Spatial distribution and cluster analysis of dengue using self organizing maps in Andhra Pradesh, India, 2011–2013

Mutheneni, Srinivasa Rao; Mopuri, Rajasekhar; Naish, Suchithra; Gunti, Deepak; Upadhyayula, Suryanarayana Murty

doi:10.1016/j.parepi.2016.11.001

Cited by 39 publications

(33 citation statements)

References 36 publications

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“…mosquitoes (no more than 500 m. 23 Therefore, the spread of dengue is mostly clustered because of the limited flight distance of mosquitoes. 24 This is also observed in the research location, where the occupancy of the population is very dense with the location of the house is very close. In fact, some of the relatives' houses are incorporated into a series of buildings.…”

Section: Discussionsupporting

confidence: 66%

Detection of Transovarial Transmission on Aedes sp. in Gombong Kebumen Central Java

2019

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Abstract. Gombong Subdistrict is one of dengue-endemic area in Kebumen Regency, Central Java. The high number of dengue cases in this area raises the question of whether there has been transovarial transmission occured from the Aedes sp. to their eggs. Transovarial transmission could be dangerous because the next generation of mosquitoes can directly become competent vectors as transmitters of the dengue virus (DENV). The purpose of this study is to detect dengue virus transovarial transmission in Aedes sp. in Gombong Subdistrict. This is a descriptive research in Gombong, Semanding, and Kali Tengah villages, Gombong Sub District. A total of 300 houses, 100 houses from each village were selected in this study. There were 600 Oviposition traps (ovitraps) were installed both inside and outside of houses for 6 days. Ovitraps were calculated by its Ovitrap Index (OI). Detection of transovarial transmission was carried by rearing field mosquitoes to Filial 1 then identified by RT-PCR assay. This study showed that OI in the three villages was higher in outdoor compared to indoor positions. All tested samples were negative DENV, indicated that there were no transovarial transmission occured at the study sites. Transmission in these study areas might still through horizontal mechanism transmission by mosquito bites. Although there is no transovarial transmission, awareness of dengue transmission must be continued by eradicating of mosquito nests such as 3M plus activities on a regular basis

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Section: Discussionsupporting

confidence: 66%

Detection of Transovarial Transmission on Aedes sp. in Gombong Kebumen Central Java

2019

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“…Hence, the present study is focused on predicting the occurrence of filariasis using various ML algorithms. ML algorithms are used extensively in epidemiological and species distribution studies [40, 41]. ML algorithms help to understand the complex non-linear associations between the response (disease occurrence) and explanatory variables, and control for interactions among explanatory variables [42].…”

Section: Discussionmentioning

confidence: 99%

Applications of machine learning techniques to predict filariasis using socio-economic factors

et al. 2019

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Filariasis is one of the major public health concerns in India. Approximately 600 million people spread across 250 districts of India are at risk of filariasis. To predict this disease, a pilot scale study was carried out in 30 villages of Karimnagar district of Telangana from 2004 to 2007 to collect epidemiological and socio-economic data. The collected data are analysed by employing various machine learning techniques such as Naïve Bayes (NB), logistic model tree, probabilistic neural network, J48 (C4.5), classification and regression tree, JRip and gradient boosting machine. The performances of these algorithms are reported using sensitivity, specificity, accuracy and area under ROC curve (AUC). Among all employed classification methods, NB yielded the best AUC of 64% and was equally statistically significant with the rest of the classifiers. Similarly, the J48 algorithm generated 23 decision rules that help in developing an early warning system to implement better prevention and control efforts in the management of filariasis.

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“…Bandar Baru Bangi emerged as land developing areas for housing estates which resulted in the growth of population density. Along with the rise of development growth, dengue epidemiology needs to be controlled to reduce the vulnerability of urban populations towards the outbreaks [32]. Bandar Baru Bangi town was listed as dengue hotspot ever since the year 2010, and this situation raised as a matter of concern for this study [33].…”

Section: Case Studymentioning

confidence: 99%

Dengue Vector Density Incident and Its Implication to Urban Livability

Majid¹,

Razman²,

Zakaria³

et al. 2020

Preprint

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Background: Malaysia's population is set to reach 33.10 million by the end of 2020. About 75% of the population of Malaysia lived in urban areas and cities. The metropolitan area of Greater Kuala Lumpur had a population of more than seven million that year, making it the largest urban area in Malaysia. Kuala Lumpur as the city centre for Greater Kuala Lumpur has been ranked as Southeast Asia's second most liveable city after Singapore. The livable city imperative is relevant because Malaysia's urbanization process is moving towards harmonization with the principles of sustainable development. Livable city involves many interdependent factors contributing to the urban quality of life. With their complete physical and social infrastructures, the urban types are an essential basis for improving the quality of life of the urbanites. However, increasing population and rapid land-use changes led to the emergence of vector-borne diseases such as dengue in an urban area. Prolong dengue outbreaks will reduce livability in urban areas. Therefore, this study aims to look at the density of dengue distribution in Bandar Baru Bangi town in 2014, 2015, 2016 and 2017.Methods: The study uses data provided from the Ministry of Health Malaysia and shows the focus of dengue cases in residential and industrial areas of Bandar Baru Bangi town. Spatial analysis using Geographical Information System (GIS) was applied to identify the locality of dengue incidence within the study area. Spatial statistical analysis of dengue cases used Kernel Density Estimation to distinguish dengue hotspots from the distribution of the exact location of dengue cases reported in Bandar Baru Bangi town.Results: Kernel density estimation showed the dengue hotspots concentrated on the east of Bandar Baru Bangi town. The results found that the highest density was in 2015 was 605 to 706 points per square kilometres. This study also discovers that most of the hotspots constructed were located in the residential area of Bandar Baru Bangi.Conclusions: This study is essential to help local authorities eradicate dengue in urban areas for future management strategies; therefore, this study is vital to help local authorities eradicate dengue in urban areas for future management strategies.

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Spatial distribution and cluster analysis of dengue using self organizing maps in Andhra Pradesh, India, 2011–2013

Cited by 39 publications

References 36 publications

Detection of Transovarial Transmission on Aedes sp. in Gombong Kebumen Central Java

Detection of Transovarial Transmission on Aedes sp. in Gombong Kebumen Central Java

Applications of machine learning techniques to predict filariasis using socio-economic factors

Dengue Vector Density Incident and Its Implication to Urban Livability

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