Dengue prediction model: A systematic review using social network analysis

Since 1968, Dengue Harmonic Fever’s incidence in Indonesia has continued to rise and has become a public health issue. Indonesia has the largest number of Dengue Harmonic Fever cases than 30 other epidemic countries worldwide. It is very important to carry out research related to dengue cases’ prediction to prevent the spread of Dengue. This literature review is intended to determine the extent of the dengue prediction approach carried out by previous researchers, and a research gap will be obtained. The algorithm used to cluster articles is a modularity algorithm, using several open-source tools to process data. The online databases used are Google Scholar and Crossref by using keywords: journal, algorithm, prediction, and Dengue. The data are taken from the expansion of 1928-2020. This study’s results are 200 articles that are suitable and divided into four clusters of important articles. Also, several important parameters were obtained in the prediction study of dengue fever, namely humidity, temperature, rainfall, and population density.

[1]  Samsuridjal Djauzi,et al.  Interleukin-18 levels in adult dengue fever and dengue hemorrhagic fever , 2004 .

[2]  Ashfaq Ahmad,et al.  Up Regulation of cystathione γ lyase and Hydrogen Sulphide in the Myocardium Inhibits the Progression of Isoproterenol–Caffeine Induced Left Ventricular Hypertrophy in Wistar Kyoto Rats , 2016, PloS one.

[3]  Y. Oyang,et al.  Comparing machine learning with case-control models to identify confirmed dengue cases , 2020, PLoS neglected tropical diseases.

[4]  E. S. Gardner.  EXPONENTIAL SMOOTHING: THE STATE OF THE ART, PART II , 2006 .

[5]  Stephen G. Kobourov,et al.  Analysis of Network Clustering Algorithms and Cluster Quality Metrics at Scale , 2016, PloS one.

[6]  F. Debnath,et al.  Dengue fever in a municipality of West Bengal, India, 2015: An outbreak investigation , 2017, Indian journal of public health.

[7]  First outbreak of dengue fever in East Sikkim in Northeastern part of India , 2019, Journal of family medicine and primary care.

[8]  Kin Keung Lai,et al.  Hybridizing Exponential Smoothing and Neural Network for Financial Time Series Predication , 2006, International Conference on Computational Science.

[9]  Erhan Guven,et al.  Ensemble method for dengue prediction , 2018, PloS one.

[10]  Patsaraporn Somboonsak.  Time Series Analysis of Dengue Fever Cases in Thailand Utilizing the SARIMA Model , 2019, ICIT.

[11]  R. Avoi,et al.  Developing a Dengue Prediction Model based on Climate in Tawau, Malaysia. , 2019, Acta tropica.

[12]  K. Shekhar,et al.  Epidemiology of Dengue/Dengue Hemorrhagic Fever in Malaysia-A Retrospective Epidemiological Study. 1973-1987. Part II: Dengue Fever (DF). , 1992, Asia-Pacific journal of public health.

[13]  E. Slud,et al.  Real-time dengue forecast for outbreak alerts in Southern Taiwan , 2020, PLoS neglected tropical diseases.

[14]  S. Polwiang.  Estimation of dengue infection for travelers in Thailand. , 2016, Travel medicine and infectious disease.

[15]  A. A. I. Perera,et al.  Prediction of Dengue Outbreaks in Sri Lanka using Artificial Neural Networks , 2014 .

[16]  Alan F. Smeaton,et al.  Advances in Visual Informatics , 2015, Lecture Notes in Computer Science.

[17]  Exponential smoothing on forecasting dengue cases in Colombo, Sri Lanka , 2020 .

[18]  L. Frewer,et al.  Food fraud and the perceived integrity of European food imports into China , 2018, PloS one.

[19]  V. Aryaprema,et al.  Breteau index as a promising early warning signal for dengue fever outbreaks in the Colombo District, Sri Lanka. , 2019, Acta tropica.

[20]  Agus Qomaruddin Munir,et al.  Sistematic Review: Model Peramalan Wabah Penyakit Demam Berdarah , 2015 .

[21]  D. Gubler,et al.  Dengue and dengue hemorrhagic fever. , 2014 .

[22]  Fabio H. Nieto.  Ex post and ex ante prediction of unobserved multivariate time series: a structural-model based approach , 2007 .

[23]  Don E. Gardner.  Weight factor selection in double exponential smoothing enrollment forecasts , 1981 .

[24]  D. Gubler,et al.  Dengue and dengue hemorrhagic fever in the Americas. , 1987, Puerto Rico health sciences journal.

[25]  C. Prem Sankar,et al.  Exploratory social network analysis of affiliation networks of Indian listed companies , 2015, Soc. Networks.

[26]  N. Alexander,et al.  Time series analysis of dengue surveillance data in two Brazilian cities. , 2018, Acta tropica.

[27]  Gisele L. Pappa,et al.  Efficient Gaussian Process-Based Inference for Modelling Spatio-Temporal Dengue Fever , 2017, 2017 Brazilian Conference on Intelligent Systems (BRACIS).

[28]  Wei Wang,et al.  A complete statistical model for calibration of RNA-seq counts using external spike-ins and maximum likelihood theory , 2019, PLoS Comput. Biol..

[29]  Ahmad Ashari,et al.  A Model for Forecasting the Number of Cases and Distribution Pattern of Dengue Hemorrhagic Fever in Indonesia , 2017 .

[30]  G. Ebrahim.  Dengue and dengue haemorrhagic fever. , 1993, Journal of tropical pediatrics.

[31]  Patsaraporn Somboonsak,et al.  Forecasting Dengue Fever Epidemics using ARIMA Model , 2019, AICCC.

[32]  Lon-Mu Liu,et al.  FORECASTING AND TIME SERIES ANALYSIS USING THE SCA STATISTICAL SYSTEM , 1994 .

[33]  T. K. Manojkumar,et al.  Dengue and Early Warning Systems: A review based on Social Network Analysis , 2020 .

[34]  Vipul Kumar Mishra,et al.  Dengue Disease Spread Prediction Using Twofold Linear Regression , 2019, 2019 IEEE 9th International Conference on Advanced Computing (IACC).

[35]  Wiwik Anggraeni,et al.  Modified Regression Approach for Predicting Number of Dengue Fever Incidents in Malang Indonesia , 2017 .

[36]  Indrajit Ghosh,et al.  Forecasting dengue epidemics using a hybrid methodology , 2019 .

[37]  Arna Fariza,et al.  Fuzzy Logic and Exponential Smoothing for Mapping Implementation of Dengue Haemorrhagic Fever in Surabaya , 2018, 2018 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC).

[38]  A. Krishnan,et al.  Seasonal Outbreak of Dengue Fever in Northern India - A Clinical Perspective and Predicting Length of Hospital Stay , 2018, International Journal of TROPICAL DISEASE & Health.

[39]  Everette S. Gardner,et al.  Exponential smoothing: The state of the art , 1985 .

[40]  Madhav Marathe,et al.  Forecasting dengue and influenza incidences using a sparse representation of Google trends, electronic health records, and time series data , 2019, PLoS Comput. Biol..

[41]  Víctor M. Guerrero,et al.  Restricted forecasts using exponential smoothing techniques , 1994 .

[42]  Anna Lena Lopez,et al.  Trends in dengue research in the Philippines: A systematic review , 2019, PLoS neglected tropical diseases.