Sociodemographic Variables Can Guide Prioritized Testing Strategies for Epidemic Control in Resource-Limited Contexts
© The Author(s) 2023. Published by Oxford University Press on behalf of Infectious Diseases Society of America. All rights reserved. For permissions, please e-mail: journals.permissionsoup.com..
BACKGROUND: Targeted surveillance allows public health authorities to implement testing and isolation strategies when diagnostic resources are limited, and can be implemented via the consideration of social network topologies. However, it remains unclear how to implement such surveillance and control when network data are unavailable.
METHODS: We evaluated the ability of sociodemographic proxies of degree centrality to guide prioritized testing of infected individuals compared to known degree centrality. Proxies were estimated via readily available sociodemographic variables (age, gender, marital status, educational attainment, household size). We simulated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemics via a susceptible-exposed-infected-recovered individual-based model on 2 contact networks from rural Madagascar to test applicability of these findings to low-resource contexts.
RESULTS: Targeted testing using sociodemographic proxies performed similarly to targeted testing using known degree centralities. At low testing capacity, using proxies reduced infection burden by 22%-33% while using 20% fewer tests, compared to random testing. By comparison, using known degree centrality reduced the infection burden by 31%-44% while using 26%-29% fewer tests.
CONCLUSIONS: We demonstrate that incorporating social network information into epidemic control strategies is an effective countermeasure to low testing capacity and can be implemented via sociodemographic proxies when social network data are unavailable.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:228 |
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| Enthalten in: |
The Journal of infectious diseases - 228(2023), 9 vom: 02. Nov., Seite 1189-1197 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Evans, Michelle V [VerfasserIn] |
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| Links: |
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| Themen: |
COVID-19 |
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| Anmerkungen: |
Date Completed 09.11.2023 Date Revised 25.04.2024 published: Print figshare: 10.6084/m9.figshare.19942139.v1 Citation Status MEDLINE |
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| doi: |
10.1093/infdis/jiad076 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM354669869 |
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| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © The Author(s) 2023. Published by Oxford University Press on behalf of Infectious Diseases Society of America. All rights reserved. For permissions, please e-mail: journals.permissionsoup.com. | ||
| 520 | |a BACKGROUND: Targeted surveillance allows public health authorities to implement testing and isolation strategies when diagnostic resources are limited, and can be implemented via the consideration of social network topologies. However, it remains unclear how to implement such surveillance and control when network data are unavailable | ||
| 520 | |a METHODS: We evaluated the ability of sociodemographic proxies of degree centrality to guide prioritized testing of infected individuals compared to known degree centrality. Proxies were estimated via readily available sociodemographic variables (age, gender, marital status, educational attainment, household size). We simulated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemics via a susceptible-exposed-infected-recovered individual-based model on 2 contact networks from rural Madagascar to test applicability of these findings to low-resource contexts | ||
| 520 | |a RESULTS: Targeted testing using sociodemographic proxies performed similarly to targeted testing using known degree centralities. At low testing capacity, using proxies reduced infection burden by 22%-33% while using 20% fewer tests, compared to random testing. By comparison, using known degree centrality reduced the infection burden by 31%-44% while using 26%-29% fewer tests | ||
| 520 | |a CONCLUSIONS: We demonstrate that incorporating social network information into epidemic control strategies is an effective countermeasure to low testing capacity and can be implemented via sociodemographic proxies when social network data are unavailable | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 650 | 4 | |a COVID-19 | |
| 650 | 4 | |a Madagascar | |
| 650 | 4 | |a epidemic control | |
| 650 | 4 | |a social network | |
| 700 | 1 | |a Ramiadantsoa, Tanjona |e verfasserin |4 aut | |
| 700 | 1 | |a Kauffman, Kayla |e verfasserin |4 aut | |
| 700 | 1 | |a Moody, James |e verfasserin |4 aut | |
| 700 | 1 | |a Nunn, Charles L |e verfasserin |4 aut | |
| 700 | 1 | |a Rabezara, Jean Yves |e verfasserin |4 aut | |
| 700 | 1 | |a Raharimalala, Prisca |e verfasserin |4 aut | |
| 700 | 1 | |a Randriamoria, Toky M |e verfasserin |4 aut | |
| 700 | 1 | |a Soarimalala, Voahangy |e verfasserin |4 aut | |
| 700 | 1 | |a Titcomb, Georgia |e verfasserin |4 aut | |
| 700 | 1 | |a Garchitorena, Andres |e verfasserin |4 aut | |
| 700 | 1 | |a Roche, Benjamin |e verfasserin |4 aut | |
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