Yano Terdsak, Phornwisetsirikun Somphorn, Susumpow Patipat, Visrutaratna Surasing, Chanachai Karoon, Phetra Polawat, Chaisowwong Warangkhana, Trakarnsirinont Pairat, Hemwan Phonpat, Kaewpinta Boontuan, Singhapreecha Charuk, Kreausukon Khwanchai, Charoenpanyanet Arisara, Robert Chongchit Sripun, Robert Lamar, Rodtian Pranee, Mahasing Suteerat, Laiya Ekkachai, Pattamakaew Sakulrat, Tankitiyanon Taweesart, Sansamur Chalutwan, Srikitjakarn Lertrak
Faculty of Veterinary Medicine, Chiang Mai University, Chiang Mai, Thailand.
Department of Livestock Development, Bangkok, Thailand.
JMIR Public Health Surveill. 2018 Mar 21;4(1):e25. doi: 10.2196/publichealth.7375.
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.
The objective of this study was to determine the effectiveness of the PODD system for the rapid detection and control of disease outbreaks.
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.
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.
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.
为实现疾病早期检测和迅速控制疫情，采用数字技术并结合参与式“同一个健康”方法创建了一种名为参与式“同一个健康”疾病检测（PODD）的新型疾病监测系统。PODD是一个社区所有的监测系统，它从志愿者报告者那里收集数据；自动识别疾病爆发；并通知地方政府、周边村庄和相关当局。该系统通过赋予社区自我保护的能力，直接为社区带来即时利益。
本研究的目的是确定PODD系统在疾病爆发快速检测和控制方面的有效性。
该系统在泰国清迈的74个地方政府进行了试点，有296名志愿者报告者参与。志愿者和地方政府是PODD系统试点的关键参与者。志愿者监测其社区内的动物和人类疾病以及环境问题，并通过PODD手机应用程序报告这些事件。地方政府负责疫情控制并为志愿者提供支持。成果映射用于评估地方政府和志愿者的表现。
根据表现，地方政府被分为A（良好）、B（中等）和C（较差）三组中的一组，大多数（46%，34/74）被归为B组。志愿者同样被分为4个表现组（A - D），同样A组表现最佳，大多数被归为B组和C组。实施16个月后，共报告了1029起异常事件，经确认均为真实报告。大多数异常报告是患病或死亡的动物（404/1029，39.26%），其次是人畜共患病和其他人类疾病（129/1029，12.54%）。检测到并成功控制了许多可能造成毁灭性影响的动物疾病疫情，包括26起鸡高死亡率疫情、4起牛病疫情、3起猪病疫情和3起鱼病疫情。在所有情况下，社区和动物管理部门合作应用社区应急计划来控制这些疫情，并且在每次疫情得到控制后，社区志愿者继续监测异常事件3周。
从设计上看，PODD最初仅针对可能演变为人类大流行病的动物疾病（如禽流感），然后根据社区需求，扩展到涵盖人类健康和环境卫生问题。
