Modelling the association of dengue fever cases with temperature and relative humidity in Jeddah, Saudi Arabia—A generalised linear model with break-point analysis

Alkhaldy, Ibrahim

doi:10.1016/j.actatropica.2016.12.034

Cited by 36 publications

(27 citation statements)

References 22 publications

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“…The seasonal pattern of DF in both Sumatra Island and Kalimantan was found to be closely related to patterns of minimum temperature [37][38][39] and relative humidity [39][40][41][42][43]. The highest peak of DF incidence in January 2016 might be related to an El Nino Southern Oscillation (ENSO) event.…”

Section: Variablesmentioning

confidence: 99%

Forest cover and climate as potential drivers for dengue fever in Sumatra and Kalimantan 2006–2016: a spatiotemporal analysis

Husnina

Clements

Wangdi

2019

Tropical Med Int Health

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Objectives To describe and quantify spatiotemporal trends of dengue fever at district level in Sumatra and Kalimantan, Indonesia in relation to forest cover and climatic factors. Methods A spatial ecological study design was used to analyse monthly surveillance data of notified dengue fever cases from January 2006 to December 2016 in the 154 districts of Sumatra and 56 districts of Kalimantan. A multivariate, zero‐inflated Poisson regression model was developed with a conditional autoregressive prior structure with posterior parameters estimated using Bayesian Markov chain Monte Carlo simulation with Gibbs sampling. Results There were 230 745 cases in Sumatra and 132 186 cases in Kalimantan during the study period. In Sumatra, the risk of dengue fever decreased by 9% (95% credible interval [CrI] 8.5–9.5%) for a 1% increase in forest cover and by 12.2% (95% CrI 11.9–12.6%) for a 1% increase in relative humidity. In Kalimantan, dengue fever risk fell by 17.6% (95% CrI 17.1–18.1%) for a 1% increase in relative humidity and rose by 7.6% (95% CrI 6.9–8.4%) for a 1 °C increase in minimum temperature. There was no significant residual spatial clustering in Sumatra after accounting for climate and demographic variables. In Kalimantan, high residual risk areas were primarily centred in North and East of the island. Conclusions Dengue fever in Sumatra and Kalimantan was highly seasonal and associated with climate factors and deforestation. Incorporation of climate indicators into risk‐based surveillance might be warranted for dengue fever in Indonesia.

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Section: Variablesmentioning

confidence: 99%

Forest cover and climate as potential drivers for dengue fever in Sumatra and Kalimantan 2006–2016: a spatiotemporal analysis

Husnina

Clements

Wangdi

2019

Tropical Med Int Health

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“…Despite the complex interplay of multiple factors that in uence the incidence of dengue, meteorological parameters play a vital role in in uencing the timing and magnitude of dengue epidemics [16,45]. With the limited success achieved during controlling dengue epidemics, recognition of vulnerable communities and evaluating the degree of vulnerability to dengue due to climate change is of paramount importance, especially in developing countries like Sri Lanka.…”

Section: Discussionmentioning

confidence: 99%

“…The rainfall events indicated a positive impact on the abundance of Aedes vectors by increasing the abundance of potential vector breeding sites either by replenishing water levels or formation of new breeding sites [21,23], and modifying the relative humidity to favourable levels for mosquito survival and longevity [26]. However, extreme rainfall events followed by ooding may ush the Aedes larvae from their breeding sites resulting in a negative impact on the vector abundance [45]. Therefore, rainfall plays a key role in governing the population dynamics of Aedes vectors mosquitoes, allowing it to be considered as risk factor for increasing the exposure of dengue.…”

Section: Exposure Index (Ei)mentioning

confidence: 99%

“…Hence, global warming would favour higher levels of vector breeding and increase the abundance of Aedes mosquitoes leading to elevated risk levels of dengue. In addition, increased temperature due to global warming may increase the DENV transmission rates, which in turn increase the vulnerability of communities to dengue infection [45].…”

Section: Exposure Index (Ei)mentioning

confidence: 99%

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Climate change induced vulnerability and adaption for dengue incidence in Colombo and Kandy Districts: The first detailed investigation in Sri Lanka

Udayanga

Gunathilaka²,

Iqbal

et al. 2020

Preprint

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Background: Assessing the vulnerability of an infectious disease such as dengue among endemic population is an important requirement to design proactive programmes in order to improve resilience capacity of vulnerable communities. Despite the significance in decision making process especially for rational resource allocation, the recognition of actual burden of dengue and the vulnerability towards dengue, has been limitedly addressed in many countries including Sri Lanka, due to deficiencies in a holistic methodology. Methods: A total of 42 variables (entomological, epidemiological, meteorological parameters, land-use practices and socio-demographic data) of all the 38 Medical Officer of Health (MOH) areas in the districts of Colombo and Kandy were considered as candidate variables for a composite index based vulnerability assessment. The Principal Component Analysis (PCA) was used in selecting and setting the weight for each indicator. Exposure, Sensitivity, Adaptive Capacity and Vulnerability of all the MOH areas for dengue incidence were calculated. Results: Out of 42 candidate variables, only 23 parameters (Exposure Index: 6 variables; Sensitivity Index: 11 variables; Adaptive Capacity Index: 6 variables) were selected as indicators to assess climate change vulnerability to dengue. Colombo Municipal Council (CMC) MOH area denoted the highest values for exposure (0.89: exceptionally high exposure), sensitivity (0.86: exceptionally high sensitivity) in the district of Colombo, while Kandy Municipal Council (KMC) area reported the highest exposure (0.79: high exposure) and sensitivity (0.77: high sensitivity) in Kandy. Piliyandala MOH area denoted the highest level of adaptive capacity (0.66) in the district of Colombo followed by Menikhinna (0.68) in Kandy. The highest vulnerability (0.45: moderate vulnerability) to dengue was indicated from CMC and the lowest indicated from Galaha MOH (0.15; very low vulnerability) in Kandy. Interestingly the Kandy Municipal Council MOH area had a notable vulnerability of 0.41 (moderate vulnerability), which was the highest within Kandy. Conclusions: In general, vulnerability for dengue was relatively higher within the MOH areas of Colombo, than in Kandy, suggesting a higher degree of potential susceptibility to dengue within and among local communities of Colombo. The Vector Controlling Entities (VCE) are recommended to consider the spatial variations in vulnerability of local communities to dengue for decision making, especially in allocation of limited financial, human and mechanical resources for dengue epidemic management

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“…They found that temperature and humidity are the main predictors of the ZCL incidence. Moreover, few studies give descriptive patterns of the temporal distributions of vector-borne diseases emergence using the GLM [15], [16]. However, there were no study modeling such relation in the case of ZCL and using the GLMs.…”

Section: Introductionmentioning

confidence: 99%

Comparative Performance Analysis for Generalized Additive and Generalized Linear Modeling in Epidemiology

Talmoudi¹,

Bellali²,

Nissaf³

et al. 2017

ijacsa

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Abstract-Most environmental-epidemiological researches emphasize modeling as the causal link of different events (e.g., hospital admission, death, disease emergency). There has been a particular concern in the use of the Generalized Linear Models (GLMs) in the field of epidemiology. However, recent studies in this field highlighted the use of non-parametric techniques, especially the Generalized Additive Models (GAMs). The aim of this work is to compare performance of both methods in the field of epidemiology. Comparison is done in terms of sharpening the relation between the predictors and the response variable as well as in predicting outbreaks. The most suitable method is then adopted to elucidate the impact of bioclimatic factors on the emergence of the zoonotic cutaneous leishmaniasis (ZCL) disease in Central Tunisia. Monthly epidemiologic and bioclimatic data from July 2009 to June 2016 are used in this study. Akaike information criterion, R-squared and F-statistic are used to compare model performance, while the root mean square error is used for checking predictive accuracy for both models. Our results show the potential of GAM model to provide a better assessment of the nonlinear relations and to give a high predictive accuracy compared to GLMs. The results also stress the inaccurate estimation of risk factors when linear trends are used to model nonlinear structured data.

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Modelling the association of dengue fever cases with temperature and relative humidity in Jeddah, Saudi Arabia—A generalised linear model with break-point analysis

Cited by 36 publications

References 22 publications

Forest cover and climate as potential drivers for dengue fever in Sumatra and Kalimantan 2006–2016: a spatiotemporal analysis

Forest cover and climate as potential drivers for dengue fever in Sumatra and Kalimantan 2006–2016: a spatiotemporal analysis

Climate change induced vulnerability and adaption for dengue incidence in Colombo and Kandy Districts: The first detailed investigation in Sri Lanka

Comparative Performance Analysis for Generalized Additive and Generalized Linear Modeling in Epidemiology

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