Identifying high-risk areas for dog-mediated rabies using Bayesian spatial regression.

Despite ongoing control efforts, rabies remains an endemic zoonotic disease in many countries. Determining high-risk areas and the space-time patterns of rabies spread, as it relates to epidemiologically important factors, can support policymakers and program managers alike to develop evidence-based targeted surveillance and control programs. In this One Health approach which selected Thailand as the example site, the location-based risk of contracting dog-mediated rabies by both human and animal populations was quantified using a Bayesian spatial regression model. Specifically, a conditional autoregressive (CAR) Bayesian zero-inflated Poisson (ZIP) regression was fitted to the reported human and animal rabies case counts of each district, from the 2012-2017 period. The human population was used as an offset. The epidemiologically important factors hypothesized as risk modifiers and therefore tested as predictors included: number of dog bites/attacks, the population of dogs and cats, number of Buddhist temples, garbage dumps, animal vaccination, post-exposure prophylaxis, poverty, and shared administrative borders. Disparate sources of data were used to improve the estimated associations and predictions. Model performance was assessed using cross-validation. Results suggested that accounting for the association between human and animal rabies with number of dog bites/attacks, number of owned and un-owned dogs; shared country borders, number of Buddhist temples, poverty levels, and accounting for spatial dependence between districts, may help to predict the risk districts for dog-mediated rabies in Thailand. The fitted values of the spatial regression were mapped to illustrate the risk of dog-mediated rabies. The cross-validation indicated an adequate performance of the spatial regression model (AUC = 0.81), suggesting that had this spatial regression approach been used to identify districts at risk in 2015, the cases reported in 2016/17 would have been predicted with model sensitivity and specificity of 0.71 and 0.80, respectively. While active surveillance is ideal, this approach of using multiple data sources to improve risk estimation may inform current rabies surveillance and control efforts including determining rabies-free zones, and the roll-out of human post-exposure prophylaxis and anti-rabies vaccines for animals in determining high-risk areas.

IHR-PVS National Bridging Workshops, a tool to operationalize the collaboration between human and animal health while advancing sector-specific goals in countries.

Collaborative, One Health approaches support governments to effectively prevent, detect and respond to emerging health challenges, such as zoonotic diseases, that arise at the human-animal-environmental interfaces. To overcome these challenges, operational and outcome-oriented tools that enable animal health and human health services to work specifically on their collaboration are required. While international capacity and assessment frameworks such as the IHR-MEF (International Health Regulations-Monitoring and Evaluation Framework) and the OIE PVS (Performance of Veterinary Services) Pathway exist, a tool and process that could assess and strengthen the interactions between human and animal health sectors was needed. Through a series of six phased pilots, the IHR-PVS National Bridging Workshop (NBW) method was developed and refined. The NBW process gathers human and animal health stakeholders and follows seven sessions, scheduled across three days. The outputs from each session build towards the next one, following a structured process that goes from gap identification to joint planning of corrective measures. The NBW process allows human and animal health sector representatives to jointly identify actions that support collaboration while advancing evaluation goals identified through the IHR-MEF and the OIE PVS Pathway. By integrating sector-specific and collaborative goals, the NBWs help countries in creating a realistic, concrete and practical joint road map for enhanced compliance to international standards as well as strengthened preparedness and response for health security at the human-animal interface.

Factors that enable effective One Health collaborations - A scoping review of the literature.

Advocates for a One Health approach recognize that global health challenges require multidisciplinary collaborative efforts. While past publications have looked at interdisciplinary competency training for collaboration, few have identified the factors and conditions that enable operational One Health. Through a scoping review of the literature, a multidisciplinary team of researchers analyzed peer-reviewed publications describing multisectoral collaborations around infectious disease-related health events. The review identified 12 factors that support successful One Health collaborations and a coordinated response to health events across three levels: two individual factors (education & training and prior experience & existing relationships), four organizational factors (organizational structures, culture, human resources and, communication), and six network factors (network structures, relationships, leadership, management, available & accessible resources, political environment). The researchers also identified the stage of collaboration during which these factors were most critical, further organizing into starting condition or process-based factors. The research found that publications on multisectoral collaboration for health events do not uniformly report on successes or challenges of collaboration and rarely identify outputs or outcomes of the collaborative process. This paper proposes a common language and framework to enable more uniform reporting, implementation, and evaluation of future One Health collaborations.

Piloting the One Health Systems Mapping and Analysis Resource Toolkit in Indonesia.

As a global network, countries are being asked to meet goals set forth in the Global Health Security Agenda (GHSA) for a workforce capable of effective and efficient prevention, detection and response to infectious disease threats. There is great need for a cross-sectoral workforce that can innovate and problem-solve. To achieve GHSA goals, countries need a way to visualize their existing system, identify opportunities for improvement, and achieve improved cross-sectoral interactions. The One Health Systems Mapping and Analysis Resource Toolkit (OH-SMART) was successfully piloted in West Sumatra, Indonesia, and was used to enhance multi-agency collaboration around infectious disease outbreaks and proved to be an adaptable, scalable process requiring minimal resources. The authors present OH-SMART as a potential tool to help countries analyze their existing health system and create relevant action steps to improve cross-sectoral collaborations.