Incorporating Queueing Theory into a Spatial Optimization Framework to Improve Mass Vaccination Programs.

Background: Mass vaccination is a cornerstone of public health emergency preparedness and response. However, injudicious placement of vaccination sites can lead to the formation of long waiting lines or queues, which discourages individuals from waiting to be vaccinated and may thus jeopardize the achievement of public health targets. Queueing theory offers a framework for modeling queue formation at vaccination sites and its effect on vaccine uptake. Methods: We developed an algorithm that integrates queueing theory within a spatial optimization framework to optimize the placement of mass vaccination sites. The algorithm was built and tested using data from a mass canine rabies vaccination campaign in Arequipa, Peru. We compared expected vaccination coverage and losses from queueing (i.e., attrition) for sites optimized with our queue-conscious algorithm to those obtained from a queue-naive version of the same algorithm. Results: Sites placed by the queue-conscious algorithm resulted in 9-19% less attrition and 1-2% higher vaccination coverage compared to sites placed by the queue-naive algorithm. Compared to the queue-naive algorithm, the queue-conscious algorithm favored placing more sites in densely populated areas to offset high arrival volumes, thereby reducing losses due to excessive queueing. These results were not sensitive to misspecification of queueing parameters or relaxation of the constant arrival rate assumption. Conclusion: One should consider losses from queueing to optimally place mass vaccination sites, even when empirically derived queueing parameters are not available. Due to the negative impacts of excessive wait times on participant satisfaction, reducing queueing attrition is also expected to yield downstream benefits and improve vaccination coverage in subsequent mass vaccination campaigns.

Optimizing the location of vaccination sites to stop a zoonotic epidemic.

Mass vaccinations are crucial public health interventions for curbing infectious diseases. Canine rabies control relies on mass dog vaccination campaigns (MDVCs) that are held annually across the globe. Dog owners must bring their pets to fixed vaccination sites, but sometimes target coverage is not achieved due to low participation. Travel distance to vaccination sites is an important barrier to participation. We aimed to increase MDVC participation in silico by optimally placing fixed-point vaccination locations. We quantified participation probability based on walking distance to the nearest vaccination site using regression models fit to participation data collected over 4 years. We used computational recursive interchange techniques to optimally place fixed-point vaccination sites and compared predicted participation with these optimally placed vaccination sites to actual locations used in previous campaigns. Algorithms that minimized average walking distance or maximized expected participation provided the best solutions. Optimal vaccination placement is expected to increase participation by 7% and improve spatial evenness of coverage, resulting in fewer under-vaccinated pockets. However, unevenness in workload across sites remained. Our data-driven algorithm optimally places limited resources to increase overall vaccination participation and equity. Field evaluations are essential to assess effectiveness and evaluate potentially longer waiting queues resulting from increased participation.

The impact of the COVID-19 pandemic on rabies reemergence in Latin America: The case of Arequipa, Peru.

In Latin America, there has been tremendous progress towards eliminating canine rabies. Major components of rabies elimination programs leading to these successes have been constant and regular surveillance for rabid dogs and uninterrupted yearly mass dog vaccination campaigns. Unfortunately, vital measures to control COVID-19 have had the negative trade-off of jeopardizing these rabies elimination and prevention activities. We aimed to assess the effect of interrupting canine rabies surveillance and mass dog vaccination campaigns on rabies trends. We built a deterministic compartment model of dog rabies dynamics to create a conceptual framework for how different disruptions may affect rabies virus transmission. We parameterized the model for conditions found in Arequipa, Peru, a city with active rabies virus transmission. We examined our results over a range of plausible values for R0 (1.36-2.0). Also, we prospectively evaluated surveillance data during the pandemic to detect temporal changes. Our model suggests that a decrease in canine vaccination coverage as well as decreased surveillance could lead to a sharp rise in canine rabies within months. These results were consistent over all plausible values of R0. Surveillance data from late 2020 and early 2021 confirms that in Arequipa, Peru, rabies cases are on an increasing trajectory. The rising rabies trends in Arequipa, if indicative to the region as whole, suggest that the achievements made in Latin America towards the elimination of dog-mediated human rabies may be in jeopardy.

The impact of the COVID-19 pandemic on rabies reemergence in Latin America: the case of Arequipa, Peru.

Over the past decades, there has been tremendous progress towards eliminating canine rabies in Latin America. Major components of rabies prevention programs in Latin America leading to these successes have been constant and intense surveillance for rabid dogs and uninterrupted yearly mass dog vaccination campaigns. However, vital measures to control COVID-19 in Latin America have had the negative trade-off of jeopardizing these rabies elimination and prevention activities. In this paper, we aimed to assess the effect of interrupting canine rabies surveillance and mass dog vaccination campaigns on rabies trends. We built a deterministic compartment model of dog rabies dynamics parameterized for conditions found in Arequipa, Peru, where there is an ongoing dog rabies epidemic. Our model suggests that a decrease in canine vaccination coverage as well as decreased surveillance leading to an increased length of survival of infected dogs could lead to a sharp rise in canine rabies and, subsequently, human rabies risk. We examined our results over the best estimate of the basic reproductive number in Arequipa (R0 = 1.44) and a range of plausible values for R0 (1.36 - 2). The rising trend was consistent. It is very possible that COVID-19 will continue to challenge our public health departments in the short- and medium-term. Innovative strategies to conduct dog vaccination and rabies surveillance during these trying times should be considered to safeguard the achievements made in Latin America towards the elimination of dog-mediated human rabies.

En las últimas décadas ha habido un tremendo progreso hacia la eliminación de la rabia canina en América Latina. Los principales componentes de los programas de prevención de la rabia en América Latina que condujeron a estos éxitos han sido la vigilancia constante e intensa de los perros con rabia y las campañas anuales de vacunación masiva ininterrumpida. Sin embargo, las medidas esenciales para controlar el COVID-19 en América Latina han tenido el balance negativo de poner en peligro estas actividades de prevención y eliminación de rabia. En este artículo, nuestro objetivo fue evaluar el efecto que la interrupción de la vigilancia de la rabia canina y las campañas de vacunación masiva de perros tendría en las tendencias de la rabia. Modelamos la dinámica de la rabia canina mediante un modelo determinístico de comportamientos parametrizado para las condiciones que se encuentran en Arequipa, Perú, donde hay una epidemia de rabia canina en curso. Nuestro modelo sugiere que una disminución en la cobertura de vacunación canina, así como una disminución en la vigilancia (que llevaría a una mayor supervivencia de los perros infectados), podría llevar a un aumento súbito de rabia canina y, seguidamente, del riesgo de rabia humana. Examinamos nuestros resultados sobre la mejor estimación del número reproductivo básico en Arequipa (R0 = 1.44) y un rango de valores plausibles para R0 (1.36 ­ 2). La tendencia al alza fue consistente. Es muy posible que el COVID-19 continúe desafiando a nuestros departamentos de salud pública a corto y mediano plazo. Por lo tanto, deben considerarse estrategias innovadoras para llevar a cabo la vacunación de perros y la vigilancia de la rabia durante estos tiempos difíciles para salvaguardar los logros alcanzados en América Latina hacia la eliminación de la rabia humana mediada por perros.

Movement patterns of free-roaming dogs on heterogeneous urban landscapes: Implications for rabies control.

In 2015, a case of canine rabies in Arequipa, Peru indicated the re-emergence of rabies virus in the city. Despite mass dog vaccination campaigns across the city and reactive ring vaccination and other control activities around positive cases (e.g. elimination of unowned dogs), the outbreak has spread. Here we explore how the urban landscape of Arequipa affects the movement patterns of free-roaming dogs, the main reservoirs of the rabies virus in the area. We tracked 23 free-roaming dogs using Global Positioning System (GPS) collars. We analyzed the spatio-temporal GPS data using the time- local convex hull method. Dog movement patterns varied across local environments. We found that water channels, an urban feature of Arequipa that are dry most of the year, promote movement. Dogs that used the water channels extensively move on average 7 times further (p = 0.002) and 1.2 times more directionally (p = 0.027) than dogs that do not use the water channels at all. They were also 1.3 times faster on average, but this difference was not statistically significant (p = 0.197). Our findings suggest that water channels can be used by dogs as 'highways' to transverse the city and have the potential to spread disease far beyond the radius of control practices. Control efforts should focus on a robust vaccination campaign attuned to the geography of the city, and not limited to small-scale rings surrounding cases.