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Eales O, Plank MJ, Cowling BJ, Howden BP, Kucharski AJ, Sullivan SG, Vandemaele K, Viboud C, Riley S, McCaw JM, Shearer FM. Key Challenges for Respiratory Virus Surveillance while Transitioning out of Acute Phase of COVID-19 Pandemic. Emerg Infect Dis 2024; 30:e230768. [PMID: 38190760 PMCID: PMC10826770 DOI: 10.3201/eid3002.230768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2024] Open
Abstract
To support the ongoing management of viral respiratory diseases while transitioning out of the acute phase of the COVID-19 pandemic, many countries are moving toward an integrated model of surveillance for SARS-CoV-2, influenza virus, and other respiratory pathogens. Although many surveillance approaches catalyzed by the COVID-19 pandemic provide novel epidemiologic insight, continuing them as implemented during the pandemic is unlikely to be feasible for nonemergency surveillance, and many have already been scaled back. Furthermore, given anticipated cocirculation of SARS-CoV-2 and influenza virus, surveillance activities in place before the pandemic require review and adjustment to ensure their ongoing value for public health. In this report, we highlight key challenges for the development of integrated models of surveillance. We discuss the relative strengths and limitations of different surveillance practices and studies as well as their contribution to epidemiologic assessment, forecasting, and public health decision-making.
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Cascante-Vega J, Galanti M, Schley K, Pei S, Shaman J. Inference of transmission dynamics and retrospective forecast of invasive meningococcal disease. PLoS Comput Biol 2023; 19:e1011564. [PMID: 37889910 PMCID: PMC10655980 DOI: 10.1371/journal.pcbi.1011564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 11/17/2023] [Accepted: 10/02/2023] [Indexed: 10/29/2023] Open
Abstract
The pathogenic bacteria Neisseria meningitidis, which causes invasive meningococcal disease (IMD), predominantly colonizes humans asymptomatically; however, invasive disease occurs in a small proportion of the population. Here, we explore the seasonality of IMD and develop and validate a suite of models for simulating and forecasting disease outcomes in the United States. We combine the models into multi-model ensembles (MME) based on the past performance of the individual models, as well as a naive equally weighted aggregation, and compare the retrospective forecast performance over a six-month forecast horizon. Deployment of the complete vaccination regimen, introduced in 2011, coincided with a change in the periodicity of IMD, suggesting altered transmission dynamics. We found that a model forced with the period obtained by local power wavelet decomposition best fit and forecast observations. In addition, the MME performed the best across the entire study period. Finally, our study included US-level data until 2022, allowing study of a possible IMD rebound after relaxation of non-pharmaceutical interventions imposed in response to the COVID-19 pandemic; however, no evidence of a rebound was found. Our findings demonstrate the ability of process-based models to retrospectively forecast IMD and provide a first analysis of the seasonality of IMD before and after the complete vaccination regimen.
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Affiliation(s)
- Jaime Cascante-Vega
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States of America
| | - Marta Galanti
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States of America
| | | | - Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States of America
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States of America
- Columbia Climate School, Columbia University, New York, New York, United States of America
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Jones RP, Ponomarenko A. Roles for Pathogen Interference in Influenza Vaccination, with Implications to Vaccine Effectiveness (VE) and Attribution of Influenza Deaths. Infect Dis Rep 2022; 14:710-758. [PMID: 36286197 PMCID: PMC9602062 DOI: 10.3390/idr14050076] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 09/15/2022] [Accepted: 09/15/2022] [Indexed: 08/29/2023] Open
Abstract
Pathogen interference is the ability of one pathogen to alter the course and clinical outcomes of infection by another. With up to 3000 species of human pathogens the potential combinations are vast. These combinations operate within further immune complexity induced by infection with multiple persistent pathogens, and by the role which the human microbiome plays in maintaining health, immune function, and resistance to infection. All the above are further complicated by malnutrition in children and the elderly. Influenza vaccination offers a measure of protection for elderly individuals subsequently infected with influenza. However, all vaccines induce both specific and non-specific effects. The specific effects involve stimulation of humoral and cellular immunity, while the nonspecific effects are far more nuanced including changes in gene expression patterns and production of small RNAs which contribute to pathogen interference. Little is known about the outcomes of vaccinated elderly not subsequently infected with influenza but infected with multiple other non-influenza winter pathogens. In this review we propose that in certain years the specific antigen mix in the seasonal influenza vaccine inadvertently increases the risk of infection from other non-influenza pathogens. The possibility that vaccination could upset the pathogen balance, and that the timing of vaccination relative to the pathogen balance was critical to success, was proposed in 2010 but was seemingly ignored. Persons vaccinated early in the winter are more likely to experience higher pathogen interference. Implications to the estimation of vaccine effectiveness and influenza deaths are discussed.
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Affiliation(s)
- Rodney P Jones
- Healthcare Analysis and Forecasting, Wantage OX12 0NE, UK
| | - Andrey Ponomarenko
- Department of Biophysics, Informatics and Medical Instrumentation, Odessa National Medical University, Valikhovsky Lane 2, 65082 Odessa, Ukraine
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Spencer JA, Shutt DP, Moser SK, Clegg H, Wearing HJ, Mukundan H, Manore CA. Distinguishing viruses responsible for influenza-like illness. J Theor Biol 2022; 545:111145. [PMID: 35490763 DOI: 10.1016/j.jtbi.2022.111145] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 04/19/2022] [Accepted: 04/21/2022] [Indexed: 10/18/2022]
Abstract
The many respiratory viruses that cause influenza-like illness (ILI) are reported and tracked as one entity, defined by the CDC as a group of symptoms that include a fever of 100 degrees Fahrenheit, a cough, and/or a sore throat. In the United States alone, ILI impacts 9-49 million people every year. While tracking ILI as a single clinical syndrome is informative in many respects, the underlying viruses differ in parameters and outbreak properties. Most existing models treat either a single respiratory virus or ILI as a whole. However, there is a need for models capable of comparing several individual viruses that cause respiratory illness, including ILI. To address this need, here we present a flexible model and simulations of epidemics for influenza, RSV, rhinovirus, seasonal coronavirus, adenovirus, and SARS/MERS, parameterized by a systematic literature review and accompanied by a global sensitivity analysis. We find that for these biological causes of ILI, their parameter values, timing, prevalence, and proportional contributions differ substantially. These results demonstrate that distinguishing the viruses that cause ILI will be an important aspect of future work on diagnostics, mitigation, modeling, and preparation for future pandemics.
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Affiliation(s)
- Julie A Spencer
- A-1 Information Systems and Modeling, Los Alamos National Laboratory, NM87545, USA.
| | - Deborah P Shutt
- A-1 Information Systems and Modeling, Los Alamos National Laboratory, NM87545, USA
| | - S Kane Moser
- B-10 Biosecurity and Public Health, Los Alamos National Laboratory, NM87545, USA
| | - Hannah Clegg
- A-1 Information Systems and Modeling, Los Alamos National Laboratory, NM87545, USA
| | - Helen J Wearing
- Department of Biology, University of New Mexico, NM87131, USA; Department of Mathematics and Statistics, University of New Mexico, NM87102, USA
| | - Harshini Mukundan
- C-PCS Physical Chemistry and Applied Spectroscopy, Los Alamos National Laboratory, NM87545, USA
| | - Carrie A Manore
- T-6 Theoretical Biology and Biophysics, Los Alamos National Laboratory, NM87545, USA
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Qi Y, Shaman J, Pei S. Quantifying the Impact of COVID-19 Nonpharmaceutical Interventions on Influenza Transmission in the United States. J Infect Dis 2021; 224:1500-1508. [PMID: 34551108 PMCID: PMC8522386 DOI: 10.1093/infdis/jiab485] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 09/20/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Nonpharmaceutical interventions (NPIs) have been implemented to suppress transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Evidence indicates that NPIs against coronavirus disease 2019 (COVID-19) may also have effects on transmission of seasonal influenza. METHODS In this study, we use an absolute humidity-driven susceptible-infectious-recovered-susceptible (SIRS) model to quantify the reduction of influenza incidence and transmission in the United States and US Department of Health and Human Services regions after implementation of NPIs in 2020. We investigate long-term effect of NPIs on influenza incidence by projecting influenza transmission at the national scale over the next 5 years, using the SIRS model. RESULTS We estimate that incidence of influenza A/H1 and B, which circulated in early 2020, was reduced by more than 60% in the United States during the first 10 weeks following implementation of NPIs. The reduction of influenza transmission exhibits clear geographical variation. After the control measures are relaxed, potential accumulation of susceptibility to influenza infection may lead to a large outbreak, the scale of which may be affected by length of the intervention period and duration of immunity to influenza. CONCLUSIONS Healthcare systems need to prepare for potential influenza patient surges and advocate vaccination and continued precautions.
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Affiliation(s)
- Yuchen Qi
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA
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Pei S, Teng X, Lewis P, Shaman J. Optimizing respiratory virus surveillance networks using uncertainty propagation. Nat Commun 2021; 12:222. [PMID: 33431854 PMCID: PMC7801666 DOI: 10.1038/s41467-020-20399-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Accepted: 12/01/2020] [Indexed: 02/07/2023] Open
Abstract
Infectious disease prevention, control and forecasting rely on sentinel observations; however, many locations lack the capacity for routine surveillance. Here we show that, by using data from multiple sites collectively, accurate estimation and forecasting of respiratory diseases for locations without surveillance is feasible. We develop a framework to optimize surveillance sites that suppresses uncertainty propagation in a networked disease transmission model. Using influenza outbreaks from 35 US states, the optimized system generates better near-term predictions than alternate systems designed using population and human mobility. We also find that monitoring regional population centers serves as a reasonable proxy for the optimized network and could direct surveillance for diseases with limited records. The proxy method is validated using model simulations for 3,108 US counties and historical data for two other respiratory pathogens - human metapneumovirus and seasonal coronavirus - from 35 US states and can be used to guide systemic allocation of surveillance efforts.
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Affiliation(s)
- Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, 10032, USA.
| | - Xian Teng
- School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Paul Lewis
- Integrated Biosurveillance Section, Armed Forces Health Surveillance Branch, Silver Spring, MD, 20904, USA
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, 10032, USA.
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