Efficient vaccination strategies for epidemic control using network information
Background: Network-based interventions against epidemic spread are most powerful when the full network structure is known. However, in practice, resource constraints require decisions to be made based on partial network information. We investigated how the accuracy of network data available at indi...
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doaj-6aa0349a032f4443b2d81ae67148d01b2020-11-25T02:45:48ZengElsevierEpidemics1755-43652019-06-0127115122Efficient vaccination strategies for epidemic control using network informationYingrui Yang0Ashley McKhann1Sixing Chen2Guy Harling3Jukka-Pekka Onnela4Department of Biostatistics, Harvard T.H. Chan School of Public Health, United StatesDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, United StatesDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, United StatesDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, United States; Institute for Global Health, University College London, United Kingdom; Africa Health Research Institute, KwaZulu-Natal, South AfricaDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, United States; Corresponding author at: Department of Biostatistics, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, MA 02115, United States.Background: Network-based interventions against epidemic spread are most powerful when the full network structure is known. However, in practice, resource constraints require decisions to be made based on partial network information. We investigated how the accuracy of network data available at individual and village levels affected network-based vaccination effectiveness. Methods: We simulated a Susceptible-Infected-Recovered process on static empirical social networks from 75 rural Indian villages. First, we used regression analysis to predict the percentage of individuals ever infected (cumulative incidence) based on village-level network properties for simulated datasets from 10 representative villages. Second, we simulated vaccinating 10% of each of the 75 empirical village networks at baseline, selecting vaccinees through one of five network-based approaches: random individuals (Random); random contacts of random individuals (Nomination); random high-degree individuals (High Degree); highest degree individuals (Highest Degree); or most central individuals (Central). The first three approaches require only sample data; the latter two require full network data. We also simulated imposing a limit on how many contacts an individual can nominate (Fixed Choice Design, FCD), which reduces the data collection burden but generates only partially observed networks. Results: In regression analysis, we found mean and standard deviation of the degree distribution to strongly predict cumulative incidence. In simulations, the Nomination method reduced cumulative incidence by one-sixth compared to Random vaccination; full network methods reduced infection by two-thirds. The High Degree approach had intermediate effectiveness. Somewhat surprisingly, FCD truncating individuals’ degrees at three was as effective as using complete networks.Conclusions:Using even partial network information to prioritize vaccines at either the village or individual level, i.e. determine the optimal order of communities or individuals within each village, substantially improved epidemic outcomes. Such approaches may be feasible and effective in outbreak settings, and full ascertainment of network structure may not be required. Keywords: Vaccination, Sociocentric networks, Agent-based modelshttp://www.sciencedirect.com/science/article/pii/S175543651830063X |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yingrui Yang Ashley McKhann Sixing Chen Guy Harling Jukka-Pekka Onnela |
spellingShingle |
Yingrui Yang Ashley McKhann Sixing Chen Guy Harling Jukka-Pekka Onnela Efficient vaccination strategies for epidemic control using network information Epidemics |
author_facet |
Yingrui Yang Ashley McKhann Sixing Chen Guy Harling Jukka-Pekka Onnela |
author_sort |
Yingrui Yang |
title |
Efficient vaccination strategies for epidemic control using network information |
title_short |
Efficient vaccination strategies for epidemic control using network information |
title_full |
Efficient vaccination strategies for epidemic control using network information |
title_fullStr |
Efficient vaccination strategies for epidemic control using network information |
title_full_unstemmed |
Efficient vaccination strategies for epidemic control using network information |
title_sort |
efficient vaccination strategies for epidemic control using network information |
publisher |
Elsevier |
series |
Epidemics |
issn |
1755-4365 |
publishDate |
2019-06-01 |
description |
Background: Network-based interventions against epidemic spread are most powerful when the full network structure is known. However, in practice, resource constraints require decisions to be made based on partial network information. We investigated how the accuracy of network data available at individual and village levels affected network-based vaccination effectiveness. Methods: We simulated a Susceptible-Infected-Recovered process on static empirical social networks from 75 rural Indian villages. First, we used regression analysis to predict the percentage of individuals ever infected (cumulative incidence) based on village-level network properties for simulated datasets from 10 representative villages. Second, we simulated vaccinating 10% of each of the 75 empirical village networks at baseline, selecting vaccinees through one of five network-based approaches: random individuals (Random); random contacts of random individuals (Nomination); random high-degree individuals (High Degree); highest degree individuals (Highest Degree); or most central individuals (Central). The first three approaches require only sample data; the latter two require full network data. We also simulated imposing a limit on how many contacts an individual can nominate (Fixed Choice Design, FCD), which reduces the data collection burden but generates only partially observed networks. Results: In regression analysis, we found mean and standard deviation of the degree distribution to strongly predict cumulative incidence. In simulations, the Nomination method reduced cumulative incidence by one-sixth compared to Random vaccination; full network methods reduced infection by two-thirds. The High Degree approach had intermediate effectiveness. Somewhat surprisingly, FCD truncating individuals’ degrees at three was as effective as using complete networks.Conclusions:Using even partial network information to prioritize vaccines at either the village or individual level, i.e. determine the optimal order of communities or individuals within each village, substantially improved epidemic outcomes. Such approaches may be feasible and effective in outbreak settings, and full ascertainment of network structure may not be required. Keywords: Vaccination, Sociocentric networks, Agent-based models |
url |
http://www.sciencedirect.com/science/article/pii/S175543651830063X |
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