Predicting dengue in the Philippines using artificial neural network
Zafra, B.
Show abstract
Dengue fever is an infectious disease caused by Flavivirus transmitted by Aedes mosquito. This disease predominantly occurs in the tropical and subtropical regions. With no specific treatment, the most effective way to prevent dengue is vector control. The dependence of Aedes mosquito population on meteorological variables make prediction of dengue infection possible using conventional statistical and epidemiologic models. However, with increasing average global temperature, the predictability of these models may be lessened employing the need for artificial neural network. This study uses artificial neural network to predict dengue incidence in the entire Philippines with humidity, rainfall, and temperature as independent variables. All generated predictive models have mean squared logarithmic error of less than 0.04.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Dynamics of residual malaria transmission in Central Western Senegal: Mapping the breeding sites of Anopheles gambiae s. l. 96%
- Malaria treatment-seeking behaviour and its associated factors: A cross-sectional study in rural East Nusa Tenggara Province, Indonesia 96%
- The knowledge and practice towards COVID-19 pandemic prevention among residents of Ethiopia. An online cross-sectional study. 96%
Similar papers in this journal
- FINE-SCALE POPULATION GENETIC STRUCTURE OF DENGUE MOSQUITO VECTOR, Aedes aegypti AND ITS ASSOCIATION TO LOCAL DENGUE INCIDENCE 96%
- Assessment of Culicidae collection methods for xenomonitoring lymphatic filariasis in malaria co-infection context in Burkina Faso 96%
- Black flies and Onchocerciasis: Knowledge, attitude and practices among inhabitants of Alabameta, Osun State, Southwestern, Nigeria 95%
Similar papers in this journal
- Epidemiological and virological factors determining dengue transmission in Sri Lanka during the COVID-19 pandemic 97%
- Determining the effects of preseasonal climate factors toward dengue early warning system in Bangladesh 97%
- Assessments of Effectiveness of Technologies Utilizations in VIHSCM Among Selected Health Facilities in Tanzania Mainland 95%
Similar papers in this journal
- Yellow fever vaccination coverage among nomadic populations in Savannah region, Ghana; a cross-sectional study following an outbreak 94%
- When it is available, will we take it? Public perception of hypothetical COVID-19 vaccine in Nigeria 94%
- COVID-19 in Hospitalized Ethiopian Children: Characteristics and Outcome Profile 94%
Similar papers in this journal
- Modeling of leptospirosis outbreaks in relation to hydroclimatic variables in the northeast of Argentina 93%
- Psychosocial Factors of Stigma and Relationship to Healthcare Service among Adolescents Living With HIV/AIDS in Kano State, Nigeria 92%
- Knockdown resistance (kdr) Associated organochlorine Resistance in mosquito-borne diseases (Culex pipiens): Systematic study of reviews and meta-analysis 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.