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1. Evolutionary principles for modifying pathogen virulence.

Author

Fieldman, Tom

Subjects
*MEDICAL personnel, *INFECTION prevention, *INFECTIOUS disease transmission, *COMMUNICABLE diseases, *DRUG resistance in microorganisms
Abstract

Current methods for combatting infectious diseases are largely limited to the prevention of infection, enhancing host immunity (via vaccination), and administration of small molecules to slow the growth of or kill pathogens (e.g. antimicrobials). Beyond efforts to deter the rise of antimicrobial resistance, little consideration is given to pathogen evolution. Natural selection will favor different levels of virulence under different circumstances. Experimental studies and a wealth of theoretical work have identified many likely evolutionary determinants of virulence. Some of these, such as transmission dynamics, are amenable to modification by clinicians and public health practitioners. In this article, we provide a conceptual overview of virulence, followed by an analysis of modifiable evolutionary determinants of virulence including vaccinations, antibiotics, and transmission dynamics. Finally, we discuss both the importance and limitations of taking an evolutionary approach to reducing pathogen virulence. [ABSTRACT FROM AUTHOR]

Published
2024
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3. A2B-COVID: A Tool for Rapidly Evaluating Potential SARS-CoV-2 Transmission Events

Author

Illingworth, Christopher J. R., Hamilton, William L., Jackson, Christopher, Warne, Ben, Popay, Ashley, Meredith, Luke, Hosmillo, Myra, Jahun, Aminu, Fieldman, Tom, Routledge, Matthew, Houldcroft, Charlotte J., Caller, Laura, Caddy, Sarah, Yakovleva, Anna, Hall, Grant, Khokhar, Fahad A., Feltwell, Theresa, Pinckert, Malte L., Georgana, Iliana, Chaudhry, Yasmin, Curran, Martin, Parmar, Surendra, Sparkes, Dominic, Rivett, Lucy, Jones, Nick K., Sridhar, Sushmita, Forrest, Sally, Dymond, Tom, Grainger, Kayleigh, Workman, Chris, Gkrania-Klotsas, Effrossyni, Brown, Nicholas M., Weekes, Michael P., Baker, Stephen, Peacock, Sharon J., Gouliouris, Theodore, Goodfellow, Ian, De Angelis, Daniela, Torok, M. Estee, Illingworth, Christopher J. R., Hamilton, William L., Jackson, Christopher, Warne, Ben, Popay, Ashley, Meredith, Luke, Hosmillo, Myra, Jahun, Aminu, Fieldman, Tom, Routledge, Matthew, Houldcroft, Charlotte J., Caller, Laura, Caddy, Sarah, Yakovleva, Anna, Hall, Grant, Khokhar, Fahad A., Feltwell, Theresa, Pinckert, Malte L., Georgana, Iliana, Chaudhry, Yasmin, Curran, Martin, Parmar, Surendra, Sparkes, Dominic, Rivett, Lucy, Jones, Nick K., Sridhar, Sushmita, Forrest, Sally, Dymond, Tom, Grainger, Kayleigh, Workman, Chris, Gkrania-Klotsas, Effrossyni, Brown, Nicholas M., Weekes, Michael P., Baker, Stephen, Peacock, Sharon J., Gouliouris, Theodore, Goodfellow, Ian, De Angelis, Daniela, and Torok, M. Estee

Abstract

Identifying linked cases of infection is a critical component of the public health response to viral infectious diseases. In a clinical context, there is a need to make rapid assessments of whether cases of infection have arrived independently onto a ward, or are potentially linked via direct transmission. Viral genome sequence data are of great value in making these assessments, but are often not the only form of data available. Here, we describe A2B-COVID, a method for the rapid identification of potentially linked cases of COVID-19 infection designed for clinical settings. Our method combines knowledge about infection dynamics, data describing the movements of individuals, and evolutionary analysis of genome sequences to assess whether data collected from cases of infection are consistent or inconsistent with linkage via direct transmission. A retrospective analysis of data from two wards at Cambridge University Hospitals NHS Foundation Trust during the first wave of the pandemic showed qualitatively different patterns of linkage between cases on designated COVID-19 and non-COVID-19 wards. The subsequent real-time application of our method to data from the second epidemic wave highlights its value for monitoring cases of infection in a clinical context.

Published
2022

4. A2B-COVID: A Tool for Rapidly Evaluating Potential SARS-CoV-2 Transmission Events

Author

Illingworth, Christopher J R, primary, Hamilton, William L, additional, Jackson, Christopher, additional, Warne, Ben, additional, Popay, Ashley, additional, Meredith, Luke, additional, Hosmillo, Myra, additional, Jahun, Aminu, additional, Fieldman, Tom, additional, Routledge, Matthew, additional, Houldcroft, Charlotte J, additional, Caller, Laura, additional, Caddy, Sarah, additional, Yakovleva, Anna, additional, Hall, Grant, additional, Khokhar, Fahad A, additional, Feltwell, Theresa, additional, Pinckert, Malte L, additional, Georgana, Iliana, additional, Chaudhry, Yasmin, additional, Curran, Martin, additional, Parmar, Surendra, additional, Sparkes, Dominic, additional, Rivett, Lucy, additional, Jones, Nick K, additional, Sridhar, Sushmita, additional, Forrest, Sally, additional, Dymond, Tom, additional, Grainger, Kayleigh, additional, Workman, Chris, additional, Gkrania-Klotsas, Effrossyni, additional, Brown, Nicholas M, additional, Weekes, Michael P, additional, Baker, Stephen, additional, Peacock, Sharon J, additional, Gouliouris, Theodore, additional, Goodfellow, Ian, additional, Angelis, Daniela De, additional, and Török, M Estée, additional

Published
2022
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5. Applying prospective genomic surveillance to support investigation of hospital-onset COVID-19

Author

Hamilton, William L, Fieldman, Tom, Jahun, Aminu, Warne, Ben, Illingworth, Christopher, Jackson, Chris, Blane, Beth, Moore, Elinor, Weekes, Michael P, Peacock, Sharon J, De Angelis, Daniela, Goodfellow, Ian, Gouliouris, Theodore, Török, M Estée, Cambridge COVID-19 Group, Jahun, Aminu [0000-0002-4585-1701], Warne, Ben [0000-0003-1326-0373], Illingworth, Christopher [0000-0002-0030-2784], Jackson, Christopher [0000-0002-6656-8913], Weekes, Michael [0000-0003-3196-5545], Peacock, Sharon [0000-0002-1718-2782], De Angelis, Daniela [0000-0001-6619-6112], and Apollo - University of Cambridge Repository

Subjects
SARS-CoV-2, COVID-19, Humans, Genomics, Prospective Studies, Hospitals
Published
2021
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6. Superspreaders drive the largest outbreaks of hospital onset COVID-19 infections

Author

Illingworth, Christopher JR, primary, Hamilton, William L, additional, Warne, Ben, additional, Routledge, Matthew, additional, Popay, Ashley, additional, Jackson, Chris, additional, Fieldman, Tom, additional, Meredith, Luke W, additional, Houldcroft, Charlotte J, additional, Hosmillo, Myra, additional, Jahun, Aminu S, additional, Caller, Laura G, additional, Caddy, Sarah L, additional, Yakovleva, Anna, additional, Hall, Grant, additional, Khokhar, Fahad A, additional, Feltwell, Theresa, additional, Pinckert, Malte L, additional, Georgana, Iliana, additional, Chaudhry, Yasmin, additional, Curran, Martin D, additional, Parmar, Surendra, additional, Sparkes, Dominic, additional, Rivett, Lucy, additional, Jones, Nick K, additional, Sridhar, Sushmita, additional, Forrest, Sally, additional, Dymond, Tom, additional, Grainger, Kayleigh, additional, Workman, Chris, additional, Ferris, Mark, additional, Gkrania-Klotsas, Effrossyni, additional, Brown, Nicholas M, additional, Weekes, Michael P, additional, Baker, Stephen, additional, Peacock, Sharon J, additional, Goodfellow, Ian G, additional, Gouliouris, Theodore, additional, de Angelis, Daniela, additional, and Török, M Estée, additional

Published
2021
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7. Author response: Superspreaders drive the largest outbreaks of hospital onset COVID-19 infections

Author

Illingworth, Christopher JR, primary, Hamilton, William L, additional, Warne, Ben, additional, Routledge, Matthew, additional, Popay, Ashley, additional, Jackson, Chris, additional, Fieldman, Tom, additional, Meredith, Luke W, additional, Houldcroft, Charlotte J, additional, Hosmillo, Myra, additional, Jahun, Aminu S, additional, Caller, Laura G, additional, Caddy, Sarah L, additional, Yakovleva, Anna, additional, Hall, Grant, additional, Khokhar, Fahad A, additional, Feltwell, Theresa, additional, Pinckert, Malte L, additional, Georgana, Iliana, additional, Chaudhry, Yasmin, additional, Curran, Martin D, additional, Parmar, Surendra, additional, Sparkes, Dominic, additional, Rivett, Lucy, additional, Jones, Nick K, additional, Sridhar, Sushmita, additional, Forrest, Sally, additional, Dymond, Tom, additional, Grainger, Kayleigh, additional, Workman, Chris, additional, Ferris, Mark, additional, Gkrania-Klotsas, Effrossyni, additional, Brown, Nicholas M, additional, Weekes, Michael P, additional, Baker, Stephen, additional, Peacock, Sharon J, additional, Goodfellow, Ian G, additional, Gouliouris, Theodore, additional, de Angelis, Daniela, additional, and Török, M Estée, additional

Published
2021
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8. Applying prospective genomic surveillance to support investigation of hospital-onset COVID-19

Author

Hamilton, William L, primary, Fieldman, Tom, additional, Jahun, Aminu, additional, Warne, Ben, additional, Illingworth, Christopher J R, additional, Jackson, Chris, additional, Blane, Beth, additional, Moore, Elinor, additional, Weekes, Michael P, additional, Peacock, Sharon J, additional, De Angelis, Daniela, additional, Goodfellow, Ian, additional, Gouliouris, Theodore, additional, Török, M Estée, additional, Curran, Martin D, additional, Parmar, Surendra, additional, Hosmillo, Myra, additional, Chaudhry, Yasmin, additional, Pinckert, Malte L, additional, Georgana, Iliana, additional, Izuagbe, Rhys, additional, Ferris, Mark, additional, Workman, Chris, additional, Jones, Nick K, additional, Rivett, Lucy, additional, Sparkes, Dominic, additional, Routledge, Matthew, additional, Enoch, David, additional, Whitehorn, James, additional, Moody, Christine, additional, Brennan, Liam, additional, Smith, Richard, additional, Tsui, Rachel, additional, Bousfield, Rachel, additional, Pocock, Joanna, additional, Toleman, Michelle, additional, Payne, Helen, additional, Coscione, Suny, additional, Myers, Chloe, additional, Sharrocks, Katherine, additional, Ravji, Pooja, additional, Leong, Clare, additional, and Ramsay, Isobel, additional

Published
2021
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9. Superspreaders drive the largest outbreaks of hospital onset COVID-19 infections

Author

Illingworth, Christopher J. R., Hamilton, William L., Warne, Ben, Routledge, Matthew, Popay, Ashley, Jackson, Chris, Fieldman, Tom, Meredith, Luke W., Houldcroft, Charlotte J., Hosmillo, Myra, Jahun, Aminu S., Caller, Laura G., Caddy, Sarah L., Yakovleva, Anna, Hall, Grant, Khokhar, Fahad A., Feltwell, Theresa, Pinckert, Malte L., Georgana, Iliana, Chaudhry, Yasmin, Curran, Martin D., Parmar, Surendra, Sparkes, Dominic, Rivett, Lucy, Jones, Nick K., Sridhar, Sushmita, Forrest, Sally, Dymond, Tom, Grainger, Kayleigh, Workman, Chris, Ferris, Mark, Gkrania-Klotsas, Effrossyni, Brown, Nicholas M., Weekes, Michael P., Baker, Stephen, Peacock, Sharon J., Goodfellow, Ian G., Gouliouris, Theodore, de Angelis, Daniela, Torok, M. Estee, Illingworth, Christopher J. R., Hamilton, William L., Warne, Ben, Routledge, Matthew, Popay, Ashley, Jackson, Chris, Fieldman, Tom, Meredith, Luke W., Houldcroft, Charlotte J., Hosmillo, Myra, Jahun, Aminu S., Caller, Laura G., Caddy, Sarah L., Yakovleva, Anna, Hall, Grant, Khokhar, Fahad A., Feltwell, Theresa, Pinckert, Malte L., Georgana, Iliana, Chaudhry, Yasmin, Curran, Martin D., Parmar, Surendra, Sparkes, Dominic, Rivett, Lucy, Jones, Nick K., Sridhar, Sushmita, Forrest, Sally, Dymond, Tom, Grainger, Kayleigh, Workman, Chris, Ferris, Mark, Gkrania-Klotsas, Effrossyni, Brown, Nicholas M., Weekes, Michael P., Baker, Stephen, Peacock, Sharon J., Goodfellow, Ian G., Gouliouris, Theodore, de Angelis, Daniela, and Torok, M. Estee

Abstract

SARS-CoV-2 is notable both for its rapid spread, and for the heterogeneity of its patterns of transmission, with multiple published incidences of superspreading behaviour. Here, we applied a novel network reconstruction algorithm to infer patterns of viral transmission occurring between patients and health care workers (HCWs) in the largest clusters of COVID-19 infection identified during the first wave of the epidemic at Cambridge University Hospitals NHS Foundation Trust, UK. Based upon dates of individuals reporting symptoms, recorded individual locations, and viral genome sequence data, we show an uneven pattern of transmission between individuals, with patients being much more likely to be infected by other patients than by HCWs. Further, the data were consistent with a pattern of superspreading, whereby 21% of individuals caused 80% of transmission events. Our study provides a detailed retrospective analysis of nosocomial SARS-CoV-2 transmission, and sheds light on the need for intensive and pervasive infection control procedures.

Published
2021

10. Superspreaders drive the largest outbreaks of hospital onset COVID-19 infection

Author

Illingworth, Chris, primary, Hamilton, William, additional, Warne, Ben, additional, Routledge, Matthew, additional, Popay, Ashley, additional, jackson, christopher, additional, Fieldman, Tom, additional, Meredith, Luke W., additional, Houldcroft, Charlotte J., additional, Hosmillo, Myra, additional, Jahun, Aminu S., additional, Caller, Laura G., additional, Caddy, Sarah L., additional, Yakovleva, Anna, additional, Hall, Grant, additional, Khokhar, Fahad A., additional, Feltwell, Theresa, additional, Pinckert, Malte L., additional, Georgana, Iliana, additional, Chaudhry, Yasmin, additional, Curran, Martin D., additional, Parmar, Surendra, additional, Sparkes, Dominic, additional, Rivett, Lucy, additional, Jones, Nick K., additional, Sridhar, Sushmita, additional, Forrest, Sally, additional, Dymond, Tom, additional, Grainger, Kayleigh, additional, Workman, Chris, additional, Gkrania-Klotsas, Effrossyni, additional, Brown, Nicholas M., additional, Baker, Stephen, additional, Weekes, Michael P., additional, Peacock, Sharon J., additional, Goodfellow, Ian, additional, Gouliouris, Theodore, additional, de Angelis, Daniela, additional, and Török, Estée, additional

Published
2021
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12. Applying prospective genomic surveillance to support investigation of hospital-onset COVID-19

Author

Curran, Martin D, Parmar, Surendra, Hosmillo, Myra, Chaudhry, Yasmin, Pinckert, Malte L, Georgana, Iliana, Izuagbe, Rhys, Ferris, Mark, Workman, Chris, Jones, Nick K, Rivett, Lucy, Sparkes, Dominic, Routledge, Matthew, Enoch, David, Whitehorn, James, Moody, Christine, Brennan, Liam, Smith, Richard, Tsui, Rachel, Bousfield, Rachel, Pocock, Joanna, Toleman, Michelle, Payne, Helen, Coscione, Suny, Myers, Chloe, Sharrocks, Katherine, Ravji, Pooja, Leong, Clare, Ramsay, Isobel, Hamilton, William L, Fieldman, Tom, Jahun, Aminu, Warne, Ben, Illingworth, Christopher J R, Jackson, Chris, Blane, Beth, Moore, Elinor, Weekes, Michael P, Peacock, Sharon J, De Angelis, Daniela, Goodfellow, Ian, Gouliouris, Theodore, and Török, M Estée

Published
2021
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14. Superspreaders drive the largest outbreaks of hospital onset COVID-19 infections

Author

Illingworth, Christopher, Hamilton, William L, Warne, Ben, Routledge, Matthew, Popay, Ashley, Jackson, Chris, Fieldman, Tom, Meredith, Luke W, Houldcroft, Charlotte J, Hosmillo, Myra, Jahun, Aminu S, Caller, Laura G, Caddy, Sarah L, Yakovleva, Anna, Hall, Grant, Khokhar, Fahad A, Feltwell, Theresa, Pinckert, Malte L, Georgana, Iliana, Chaudhry, Yasmin, Curran, Martin D, Parmar, Surendra, Sparkes, Dominic, Rivett, Lucy, Jones, Nick K, Sridhar, Sushmita, Forrest, Sally, Dymond, Tom, Grainger, Kayleigh, Workman, Chris, Ferris, Mark, Gkrania-Klotsas, Effrossyni, Brown, Nicholas M, Weekes, Michael P, Baker, Stephen, Peacock, Sharon J, Goodfellow, Ian G, Gouliouris, Theodore, De Angelis, Daniela, and Török, M Estée

Subjects
Male, Infectious disease, Evolutionary Biology, Superspreader, 1. No poverty, Sars-cov-2, COVID-19, Middle Aged, Microbiology, Hospitals, 3. Good health, Virus, Nosocomial Transmission, Disease Outbreaks, Hospital, FOS: Biological sciences, Humans, Female, Retrospective Studies
Abstract

SARS-CoV-2 is notable both for its rapid spread, and for the heterogeneity of its patterns of transmission, with multiple published incidences of superspreading behaviour. Here, we applied a novel network reconstruction algorithm to infer patterns of viral transmission occurring between patients and health care workers (HCWs) in the largest clusters of COVID-19 infection identified during the first wave of the epidemic at Cambridge University Hospitals NHS Foundation Trust, UK. Based upon dates of individuals reporting symptoms, recorded individual locations, and viral genome sequence data, we show an uneven pattern of transmission between individuals, with patients being much more likely to be infected by other patients than by HCWs. Further, the data were consistent with a pattern of superspreading, whereby 21% of individuals caused 80% of transmission events. Our study provides a detailed retrospective analysis of nosocomial SARS-CoV-2 transmission, and sheds light on the need for intensive and pervasive infection control procedures.

15. Superspreaders drive the largest outbreaks of hospital onset COVID-19 infections

Author

Illingworth, Christopher, Hamilton, William L, Warne, Ben, Routledge, Matthew, Popay, Ashley, Jackson, Chris, Fieldman, Tom, Meredith, Luke W, Houldcroft, Charlotte J, Hosmillo, Myra, Jahun, Aminu S, Caller, Laura G, Caddy, Sarah L, Yakovleva, Anna, Hall, Grant, Khokhar, Fahad A, Feltwell, Theresa, Pinckert, Malte L, Georgana, Iliana, Chaudhry, Yasmin, Curran, Martin D, Parmar, Surendra, Sparkes, Dominic, Rivett, Lucy, Jones, Nick K, Sridhar, Sushmita, Forrest, Sally, Dymond, Tom, Grainger, Kayleigh, Workman, Chris, Ferris, Mark, Gkrania-Klotsas, Effrossyni, Brown, Nicholas M, Weekes, Michael P, Baker, Stephen, Peacock, Sharon J, Goodfellow, Ian G, Gouliouris, Theodore, De Angelis, Daniela, and Török, M Estée

Subjects
superspreader, Evolutionary Biology, Microbiology and Infectious Disease, SARS-CoV-2, FOS: Biological sciences, nosocomial transmission, hospital, 3. Good health, Research Article, Virus
Abstract

Funder: Academy of Medical Sciences; FundRef: http://dx.doi.org/10.13039/501100000691, Funder: NIHR; FundRef: http://dx.doi.org/10.13039/501100000272, Funder: National Institute for Health Research; FundRef: http://dx.doi.org/10.13039/501100000272, Funder: The Health Foundation, SARS-CoV-2 is notable both for its rapid spread, and for the heterogeneity of its patterns of transmission, with multiple published incidences of superspreading behaviour. Here, we applied a novel network reconstruction algorithm to infer patterns of viral transmission occurring between patients and health care workers (HCWs) in the largest clusters of COVID-19 infection identified during the first wave of the epidemic at Cambridge University Hospitals NHS Foundation Trust, UK. Based upon dates of individuals reporting symptoms, recorded individual locations, and viral genome sequence data, we show an uneven pattern of transmission between individuals, with patients being much more likely to be infected by other patients than by HCWs. Further, the data were consistent with a pattern of superspreading, whereby 21% of individuals caused 80% of transmission events. Our study provides a detailed retrospective analysis of nosocomial SARS-CoV-2 transmission, and sheds light on the need for intensive and pervasive infection control procedures.

16. Superspreaders drive the largest outbreaks of hospital onset COVID-19 infections

Author

Illingworth, Christopher, Hamilton, William L, Warne, Ben, Routledge, Matthew, Popay, Ashley, Jackson, Chris, Fieldman, Tom, Meredith, Luke W, Houldcroft, Charlotte J, Hosmillo, Myra, Jahun, Aminu S, Caller, Laura G, Caddy, Sarah L, Yakovleva, Anna, Hall, Grant, Khokhar, Fahad A, Feltwell, Theresa, Pinckert, Malte L, Georgana, Iliana, Chaudhry, Yasmin, Curran, Martin D, Parmar, Surendra, Sparkes, Dominic, Rivett, Lucy, Jones, Nick K, Sridhar, Sushmita, Forrest, Sally, Dymond, Tom, Grainger, Kayleigh, Workman, Chris, Ferris, Mark, Gkrania-Klotsas, Effrossyni, Brown, Nicholas M, Weekes, Michael P, Baker, Stephen, Peacock, Sharon J, Goodfellow, Ian G, Gouliouris, Theodore, De Angelis, Daniela, and Török, M Estée

Subjects
superspreader, Male, SARS-CoV-2, infectious disease, evolutionary biology, microbiology, 1. No poverty, nosocomial transmission, COVID-19, virus, Middle Aged, Hospitals, 3. Good health, Disease Outbreaks, Humans, Female, hospital, Retrospective Studies
Abstract

SARS-CoV-2 is notable both for its rapid spread, and for the heterogeneity of its patterns of transmission, with multiple published incidences of superspreading behaviour. Here, we applied a novel network reconstruction algorithm to infer patterns of viral transmission occurring between patients and health care workers (HCWs) in the largest clusters of COVID-19 infection identified during the first wave of the epidemic at Cambridge University Hospitals NHS Foundation Trust, UK. Based upon dates of individuals reporting symptoms, recorded individual locations, and viral genome sequence data, we show an uneven pattern of transmission between individuals, with patients being much more likely to be infected by other patients than by HCWs. Further, the data were consistent with a pattern of superspreading, whereby 21% of individuals caused 80% of transmission events. Our study provides a detailed retrospective analysis of nosocomial SARS-CoV-2 transmission, and sheds light on the need for intensive and pervasive infection control procedures.

17. Comments.

Author

Chiang, Biona, Fieldman, Tom, Hayes, Jonathan, and Pearson, Ric

Subjects
BUSINESS enterprises, REAL property, REAL estate business, CHILD care services
Abstract

Several letters to the editor are presented in response to articles in previous issues, namely, an article about the Best Companies to Work for in British Columbia (BC) in the December 2012 issue, as well as an online letter talking about the 2012 real estate forecast and an online letter asking for an article about the effects of the lack of affordable childcare on businesses.

Published
2013

18. Labour makes late bid for engineers' vote.

Author

Fieldman, Tom

Subjects
VOTER turnout, CIVIL engineers
Abstract

The article reports on the late bid made by the Labour Party for the vote of civil engineers in the General Election in Great Britain.

Published
2010

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