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1. Evaluation of FluSight influenza forecasting in the 2021–22 and 2022–23 seasons with a new target laboratory-confirmed influenza hospitalizations

Author

Mathis, Sarabeth M., Webber, Alexander E., León, Tomás M., Murray, Erin L., Sun, Monica, White, Lauren A., Brooks, Logan C., Green, Alden, Hu, Addison J., Rosenfeld, Roni, Shemetov, Dmitry, Tibshirani, Ryan J., McDonald, Daniel J., Kandula, Sasikiran, Pei, Sen, Yaari, Rami, Yamana, Teresa K., Shaman, Jeffrey, Agarwal, Pulak, Balusu, Srikar, Gururajan, Gautham, Kamarthi, Harshavardhan, Prakash, B. Aditya, Raman, Rishi, Zhao, Zhiyuan, Rodríguez, Alexander, Meiyappan, Akilan, Omar, Shalina, Baccam, Prasith, Gurung, Heidi L., Suchoski, Brad T., Stage, Steve A., Ajelli, Marco, Kummer, Allisandra G., Litvinova, Maria, Ventura, Paulo C., Wadsworth, Spencer, Niemi, Jarad, Carcelen, Erica, Hill, Alison L., Loo, Sara L., McKee, Clifton D., Sato, Koji, Smith, Claire, Truelove, Shaun, Jung, Sung-mok, Lemaitre, Joseph C., Lessler, Justin, McAndrew, Thomas, Ye, Wenxuan, Bosse, Nikos, Hlavacek, William S., Lin, Yen Ting, Mallela, Abhishek, Gibson, Graham C., Chen, Ye, Lamm, Shelby M., Lee, Jaechoul, Posner, Richard G., Perofsky, Amanda C., Viboud, Cécile, Clemente, Leonardo, Lu, Fred, Meyer, Austin G., Santillana, Mauricio, Chinazzi, Matteo, Davis, Jessica T., Mu, Kunpeng, Pastore y Piontti, Ana, Vespignani, Alessandro, Xiong, Xinyue, Ben-Nun, Michal, Riley, Pete, Turtle, James, Hulme-Lowe, Chis, Jessa, Shakeel, Nagraj, V. P., Turner, Stephen D., Williams, Desiree, Basu, Avranil, Drake, John M., Fox, Spencer J., Suez, Ehsan, Cojocaru, Monica G., Thommes, Edward W., Cramer, Estee Y., Gerding, Aaron, Stark, Ariane, Ray, Evan L., Reich, Nicholas G., Shandross, Li, Wattanachit, Nutcha, Wang, Yijin, Zorn, Martha W., Aawar, Majd Al, Srivastava, Ajitesh, Meyers, Lauren A., Adiga, Aniruddha, Hurt, Benjamin, Kaur, Gursharn, Lewis, Bryan L., Marathe, Madhav, Venkatramanan, Srinivasan, Butler, Patrick, Farabow, Andrew, Ramakrishnan, Naren, Muralidhar, Nikhil, Reed, Carrie, Biggerstaff, Matthew, and Borchering, Rebecca K.

Published
2024
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2. Impacts of human mobility on the citywide transmission dynamics of 18 respiratory viruses in pre- and post-COVID-19 pandemic years

Author

Perofsky, Amanda C., Hansen, Chelsea L., Burstein, Roy, Boyle, Shanda, Prentice, Robin, Marshall, Cooper, Reinhart, David, Capodanno, Ben, Truong, Melissa, Schwabe-Fry, Kristen, Kuchta, Kayla, Pfau, Brian, Acker, Zack, Lee, Jover, Sibley, Thomas R., McDermot, Evan, Rodriguez-Salas, Leslie, Stone, Jeremy, Gamboa, Luis, Han, Peter D., Adler, Amanda, Waghmare, Alpana, Jackson, Michael L., Famulare, Michael, Shendure, Jay, Bedford, Trevor, Chu, Helen Y., Englund, Janet A., Starita, Lea M., and Viboud, Cécile

Published
2024
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3. Fine-scale spatial and social patterns of SARS-CoV-2 transmission from identical pathogen sequences

Author

Tran-Kiem, Cécile, primary, Paredes, Miguel I., additional, Perofsky, Amanda C., additional, Frisbie, Lauren A., additional, Xie, Hong, additional, Kong, Kevin, additional, Weixler, Amelia, additional, Greninger, Alexander L., additional, Roychoudhury, Pavitra, additional, Peterson, JohnAric M., additional, Delgado, Andrew, additional, Holstead, Holly, additional, MacKellar, Drew, additional, Dykema, Philip, additional, Gamboa, Luis, additional, Frazar, Chris D., additional, Ryke, Erica, additional, Stone, Jeremy, additional, Reinhart, David, additional, Starita, Lea, additional, Thibodeau, Allison, additional, Yun, Cory, additional, Aragona, Frank, additional, Black, Allison, additional, Viboud, Cécile, additional, and Bedford, Trevor, additional

Published
2024
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4. Local-scale phylodynamics reveal differential community impact of SARS-CoV-2 in a metropolitan US county

Author

Paredes, Miguel I., primary, Perofsky, Amanda C., additional, Frisbie, Lauren, additional, Moncla, Louise H., additional, Roychoudhury, Pavitra, additional, Xie, Hong, additional, Bakhash, Shah A. Mohamed, additional, Kong, Kevin, additional, Arnould, Isabel, additional, Nguyen, Tien V., additional, Wendm, Seffir T., additional, Hajian, Pooneh, additional, Ellis, Sean, additional, Mathias, Patrick C., additional, Greninger, Alexander L., additional, Starita, Lea M., additional, Frazar, Chris D., additional, Ryke, Erica, additional, Zhong, Weizhi, additional, Gamboa, Luis, additional, Threlkeld, Machiko, additional, Lee, Jover, additional, Stone, Jeremy, additional, McDermot, Evan, additional, Truong, Melissa, additional, Shendure, Jay, additional, Oltean, Hanna N., additional, Viboud, Cécile, additional, Chu, Helen, additional, Müller, Nicola F., additional, and Bedford, Trevor, additional

Published
2024
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5. Antigenic drift and subtype interference shape A(H3N2) epidemic dynamics in the United States

Author

Perofsky, Amanda C, primary, Huddleston, John, additional, Hansen, Chelsea, additional, Barnes, John R, additional, Rowe, Thomas, additional, Xu, Xiyan, additional, Kondor, Rebecca, additional, Wentworth, David E, additional, Lewis, Nicola, additional, Whittaker, Lynne, additional, Ermetal, Burcu, additional, Harvey, Ruth, additional, Galiano, Monica, additional, Daniels, Rodney Stuart, additional, McCauley, John W, additional, Fujisaki, Seiichiro, additional, Nakamura, Kazuya, additional, Kishida, Noriko, additional, Watanabe, Shinji, additional, Hasegawa, Hideki, additional, Sullivan, Sheena G, additional, Barr, Ian G, additional, Subbarao, Kanta, additional, Krammer, Florian, additional, Bedford, Trevor, additional, and Viboud, Cécile, additional

Published
2024
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6. Evaluation of FluSight influenza forecasting in the 2021-22 and 2022-23 seasons with a new target laboratory-confirmed influenza hospitalizations

Author

Mathis, Sarabeth M., primary, Webber, Alexander E., additional, León, Tomás M., additional, Murray, Erin L., additional, Sun, Monica, additional, White, Lauren A., additional, Brooks, Logan C., additional, Green, Alden, additional, Hu, Addison J., additional, McDonald, Daniel J., additional, Rosenfeld, Roni, additional, Shemetov, Dmitry, additional, Tibshirani, Ryan J., additional, Kandula, Sasikiran, additional, Pei, Sen, additional, Shaman, Jeffrey, additional, Yaari, Rami, additional, Yamana, Teresa K., additional, Agarwal, Pulak, additional, Balusu, Srikar, additional, Gururajan, Gautham, additional, Kamarthi, Harshavardhan, additional, Prakash, B. Aditya, additional, Raman, Rishi, additional, Rodríguez, Alexander, additional, Zhao, Zhiyuan, additional, Meiyappan, Akilan, additional, Omar, Shalina, additional, Baccam, Prasith, additional, Gurung, Heidi L., additional, Stage, Steve A., additional, Suchoski, Brad T., additional, Ajelli, Marco, additional, Kummer, Allisandra G., additional, Litvinova, Maria, additional, Ventura, Paulo C., additional, Wadsworth, Spencer, additional, Niemi, Jarad, additional, Carcelen, Erica, additional, Hill, Alison L, additional, Jung, Sung-mok, additional, Lemaitre, Joseph C., additional, Lessler, Justin, additional, Loo, Sara L, additional, McKee, Clifton D., additional, Sato, Koji, additional, Smith, Claire, additional, Truelove, Shaun, additional, McAndrew, Thomas, additional, Ye, Wenxuan, additional, Bosse, Nikos, additional, Hlavacek, William S., additional, Lin, Yen Ting, additional, Mallela, Abhishek, additional, Chen, Ye, additional, Lamm, Shelby M., additional, Lee, Jaechoul, additional, Posner, Richard G., additional, Perofsky, Amanda C., additional, Viboud, Cécile, additional, Clemente, Leonardo, additional, Lu, Fred, additional, Meyer, Austin G, additional, Santillana, Mauricio, additional, Chinazzi, Matteo, additional, Davis, Jessica T., additional, Mu, Kunpeng, additional, Pastore y Piontti, Ana, additional, Vespignani, Alessandro, additional, Xiong, Xinyue, additional, Ben-Nun, Michal, additional, Riley, Pete, additional, Turtle, James, additional, Hulme-Lowe, Chis, additional, Jessa, Shakeel, additional, Nagraj, V.P., additional, Turner, Stephen D., additional, Williams, Desiree, additional, Basu, Avranil, additional, Drake, John M., additional, Fox, Spencer J., additional, Gibson, Graham C., additional, Suez, Ehsan, additional, Thommes, Edward W., additional, Cojocaru, Monica G., additional, Cramer, Estee Y., additional, Gerding, Aaron, additional, Stark, Ariane, additional, Ray, Evan L., additional, Reich, Nicholas G., additional, Shandross, Li, additional, Wattanachit, Nutcha, additional, Wang, Yijin, additional, Zorn, Martha W., additional, Aawar, Majd Al, additional, Srivastava, Ajitesh, additional, Meyers, Lauren A., additional, Adiga, Aniruddha, additional, Hurt, Benjamin, additional, Kaur, Gursharn, additional, Lewis, Bryan L., additional, Marathe, Madhav, additional, Venkatramanan, Srinivasan, additional, Butler, Patrick, additional, Farabow, Andrew, additional, Muralidhar, Nikhil, additional, Ramakrishnan, Naren, additional, Reed, Carrie, additional, Biggerstaff, Matthew, additional, and Borchering, Rebecca K., additional

Published
2023
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7. Human mobility impacts the transmission of common respiratory viruses: A modeling study of the Seattle metropolitan area

Author

Perofsky, Amanda C., primary, Hansen, Chelsea, additional, Burstein, Roy, additional, Boyle, Shanda, additional, Prentice, Robin, additional, Marshall, Cooper, additional, Reinhart, David, additional, Capodanno, Ben, additional, Truong, Melissa, additional, Schwabe-Fry, Kristen, additional, Kuchta, Kayla, additional, Pfau, Brian, additional, Acker, Zack, additional, Lee, Jover, additional, Sibley, Thomas R., additional, McDermot, Evan, additional, Rodriguez-Salas, Leslie, additional, Stone, Jeremy, additional, Gamboa, Luis, additional, Han, Peter D., additional, Adler, Amanda, additional, Waghmare, Alpana, additional, Jackson, Michael L., additional, Famulare, Mike, additional, Shendure, Jay, additional, Bedford, Trevor, additional, Chu, Helen Y., additional, Englund, Janet A., additional, Starita, Lea M., additional, and Viboud, Cecile, additional

Published
2023
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8. Antigenic drift and subtype interference shape A(H3N2) epidemic dynamics in the United States

Author

Perofsky, Amanda C, primary, Huddleston, John, additional, Hansen, Chelsea, additional, Barnes, John R, additional, Rowe, Thomas, additional, Xu, Xiyan, additional, Kondor, Rebecca, additional, Wentworth, David E, additional, Lewis, Nicola, additional, Whittaker, Lynne, additional, Ermetal, Burcu, additional, Harvey, Ruth, additional, Galiano, Monica, additional, Daniels, Rodney Stuart, additional, McCauley, John W, additional, Fujisaki, Seiichiro, additional, Nakamura, Kazuya, additional, Kishida, Noriko, additional, Watanabe, Shinji, additional, Hasegawa, Hideki, additional, Sullivan, Sheena G, additional, Barr, Ian G, additional, Subbarao, Kanta, additional, Krammer, Florian, additional, Bedford, Trevor, additional, and Viboud, Cécile, additional

Published
2023
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11. Local-Scale phylodynamics reveal differential community impact of SARS-CoV-2 in metropolitan US county

Author

Paredes, Miguel I., primary, Perofsky, Amanda C., additional, Frisbie, Lauren, additional, Moncla, Louise H., additional, Roychoudhury, Pavitra, additional, Xie, Hong, additional, Mohamed Bakhash, Shah A., additional, Kong, Kevin, additional, Arnould, Isabel, additional, Nguyen, Tien V., additional, Wendm, Seffir T., additional, Hajian, Pooneh, additional, Ellis, Sean, additional, Mathias, Patrick C., additional, Greninger, Alexander L., additional, Starita, Lea M., additional, Frazar, Chris D., additional, Ryke, Erica, additional, Zhong, Weizhi, additional, Gamboa, Luis, additional, Threlkeld, Machiko, additional, Lee, Jover, additional, Stone, Jeremy, additional, McDermot, Evan, additional, Truong, Melissa, additional, Shendure, Jay, additional, Oltean, Hanna N., additional, Viboud, Cécile, additional, Chu, Helen, additional, Müller, Nicola F., additional, and Bedford, Trevor, additional

Published
2022
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12. Trends in Risk Factors and Symptoms Associated With SARS-CoV-2 and Rhinovirus Test Positivity in King County, Washington, June 2020 to July 2022

Author

Hansen, Chelsea, primary, Perofsky, Amanda C., additional, Burstein, Roy, additional, Famulare, Michael, additional, Boyle, Shanda, additional, Prentice, Robin, additional, Marshall, Cooper, additional, McCormick, Benjamin J. J., additional, Reinhart, David, additional, Capodanno, Ben, additional, Truong, Melissa, additional, Schwabe-Fry, Kristen, additional, Kuchta, Kayla, additional, Pfau, Brian, additional, Acker, Zack, additional, Lee, Jover, additional, Sibley, Thomas R., additional, McDermot, Evan, additional, Rodriguez-Salas, Leslie, additional, Stone, Jeremy, additional, Gamboa, Luis, additional, Han, Peter D., additional, Duchin, Jeffery S., additional, Waghmare, Alpana, additional, Englund, Janet A., additional, Shendure, Jay, additional, Bedford, Trevor, additional, Chu, Helen Y., additional, Starita, Lea M., additional, and Viboud, Cécile, additional

Published
2022
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13. Trends in risk factors and symptoms associated with SARS-CoV-2 and Rhinovirus test positivity in King County, Washington: A Test-Negative Design Study of the Greater Seattle Coronavirus Assessment Network

Author

Hansen, Chelsea L., primary, Perofsky, Amanda, additional, Burstein, Roy, additional, Famulare, Michael, additional, Boyle, Shanda, additional, Prentice, Robin, additional, Marshall, Cooper, additional, McCormick, Benjamin JJ, additional, Reinhart, David, additional, Capodanno, Ben, additional, Truong, Melissa, additional, Schwabe-Fry, Kristen, additional, Kuchta, Kayla, additional, Pfau, Brian, additional, Acker, Zack, additional, Lee, Jover, additional, Sibley, Thomas R., additional, McDermot, Evan, additional, Rodriguez-Salas, Leslie, additional, Stone, Jeremy, additional, Gamboa, Luis, additional, Han, Peter D., additional, Duchin, Jeffery S., additional, Waghmare, Alpana, additional, Englund, Janet A., additional, Shendure, Jay, additional, Bedford, Trevor, additional, Chu, Helen Y., additional, Starita, Lea M., additional, and Viboud, Cécile, additional

Published
2022
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14. Direct and Indirect Effects of the Coronavirus Disease 2019 Pandemic on Private Healthcare Utilization in South Africa, March 2020–September 2021

Author

Perofsky, Amanda C, primary, Tempia, Stefano, additional, Bingham, Jeremy, additional, Maslo, Caroline, additional, Toubkin, Mande, additional, Laubscher, Anchen, additional, Walaza, Sibongile, additional, Pulliam, Juliet R C, additional, Viboud, Cécile, additional, and Cohen, Cheryl, additional

Published
2022
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17. A Heterogeneous Swine Show Circuit Drives Zoonotic Transmission of Influenza A Viruses in the United States

Author

Nelson, Martha I., primary, Perofsky, Amanda, additional, McBride, Dillon S., additional, Rambo-Martin, Benjamin L., additional, Wilson, Malania M., additional, Barnes, John R., additional, van Bakel, Harm, additional, Khan, Zenab, additional, Dutta, Jayeeta, additional, Nolting, Jacqueline M., additional, and Bowman, Andrew S., additional

Published
2020
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24. Ecological, evolutionary, and behavioral determinants of gut microbiomes in Malagasy mammals

Author

Perofsky, Amanda Claire

Subjects
Primate, Lemur, Mammal, Verreaux's sifaka, Propithecus verreauxi, Madagascar, Social network, Contact network, Gut microbiome, Microbial ecology, Microbial communities, Bacteria transmission, Evolution, Animal behavior
Abstract

Mammalian gut microbial communities govern host development, metabolism, immune function, and physiology, through interactions that range from commensal and mutualistic to pathogenic. However, we know little about the relative contributions of vertical, horizontal, and environmental transmission to gut microbiome composition, despite its importance to host health. To address this knowledge gap, I integrated field-collected data, molecular analyses, and computational and statistical approaches to examine how social networks and proximity to other host species influence bacteria transmission and gut microbiome composition in the wild mammals inhabiting Kirindy Mitea Biosphere Reserve in western Madagascar. In Chapter 1, I estimated the impacts of multilevel social structure, individual demographic traits, diet, scent-marking, and habitat overlap on gut bacteria acquisition in a wild lemur population (Verreaux’s sifaka, Propithecus verreauxi). The gut microbiomes of wild sifaka clearly reflected their social group membership, and both grooming and scent-marking behaviors promote microbial exchange and within-host diversity. In Chapter 2, I extended this work by examining how sifaka social dynamics influenced gut microbial turnover within individual animals and social groups over the course of several years. Sifaka social groups harbored distinct gut microbial communities throughout the study period, and compositional changes in the microbiota of individual animals were influenced by the gain or loss of unique social partners and dispersal between groups. In Chapter 3, I assayed the gut microbial communities of six sympatric (i.e., co-occurring) mammal populations— three lemur and three non-primate species – and assessed evolutionary, dietary, and behavioral predictors of microbiome functionality and composition. The ecological relationships among mammalian gut microbiomes mirrored their hosts’ phylogeny, with the three lemur species clustering separately from the other mammal species. The predicted functionality of lemur microbiomes differentiated according to diet, and distantly-related terrestrial mammals exhibited overlapping microbial communities, suggesting that ground dwelling facilitates the indirect horizontal transmission of gut bacteria among sympatric wild mammals. Together, these findings demonstrate that patterns of gut microbiome composition in wild mammals are scale-dependent: host phylogeny, diet, and substrate use shape microbial variation among sympatric mammal taxa, while social groupings and social contacts constrain the horizontal transmission of gut bacteria within a single host population

Published
2018

25. Title evaluation of FluSight influenza forecasting in the 2021-22 and 2022-23 seasons with a new target laboratory-confirmed influenza hospitalizations.

Author

Mathis SM, Webber AE, León TM, Murray EL, Sun M, White LA, Brooks LC, Green A, Hu AJ, Rosenfeld R, Shemetov D, Tibshirani RJ, McDonald DJ, Kandula S, Pei S, Yaari R, Yamana TK, Shaman J, Agarwal P, Balusu S, Gururajan G, Kamarthi H, Prakash BA, Raman R, Zhao Z, Rodríguez A, Meiyappan A, Omar S, Baccam P, Gurung HL, Suchoski BT, Stage SA, Ajelli M, Kummer AG, Litvinova M, Ventura PC, Wadsworth S, Niemi J, Carcelen E, Hill AL, Loo SL, McKee CD, Sato K, Smith C, Truelove S, Jung SM, Lemaitre JC, Lessler J, McAndrew T, Ye W, Bosse N, Hlavacek WS, Lin YT, Mallela A, Gibson GC, Chen Y, Lamm SM, Lee J, Posner RG, Perofsky AC, Viboud C, Clemente L, Lu F, Meyer AG, Santillana M, Chinazzi M, Davis JT, Mu K, Pastore Y Piontti A, Vespignani A, Xiong X, Ben-Nun M, Riley P, Turtle J, Hulme-Lowe C, Jessa S, Nagraj VP, Turner SD, Williams D, Basu A, Drake JM, Fox SJ, Suez E, Cojocaru MG, Thommes EW, Cramer EY, Gerding A, Stark A, Ray EL, Reich NG, Shandross L, Wattanachit N, Wang Y, Zorn MW, Aawar MA, Srivastava A, Meyers LA, Adiga A, Hurt B, Kaur G, Lewis BL, Marathe M, Venkatramanan S, Butler P, Farabow A, Ramakrishnan N, Muralidhar N, Reed C, Biggerstaff M, and Borchering RK

Subjects
Humans, Models, Statistical, Influenza, Human epidemiology, Hospitalization statistics & numerical data, Forecasting methods, Seasons
Abstract

Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. For the 2021-22 and 2022-23 influenza seasons, 26 forecasting teams provided national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one-to-four weeks ahead. Forecast skill is evaluated using the Weighted Interval Score (WIS), relative WIS, and coverage. Six out of 23 models outperform the baseline model across forecast weeks and locations in 2021-22 and 12 out of 18 models in 2022-23. Averaging across all forecast targets, the FluSight ensemble is the 2 nd most accurate model measured by WIS in 2021-22 and the 5 th most accurate in the 2022-23 season. Forecast skill and 95% coverage for the FluSight ensemble and most component models degrade over longer forecast horizons. In this work we demonstrate that while the FluSight ensemble was a robust predictor, even ensembles face challenges during periods of rapid change., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Published
2024
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26. Fine-scale spatial and social patterns of SARS-CoV-2 transmission from identical pathogen sequences.

Author

Tran-Kiem C, Paredes MI, Perofsky AC, Frisbie LA, Xie H, Kong K, Weixler A, Greninger AL, Roychoudhury P, Peterson JM, Delgado A, Halstead H, MacKellar D, Dykema P, Gamboa L, Frazar CD, Ryke E, Stone J, Reinhart D, Starita L, Thibodeau A, Yun C, Aragona F, Black A, Viboud C, and Bedford T

Abstract

Pathogen genomics can provide insights into disease transmission patterns, but new methods are needed to handle modern large-scale pathogen genome datasets. Genetically proximal viruses indicate epidemiological linkage and are informative about transmission events. Here, we leverage pairs of identical sequences using 114,298 SARS-CoV-2 genomes collected via sentinel surveillance from March 2021 to December 2022 in Washington State, USA, with linked age and residence information to characterize fine-scale transmission. The location of pairs of identical sequences is highly consistent with expectations from mobility and social contact data. Outliers in the relationship between genetic and mobility data can be explained by SARS-CoV-2 transmission between postal codes with male prisons, consistent with transmission between prison facilities. Transmission patterns between age groups vary across spatial scales. Finally, we use the timing of sequence collection to understand the age groups driving transmission. This work improves our ability to characterize transmission from large pathogen genome datasets., Competing Interests: Competing interests ALG reports contract testing from Abbott, Cepheid, Novavax, Pfizer, Janssen and Hologic, research support from Gilead, and salary and stock grants for LabCorp an immediate family member, outside of the described work. All other authors declare no competing interests.

Published
2024
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27. Antigenic drift and subtype interference shape A(H3N2) epidemic dynamics in the United States.

Author

Perofsky AC, Huddleston J, Hansen C, Barnes JR, Rowe T, Xu X, Kondor R, Wentworth DE, Lewis N, Whittaker L, Ermetal B, Harvey R, Galiano M, Daniels RS, McCauley JW, Fujisaki S, Nakamura K, Kishida N, Watanabe S, Hasegawa H, Sullivan SG, Barr IG, Subbarao K, Krammer F, Bedford T, and Viboud C

Abstract

Influenza viruses continually evolve new antigenic variants, through mutations in epitopes of their major surface proteins, hemagglutinin (HA) and neuraminidase (NA). Antigenic drift potentiates the reinfection of previously infected individuals, but the contribution of this process to variability in annual epidemics is not well understood. Here we link influenza A(H3N2) virus evolution to regional epidemic dynamics in the United States during 1997-2019. We integrate phenotypic measures of HA antigenic drift and sequence-based measures of HA and NA fitness to infer antigenic and genetic distances between viruses circulating in successive seasons. We estimate the magnitude, severity, timing, transmission rate, age-specific patterns, and subtype dominance of each regional outbreak and find that genetic distance based on broad sets of epitope sites is the strongest evolutionary predictor of A(H3N2) virus epidemiology. Increased HA and NA epitope distance between seasons correlates with larger, more intense epidemics, higher transmission, greater A(H3N2) subtype dominance, and a greater proportion of cases in adults relative to children, consistent with increased population susceptibility. Based on random forest models, A(H1N1) incidence impacts A(H3N2) epidemics to a greater extent than viral evolution, suggesting that subtype interference is a major driver of influenza A virus infection dynamics, presumably via heterosubtypic cross-immunity., Competing Interests: Competing interests The WHO Collaborating Centre for Reference and Research on Influenza in Melbourne has a collaborative research and development agreement (CRADA) with CSL Seqirus for isolation of candidate vaccine viruses in cells and an agreement with IFPMA for isolation of candidate vaccine viruses in eggs. SGS reports honoraria from CSL Seqirus, Moderna, Pfizer, and Evo Health. The Icahn School of Medicine at Mount Sinai has filed patent applications relating to influenza virus vaccines, SARS-CoV-2 serological assays, and SARS-CoV-2 vaccines which list FK as co-inventor. Mount Sinai has spun out companies, Kantaro and Castlevax, to market the SARS-CoV-2 related technologies. FK has consulted for Merck and Pfizer (before 2020), and is currently consulting for Pfizer, Seqirus, 3rd Rock Ventures, GSK and Avimex. The Krammer laboratory is also collaborating with Pfizer on animal models of SARS-CoV-2 and with Dynavax on universal influenza virus vaccines. All other authors declare no competing interests.

Published
2024
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28. Evaluation of FluSight influenza forecasting in the 2021-22 and 2022-23 seasons with a new target laboratory-confirmed influenza hospitalizations.

Author

Mathis SM, Webber AE, León TM, Murray EL, Sun M, White LA, Brooks LC, Green A, Hu AJ, McDonald DJ, Rosenfeld R, Shemetov D, Tibshirani RJ, Kandula S, Pei S, Shaman J, Yaari R, Yamana TK, Agarwal P, Balusu S, Gururajan G, Kamarthi H, Prakash BA, Raman R, Rodríguez A, Zhao Z, Meiyappan A, Omar S, Baccam P, Gurung HL, Stage SA, Suchoski BT, Ajelli M, Kummer AG, Litvinova M, Ventura PC, Wadsworth S, Niemi J, Carcelen E, Hill AL, Jung SM, Lemaitre JC, Lessler J, Loo SL, McKee CD, Sato K, Smith C, Truelove S, McAndrew T, Ye W, Bosse N, Hlavacek WS, Lin YT, Mallela A, Chen Y, Lamm SM, Lee J, Posner RG, Perofsky AC, Viboud C, Clemente L, Lu F, Meyer AG, Santillana M, Chinazzi M, Davis JT, Mu K, Piontti APY, Vespignani A, Xiong X, Ben-Nun M, Riley P, Turtle J, Hulme-Lowe C, Jessa S, Nagraj VP, Turner SD, Williams D, Basu A, Drake JM, Fox SJ, Gibson GC, Suez E, Thommes EW, Cojocaru MG, Cramer EY, Gerding A, Stark A, Ray EL, Reich NG, Shandross L, Wattanachit N, Wang Y, Zorn MW, Al Aawar M, Srivastava A, Meyers LA, Adiga A, Hurt B, Kaur G, Lewis BL, Marathe M, Venkatramanan S, Butler P, Farabow A, Muralidhar N, Ramakrishnan N, Reed C, Biggerstaff M, and Borchering RK

Abstract

Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. Forecasting teams were asked to provide national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one through four weeks ahead for the 2021-22 and 2022-23 influenza seasons. Across both seasons, 26 teams submitted forecasts, with the submitting teams varying between seasons. Forecast skill was evaluated using the Weighted Interval Score (WIS), relative WIS, and coverage. Six out of 23 models outperformed the baseline model across forecast weeks and locations in 2021-22 and 12 out of 18 models in 2022-23. Averaging across all forecast targets, the FluSight ensemble was the 2 nd most accurate model measured by WIS in 2021-22 and the 5 th most accurate in the 2022-23 season. Forecast skill and 95% coverage for the FluSight ensemble and most component models degraded over longer forecast horizons and during periods of rapid change. Current influenza forecasting efforts help inform situational awareness, but research is needed to address limitations, including decreased performance during periods of changing epidemic dynamics., Competing Interests: Competing interests: E.W.T. is an employee of Sanofi, which manufactures influenza vaccines. J.S. and Columbia University disclose partial ownership of SK Analytics. J.S. discloses consulting for BNI.

Published
2023
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29. Local-Scale phylodynamics reveal differential community impact of SARS-CoV-2 in metropolitan US county.

Author

Paredes MI, Perofsky AC, Frisbie L, Moncla LH, Roychoudhury P, Xie H, Mohamed Bakhash SA, Kong K, Arnould I, Nguyen TV, Wendm ST, Hajian P, Ellis S, Mathias PC, Greninger AL, Starita LM, Frazar CD, Ryke E, Zhong W, Gamboa L, Threlkeld M, Lee J, Stone J, McDermot E, Truong M, Shendure J, Oltean HN, Viboud C, Chu H, Müller NF, and Bedford T

Abstract

SARS-CoV-2 transmission is largely driven by heterogeneous dynamics at a local scale, leaving local health departments to design interventions with limited information. We analyzed SARS-CoV-2 genomes sampled between February 2020 and March 2022 jointly with epidemiological and cell phone mobility data to investigate fine scale spatiotemporal SARS-CoV-2 transmission dynamics in King County, Washington, a diverse, metropolitan US county. We applied an approximate structured coalescent approach to model transmission within and between North King County and South King County alongside the rate of outside introductions into the county. Our phylodynamic analyses reveal that following stay-at-home orders, the epidemic trajectories of North and South King County began to diverge. We find that South King County consistently had more reported and estimated cases, COVID-19 hospitalizations, and longer persistence of local viral transmission when compared to North King County, where viral importations from outside drove a larger proportion of new cases. Using mobility and demographic data, we also find that South King County experienced a more modest and less sustained reduction in mobility following stay-at-home orders than North King County, while also bearing more socioeconomic inequities that might contribute to a disproportionate burden of SARS-CoV-2 transmission. Overall, our findings suggest a role for local-scale phylodynamics in understanding the heterogeneous transmission landscape., Competing Interests: Declaration of interests ALG reports contract testing from Abbott, Cepheid, Novavax, Pfizer, Janssen and Hologic and research support from Gilead and Merck, outside of the described work. All other authors declare no competing interests.

Published
2022
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