Implementation of a Vaccination Program Based on Epidemic Geospatial Attributes: COVID-19 Pandemic in Ohio as a Case Study and Proof of Concept
Geospatial vaccine uptake is a critical factor in designing strategies that maximize the population-level impact of a vaccination program. This study uses an innovative spatiotemporal model to assess the impact of vaccination distribution strategies based on disease geospatial attributes and populat...
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2021
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oai:doaj.org-article:fb897a655e5e409da0c85f5fd550a2dd2021-11-25T19:10:26ZImplementation of a Vaccination Program Based on Epidemic Geospatial Attributes: COVID-19 Pandemic in Ohio as a Case Study and Proof of Concept10.3390/vaccines91112422076-393Xhttps://doaj.org/article/fb897a655e5e409da0c85f5fd550a2dd2021-10-01T00:00:00Zhttps://www.mdpi.com/2076-393X/9/11/1242https://doaj.org/toc/2076-393XGeospatial vaccine uptake is a critical factor in designing strategies that maximize the population-level impact of a vaccination program. This study uses an innovative spatiotemporal model to assess the impact of vaccination distribution strategies based on disease geospatial attributes and population-level risk assessment. For proof of concept, we adapted a spatially explicit COVID-19 model to investigate a hypothetical geospatial targeting of COVID-19 vaccine rollout in Ohio, United States, at the early phase of COVID-19 pandemic. The population-level deterministic compartmental model, incorporating spatial-geographic components at the county level, was formulated using a set of differential equations stratifying the population according to vaccination status and disease epidemiological characteristics. Three different hypothetical scenarios focusing on geographical subpopulation targeting (areas with high versus low infection intensity) were investigated. Our results suggest that a vaccine program that distributes vaccines equally across the entire state effectively averts infections and hospitalizations (2954 and 165 cases, respectively). However, in a context with equitable vaccine allocation, the number of COVID-19 cases in high infection intensity areas will remain high; the cumulative number of cases remained >30,000 cases. A vaccine program that initially targets high infection intensity areas has the most significant impact in reducing new COVID-19 cases and infection-related hospitalizations (3756 and 213 infections, respectively). Our approach demonstrates the importance of factoring geospatial attributes to the design and implementation of vaccination programs in a context with limited resources during the early stage of the vaccine rollout.Susanne F. AwadGodfrey MusukaZindoga MukandavireDillon FroassNeil J. MacKinnonDiego F. CuadrosMDPI AGarticlevaccination programgeospatial attributesspatial epidemiologydisease mappingCOVID-19mathematical modelMedicineRENVaccines, Vol 9, Iss 1242, p 1242 (2021) |
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vaccination program geospatial attributes spatial epidemiology disease mapping COVID-19 mathematical model Medicine R |
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vaccination program geospatial attributes spatial epidemiology disease mapping COVID-19 mathematical model Medicine R Susanne F. Awad Godfrey Musuka Zindoga Mukandavire Dillon Froass Neil J. MacKinnon Diego F. Cuadros Implementation of a Vaccination Program Based on Epidemic Geospatial Attributes: COVID-19 Pandemic in Ohio as a Case Study and Proof of Concept |
description |
Geospatial vaccine uptake is a critical factor in designing strategies that maximize the population-level impact of a vaccination program. This study uses an innovative spatiotemporal model to assess the impact of vaccination distribution strategies based on disease geospatial attributes and population-level risk assessment. For proof of concept, we adapted a spatially explicit COVID-19 model to investigate a hypothetical geospatial targeting of COVID-19 vaccine rollout in Ohio, United States, at the early phase of COVID-19 pandemic. The population-level deterministic compartmental model, incorporating spatial-geographic components at the county level, was formulated using a set of differential equations stratifying the population according to vaccination status and disease epidemiological characteristics. Three different hypothetical scenarios focusing on geographical subpopulation targeting (areas with high versus low infection intensity) were investigated. Our results suggest that a vaccine program that distributes vaccines equally across the entire state effectively averts infections and hospitalizations (2954 and 165 cases, respectively). However, in a context with equitable vaccine allocation, the number of COVID-19 cases in high infection intensity areas will remain high; the cumulative number of cases remained >30,000 cases. A vaccine program that initially targets high infection intensity areas has the most significant impact in reducing new COVID-19 cases and infection-related hospitalizations (3756 and 213 infections, respectively). Our approach demonstrates the importance of factoring geospatial attributes to the design and implementation of vaccination programs in a context with limited resources during the early stage of the vaccine rollout. |
format |
article |
author |
Susanne F. Awad Godfrey Musuka Zindoga Mukandavire Dillon Froass Neil J. MacKinnon Diego F. Cuadros |
author_facet |
Susanne F. Awad Godfrey Musuka Zindoga Mukandavire Dillon Froass Neil J. MacKinnon Diego F. Cuadros |
author_sort |
Susanne F. Awad |
title |
Implementation of a Vaccination Program Based on Epidemic Geospatial Attributes: COVID-19 Pandemic in Ohio as a Case Study and Proof of Concept |
title_short |
Implementation of a Vaccination Program Based on Epidemic Geospatial Attributes: COVID-19 Pandemic in Ohio as a Case Study and Proof of Concept |
title_full |
Implementation of a Vaccination Program Based on Epidemic Geospatial Attributes: COVID-19 Pandemic in Ohio as a Case Study and Proof of Concept |
title_fullStr |
Implementation of a Vaccination Program Based on Epidemic Geospatial Attributes: COVID-19 Pandemic in Ohio as a Case Study and Proof of Concept |
title_full_unstemmed |
Implementation of a Vaccination Program Based on Epidemic Geospatial Attributes: COVID-19 Pandemic in Ohio as a Case Study and Proof of Concept |
title_sort |
implementation of a vaccination program based on epidemic geospatial attributes: covid-19 pandemic in ohio as a case study and proof of concept |
publisher |
MDPI AG |
publishDate |
2021 |
url |
https://doaj.org/article/fb897a655e5e409da0c85f5fd550a2dd |
work_keys_str_mv |
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