Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Castillo-Neyra R

PROVIDER: S-EPMC11237137 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature

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

Castillo-Neyra Ricardo R Xie Sherrie S Bellotti Brinkley Raynor BR Diaz Elvis W EW Saxena Aris A Toledo Amparo M AM Condori-Luna Gian Franco GF Rieders Maria M Bhattacharya Bhaswar B BB Levy Michael Z MZ

Scientific reports 20240710 1

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 va ...[more]

PMID: 38987306

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Project description:Outbreaks of infectious viruses resulting from spillover events from bats have brought much attention to bat-borne zoonoses, which has motivated increased ecological and epidemiological studies on bat populations. Field sampling methods often collect pooled samples of bat excreta from plastic sheets placed under-roosts. However, positive bias is introduced because multiple individuals may contribute to pooled samples, making studies of viral dynamics difficult. Here, we explore the general issue of bias in spatial sample pooling using Hendra virus in Australian bats as a case study. We assessed the accuracy of different under-roost sampling designs using generalized additive models and field data from individually captured bats and pooled urine samples. We then used theoretical simulation models of bat density and under-roost sampling to understand the mechanistic drivers of bias. The most commonly used sampling design estimated viral prevalence 3.2 times higher than individual-level data, with positive bias 5-7 times higher than other designs due to spatial autocorrelation among sampling sheets and clustering of bats in roosts. Simulation results indicate using a stratified random design to collect 30-40 pooled urine samples from 80 to 100 sheets, each with an area of 0.75-1 m2, and would allow estimation of true prevalence with minimum sampling bias and false negatives. These results show that widely used under-roost sampling techniques are highly sensitive to viral presence, but lack specificity, providing limited information regarding viral dynamics. Improved estimation of true prevalence can be attained with minor changes to existing designs such as reducing sheet size, increasing sheet number, and spreading sheets out within the roost area. Our findings provide insight into how spatial sample pooling is vulnerable to bias for a wide range of systems in disease ecology, where optimal sampling design is influenced by pathogen prevalence, host population density, and patterns of aggregation.

Project description:BackgroundLarge-scale vaccination is fundamental to combatting COVID-19. In March 2021, the UK's vaccination programme had delivered vaccines to large proportions of older and more vulnerable population groups; however, there was concern that uptake would be lower among young people. This research was designed to elicit the preferences of 18-29-year-olds regarding key delivery characteristics and assess the influence of these on intentions to get vaccinated, to inform planning for this cohort.MethodsFrom 25 March to 2 April 2021, an online sample of 2012 UK adults aged 18-29 years participated in a Discrete Choice Experiment. Participants made six choices, each involving two SMS invitations to book a vaccination appointment and an opt-out. Invitations had four attributes (1 × 5 levels, 3 × 3 levels): delivery mode, appointment timing, proximity, and sender. These were systematically varied according to a d-optimal design. Responses were analysed using a mixed logit model.ResultsThe main effects logit model revealed a large alternative-specific constant (β = 1.385, SE = 0.067, p < 0.001), indicating a strong preference for 'opting in' to appointment invitations. Pharmacies were dispreferred to the local vaccination centre (β = - 0.256, SE = 0.072, p < 0.001), appointments in locations that were 30-45 min travel time from one's premises were dispreferred to locations that were less than 15 min away (β = - 0.408, SE = 0.054, p < 0.001), and, compared to invitations from the NHS, SMSs forwarded by 'a friend' were dispreferred (β = - 0.615, SE = 0.056, p < 0.001) but invitations from the General Practitioner were preferred (β = 0.105, SE = 0.048, p = 0.028).ConclusionsThe results indicated that the existing configuration of the UK's vaccination programme was well-placed to deliver vaccines to 18-29-year-olds; however, some adjustments might enhance acceptance. Local pharmacies were not preferred; long travel times were a disincentive but close proximity (0-15 min from one's premises) was not necessary; and either the 'NHS' or 'Your GP' would serve as adequate invitation sources. This research informed COVID-19 policy in the UK, and contributes to a wider body of Discrete Choice Experiment evidence on citizens' preferences, requirements and predicted behaviours regarding COVID-19.

Dataset Information

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

Publications

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

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