A spatio-temporal climate-based model of early dengue fever warning in southern Taiwan

Abstract

Dengue Fever (DF) has been identified by the World Health organization (WHO) as one of the most serious vector-borne infectious diseases in tropical and subtropical areas. During 2007, in particular, there were over 2,000 DF cases in Taiwan, which was the highest number of cases in the recorded history of Taiwan epidemics. Most DF studies have focused mainly on temporal DF patterns and its close association with climatic covariates, whereas they have understated spatial DF patterns (spatial dependence and clustering) and composite space-time effects. The present study proposes a spatio-temporal DF prediction approach based on stochastic Bayesian Maximum Entropy (BME) analysis. Core and site-specific knowledge bases are considered, including climate and health datasets under conditions of uncertainty, space-time dependence functions, and a Poisson regression model of climatic variables contributing to DF occurrences in southern Taiwan during 2007. The results show that the DF outbreaks in the study area are highly influenced by climatic conditions. Furthermore, the analysis can provide the required ''oneweek-ahead'' outbreak warnings based on spatio-temporal predictions of DF distributions. Therefore, the proposed approach can provide the Taiwan Disease Control Agency with a valuable tool to timely identify, control, and even efficiently prevent DF spreading across space-time.

Spatio-Temporal Extension and Spatial Analyses of Dengue from Rawalpindi, Islamabad and Swat during 2010–2014

“…The rapid transport facilities and frequent visits to infested areas have been confirmed in previous studies [22,47]. Hydrology or physical existing water channels like rivers, streams and canals play an important role in ambient temperature and humidity profile of a locale [48].…”

“…Aedes Aegypti and Aedes Albopictus have overlapping habitat distributions. The sign and strength of climate-DENV associations depend largely on local climate context [22]. Specific and relative humidity increases along the rivers and streams running through urban areas.…”

Spatial dynamic patterns of hand-foot-mouth disease in the People’s Republic of China

“…Raw data usually contain too much noise to display (Riley, 2008;Zhou, 2009;Christakos, 2010;Wang et al, 2011a;Yu et al, 2011). However, because spatio-temporal processes are integrated and based on data from all domains, the study of a single domain would neglect information from the other domains.…”

Climate and Non-Climate Drivers of Dengue Epidemics in Southern Coastal Ecuador

“…28 To date, there has been limited success in developing an operational EWS for dengue, although several studies have shown the potential to develop such a system. [29][30][31] Aims of the study. The objective of this study is to assess the importance of climate and non-climate drivers of interannual variability in dengue fever in southern coastal Ecuador.…”