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Article ; Online: Quantifying antibody kinetics and RNA detection during early-phase SARS-CoV-2 infection by time since symptom onset.

Borremans, Benny / Gamble, Amandine / Prager, K C / Helman, Sarah K / McClain, Abby M / Cox, Caitlin / Savage, Van / Lloyd-Smith, James O

eLife

2020 Volume 9

Abstract: Understanding and mitigating SARS-CoV-2 transmission hinges on antibody and viral RNA data that inform exposure and shedding, but extensive variation in assays, study group demographics and laboratory protocols across published studies confounds ... ...

Abstract	Understanding and mitigating SARS-CoV-2 transmission hinges on antibody and viral RNA data that inform exposure and shedding, but extensive variation in assays, study group demographics and laboratory protocols across published studies confounds inference of true biological patterns. Our meta-analysis leverages 3214 datapoints from 516 individuals in 21 studies to reveal that seroconversion of both IgG and IgM occurs around 12 days post-symptom onset (range 1-40), with extensive individual variation that is not significantly associated with disease severity. IgG and IgM detection probabilities increase from roughly 10% at symptom onset to 98-100% by day 22, after which IgM wanes while IgG remains reliably detectable. RNA detection probability decreases from roughly 90% to zero by day 30, and is highest in feces and lower respiratory tract samples. Our findings provide a coherent evidence base for interpreting clinical diagnostics, and for the mathematical models and serological surveys that underpin public health policies.
MeSH term(s)	Antibodies, Viral/blood ; Antibodies, Viral/isolation & purification ; Betacoronavirus/genetics ; Betacoronavirus/immunology ; COVID-19 ; COVID-19 Testing ; Clinical Laboratory Techniques/methods ; Coronavirus Infections/blood ; Coronavirus Infections/diagnosis ; Coronavirus Infections/immunology ; Coronavirus Infections/virology ; Enzyme-Linked Immunosorbent Assay ; Humans ; Immunoglobulin G/blood ; Immunoglobulin G/isolation & purification ; Immunoglobulin M/blood ; Immunoglobulin M/isolation & purification ; Pandemics ; Pneumonia, Viral/blood ; Pneumonia, Viral/diagnosis ; Pneumonia, Viral/immunology ; Pneumonia, Viral/virology ; RNA, Viral/analysis ; RNA, Viral/isolation & purification ; SARS-CoV-2
Chemical Substances	Antibodies, Viral ; Immunoglobulin G ; Immunoglobulin M ; RNA, Viral
Keywords	covid19
Language	English
Publishing date	2020-09-07
Publishing country	England
Document type	Journal Article ; Meta-Analysis ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2687154-3
ISSN	2050-084X ; 2050-084X
ISSN (online)	2050-084X
ISSN	2050-084X
DOI	10.7554/eLife.60122
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Estimating prevalence and test accuracy in disease ecology: How Bayesian latent class analysis can boost or bias imperfect test results.

Helman, Sarah K / Mummah, Riley O / Gostic, Katelyn M / Buhnerkempe, Michael G / Prager, Katherine C / Lloyd-Smith, James O

Ecology and evolution

2020 Volume 10, Issue 14, Page(s) 7221–7232

Abstract: Obtaining accurate estimates of disease prevalence is crucial for the monitoring and management of wildlife populations but can be difficult if different diagnostic tests yield conflicting results and if the accuracy of each diagnostic test is unknown. ... ...

Abstract	Obtaining accurate estimates of disease prevalence is crucial for the monitoring and management of wildlife populations but can be difficult if different diagnostic tests yield conflicting results and if the accuracy of each diagnostic test is unknown. Bayesian latent class analysis (BLCA) modeling offers a potential solution, providing estimates of prevalence levels and diagnostic test accuracy under the realistic assumption that no diagnostic test is perfect.In typical applications of this approach, the specificity of one test is fixed at or close to 100%, allowing the model to simultaneously estimate the sensitivity and specificity of all other tests, in addition to infection prevalence. In wildlife systems, a test with near-perfect specificity is not always available, so we simulated data to investigate how decreasing this fixed specificity value affects the accuracy of model estimates.We used simulations to explore how the trade-off between diagnostic test specificity and sensitivity impacts prevalence estimates and found that directional biases depend on pathogen prevalence. Both the precision and accuracy of results depend on the sample size, the diagnostic tests used, and the true infection prevalence, so these factors should be considered when applying BLCA to estimate disease prevalence and diagnostic test accuracy in wildlife systems. A wildlife disease case study, focusing on leptospirosis in California sea lions, demonstrated the potential for Bayesian latent class methods to provide reliable estimates under real-world conditions.We delineate conditions under which BLCA improves upon the results from a single diagnostic across a range of prevalence levels and sample sizes, demonstrating when this method is preferable for disease ecologists working in a wide variety of pathogen systems.
Language	English
Publishing date	2020-06-15
Publishing country	England
Document type	Journal Article
ZDB-ID	2635675-2
ISSN	2045-7758
ISSN	2045-7758
DOI	10.1002/ece3.6448
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: SARS-CoV-2: Cross-scale Insights from Ecology and Evolution.

Snedden, Celine E / Makanani, Sara K / Schwartz, Shawn T / Gamble, Amandine / Blakey, Rachel V / Borremans, Benny / Helman, Sarah K / Espericueta, Luisa / Valencia, Alondra / Endo, Andrew / Alfaro, Michael E / Lloyd-Smith, James O

Trends in microbiology

2021 Volume 29, Issue 7, Page(s) 593–605

Abstract: Ecological and evolutionary processes govern the fitness, propagation, and interactions of organisms through space and time, and viruses are no exception. While coronavirus disease 2019 (COVID-19) research has primarily emphasized virological, clinical, ... ...

Abstract	Ecological and evolutionary processes govern the fitness, propagation, and interactions of organisms through space and time, and viruses are no exception. While coronavirus disease 2019 (COVID-19) research has primarily emphasized virological, clinical, and epidemiological perspectives, crucial aspects of the pandemic are fundamentally ecological or evolutionary. Here, we highlight five conceptual domains of ecology and evolution - invasion, consumer-resource interactions, spatial ecology, diversity, and adaptation - that illuminate (sometimes unexpectedly) the emergence and spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We describe the applications of these concepts across levels of biological organization and spatial scales, including within individual hosts, host populations, and multispecies communities. Together, these perspectives illustrate the integrative power of ecological and evolutionary ideas and highlight the benefits of interdisciplinary thinking for understanding emerging viruses.
MeSH term(s)	Animals ; COVID-19/epidemiology ; COVID-19/virology ; Chiroptera/virology ; Disease Reservoirs/veterinary ; Disease Reservoirs/virology ; Ecology ; Evolution, Molecular ; Humans ; SARS-CoV-2/genetics ; Zoonoses/virology
Language	English
Publishing date	2021-03-26
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S. ; Review
ZDB-ID	1158963-2
ISSN	1878-4380 ; 0966-842X
ISSN (online)	1878-4380
ISSN	0966-842X
DOI	10.1016/j.tim.2021.03.013
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Pathogenic Leptospira are widespread in the urban wildlife of southern California.

Scientific reports

2023 Volume 13, Issue 1, Page(s) 14368

Abstract: Leptospirosis, the most widespread zoonotic disease in the world, is broadly understudied in multi-host wildlife systems. Knowledge gaps regarding Leptospira circulation in wildlife, particularly in densely populated areas, contribute to frequent ... ...

Abstract	Leptospirosis, the most widespread zoonotic disease in the world, is broadly understudied in multi-host wildlife systems. Knowledge gaps regarding Leptospira circulation in wildlife, particularly in densely populated areas, contribute to frequent misdiagnoses in humans and domestic animals. We assessed Leptospira prevalence levels and risk factors in five target wildlife species across the greater Los Angeles region: striped skunks (Mephitis mephitis), raccoons (Procyon lotor), coyotes (Canis latrans), Virginia opossums (Didelphis virginiana), and fox squirrels (Sciurus niger). We sampled more than 960 individual animals, including over 700 from target species in the greater Los Angeles region, and an additional 266 sampled opportunistically from other California regions and species. In the five target species seroprevalences ranged from 5 to 60%, and infection prevalences ranged from 0.8 to 15.2% in all except fox squirrels (0%). Leptospira phylogenomics and patterns of serologic reactivity suggest that mainland terrestrial wildlife, particularly mesocarnivores, could be the source of repeated observed introductions of Leptospira into local marine and island ecosystems. Overall, we found evidence of widespread Leptospira exposure in wildlife across Los Angeles and surrounding regions. This indicates exposure risk for humans and domestic animals and highlights that this pathogen can circulate endemically in many wildlife species even in densely populated urban areas.
MeSH term(s)	Animals ; Humans ; Leptospira/genetics ; Animals, Wild ; Ecosystem ; Mephitidae ; Los Angeles ; Animals, Domestic ; Coyotes ; Didelphis ; Geraniaceae ; Raccoons ; Sciuridae
Language	English
Publishing date	2023-09-01
Publishing country	England
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-40322-2
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: SARS-CoV-2: Cross-scale Insights from Ecology and Evolution

Trends in microbiology. 2021 July, v. 29, no. 7

2021

Abstract	Ecological and evolutionary processes govern the fitness, propagation, and interactions of organisms through space and time, and viruses are no exception. While coronavirus disease 2019 (COVID-19) research has primarily emphasized virological, clinical, and epidemiological perspectives, crucial aspects of the pandemic are fundamentally ecological or evolutionary. Here, we highlight five conceptual domains of ecology and evolution – invasion, consumer-resource interactions, spatial ecology, diversity, and adaptation – that illuminate (sometimes unexpectedly) the emergence and spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We describe the applications of these concepts across levels of biological organization and spatial scales, including within individual hosts, host populations, and multispecies communities. Together, these perspectives illustrate the integrative power of ecological and evolutionary ideas and highlight the benefits of interdisciplinary thinking for understanding emerging viruses.
Keywords	COVID-19 infection ; Severe acute respiratory syndrome coronavirus 2 ; ecology ; evolution ; pandemic ; space and time
Language	English
Dates of publication	2021-07
Size	p. 593-605.
Publishing place	Elsevier Ltd
Document type	Article
Note	NAL-AP-2-clean
ZDB-ID	1158963-2
ISSN	1878-4380 ; 0966-842X
ISSN (online)	1878-4380
ISSN	0966-842X
DOI	10.1016/j.tim.2021.03.013
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Quantifying antibody kinetics and RNA detection during early-phase SARS-CoV-2 infection by time since symptom onset

Borremans, Benny / Gamble, Amandine / Prager, KC / Helman, Sarah K / McClain, Abby M / Cox, Caitlin / Savage, Van / Lloyd-Smith, James O

eLife

2020 Volume 9

Abstract	Understanding and mitigating SARS-CoV-2 transmission hinges on antibody and viral RNA data that inform exposure and shedding, but extensive variation in assays, study group demographics and laboratory protocols across published studies confounds inference of true biological patterns. Our meta-analysis leverages 3214 datapoints from 516 individuals in 21 studies to reveal that seroconversion of both IgG and IgM occurs around 12 days post-symptom onset (range 1–40), with extensive individual variation that is not significantly associated with disease severity. IgG and IgM detection probabilities increase from roughly 10% at symptom onset to 98–100% by day 22, after which IgM wanes while IgG remains reliably detectable. RNA detection probability decreases from roughly 90% to zero by day 30, and is highest in feces and lower respiratory tract samples. Our findings provide a coherent evidence base for interpreting clinical diagnostics, and for the mathematical models and serological surveys that underpin public health policies.
Keywords	General Biochemistry, Genetics and Molecular Biology ; General Immunology and Microbiology ; General Neuroscience ; General Medicine ; covid19
Language	English
Publisher	eLife Sciences Publications, Ltd
Publishing country	uk
Document type	Article ; Online
ZDB-ID	2687154-3
ISSN	2050-084X
ISSN	2050-084X
DOI	10.7554/elife.60122
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Quantifying antibody kinetics and RNA detection during early-phase SARS-CoV-2 infection by time since symptom onset

Borremans, Benny / Gamble, Amandine / Prager, K C / Helman, Sarah K / McClain, Abby M / Cox, Caitlin / Savage, Van / Lloyd-Smith, James O

eLife (Cambridge)

Abstract	Understanding and mitigating SARS-CoV-2 transmission hinges on antibody and viral RNA data that inform exposure and shedding, but extensive variation in assays, study group demographics and laboratory protocols across published studies confounds inference of true biological patterns. Our meta-analysis leverages 3214 datapoints from 516 individuals in 21 studies to reveal that seroconversion of both IgG and IgM occurs around 12 days post-symptom onset (range 1-40), with extensive individual variation that is not significantly associated with disease severity. IgG and IgM detection probabilities increase from roughly 10% at symptom onset to 98-100% by day 22, after which IgM wanes while IgG remains reliably detectable. RNA detection probability decreases from roughly 90% to zero by day 30, and is highest in feces and lower respiratory tract samples. Our findings provide a coherent evidence base for interpreting clinical diagnostics, and for the mathematical models and serological surveys that underpin public health policies.
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #745648
Database	COVID19

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Article ; Online: Quantifying antibody kinetics and RNA shedding during early-phase SARS-CoV-2 infection

Borremans, Benny / Gamble, Amandine / Prager, K C / Helman, Sarah K / McClain, Abby M / Cox, Caitlin / Savage, Van / Lloyd-Smith, James O

medRxiv

Abstract: Our ability to understand and mitigate the spread of SARS-CoV-2 depends largely on antibody and viral RNA data that provide information about past exposure and shedding. Five months into the outbreak there is an impressive number of studies reporting ... ...

Abstract	Our ability to understand and mitigate the spread of SARS-CoV-2 depends largely on antibody and viral RNA data that provide information about past exposure and shedding. Five months into the outbreak there is an impressive number of studies reporting antibody kinetics and RNA shedding dynamics, but extensive variation in detection assays, study group demographics, and laboratory protocols has presented a challenge for inferring the true biological patterns. Here, we combine existing data on SARS-CoV-2 IgG, IgM and RNA kinetics using a formal quantitative approach that enables integration of 3,214 data points from 516 individuals, published in 22 studies. This allows us to determine the mean values and distributions of IgG and IgM seroconversion times and titer kinetics, and to characterize how antibody and RNA detection probabilities change during the early phase of infection. We observe extensive variation in antibody response patterns and RNA detection patterns, explained by both individual heterogeneity and protocol differences such as targeted antigen and sample type. These results provide a robust reference for clinical management of individual patients, and a foundation for the mathematical models and serological surveys that underpin public health policies.
Keywords	covid19
Language	English
Publishing date	2020-05-20
Publisher	Cold Spring Harbor Laboratory Press
Document type	Article ; Online
DOI	10.1101/2020.05.15.20103275
Database	COVID19

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Book ; Online: Author response

Borremans, Benny / Gamble, Amandine / Prager, KC / Helman, Sarah K / McClain, Abby M / Cox, Caitlin / Savage, Van / Lloyd-Smith, James O

Quantifying antibody kinetics and RNA detection during early-phase SARS-CoV-2 infection by time since symptom onset

2020

Keywords	covid19
Publisher	eLife Sciences Publications, Ltd
Publishing country	uk
Document type	Book ; Online
DOI	10.7554/elife.60122.sa2
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Quantifying antibody kinetics and RNA detection during early-phase SARS-CoV-2 infection by time since symptom onset.

Borremans, Benny / Gamble, Amandine / Prager, KC / Helman, Sarah K / McClain, Abby M / Cox, Caitlin / Savage, Van / Lloyd-Smith, James O

2020

Abstract	Understanding and mitigating SARS-CoV-2 transmission hinges on antibody and viral RNA data that inform exposure and shedding, but extensive variation in assays, study group demographics and laboratory protocols across published studies confounds inference of true biological patterns. Our meta-analysis leverages 3214 datapoints from 516 individuals in 21 studies to reveal that seroconversion of both IgG and IgM occurs around 12 days post-symptom onset (range 1-40), with extensive individual variation that is not significantly associated with disease severity. IgG and IgM detection probabilities increase from roughly 10% at symptom onset to 98-100% by day 22, after which IgM wanes while IgG remains reliably detectable. RNA detection probability decreases from roughly 90% to zero by day 30, and is highest in feces and lower respiratory tract samples. Our findings provide a coherent evidence base for interpreting clinical diagnostics, and for the mathematical models and serological surveys that underpin public health policies.
Keywords	Humans ; Pneumonia ; Viral ; Coronavirus Infections ; Immunoglobulin G ; Immunoglobulin M ; RNA ; Antibodies ; Enzyme-Linked Immunosorbent Assay ; Clinical Laboratory Techniques ; Pandemics ; Betacoronavirus ; COVID-19 ; SARS-CoV-2 ; antibody kinetics ; detection probability ; epidemiology ; global health ; human ; meta-analysis ; Biochemistry and Cell Biology ; covid19
Publishing date	2020-09-07
Publisher	eScholarship, University of California
Publishing country	us
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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