Optimizing laboratory-based surveillance networks for monitoring multi-genotype or multi-serotype infections.
With the aid of laboratory typing techniques, infectious disease surveillance networks have the opportunity to obtain powerful information on the emergence, circulation, and evolution of multiple genotypes, serotypes or other subtypes of pathogens, informing understanding of transmission dynamics an...
| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-09-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1010575 |
| _version_ | 1811237686866870272 |
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| author | Qu Cheng Philip A Collender Alexandra K Heaney Aidan McLoughlin Yang Yang Yuzi Zhang Jennifer R Head Rohini Dasan Song Liang Qiang Lv Yaqiong Liu Changhong Yang Howard H Chang Lance A Waller Jon Zelner Joseph A Lewnard Justin V Remais |
| author_facet | Qu Cheng Philip A Collender Alexandra K Heaney Aidan McLoughlin Yang Yang Yuzi Zhang Jennifer R Head Rohini Dasan Song Liang Qiang Lv Yaqiong Liu Changhong Yang Howard H Chang Lance A Waller Jon Zelner Joseph A Lewnard Justin V Remais |
| author_sort | Qu Cheng |
| collection | DOAJ |
| description | With the aid of laboratory typing techniques, infectious disease surveillance networks have the opportunity to obtain powerful information on the emergence, circulation, and evolution of multiple genotypes, serotypes or other subtypes of pathogens, informing understanding of transmission dynamics and strategies for prevention and control. The volume of typing performed on clinical isolates is typically limited by its ability to inform clinical care, cost and logistical constraints, especially in comparison with the capacity to monitor clinical reports of disease occurrence, which remains the most widespread form of public health surveillance. Viewing clinical disease reports as arising from a latent mixture of pathogen subtypes, laboratory typing of a subset of clinical cases can provide inference on the proportion of clinical cases attributable to each subtype (i.e., the mixture components). Optimizing protocols for the selection of isolates for typing by weighting specific subpopulations, locations, time periods, or case characteristics (e.g., disease severity), may improve inference of the frequency and distribution of pathogen subtypes within and between populations. Here, we apply the Disease Surveillance Informatics Optimization and Simulation (DIOS) framework to simulate and optimize hand foot and mouth disease (HFMD) surveillance in a high-burden region of western China. We identify laboratory surveillance designs that significantly outperform the existing network: the optimal network reduced mean absolute error in estimated serotype-specific incidence rates by 14.1%; similarly, the optimal network for monitoring severe cases reduced mean absolute error in serotype-specific incidence rates by 13.3%. In both cases, the optimal network designs achieved improved inference without increasing subtyping effort. We demonstrate how the DIOS framework can be used to optimize surveillance networks by augmenting clinical diagnostic data with limited laboratory typing resources, while adapting to specific, local surveillance objectives and constraints. |
| first_indexed | 2024-04-12T12:28:38Z |
| format | Article |
| id | doaj.art-61dbc7776e7444d0880956049a98cf6b |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-12T12:28:38Z |
| publishDate | 2022-09-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-61dbc7776e7444d0880956049a98cf6b2022-12-22T03:33:06ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582022-09-01189e101057510.1371/journal.pcbi.1010575Optimizing laboratory-based surveillance networks for monitoring multi-genotype or multi-serotype infections.Qu ChengPhilip A CollenderAlexandra K HeaneyAidan McLoughlinYang YangYuzi ZhangJennifer R HeadRohini DasanSong LiangQiang LvYaqiong LiuChanghong YangHoward H ChangLance A WallerJon ZelnerJoseph A LewnardJustin V RemaisWith the aid of laboratory typing techniques, infectious disease surveillance networks have the opportunity to obtain powerful information on the emergence, circulation, and evolution of multiple genotypes, serotypes or other subtypes of pathogens, informing understanding of transmission dynamics and strategies for prevention and control. The volume of typing performed on clinical isolates is typically limited by its ability to inform clinical care, cost and logistical constraints, especially in comparison with the capacity to monitor clinical reports of disease occurrence, which remains the most widespread form of public health surveillance. Viewing clinical disease reports as arising from a latent mixture of pathogen subtypes, laboratory typing of a subset of clinical cases can provide inference on the proportion of clinical cases attributable to each subtype (i.e., the mixture components). Optimizing protocols for the selection of isolates for typing by weighting specific subpopulations, locations, time periods, or case characteristics (e.g., disease severity), may improve inference of the frequency and distribution of pathogen subtypes within and between populations. Here, we apply the Disease Surveillance Informatics Optimization and Simulation (DIOS) framework to simulate and optimize hand foot and mouth disease (HFMD) surveillance in a high-burden region of western China. We identify laboratory surveillance designs that significantly outperform the existing network: the optimal network reduced mean absolute error in estimated serotype-specific incidence rates by 14.1%; similarly, the optimal network for monitoring severe cases reduced mean absolute error in serotype-specific incidence rates by 13.3%. In both cases, the optimal network designs achieved improved inference without increasing subtyping effort. We demonstrate how the DIOS framework can be used to optimize surveillance networks by augmenting clinical diagnostic data with limited laboratory typing resources, while adapting to specific, local surveillance objectives and constraints.https://doi.org/10.1371/journal.pcbi.1010575 |
| spellingShingle | Qu Cheng Philip A Collender Alexandra K Heaney Aidan McLoughlin Yang Yang Yuzi Zhang Jennifer R Head Rohini Dasan Song Liang Qiang Lv Yaqiong Liu Changhong Yang Howard H Chang Lance A Waller Jon Zelner Joseph A Lewnard Justin V Remais Optimizing laboratory-based surveillance networks for monitoring multi-genotype or multi-serotype infections. PLoS Computational Biology |
| title | Optimizing laboratory-based surveillance networks for monitoring multi-genotype or multi-serotype infections. |
| title_full | Optimizing laboratory-based surveillance networks for monitoring multi-genotype or multi-serotype infections. |
| title_fullStr | Optimizing laboratory-based surveillance networks for monitoring multi-genotype or multi-serotype infections. |
| title_full_unstemmed | Optimizing laboratory-based surveillance networks for monitoring multi-genotype or multi-serotype infections. |
| title_short | Optimizing laboratory-based surveillance networks for monitoring multi-genotype or multi-serotype infections. |
| title_sort | optimizing laboratory based surveillance networks for monitoring multi genotype or multi serotype infections |
| url | https://doi.org/10.1371/journal.pcbi.1010575 |
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