic information system, Modeling