The Regional Epidemiology Surveillance Unit of Region XI (RESU XI) regularly gathers and summarizes all reports on diseases of epidemic potential in Davao Region. The summary surveillance report, which is released on a weekly basis, reflects the number of patients reported to have particular reportable conditions based on presenting signs and/or symptoms, clinical suspicion, clinical diagnoses, or laboratory-confirmed diagnoses. This infographic shows the number of patients suspected to have dengue who were reported to the RESU XI from January 2008 to October 2019. A patient suspected to have dengue is a previously well person who develops an acute febrile illness for 2-7 days duration, and has at least two of the following signs and symptoms: headache, body malaise, myalgia, arthralgia, retro-orbital pain, anorexia, nausea, vomiting, diarrhea, flushed skin, and/or rash (petechial, Herman’s sign).1 These dengue reports are prepared by the 10 identified sentinel hospitals throughout Davao Region (6 in Davao City; 1 each in Davao del Norte, Davao Del Sur, Davao Oriental, and Compostela Valley). The first six graphs show data from Davao City and the individual provinces in Davao Region. Reports were counted based on the city or province where the patients came from. The large graph at the bottom represents overall counts for the entire Davao Region. Each bar in a graph represents the monthly number of patients reported.
Data Visualization ; Dengue

Objectives: Hand, foot and mouth disease (HFMD) is a widespread infectious disease that causes a significant disease burden on society. To achieve early intervention and to prevent outbreaks of disease, we propose a novel warning model that can accurately predict the incidence of HFMD. Methods: We propose a spatial-temporal graph convolutional network (STGCN) that combines spatial factors for surrounding cities with historical incidence over a certain time period to predict the future occurrence of HFMD in Guangdong and Shandong between 2011 and 2019. The 2011-2018 data served as the training and verification set, while data from 2019 served as the prediction set. Six important parameters were selected and verified in this model and the deviation was displayed by the root mean square error and the mean absolute error. Results: As the first application using a STGCN for disease forecasting, we succeeded in accurately predicting the incidence of HFMD over a 12-week period at the prefecture level, especially for cities of significant concern. Conclusions This model provides a novel approach for infectious disease prediction and may help health administrative departments implement effective control measures up to 3 months in advance, which may significantly reduce the morbidity associated with HFMD in the future.
China/epidemiology* ; Cities/epidemiology* ; Data Visualization ; Disease Outbreaks/statistics & numerical data* ; Forecasting/methods* ; Hand, Foot and Mouth Disease/prevention & control* ; Humans ; Incidence ; Neural Networks, Computer ; Reproducibility of Results ; Spatio-Temporal Analysis ; Time Factors