Sensitivity of an international notification system for wildlife diseases: A case study using the OIE-WAHIS data on tularemia.

The World Organization for Animal Health (OIE) has recently developed a Wildlife Health Framework to respond to the need of members to manage the risk from emerging diseases at the animal-human-ecosystem interface. One of its objectives is to improve surveillance systems, early detection and notification of wildlife diseases. Members share information on disease occurrence by reporting through the OIE World Animal Health Information System (OIE-WAHIS-formerly known as 'WAHIS'). To evaluate the capacity of a surveillance system to detect disease events, it is important to quantify the gap between all known events and those officially notified to the OIE. This study used capture-recapture analysis to estimate the sensitivity of the OIE-WAHIS system for a OIE-listed wildlife disease by comparing information from publicly available sources to identify undetected events. This article presents a case study of the occurrence of tularemia in lagomorphs among selected North American and European countries during the period 2014-2019. First, an analysis using three data sources (OIE-WAHIS, ProMED, WHO-EIOS [Epidemic Intelligence from Open Sources]) was conducted. Subsequent analysis then explored the model integrating information from a fourth source (scientific literature collected in PubMed). Two models were built to evaluate both the sensitivity of the OIE-WAHIS using media reports (ProMED and WHO-EIOS), which is likely to represent current closer to real-time events, and published scientific data, which is more useful for retrospective analysis. Using the three-source approach, the predicted number of tularemia events was 93 (95% CI: 75-114), with an OIE-WAHIS sensitivity of 90%. In the four-source approach, the number of predicted events increased to 120 (95% CI: 99-143), dropping the sensitivity of the OIE-WAHIS to 70%. The results indicate a good sensitivity of the OIE-WAHIS system using the three-source approach, but lower sensitivity when including information from the scientific literature. Further analysis should be undertaken to identify diseases and regions for which international reporting presents a low sensitivity. This will enable evaluation and prioritization of underreported OIE-listed wildlife diseases and identify areas of focus as part of the Wildlife Health Framework. This study also highlights the need for stronger collaborations between academia and National Veterinary Services to enhance surveillance systems for notifiable diseases.

Application of the GARC Data Logger-a custom-developed data collection device-to capture and monitor mass dog vaccination campaigns in Namibia.

Domestic dogs are responsible for 99% of all cases of human rabies and thus, mass dog vaccination has been demonstrated to be the most effective approach towards the elimination of dog-mediated human rabies. Namibia demonstrated the feasibility of this approach by applying government-led strategic rabies vaccination campaigns to reduce both human and dog rabies incidences in the Northern Communal Areas of Namibia since 2016. The lessons learnt using paper-based form for data capturing and management of mass dog vaccination campaign during the pilot and roll out phase of the project (2016-2018) led to the implementation of a simple and accurate data collection tool in the second phase (2019-2022) of the rabies elimination program. In this paper, we describe the implementation of such custom-developed vaccination tracking device, i.e. the Global Alliance for Rabies Control (GARC) Data Logger (GDL), and the integration of the collected data into a website-based rabies surveillance system (Rabies Epidemiological Bulletin-REB) during 2019 and 2020 campaigns. A total of 10,037 dogs and 520 cats were vaccinated during the 2019 campaign and 13,219 dogs and 1,044 cats during the 2020 campaign. The vaccination data were recorded with the GDL and visualized via REB. Subsequent GIS-analysis using gridded population data revealed a suboptimal vaccination coverage in the great majority of grid cells (82%) with a vaccination coverage below 50%. Spatial regression analysis identified the number of schools, estimated human density, and adult dog population were associated with the vaccination performance. However, there was an inverse correlation to human densities. Nonetheless, the use of the GDL improved data capturing and monitoring capacity of the campaign, enabling the Namibian government to improve strategies for the vaccination of at-risk areas towards achieving adequate vaccination coverage which would effectively break the transmission of rabies.