BackgroundAiming 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.ObjectiveThe objective of this study was to determine the effectiveness of the PODD system for the rapid detection and control of disease outbreaks.MethodsThe 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.ResultsLGs 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.ConclusionsBy 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.

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This study aimed to design and test a COVID-19 surveillance system model for community-industry population. A prospective cohort study was conducted from May to December, 2020. Researchers designed a COVID-19 surveillance system and presented it to stakeholders from the community-industry setting in Lamphun and Chiang Mai provinces, Thailand. The model was adjusted following feedback and tested. The model was an Active surveillance for early Alert and rapid Action using Big data and mobile phone application technology for a Community-industry setting (3ABC model). The major components were active surveillance, community-based surveillance, event-based surveillance, and early warning and rapid response. A drive-thru testing unit was operated to enable early detection. Alerts and recommended action on individual and administrative levels were sent via an application and networks. In the testing of the model, risk assessment was initially conducted with regard to COVID-19 transmission in the factories. Researchers provided recommendations based on findings. The improvements included human resource management, systems, and structure. The 3ABC model work well as designed. The participants actively reported events daily including prevention and control activities, animal diseases (foot-and-mouth disease in buffalos and hog cholera), human diseases (dengue and chikungunya), and absent of COVID-19 outbreak. Only five quarantined COVID-19 cases whom were monitored. Daily reports of no abnormal event was also high (70.2% to 71.1%). It is practical and feasible to implement the 3ABC model in a community-industry setting. A further study for a longer period to verify its level of effectiveness should be done. Keywords: Infectious disease, Epidemic model, Surveillance, Mobile application, Model evaluation

By exclusion of other possible aetiological agents, strong circumstantial evidence is presented of Trypanosoma evansi infection being the cause of late gestation abortion and stillbirth in buffaloes.

The occurrence of Salmonella in food of animal origin in Chiang Mai province was investigated by using a cross-sectional study during several phases of the pork production chain (cutting, transport, and retail) and of the environment in the cutting unit of a slaughterhouse. In total, 173 pork samples were obtained during the cutting phase, 173 samples from transported pork, 200 samples from retail products, and 300 samples from the slaughterhouse environment. Salmonella was detected in 55.5% of freshly cut pork, 70.5% of transported pork, and 34.5% of retail products. The five most prevalent Salmonella serotypes identified were Rissen (45.3%), Typhimurium (16.3%), Krefeld (10.6%), Stanley (6.3%), and Lagos (6.0%). Carcass contamination prior to cutting and in the slaughterhouse environment appeared to be important sources of Salmonella in transported pork and retail products. As Salmonella was also found during early stages of the slaughter process, attention should focus on all stages of the pork production chain to reduce contamination level and consumer risk of infection.