Climatic factorsinfluencing dengue cases in Dhaka city: a model for dengue predictionMd. Nazmul Karim, Saif Ullah Munshi*, Nazneen Anwar & Md. Shah Alam**WHO-Bangladesh, *Department of Virology, Bangabandhu Sheikh Mujib MedicalUniversity & **The Bangladesh Meteorological Department, Dhaka, BangladeshReceived November 2, 2010 Linear regression is used to normalize thedata. Average monthly humidity, rainfall, minimum and maximum temperature wereused as independent variables and number of dengue cases reported monthly wasused as dependent variable. Accuracy of the model for predicting outbreak wasassessed through receiver operative characteristics (ROC) curve. The predictionmodel had some limitations in predicting the monthly number of dengue cases, itcould forecast possible outbreak two months in advance with considerableaccuracy. DENGUE DISEASEPREDICTION USING WEKA DATA MINING TOOLKASHISH ARA SHAKIL, SHADMA ANIS AND MANSAF ALAM Department of Computer Science, Jamia Millia Islamia New Delhi, India [email protected]

co.in,[email protected] and [email protected] Datasets that are available for dengue describe information about thepatients suffering with dengue disease and without dengue disease along withtheir symptoms like: Fever Temperature, WBC, Platelets, Severe Headache,Vomiting, Metallic Taste, Joint Pain, Appetite, Diarrhea, Hematocrit,Hemoglobin, and how many days suffer in different city. Weka tool is used forclassification of data.

Mobile Applicationfor Dengue Fever Monitoring and Tracking via GPS: Case Study for FijiEmmenual Reddy1,3, Sarnil Kumar1,3, Nicholas Rollings2,3, and RohitashChandra1,31 School of Computing, Information and Mathematical Statistics,The University of the South Paci?c, Laucala Campus, Suva, Fiji.2 Geography, Earth Science and Environment,The University of the South Paci?c, Laucala Campus, Suva, Fiji.3 Arti?cial Intelligence and Cybernetics Research Group, SoftwareFoundation,Nausori, Fiji{emmenual.reddy,nicholas.

rollings,rohitash.chandra}@usp.ac.

fjhttp://www.usp.ac.fjBased on thesymptoms details given by the user the amount of disease spread in a particulararea is found.

It helps authorities to helps the affected people. Need for GIS based dengue surveillance with Googleinternet real timemapping for epidemic control in IndiaPalaniyandi MRemote sensing and GIS laboratory, Vector ControlResearch Centre, (ICMR),Indira Nagar, Pondicherry-605006, [email protected] JOURNAL OF GEOMATICS AND GEOSCIENCESVolume 5, No 1, 2014Submitted on June 2014 published on August 2014 132  The information relevant to the geographical site specification of dengue vectorsbreeding habitats, vector abundance, vector density, etc., could be recordedusing global positioning systems (GPS).

This information could be mapped andoverlay on the the climatic layers of climate variables (Temperature, relativehumidity, saturation deficiency and Rainfall)under the geographical informationsystems (GIS) software platform for spatial analysis(cluster analysis, nearestneighbourhood analysis, fussy analysis, probability of maximum and minimumlikelihood analysis etc.,) for prediction of disease epidemics 7 days inadvance. Dengue FeverPrediction: A Data Mining Problem Kamran Shaukat1*, Nayyer Masood2, Sundas Mehreen1 and Ulya Azmeen1 IT Department, University of the Punjab, Jhelum Campus, Pakistan 2 Mohammad Ali Jinnah University, Islamabad Campus. Pakistan Dengue fever is used in classification techniques to evaluate and comparetheir performance. The dataset was collected from District Headquarter Hospital(DHQ) Jhelum.

For properly categorizing our dataset, different classificationtechniques are used. These techniques are Naïve Bayesian, REP Tree, Randomtree, J48 and SMO. WEKA was used as Data mining tool for classification ofdata.Nation-Wide, Web-Based,Geographic Information System for the IntegratedSurveillance and Control ofDengue Fever in MexicoJuan Eugenio Hernández-Ávila,Mario-Henry Rodríguez,René Santos-Luna,Veronica Sánchez-Castañeda,Susana Román-PérezPublished: August 6, 2013https://doi.org/10.1371/journal.pone.

0070231 Dengue-GIS provides the geographical detailneeded to plan, asses and evaluate the impact of control activities. The systemis beginning to be adopted as a knowledge base by vector control programs. Itis used to generate evidence on impact and cost-effectiveness of controlactivities, promoting the use of information for decision making at all levelsof the vector control program. Dengue-GIS has also been used as a hypothesisgenerator for the academic community.

Spatial point analysis based ondengue surveys at household level in central BrazilGersonLaurindo Barbosa, 1, * Maria Rita Donalísio, 2 Celso Stephan, 2 Roberto Wagner Lourenço, 3 Valmir Roberto Andrade, 1 Marylene de BritoArduino, 1 and Virgilia Luna Castor de Lima 1 Thegoal of this spatial point analysis was to identify potential high-riskintra-urban areas of dengue, using data collected at household level fromsurveysFirst survey screened 1,586 asymptomatic individuals older than 5 yearsof age. Second survey 2,906 asymptomatic volunteers, same age-groups, wereselected by multistage sampling (census tracts; blocks; households) usingavailable digital maps. Sera from participants were tested by denguevirus-specific IgM/IgG by EIA. A Generalized Additive Model (GAM) was used todetect the spatial varying risk over the region.

Initially without any fixedcovariates, to depict the overall risk map, followed by a model including themain covariates and the year, where the resulting maps show the risk associatedwith living place, controlled for the individual risk factors. This method hasthe advantage to generate smoothed risk factors maps, adjusted bysocio-demographic covariates.. Data from household surveys pointed out that lowprevalence areasA study findsthat it is possible to forecast the outbreak of the diseaseJoão B Siqueira-Junior,Ivan J Maciel,Christovam Barcellos,Wayner V Souza,Marilia S Carvalho,Nazareth E Nascimento,Renato M Oliveira,Otaliba Morais-Neto andCelina MT MartelliEmail authorBMCPublic Health20088:361https://doi.org/10.1186/1471-2458-8-361© Siqueira-Junior et al; licensee BioMed Central Ltd. 2008Received: 19 October 2007Accepted: 20 October 2008Published: 20 October 2008 Scientistshave reached this conclusion after evaluating the relationship of climaticfactors to the spread of dengue in different climatic zones in India — Punjab,Haryana, Rajasthan, Gujarat, and Kerala.

They focussed on changes in a factorcalled extrinsic incubation period (EIP) of the dengue virus, by taking intoaccount daily and monthly mean temperatures in these areas The EIP is the timetaken for incubation of the virus in the mosquito.Lower temperatures (17-18°C)result in longer EIPs thereby leading to decreased virus transmission. Withincreasing temperatures, feeding increases because of the enhanced metabolismof the mosquito, leading to shorter EIPs. Even a five-day decrease in theincubation period can hike the transmission rate by three times, and with anincrease in temperature from 17 to 30°C, dengue transmission increasesfourfold.

A further increase in temperature beyond 35°C is detrimental to themosquito’s survival.       

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