Meteorological Factors–Based Spatio-Temporal Mapping and Predicting Malaria in Central China

Abstract

Despite significant reductions in the overall burden of malaria in the 20th century, this disease still represents a significant public health problem in China, especially in central areas. Understanding the spatio-temporal distribution of malaria is essential in the planning and implementing of effective control measures. In this study, normalized meteorological factors were incorporated in spatio-temporal models. Seven models were established in WinBUGS software by using Bayesian hierarchical models and Markov Chain Monte Carlo methods. M1, M2, and M3 modeled separate meteorological factors, and M3, which modeled rainfall performed better than M1 and M2, which modeled average temperature and relative humidity, respectively. M7 was the best fitting models on the basis of based on deviance information criterion and predicting errors. The results showed that the way rainfall influencing malaria incidence was different from other factors, which could be interpreted as rainfall having a greater influence than other factors.

Malaria transmission modelling: a network perspective

“…Malaria transmission can be affected by multiple factors, such as biology, environment, and socio-economy, that directly impinge on the interactions among hosts, vectors, and parasites at varying degrees and scales [17-19]. A feasible means to model malaria transmission is to rely on passive case reporting surveillance systems or surveys through local, national, or regional public health and medical organizations.…”

“…To handle the case of network inference that potentially involves large-scale and/or dynamically-evolving surveillance data, e.g., over decades of temporal intervals [19,64,65], thousands of locations in different levels of spatial categories [64,65], and multiple environmental or biological factors considered [65,66], alternative optimization methods can be considered; one of the possible ways is to utilize autonomy-oriented computing (AOC) [15,27,67]. One can take further advantage of the autonomy aspect emphasized in the AOC paradigm and consider that some active surveillance strategies can be employed.…”

Elimination of malaria due to Plasmodium vivax in central part of the People’s Republic of China: analysis and prediction based on modelling

“…This may be explained by the local government relaxing existing control measures prematurely or misunderstanding the differences between control phases. Alternatively, it could be a side effect of the improved water quality of Huang-Huai River since this amplified the rice planting area, a situation that expanded the breeding habitats of the Anopheles vector (Huang et al, 2011). It could also have to do with long-term dormant liver stages of P. vivax resulting in relapse of the infection after the end of treatment (Mueller et al, 2009).…”

“…The number of cases here reportedly reached 22 million, accounting for 91.2% of the total number of all cases in P.R. China in 1970 (Huang et al, 2011). Since then, the number of cases decreased dramatically (Xu et al, 1994;Li et al, 1999).…”

“…However, by the end of the 20 th century, malaria due to P. vivax re-emerged and the situation was again dire in the provinces along the Huang-Huai River (Yellow River), especially in the Henan and Anhui provinces (Sleigh et al, 1998;Zhou et al, 2005). A total of 34,933 malaria cases were reported in 2006, the highest number of malaria cases so far in the new millennium (Huang et al, 2011), resulting in the immediate launch of the national malaria control programme (NMEP). Good results were achieved and the progress has been followed up by the Action Plan of the China Malaria Elimination (APCME) taking over in 2010 with the explicit aim of malaria elimination.…”