This is the first study spatially and temporally exploring the effect of weather variables and vector parameters on malaria incidence in China. The data involved in this study were relevant because all of them were obtained from national monitoring data. In this study, clear spatial heterogeneity and temporal clustering of malaria incidence could be found, with higher incidence distributed in the Southern Yongcheng spatially and in July to November temporally. The finding indicated that areas and months with higher malaria transmission risk should be focused on more public health attention and resources. Spatial heterogeneity of malaria incidence (higher incidence in the south) could be explained by the spatial variability of temperature and the distribution of malaria imported cases. First of all, Southern Yongcheng prefecture has been confronted with higher pressure from imported cases, as its was adjacent to Suixi, Guoyang, Xiao, and Huaibei county in Anhui province, which were identified as high-endemic areas of malaria (incidence
]. Furthermore, its spatial decreasing pattern from the north to south was somehow in accordance of temperature change, particularly in 2006. Only temperature was selected to assess the influencing factor associated with spatial heterogeneity owing to the founding from previous studies that temperature might be a major determinant of malaria incidence in China . Therefore, public resource should be allocated proportionally in different areas based on the risk predicted by imported possibility and average temperature, and close collaboration should be established between Henan and Anhui to effectively control and prevent malaria together. From the perspective of time, annual malaria incidence in Yongcheng prefecture showed an obvious decrease from 2006 to 2010. However, prevention and control of malaria should not be taken slightly, because Yongcheng prefecture is still at risk for malaria due to the existing of An. sinensis
, suitable weather condition for the growing of An. sinensis
, and the risk has been possibly varying with weather changes and population movement [39,40]. Determining the principal influencing factors of malaria incidence would be beneficial for malaria risk assessment and thus providing a basis for the policy making for malaria control technologies.
Malaria is a vector-borne infectious disease, and as such, is sensitive to environmental change [40-42]. Anopheles density, as a proximate environmental factor of malaria transmission, play an important role in estimating and predicting malaria risk . Climatic variables have also been established as important environmental drivers of malaria transmission , because of their impacts on the growth and reproduction rates of mosquitoes, the temporal activity pattern of the population as well as the life cycle of Plasmodium [44-47].
The best-fit model (model 2) derived from the study was reliable and had a good fit and predictive validity (QIC
16.934, P<0.001, R2
0.818), which provided insights into the most important drivers of P. vivax
malaria, including maximum temperature, average humidity and incidence of previous month that influenced seasonal fluctuation of P. vivax
malaria incidence. The result that temperature rise would contribute to malaria transmission was in agreement with some researchers [46-52], although there were still some other researchers who argued that this relationship was not significant [19,53], or that it was uncertain . It has been demonstrated that temperature increase would improve the survival chances of Anopheles
and thus contribute to the malaria transmission [55,56] Moreover, relative humidity exerted an influence on the survival of mosquito eggs and adults and the moderate increase in malaria risk associated with average humidity observed in this study was consistent with previous findings . Conversely, some literature found a correlation between rainfall and malaria , while other studies found no correlation . In this study, rainfall failed to enter the best-fit model as a predictor of malaria epidemics. The phenomenon could be explained by the complex nonlinear association between rainfall and malaria incidence. Rainfall is beneficial to the growth and reproduction of mosquito if it is moderate, because it often leads to puddles and increased local humidity; however, excessive rain can also wash away eggs and completely destroy breeding sites . This result indicated that it was not necessary to consider rainfall as a predictor in Yongcheng, which made malaria surveillance simpler in this area.
In addition to factors mentioned above, although An. sinensis
densities failed to enter into best-fit model (model 2), Dbait
was included in model 1. The reasons why Dbait
rather than Dnet
were included in model 1 probably lay in two aspects. Firstly, An. sinensis
is slightly exophagic (biting outdoors) , and thus Dbait
is more representative of malaria transmission in Yongcheng. Secondly, most of the malaria cases in Yongcheng were farmers, and they would like to sleep and work outdoors in the summer without effective protections, thus having more opportunities to be infected [5
]. Therefore, Dbait
would contribute more to the prediction of malaria incidence. Moreover, comparing the time series plots of Dbait
(Figure ), expected values predicted by model 1(Figure ) and actual values (Figure ), we found out expected values were over-estimated in 2009 and 2010 when actual malaria incidence was relatively low, which mainly due to the existence of predictor Dbait
reasoned by their similar rising tendency. It could be concluded that An. sinensis
density probably had its shortcomings as a routine monitoring and predicting index. An. sinensis
density may be a better predictor of malaria incidence when transmission is relatively high, as many of the female mosquitoes may have been infected by Plasmodium
, and increase in An. sinensis
density would lead to a direct rise in malaria incidence. However, when the incidence is at a low level, most of the female mosquitoes are possibly free of Plasmodium
. In this circumstance, when a healthy human being is bitten by a female An. sinensis
the probability of infection by Plasmodium
is low. Therefore, we concluded that rise in An. sinensis
density would possibly contribute little to the increase of malaria incidence in low transmission areas, which agreed with the result of another study that malaria transmission potential would be very low in spite of a high human biting rate in unstable malaria areas . Therefore, control target should vary with the severity of malaria epidemics. When malaria incidence is high, public resource allocation should be focused on mosquito control and elimination; however, when malaria incidence is low, the key control point should lie in controlling sources of infections. Furthermore, it is necessary to find a substitute, such as entomological infection rate (EIR), which can overcome the weakness of An. sinensis
density as an indicator for malaria surveillance and prediction, although it can be used as a good index for predicting malaria potential risk as a previous study showed .
As far as the lag effect was concerned, this study found significant one month lag effects of entomological and meteorological variables on malaria incidence, and this finding was supported by several earlier studies [17,19,35,49]. This phenomenon could be explained by the approximately one month duration of malaria infection cycle. The time incorporates several processes above. An adult mosquito first bites an infected human, and then the parasite develops in the adult mosquito (Extrinsic Incubation Period). Ten days later when the P. vivax
sporozoites move the salivary glands, the mosquito transmits malaria to a human when it takes another blood meal. Once the person is infected, time to development of malaria symptoms and infectivity (Intrinsic Incubation Period) takes about another 1–2
weeks [40,61]. Knowing the approximate lag size of effects on malaria incidence would benefit us to get prepared for quick and effective response on malaria epidemic easily at least one month in advance.
The transmission of malaria is complicated, and we still need further research to figure it out. For example, the temporal variation in malaria incidence could be also partially explained by continuous and effective control efforts Chinese local and national health agencies made, such as treatment in the rest period of malaria(conducted from 2004)  and comprehensive vector control action characterized by biological larviciding and residual spray(conducted from 2007) [8
]. Human interventions are failed to be inputted into the predictive model in this study because it is difficult to measured and quantify.