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Cheng Q, Collender PA, Heaney AK, McLoughlin A, Yang Y, Zhang Y, Head JR, Dasan R, Liang S, Lv Q, Liu Y, Yang C, Chang HH, Waller LA, Zelner J, Lewnard JA, Remais JV. Optimizing laboratory-based surveillance networks for monitoring multi-genotype or multi-serotype infections. PLoS Comput Biol 2022; 18:e1010575. [PMID: 36166479 PMCID: PMC9543988 DOI: 10.1371/journal.pcbi.1010575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 10/07/2022] [Accepted: 09/15/2022] [Indexed: 11/18/2022] Open
Abstract
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.
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Affiliation(s)
- Qu Cheng
- Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, Berkeley, California, United States of America
| | - Philip A. Collender
- Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, Berkeley, California, United States of America
| | - Alexandra K. Heaney
- Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, Berkeley, California, United States of America
| | - Aidan McLoughlin
- Division of Biostatistics, School of Public Health, University of California, Berkeley, Berkeley, California, United States of America
| | - Yang Yang
- College of Public Health and Health Professions and Emerging Pathogens Institute, University of Florida, Gainesville, Florida, United States of America
| | - Yuzi Zhang
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Jennifer R. Head
- Division of Epidemiology, School of Public Health, University of California, Berkeley, Berkeley, California, United States of America
| | - Rohini Dasan
- Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, Berkeley, California, United States of America
| | - Song Liang
- Department of Environmental and Global Health College of Public Health and Health Professions, University of Florida, Gainesville, Florida, United States of America
| | - Qiang Lv
- Institute of Health Informatics, Sichuan Center for Disease Control and Prevention, Chengdu, Sichuan, People’s Republic of China
| | - Yaqiong Liu
- Institute of Acute Infectious Disease Control and Prevention, Sichuan Center for Disease Control and Prevention, Chengdu, Sichuan, People’s Republic of China
| | - Changhong Yang
- Division of Business Management and Quality Control, Sichuan Center for Disease Control and Prevention, Chengdu, Sichuan, People’s Republic of China
| | - Howard H. Chang
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Lance A. Waller
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Jon Zelner
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
- Center for Social Epidemiology and Population Health, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Joseph A. Lewnard
- Division of Epidemiology, School of Public Health, University of California, Berkeley, Berkeley, California, United States of America
| | - Justin V. Remais
- Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, Berkeley, California, United States of America
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