DOT: a crowdsourcing Mobile application for disease outbreak detection and surveillance in Mauritius

• Published in: Health and technology Vol. 10; no. 5; pp. 1115 - 1127
• Main Authors: Khedo, Kavi, Baichoo, Shakuntala, Nagowah, Soulakshmee Devi, Mungloo-Dilmohamud, Zahra, Cadersaib, Zarine, Cheerkoot-Jalim, Sudha, Nagowah, Leckraj, Sookha, Lownish
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2020; Springer Nature B.V
• Subjects: Algorithms; Applications programs; Big Data; Biological and Medical Physics; Biomedical Engineering and Bioengineering; Biomedicine; Biophysics; Computational Biology/Bioinformatics; Conjunctivitis; Crowdsourcing; Data analysis; Data collection; Data mining; Decision making; Disease; Ear; Engineering; Epidemics; Graphical representations; Health hazards; Health surveillance; Medicine/Public Health; Mobile communications networks; Mobile computing; Original Paper; Outbreaks; Pandemics; Probabilistic methods; Public health; R & D/Technology Policy; Real time; Signs and symptoms; Social networks; Surveillance systems; Swine flu; United States > US; Mauritius; Crowdsourcing; Mobile applications; Disease outbreaks; Communicable diseases
• ISSN: 2190-7188; 2190-7196; 2190-7196
• DOI: 10.1007/s12553-020-00456-z

Early detection of disease outbreaks is crucial and even small improvements in detection can significantly impact on a country’s public health. In this work, we investigate the use of a crowdsourcing application and a real-time disease outbreak surveillance system for five diseases; Influenza, Gastroenteritis, Upper Respiratory Tract Infection (URTI), Scabies and Conjunctivitis, that are closely monitored in Mauritius. We also analyze and correlate the collected data with past statistics. A crowdsourcing mobile application known as Disease Outbreak Tracker ( DOT ) was implemented and made public. A real-time disease surveillance system using the Early Aberration Reporting System algorithm (EARS) for analysis of the collected data was also implemented. The collected data were correlated to historical data for 2017. Data were successfully collected and plotted on a daily basis. The results show that a few cases of Flu and Scabies were reported in some districts. The EARS methods C1, C2 and C3 also depicted spikes above the set threshold on some days. The study covers data collected over a period of one month. Once symptoms data were collected using DOT , probabilistic methods were used to find the disease or diseases that the user was suffering from. The data were further processed to find the extent of the disease outbreak district-wise, per disease. These data were represented graphically for a rapid understanding of the situation in each district. Our findings concur with existing data for the same period for previous years showing that the crowdsourcing application can aid in the detection of disease outbreaks.