Climate Variability and Dengue Fever in Warm and Humid Mexico

Colón‐González, Felipe J.; Lake, Iain R.; Bentham, Graham

doi:10.4269/ajtmh.2011.10-0609

Cited by 90 publications

(92 citation statements)

References 31 publications

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“…Additionally, ONI remains the most important climate variable when Tmin and precipitation are included in the models, indicating that the influence of ENSO on dengue extends beyond these variables, which was found in a previous study in Mexico. 17 We found that ENSO has a strong influence on anomalies in Tmin, which was indicated by a positive association during both time periods (1995-2010 and 2001-2010) (Figure 3). Anomalies in Tmax and Tmean were also positively associated with ENSO, although the association was weaker.…”

Section: Discussionmentioning

confidence: 81%

“…17,[46][47][48] Likewise, field studies in this region found that Tmin and precipitation were the most important local climate predictors of seasonal Ae. aegypti population dynamics (StewartIbarra AM and others, unpublished data).…”

Section: Discussionmentioning

confidence: 99%

“…16 Dengue transmission in Mexico has been shown to be strongly associated with ENSO and minimum temperature, although not with precipitation. 17 Dengue hemorrhagic fever (DHF) epidemics in Colombia, Suriname, French Guiana, and Indonesia were found to be associated with El Niñ o events, although the effects of El Niñ o on local climate varied by region. 18 Other studies found that ENSO and local climate were not important determinants of interannual variability in dengue incidence in Mexico, Puerto Rico, and Thailand, [19][20][21][22] highlighting the importance of identifying and assessing the effects of non-climate factors in analyses of interannual variability.…”

Section: Introductionmentioning

confidence: 99%

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

Stewart-Ibarra¹,

Lowe²

2013

The American Society of Tropical Medicine and Hygiene

138

162

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Abstract. We report a statistical mixed model for assessing the importance of climate and non-climate drivers of interannual variability in dengue fever in southern coastal Ecuador. Local climate data and Pacific sea surface temperatures (Oceanic Niñ o Index [ONI]) were used to predict dengue standardized morbidity ratios (SMRs;1995-2010. Unobserved confounding factors were accounted for using non-structured yearly random effects. We found that ONI, rainfall, and minimum temperature were positively associated with dengue, with more cases of dengue during El Niñ o events. We assessed the influence of non-climatic factors on dengue SMR using a subset of data (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010) and found that the percent of households with Aedes aegypti immatures was also a significant predictor. Our results indicate that monitoring the climate and non-climate drivers identified in this study could provide some predictive lead for forecasting dengue epidemics, showing the potential to develop a dengue early-warning system in this region.

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Section: Discussionmentioning

confidence: 81%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Stewart-Ibarra¹,

Lowe²

2013

The American Society of Tropical Medicine and Hygiene

138

162

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“…For this investigation, we selected two pathogens: influenza and dengue. The rationale for selecting these diseases is that they both have strong seasonality-which can drive accelerating spread in some models 6,7 -and have rich historical data sets. However, because dengue is a vectored pathogen, and influenza is not, we expect the importance of relational exchange to be far greater for influenza 8,9,10 .…”

mentioning

confidence: 99%

The effect of a prudent adaptive behaviour on disease transmission

2016

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Infectious diseases often spread faster near their peak than would be predicted given early data on transmission . Despite the commonality of this phenomena, there are no known general mechanisms able to cause an exponentially spreading disease to begin spreading faster. Indeed most features of real world social networks, e.g. clustering 1,2 and community structure 3 , and of human behaviour, e.g. social distancing 4 and increased hygiene 5 , will slow disease spread. Here, we consider a model where individuals with essential societal roles-e.g. teachers, first responders, health-care workers, etc.-who fall ill are replaced with healthy individuals. We refer to this process as relational exchange. Relational exchange is also a behavioural process, but one whose effect on disease transmission is less obvious. By incorporating this behaviour into a dynamic network model, we demonstrate that replacing individuals can accelerate disease transmission. Furthermore, we find that the effects of this process are trivial when considering a standard mass-action model, but dramatic when considering network structure.This result highlights another critical shortcoming in mass-action models, namely their inability to account for behavioural processes. Lastly, using empirical data, we find that this mechanism parsimoniously explains observed patterns across more than seventeen years of influenza and dengue virus data. We anticipate that our findings will advance the emerging field of disease forecasting and will better inform public health decision making during outbreaks.

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“…2 Climate influences dengue transmission through impacts on the vector (e.g., growth and development, availability of habitat, survivorship) 3 and impacts on the virus (e.g., length of extrinsic incubation period, [EIP]). 4,5 Correlations have been found between weather and climate variability and dengue incidence, including distinct seasonal variability, 6 El Niñ o Southern Oscillation index variability, [7][8][9][10][11] and monthly [12][13][14] and weekly 15 weather variability. The potential impact of climate change on dengue has been extensively explored and estimated via scenario-based modeling, with prediction of expansion in the geographic distribution of dengue under climate change scenarios.…”

Section: Introductionmentioning

confidence: 99%

Weather-Driven Variation in Dengue Activity in Australia Examined Using a Process-Based Modeling Approach

Bannister-Tyrrell¹,

Williams²,

Ritchie³

et al. 2013

The American Society of Tropical Medicine and Hygiene

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Abstract. The impact of weather variation on dengue transmission in Cairns, Australia, was determined by applying a process-based dengue simulation model (DENSiM) that incorporated local meteorologic, entomologic, and demographic data. Analysis showed that inter-annual weather variation is one of the significant determinants of dengue outbreak receptivity. Cross-correlation analyses showed that DENSiM simulated epidemics of similar relative magnitude and timing to those historically recorded in reported dengue cases in Cairns during 1991-2009, (r = 0.372, P 0.01). The DENSiM model can now be used to study the potential impacts of future climate change on dengue transmission. Understanding the impact of climate variation on the geographic range, seasonality, and magnitude of dengue transmission will enhance development of adaptation strategies to minimize future disease burden in Australia.

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Climate Variability and Dengue Fever in Warm and Humid Mexico

Cited by 90 publications

References 31 publications

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

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

The effect of a prudent adaptive behaviour on disease transmission

Weather-Driven Variation in Dengue Activity in Australia Examined Using a Process-Based Modeling Approach

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