Research on Climate and Dengue in Malaysia: A Systematic Review

Hii, Yien Ling; Zaki, Rafdzah Ahmad; Aghamohammadi, Nasrin; Rocklöv, Joacim

doi:10.1007/s40572-016-0078-z

Cited by 65 publications

(47 citation statements)

References 20 publications

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“…another study Ahmad et al (2016a) observed maximum activity of ChE in Anabas testudineus muscles in the range of 25°C to 40°C, while the maximum activity of blood ChE was observed at 30°C. It can be concluded that this particular enzyme works best at temperature ranging from 20°C to 40°C that prevails in Malaysia, a tropical country with warm temperature, high humidity and copious rainfall and average temperature of around 27°C (Hii et al, 2016). The results this study indicate that cadmium induces toxicity on C. gariepinus, and AChE present in the gills of C. gariepinus may serve as a significant biomarker of cadmium induced neurotoxicity.…”

Section: Half Maximal Inhibitory Concentration (Ic50)mentioning

confidence: 65%

Histopathological and cholinesterase changes in the gills of Clarias gariepinus as a result of cadmium exposure

Pariza¹,

Ahmad²,

Fadzil³

et al. 2019

JEB

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Section: Half Maximal Inhibitory Concentration (Ic50)mentioning

confidence: 65%

Histopathological and cholinesterase changes in the gills of Clarias gariepinus as a result of cadmium exposure

Pariza¹,

Ahmad²,

Fadzil³

et al. 2019

JEB

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“…Climatic factors can enhance vector population growth and increase the risk of DENV vectors biting susceptible humans [39][40][41]. Dengue transmission is often seasonal and driven by many climatic factors including temperature, short and long term rainfall and relative humidity influencing mosquito vector growth and density [42][43][44][45].…”

Section: Seasonal and Environmental Factorsmentioning

confidence: 99%

Dengue Fever and Severe Dengue in Barbados, 2008–2016

et al. 2020

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Analysis of the temporal, seasonal and demographic distribution of dengue virus (DENV) infections in Barbados was conducted using national surveillance data from a total of 3994 confirmed dengue cases. Diagnosis was confirmed either by DENV-specific real time reverse transcriptase polymerase chain reaction (rRT-PCR), or non-structural protein 1 (NS1) antigen or enzyme linked immunosorbent assay (ELISA) tests; a case fatality rate of 0.4% (10/3994) was observed. The prevalence rate of dengue fever (DF) varied from 27.5 to 453.9 cases per 100,000 population among febrile patients who sought medical attention annually. DF cases occurred throughout the year with low level of transmission observed during the dry season (December to June), then increased transmission during rainy season (July to November) peaking in October. Three major dengue epidemics occurred in Barbados during 2010, 2013 and possibly 2016 with an emerging three-year interval. DF prevalence rate among febrile patients who sought medical attention overall was highest among the 10-19 years old age group. The highest DF hospitalisation prevalence rate was observed in 2013. Multiple serotypes circulated during the study period and Dengue virus serotype 2 (DENV-2) was the most prevalent serotype during 2010, whilst DENV-1 was the most prevalent serotype in 2013. Two DENV-1 strains from the 2013 DENV epidemic were genetically more closely related to South East Asian strains, than Caribbean or South American strains, and represent the first ever sequencing of DENV strains in Barbados. However, the small sample size (n = 2) limits any meaningful conclusions. DF prevalence rates were not significantly different between females and males. Public health planning should consider DENV inter-epidemic periodicity, the current COVID-19 pandemic and similar clinical symptomology between DF and COVID-19. The implementation of routine sequencing of DENV strains to obtain critical data can aid in battling DENV epidemics in Barbados.

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“…Therefore, establishing related risk factors is critical for prediction systems [16]. Given that climate factors play a key role in this disease, identifying the relation between weather information and dengue outbreak incidence is a major task in establishing an accurate prediction system for future outbreaks [17,18,19]. In the current study, important climatic risk factors, such as temperature, relative humidity and rainfall amount, were examined.…”

Section: Introductionmentioning

confidence: 99%

“…Hybrid models have been used in outbreak prediction research. A hybrid model is an example of an integrated model, and many models based on genetic algorithms are available to determine the weight in a neural network model [14,17,18,19,25,26]. In Singapore, researchers found signi cant correlated dengue cases with climatic variables by using a Poisson regression model [27].…”

Section: Introductionmentioning

confidence: 99%

Identification of Significant Climatic Risk Factors and Machine Learning Models in Dengue Outbreak Prediction

Nejad

Varathan

2020

Preprint

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Background: Dengue fever is a widespread viral disease and one of the world’s major pandemic vector-borne infections, causing serious hazard to humanity. The World Health Organisation (WHO) reported that the incidence of dengue fever has increased dramatically across the world in recent decades. WHO currently estimates an annual incidence of 50–100 million dengue infections worldwide. To date, no tested vaccine or treatment is available to stop or prevent dengue fever. Thus, the importance of predicting dengue outbreaks is significant. The current issue that should be addressed in dengue outbreak prediction is accuracy. A limited number of studies have conducted an in-depth analysis of climate factors in dengue outbreak prediction. Methods: The most important climatic factors that contribute to dengue outbreaks were identified in the current work. Correlation analyses were performed in order to determine these factors and these factors were used as input parameters for machine learning models. Top five machine learning classification models (Bayes network (BN) models, support vector machine (SVM), RBF tree, decision table and naive Bayes) were chosen based on past research. The models were then tested and evaluated on the basis of four-years data (January 2010 to December 2013) collected in Malaysia. Results: This research has two major contributions. A new risk factor, called the TempeRain Factor (TRF), was identified and used as an input parameter for the model of dengue outbreak prediction. Moreover, TRF was applied to demonstrate its strong impact on dengue outbreaks. Experimental results showed that the Bayes Network model with the new meteorological risk factor identified in this study increased accuracy to 92.35% and reduced the root-mean-square error to 0.26 for predicting dengue outbreaks. Conclusions: This research explored the factors used in dengue outbreak prediction systems. The major contribution of this study is identifying new significant factors that contribute to dengue outbreak prediction. From the evaluation result, we obtained a significant improvement in the accuracy of a machine learning model for dengue outbreak prediction.

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Research on Climate and Dengue in Malaysia: A Systematic Review

Cited by 65 publications

References 20 publications

Histopathological and cholinesterase changes in the gills of Clarias gariepinus as a result of cadmium exposure

Histopathological and cholinesterase changes in the gills of Clarias gariepinus as a result of cadmium exposure

Dengue Fever and Severe Dengue in Barbados, 2008–2016

Identification of Significant Climatic Risk Factors and Machine Learning Models in Dengue Outbreak Prediction

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