Dengue fever and El Niño/Southern Oscillation in Queensland, Australia: a time series predictive model

Hu, Wenbiao; Clements, Archie; Williams, Gail M.; Tong, Shilu

doi:10.1136/oem.2008.044966

Cited by 88 publications

(85 citation statements)

References 38 publications

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“…Previous studies showed that, in Queensland, the dengue epidemic often had a cycle of 5 -6 years and was expected to reach a 6-year peak (Hu et al, 2010, Hanna, 2009 Table 1). In our space-time clustering analysis, most of the dengue cases occurred during summer and autumn (i.e., the first quarter of the year), when the rainfall and temperature are highest of the year.…”

Section: Discussionmentioning

confidence: 99%

Hot spot detection and spatio-temporal dynamics of dengue in Queensland, Australia

Naish

Tong

2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VI, WG VI/4KEY WORDS: Clusters, Dengue, Geographical information systems, Spatiotemporal, Spatial autocorrelation ABSTRACT:Dengue has been a major public health concern in Australia since it re-emerged in Queensland in 1992Queensland in -1993. This study explored spatio-temporal distribution and clustering of locally-acquired dengue cases in Queensland State, Australia and identified target areas for effective interventions. A computerised locally-acquired dengue case dataset was collected from Queensland Health for Queensland from 1993 to 2012. Descriptive spatial and temporal analyses were conducted using geographic information system tools and geostatistical techniques. Dengue hot spots were detected using SatScan method. Descriptive spatial analysis showed that a total of 2,398 locally-acquired dengue cases were recorded in central and northern regions of tropical Queensland. A seasonal pattern was observed with most of the cases occurring in autumn. Spatial and temporal variation of dengue cases was observed in the geographic areas affected by dengue over time. Tropical areas are potential high-risk areas for mosquito-borne diseases such as dengue. This study demonstrated that the locally-acquired dengue cases have exhibited a spatial and temporal variation over the past twenty years in tropical Queensland, Australia. There is a clear evidence for the existence of statistically significant clusters of dengue and these clusters varied over time. These findings enabled us to detect and target dengue clusters suggesting that the use of geospatial information can assist the health authority in planning dengue control activities and it would allow for better design and implementation of dengue management programs.* Corresponding author. This is useful to know for communication with the appropriate person in cases with more than one author.

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Section: Discussionmentioning

confidence: 99%

Hot spot detection and spatio-temporal dynamics of dengue in Queensland, Australia

Naish

Tong

2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…For Queensland Australia, Hu et al [66] applied a seasonal auto-regressive integrated moving average model for the period 1993 to 2005 to the analysis of the numbers of notified dengue fever cases and the numbers of postcode areas with dengue fever cases in relation to ENSO as described by the SOI. They found that a decrease in the average SOI (warm phase conditions) during the preceding 3-12 months was significantly associated with an increase in the monthly numbers of postcode areas with dengue fever cases.…”

Section: Denguementioning

confidence: 99%

El Niño Southern Oscillation (ENSO) and Health: An Overview for Climate and Health Researchers

McGregor

Ebi

2018

Atmosphere

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Abstract:The El Niño Southern Oscillation (ENSO) is an important mode of climatic variability that exerts a discernible impact on ecosystems and society through alterations in climate patterns. For this reason, ENSO has attracted much interest in the climate and health science community, with many analysts investigating ENSO health links through considering the degree of dependency of the incidence of a range of climate diseases on the occurrence of El Niño events. Because of the mounting interest in the relationship between ENSO as a major mode of climatic variability and health, this paper presents an overview of the basic characteristics of the ENSO phenomenon and its climate impacts, discusses the use of ENSO indices in climate and health research, and outlines the present understanding of ENSO health associations. Also touched upon are ENSO-based seasonal health forecasting and the possible impacts of climate change on ENSO and the implications this holds for future assessments of ENSO health associations. The review concludes that there is still some way to go before a thorough understanding of the association between ENSO and health is achieved, with a need to move beyond analyses undertaken through a purely statistical lens, with due acknowledgement that ENSO is a complex non-canonical phenomenon, and that simple ENSO health associations should not be expected.

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“…The r coefficient evaluates the synchronism between DF and SOI time series and shows also the number of months, in which a positive association between both series occurs. A forecast model for estimating the potential impact of ENSO on DF transmission in Queensland, Australia has been developed by Hu et al (2010). The results show that a SOI decrease is significantly associated with an increase in the number of monthly DF cases.…”

Section: Introductionmentioning

confidence: 99%

Geographical distribution of the association between El Niño South Oscillation and dengue fever in the Americas: a continental analysis using geographical information system-based techniques

Ferreira

2014

Geospat Health

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Abstract. El Niño South Oscillation (ENSO) is one climatic phenomenon related to the inter-annual variability of global meteorological patterns influencing sea surface temperature and rainfall variability. It influences human health indirectly through extreme temperature and moisture conditions that may accelerate the spread of some vector-borne viral diseases, like dengue fever (DF). This work examines the spatial distribution of association between ENSO and DF in the countries of the Americas during 1995-2004, which includes the 1997-1998 El Niño, one of the most important climatic events of 20 th century. Data regarding the South Oscillation index (SOI), indicating El Niño-La Niña activity, were obtained from Australian Bureau of Meteorology. The annual DF incidence (AI y ) by country was computed using Pan-American Health Association data. SOI and AI y values were standardised as deviations from the mean and plotted in bars-line graphics. The regression coefficient values between SOI and AI y (r SOI,AI ) were calculated and spatially interpolated by an inverse distance weighted algorithm. The results indicate that among the five years registering high number of cases (1998, 2002, 2001, 2003 and 1997), four had El Niño activity. In the southern hemisphere, the annual spatial weighted mean centre of epidemics moved southward, from 6°31' S in 1995 to 21°12' S in 1999 and the r SOI,AI values were negative in Cuba, Belize, Guyana and Costa Rica, indicating a synchrony between higher DF incidence rates and a higher El Niño activity. The r SOI,AI map allows visualisation of a graded surface with higher values of ENSO-DF associations for Mexico, Central America, northern Caribbean islands and the extreme north-northwest of South America.

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Dengue fever and El Niño/Southern Oscillation in Queensland, Australia: a time series predictive model

Abstract: Climate variability is directly and/or indirectly associated with dengue transmission and the development of an SOI-based epidemic forecasting system is possible for dengue fever in Queensland, Australia.

Cited by 88 publications

References 38 publications

Hot spot detection and spatio-temporal dynamics of dengue in Queensland, Australia

Hot spot detection and spatio-temporal dynamics of dengue in Queensland, Australia

El Niño Southern Oscillation (ENSO) and Health: An Overview for Climate and Health Researchers

Geographical distribution of the association between El Niño South Oscillation and dengue fever in the Americas: a continental analysis using geographical information system-based techniques

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