Statistical identifiability and sample size calculations for serial seroepidemiology
Inference on disease dynamics is typically performed using case reporting time series of symptomatic disease. The inferred dynamics will vary depending on the reporting patterns and surveillance system for the disease in question, and the inference will miss mild or underreported epidemics. To elimi...
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| Format: | Article |
| Language: | English |
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Elsevier
2015-09-01
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| Series: | Epidemics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1755436515000274 |
| _version_ | 1818700900872486912 |
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| author | Dao Nguyen Vinh Maciej F. Boni |
| author_facet | Dao Nguyen Vinh Maciej F. Boni |
| author_sort | Dao Nguyen Vinh |
| collection | DOAJ |
| description | Inference on disease dynamics is typically performed using case reporting time series of symptomatic disease. The inferred dynamics will vary depending on the reporting patterns and surveillance system for the disease in question, and the inference will miss mild or underreported epidemics. To eliminate the variation introduced by differing reporting patterns and to capture asymptomatic or subclinical infection, inferential methods can be applied to serological data sets instead of case reporting data. To reconstruct complete disease dynamics, one would need to collect a serological time series. In the statistical analysis presented here, we consider a particular kind of serological time series with repeated, periodic collections of population-representative serum. We refer to this study design as a serial seroepidemiology (SSE) design, and we base the analysis on our epidemiological knowledge of influenza. We consider a study duration of three to four years, during which a single antigenic type of influenza would be circulating, and we evaluate our ability to reconstruct disease dynamics based on serological data alone. We show that the processes of reinfection, antibody generation, and antibody waning confound each other and are not always statistically identifiable, especially when dynamics resemble a non-oscillating endemic equilibrium behavior. We introduce some constraints to partially resolve this confounding, and we show that transmission rates and basic reproduction numbers can be accurately estimated in SSE study designs. Seasonal forcing is more difficult to identify as serology-based studies only detect oscillations in antibody titers of recovered individuals, and these oscillations are typically weaker than those observed for infected individuals. To accurately estimate the magnitude and timing of seasonal forcing, serum samples should be collected every two months and 200 or more samples should be included in each collection; this sample size estimate is sensitive to the antibody waning rate and the assumed level of seasonal forcing. |
| first_indexed | 2024-12-17T15:12:18Z |
| format | Article |
| id | doaj.art-fa0d9f807ad04a9e898e65564a17b597 |
| institution | Directory Open Access Journal |
| issn | 1755-4365 1878-0067 |
| language | English |
| last_indexed | 2024-12-17T15:12:18Z |
| publishDate | 2015-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Epidemics |
| spelling | doaj.art-fa0d9f807ad04a9e898e65564a17b5972022-12-21T21:43:39ZengElsevierEpidemics1755-43651878-00672015-09-0112C303910.1016/j.epidem.2015.02.005Statistical identifiability and sample size calculations for serial seroepidemiologyDao Nguyen Vinh0Maciej F. Boni1Oxford University Clinical Research Unit, Wellcome Trust Major Overseas Programme, Ho Chi Minh City, Viet NamOxford University Clinical Research Unit, Wellcome Trust Major Overseas Programme, Ho Chi Minh City, Viet NamInference on disease dynamics is typically performed using case reporting time series of symptomatic disease. The inferred dynamics will vary depending on the reporting patterns and surveillance system for the disease in question, and the inference will miss mild or underreported epidemics. To eliminate the variation introduced by differing reporting patterns and to capture asymptomatic or subclinical infection, inferential methods can be applied to serological data sets instead of case reporting data. To reconstruct complete disease dynamics, one would need to collect a serological time series. In the statistical analysis presented here, we consider a particular kind of serological time series with repeated, periodic collections of population-representative serum. We refer to this study design as a serial seroepidemiology (SSE) design, and we base the analysis on our epidemiological knowledge of influenza. We consider a study duration of three to four years, during which a single antigenic type of influenza would be circulating, and we evaluate our ability to reconstruct disease dynamics based on serological data alone. We show that the processes of reinfection, antibody generation, and antibody waning confound each other and are not always statistically identifiable, especially when dynamics resemble a non-oscillating endemic equilibrium behavior. We introduce some constraints to partially resolve this confounding, and we show that transmission rates and basic reproduction numbers can be accurately estimated in SSE study designs. Seasonal forcing is more difficult to identify as serology-based studies only detect oscillations in antibody titers of recovered individuals, and these oscillations are typically weaker than those observed for infected individuals. To accurately estimate the magnitude and timing of seasonal forcing, serum samples should be collected every two months and 200 or more samples should be included in each collection; this sample size estimate is sensitive to the antibody waning rate and the assumed level of seasonal forcing.http://www.sciencedirect.com/science/article/pii/S1755436515000274InfluenzaSeroepidemiologySerial seroepidemiologyAntibody waningStatistical identifiabilityMaximum likelihoodComplete disease dynamics |
| spellingShingle | Dao Nguyen Vinh Maciej F. Boni Statistical identifiability and sample size calculations for serial seroepidemiology Epidemics Influenza Seroepidemiology Serial seroepidemiology Antibody waning Statistical identifiability Maximum likelihood Complete disease dynamics |
| title | Statistical identifiability and sample size calculations for serial seroepidemiology |
| title_full | Statistical identifiability and sample size calculations for serial seroepidemiology |
| title_fullStr | Statistical identifiability and sample size calculations for serial seroepidemiology |
| title_full_unstemmed | Statistical identifiability and sample size calculations for serial seroepidemiology |
| title_short | Statistical identifiability and sample size calculations for serial seroepidemiology |
| title_sort | statistical identifiability and sample size calculations for serial seroepidemiology |
| topic | Influenza Seroepidemiology Serial seroepidemiology Antibody waning Statistical identifiability Maximum likelihood Complete disease dynamics |
| url | http://www.sciencedirect.com/science/article/pii/S1755436515000274 |
| work_keys_str_mv | AT daonguyenvinh statisticalidentifiabilityandsamplesizecalculationsforserialseroepidemiology AT maciejfboni statisticalidentifiabilityandsamplesizecalculationsforserialseroepidemiology |