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JMIR Public Health Surveill. 2018 Mar 21;4(1):e25. doi: 10.2196/publichealth.7375.

A Participatory System for Preventing Pandemics of Animal Origins: Pilot Study of the Participatory One Health Disease Detection (PODD) System.

Author information

1
Faculty of Veterinary Medicine, Chiang Mai University, Chiang Mai, Thailand.
2
Department of Livestock Development, Bangkok, Thailand.
3
Opendream Co Ltd, Bangkok, Thailand.
4
Chiang Mai Provincial Public Health Office, Chiang Mai, Thailand.
5
Faculty of Political Science, Chiang Mai University, Chiang Mai, Thailand.
6
Faculty of Social Sciences, Chiang Mai University, Chiang Mai, Thailand.
7
Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand.
8
Faculty of Economics, Chiang Mai University, Chiang Mai, Thailand.
#
Contributed equally

Abstract

BACKGROUND:

Aiming for early disease detection and prompt outbreak control, digital technology with a participatory One Health approach was used to create a novel disease surveillance system called Participatory One Health Disease Detection (PODD). PODD is a community-owned surveillance system that collects data from volunteer reporters; identifies disease outbreak automatically; and notifies the local governments (LGs), surrounding villages, and relevant authorities. This system provides a direct and immediate benefit to the communities by empowering them to protect themselves.

OBJECTIVE:

The objective of this study was to determine the effectiveness of the PODD system for the rapid detection and control of disease outbreaks.

METHODS:

The system was piloted in 74 LGs in Chiang Mai, Thailand, with the participation of 296 volunteer reporters. The volunteers and LGs were key participants in the piloting of the PODD system. Volunteers monitored animal and human diseases, as well as environmental problems, in their communities and reported these events via the PODD mobile phone app. LGs were responsible for outbreak control and provided support to the volunteers. Outcome mapping was used to evaluate the performance of the LGs and volunteers.

RESULTS:

LGs were categorized into one of the 3 groups based on performance: A (good), B (fair), and C (poor), with the majority (46%,34/74) categorized into group B. Volunteers were similarly categorized into 4 performance groups (A-D), again with group A showing the best performance, with the majority categorized into groups B and C. After 16 months of implementation, 1029 abnormal events had been reported and confirmed to be true reports. The majority of abnormal reports were sick or dead animals (404/1029, 39.26%), followed by zoonoses and other human diseases (129/1029, 12.54%). Many potentially devastating animal disease outbreaks were detected and successfully controlled, including 26 chicken high mortality outbreaks, 4 cattle disease outbreaks, 3 pig disease outbreaks, and 3 fish disease outbreaks. In all cases, the communities and animal authorities cooperated to apply community contingency plans to control these outbreaks, and community volunteers continued to monitor the abnormal events for 3 weeks after each outbreak was controlled.

CONCLUSIONS:

By design, PODD initially targeted only animal diseases that potentially could emerge into human pandemics (eg, avian influenza) and then, in response to community needs, expanded to cover human health and environmental health issues.

KEYWORDS:

PODD; backyard chicken; community-owned disease surveillance system; mobile app; one health; pandemic prevention; participatory approach

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