scholarly journals Prediction of Dengue Incidence in the Northeast Malaysia Based on Weather Data Using the Generalized Additive Model

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Afiqah Syamimi Masrani ◽  
Nik Rosmawati Nik Husain ◽  
Kamarul Imran Musa ◽  
Ahmad Syaarani Yasin
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Warning System ◽  
Weather Data ◽  
Maximum Temperature ◽  
Climate Data ◽  
Weather Variables ◽  
Relevant Variable

Introduction. Dengue, a vector-borne viral illness, shows worldwide widening spatial distribution beyond its point of origination, namely, the tropical belt. The persistent hyperendemicity in Malaysia has resulted in the formation of the dengue early warning system. However, weather variables are yet to be fully utilized for prevention and control activities, particularly in east-coast peninsular Malaysia where limited studies have been conducted. We aim to provide a time-based estimate of possible dengue incidence increase following weather-related changes, thereby highlighting potential dengue outbreaks. Method. All serologically confirmed dengue patients in Kelantan, a northeastern state in Malaysia, registered in the eDengue system with an onset of disease from January 2016 to December 2018, were included in the study with the exclusion of duplicate entry. Using a generalized additive model, climate data collected from the Kota Bharu weather station (latitude 6°10 ′ N, longitude 102°18 ′ E) was analysed with dengue data. Result. A cyclical pattern of dengue cases was observed with annual peaks coinciding with the intermonsoon period. Our analysis reveals that maximum temperature, mean temperature, rainfall, and wind speed have a significant nonlinear effect on dengue cases in Kelantan. Our model can explain approximately 8.2% of dengue incidence variabilities. Conclusion. Weather variables affect nearly 10% of the dengue incidences in Northeast Malaysia, thereby making it a relevant variable to be included in a dengue early warning system. Interventions such as vector control activities targeting the intermonsoon period are recommended.

scholarly journals Prediction of epidemic cholera due toVibrio choleraeO1 in children younger than 10 years using climate data in Bangladesh

2007 ◽  
Vol 136 (1) ◽  
pp. 73-79 ◽  
Author(s):  
F. MATSUDA ◽  
S. ISHIMURA ◽  
Y. WAGATSUMA ◽  
T. HIGASHI ◽  
T. HAYASHI ◽  
...  
Keyword(s):  
Vibrio Cholerae ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Dhaka City ◽  
Climate Variables ◽  
Climate Data ◽  
Number Of Children ◽  
Number Of Patients ◽  
Autoregression Model

SUMMARYTo determine if a prediction of epidemic cholera using climate data can be made, we performed autoregression analysis using the data recorded in Dhaka City, Bangladesh over a 20-year period (1983–2002) comparing the number of children aged <10 years who were infected withVibrio choleraeO1 to the maximum and minimum temperatures and rainfall. We formulated a simple autoregression model that predicts the monthly number of patients using earlier climate variables. The monthly number of patients predicted by this model agreed well with the actual monthly number of patients where the Pearson's correlation coefficient was 0·95. Arbitrarily defined, 39·4% of the predicted numbers during the study period were within 0·8–1·2 times the observed numbers. This prediction model uses the climate data recorded 2–4 months before. Therefore, our approach may be a good basis for establishing a practical early warning system for epidemic cholera.

scholarly journals Research on intelligent early warning system of rail transit line trip based on pscada

2021 ◽  
Vol 2113 (1) ◽  
pp. 012079
Author(s):  
Yuqing Zhang ◽  
Xiaohong Zhang
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Weather Data ◽  
Support Vector ◽  
Rail Transit ◽  
Good Prediction ◽  
Data Set ◽  
The Real

Abstract In this paper, an intelligent early warning scheme for rail transit line trip based on PSCADA system is proposed. The scheme takes into account the defects of low prediction accuracy and real-time prediction caused by the lack of power data in the traditional line trip prediction method. At the same time, a large number of power data generated by PSCADA system in the long-term application process are ignored in the field of rail transit[1]. Based on this situation, the prediction data set is constructed by combining the historical power data collected by PSCADA system in rail transit and the lightning weather data in traditional prediction methods. On this basis, the lightgbm machine learning intelligent algorithm is used to compare the similar support vector machine (SVM) and logistic regression algorithm to obtain a model with good prediction effect. In practical application, the real-time data set is constructed by using the real-time power data and real-time weather data collected by PSCADA system to predict, and an intelligent early warning system with the dual advantages of real-time and high accuracy is obtained.

MODIFIKASI ASESMEN EARLY WARNING SYSTEM UPAYA PENINGKATAN PENERAPAN KESELAMATAN PASIEN

2020 ◽  
Vol 6 (2) ◽  
pp. 112
Author(s):  
Veronika Hutabarat ◽  
Enie Novieastari ◽  
Satinah Satinah
Keyword(s):  
Action Research ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Planning Action

Salah satu faktor dalam meningkatkan penerapan keselamatan pasien adalah ketersediaan dan efektifitas prasarana dalam rumah sakit. Early warning system (EWS) merupakan prasarana dalam mendeteksi perubahan dini  kondisi pasien. Penatalaksanaan EWS masih kurang efektif karena parameter dan nilai rentang scorenya belum sesuai dengan kondisi pasien. Tujuan penulisan untuk mengidentifikasi efektifitas EWS dalam penerapan keselamatan pasien. Metode penulisan action research melalui proses diagnosa, planning action, intervensi, evaluasi dan  refleksi. Responden dalam penelitian ini adalah  perawat yang bertugas di area respirasi dan pasien dengan kasus kompleks respirasi di Rumah Sakit Pusat Rujukan Pernapasan Persahabatan Jakarta. Analisis masalah dilakukan dengan menggunakan diagram fishbone. Masalah yang muncul belum optimalnya implementasi early warning system dalam penerapan keselamatan pasien. Hasilnya 100% perawat mengatakan REWS membantu mendeteksi kondisi pasien, 97,4 % perawat mengatakan lebih efektif dan 92,3 % perawat mengatakan lebih efesien mendeteksi perubahan kondisi pasien. Modifikasi EWS menjadi REWS lebih efektif dan efesien dilakukan karena disesuaikan dengan jenis dan kekhususan Rumah Sakit dan berdampak terhadap kualitas asuhan keperawatan dalam menerapkan keselamatan pasien. Rekomendasi perlu dilakukan monitoring evaluasi terhadap implementasi t.erhadap implementasi REWS dan pengembangan aplikasi berbasis tehnologi

Development of a Pediatric Early Warning System Using Data-Driven Vital Signs

PEDIATRICS ◽  
2016 ◽  
Vol 137 (Supplement 3) ◽  
pp. 256A-256A
Author(s):  
Catherine Ross ◽  
Iliana Harrysson ◽  
Lynda Knight ◽  
Veena Goel ◽  
Sarah Poole ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Vital Signs ◽  
Warning System ◽  
Data Driven ◽  
Using Data

KAJIAN KEKUATAN SINYAL RADIO (RSSI) XBEE DALAM RANGKA PEMASANGAN LANDSLIDE EARLY WARNING SYSTEM (LEWS) DI KABUPATEN GARUT, TASIKMALAYA DAN MAJALENGKA

2019 ◽  
Vol 3 (2) ◽  
pp. 88
Author(s):  
Riski Fitriani
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Received Signal Strength Indicator ◽  
Landslide Early Warning ◽  
Point To Point

Salah satu inovasi untuk menanggulangi longsor adalah dengan melakukan pemasangan Landslide Early Warning System (LEWS). Media transmisi data dari LEWS yang dikembangkan menggunakan sinyal radio Xbee. Sehingga sebelum dilakukan pemasangan LEWS, perlu dilakukan kajian kekuatan sinyal tersebut di lokasi yang akan terpasang yaitu Garut, Tasikmalaya, dan Majalengka. Kajian dilakukan menggunakan 2 jenis Xbee yaitu Xbee Pro S2B 2,4 GHz dan Xbee Pro S5 868 MHz. Setelah dilakukan kajian, Xbee 2,4 GHz tidak dapat digunakan di lokasi pengujian Garut dan Majalengka karena jarak modul induk dan anak cukup jauh serta terlalu banyak obstacle. Topologi yang digunakan yaitu topologi pair/point to point, dengan mengukur nilai RSSI menggunakan software XCTU. Semakin kecil nilai Received Signal Strength Indicator (RSSI) dari nilai receive sensitivity Xbee maka kualitas sinyal semakin baik. Pengukuran dilakukan dengan meninggikan antena Xbee dengan beberapa variasi ketinggian untuk mendapatkan kualitas sinyal yang lebih baik. Hasilnya diperoleh beberapa rekomendasi tinggi minimal antena Xbee yang terpasang di tiap lokasi modul anak pada 3 kabupaten.

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