Optimizing Spatio-Temporal Allocation of the COVID-19 Vaccine Under Different Epidemiological Landscapes
An efficient and safe vaccine is expected to allow people to return to normal life as soon as possible. However, vaccines for new diseases are likely to be in short supply during the initial deployment due to narrow production capacity and logistics. There is an urgent need to optimize the allocatio...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-06-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2022.921855/full |
| _version_ | 1811237291479269376 |
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| author | Wen Cao Jingwen Zhu Xinyi Wang Xiaochong Tong Yuzhen Tian Haoran Dai Zhigang Ma |
| author_facet | Wen Cao Jingwen Zhu Xinyi Wang Xiaochong Tong Yuzhen Tian Haoran Dai Zhigang Ma |
| author_sort | Wen Cao |
| collection | DOAJ |
| description | An efficient and safe vaccine is expected to allow people to return to normal life as soon as possible. However, vaccines for new diseases are likely to be in short supply during the initial deployment due to narrow production capacity and logistics. There is an urgent need to optimize the allocation of limited vaccines to improve the population effectiveness of vaccination. Existing studies mostly address a single epidemiological landscape. The robustness of the effectiveness of other proposed strategies is difficult to guarantee under other landscapes. In this study, a novel vaccination allocation model based on spatio-temporal heterogeneity of epidemiological landscapes is proposed. This model was combined with optimization algorithms to determine the near-optimal spatio-temporal allocation for vaccines with different effectiveness and coverage. We fully simulated the epidemiological landscapes during vaccination, and then minimized objective functions independently under various epidemiological landscapes and degrees of viral transmission. We find that if all subregions are in the middle or late stages of the pandemic, the difference between the effectiveness of the near-optimal and pro-rata strategies is very small in most cases. In contrast, under other epidemiological landscapes, when minimizing deaths, the optimizer tends to allocate the remaining doses to sub-regions with relatively higher risk and expected coverage after covering the elderly. While to minimize symptomatic infections, allocating vaccines first to the higher-risk sub-regions is near-optimal. This means that the pro-rata allocation is a good option when the subregions are all in the middle to late stages of the pandemic. Moreover, we suggest that if all subregions are in the period of rapid virus transmission, vaccines should be administered to older adults in all subregions simultaneously, while when the epidemiological dynamics of the subregions are significantly different, priority can be given to older adults in subregions that are still in the early stages of the pandemic. After covering the elderly in the region, high-risk sub-regions can be prioritized. |
| first_indexed | 2024-04-12T12:21:11Z |
| format | Article |
| id | doaj.art-9dbf114d32434aceadcc2acd6d479bb2 |
| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-04-12T12:21:11Z |
| publishDate | 2022-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj.art-9dbf114d32434aceadcc2acd6d479bb22022-12-22T03:33:17ZengFrontiers Media S.A.Frontiers in Public Health2296-25652022-06-011010.3389/fpubh.2022.921855921855Optimizing Spatio-Temporal Allocation of the COVID-19 Vaccine Under Different Epidemiological LandscapesWen Cao0Jingwen Zhu1Xinyi Wang2Xiaochong Tong3Yuzhen Tian4Haoran Dai5Zhigang Ma6Department of Remote Sensing and Geographic Information Science, School of Geoscience and Technology, Zhengzhou University, Zhengzhou, ChinaDepartment of Remote Sensing and Geographic Information Science, School of Geoscience and Technology, Zhengzhou University, Zhengzhou, ChinaDepartment of Remote Sensing and Geographic Information Science, School of Geoscience and Technology, Zhengzhou University, Zhengzhou, ChinaDepartment of Photogrammetry and Remote Sensing, School of Geospatial Information, University of Information Engineering, Zhengzhou, ChinaDepartment of Remote Sensing and Geographic Information Science, School of Geoscience and Technology, Zhengzhou University, Zhengzhou, ChinaDepartment of Remote Sensing and Geographic Information Science, School of Geoscience and Technology, Zhengzhou University, Zhengzhou, ChinaPIESAT Institute of Applied Beidou Navigation Technologies at Zhengzhou, Zhengzhou, ChinaAn efficient and safe vaccine is expected to allow people to return to normal life as soon as possible. However, vaccines for new diseases are likely to be in short supply during the initial deployment due to narrow production capacity and logistics. There is an urgent need to optimize the allocation of limited vaccines to improve the population effectiveness of vaccination. Existing studies mostly address a single epidemiological landscape. The robustness of the effectiveness of other proposed strategies is difficult to guarantee under other landscapes. In this study, a novel vaccination allocation model based on spatio-temporal heterogeneity of epidemiological landscapes is proposed. This model was combined with optimization algorithms to determine the near-optimal spatio-temporal allocation for vaccines with different effectiveness and coverage. We fully simulated the epidemiological landscapes during vaccination, and then minimized objective functions independently under various epidemiological landscapes and degrees of viral transmission. We find that if all subregions are in the middle or late stages of the pandemic, the difference between the effectiveness of the near-optimal and pro-rata strategies is very small in most cases. In contrast, under other epidemiological landscapes, when minimizing deaths, the optimizer tends to allocate the remaining doses to sub-regions with relatively higher risk and expected coverage after covering the elderly. While to minimize symptomatic infections, allocating vaccines first to the higher-risk sub-regions is near-optimal. This means that the pro-rata allocation is a good option when the subregions are all in the middle to late stages of the pandemic. Moreover, we suggest that if all subregions are in the period of rapid virus transmission, vaccines should be administered to older adults in all subregions simultaneously, while when the epidemiological dynamics of the subregions are significantly different, priority can be given to older adults in subregions that are still in the early stages of the pandemic. After covering the elderly in the region, high-risk sub-regions can be prioritized.https://www.frontiersin.org/articles/10.3389/fpubh.2022.921855/fullCOVID-19epidemiological landscapesoptimal vaccine allocationpolicy decisionvaccination |
| spellingShingle | Wen Cao Jingwen Zhu Xinyi Wang Xiaochong Tong Yuzhen Tian Haoran Dai Zhigang Ma Optimizing Spatio-Temporal Allocation of the COVID-19 Vaccine Under Different Epidemiological Landscapes Frontiers in Public Health COVID-19 epidemiological landscapes optimal vaccine allocation policy decision vaccination |
| title | Optimizing Spatio-Temporal Allocation of the COVID-19 Vaccine Under Different Epidemiological Landscapes |
| title_full | Optimizing Spatio-Temporal Allocation of the COVID-19 Vaccine Under Different Epidemiological Landscapes |
| title_fullStr | Optimizing Spatio-Temporal Allocation of the COVID-19 Vaccine Under Different Epidemiological Landscapes |
| title_full_unstemmed | Optimizing Spatio-Temporal Allocation of the COVID-19 Vaccine Under Different Epidemiological Landscapes |
| title_short | Optimizing Spatio-Temporal Allocation of the COVID-19 Vaccine Under Different Epidemiological Landscapes |
| title_sort | optimizing spatio temporal allocation of the covid 19 vaccine under different epidemiological landscapes |
| topic | COVID-19 epidemiological landscapes optimal vaccine allocation policy decision vaccination |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2022.921855/full |
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