Roger G. Bowers

Roger G. Bowers

Auto-generated profile, based on publicly available information

Roger G. Bowers

The University of Liverpool

Research Focus & Metrics

Academic Career Length

61Years (1966-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
1 August 2002 in Epidemiology and Infection
Verified
Journal Article
Open access
PDF file
MBM. Begon
M. Begon
MBM. Bennett
M. Bennett
RBR. G. Bowers
R. G. Bowers
NFN. P. French
N. P. French
SHS. M. Hazel
S. M. Hazel
JTJ. Turner
J. Turner
Transmission is the driving force in the dynamics of any infectious disease. A crucial element in understanding disease dynamics, therefore, is the ‘transmission term’ describing the rate at which susceptible hosts are ‘converted’ into infected hosts by their contact with infectious material. Recently, the conventional form of this term has been increasingly questioned, and new terminologies and conventions have been proposed. Here, therefore, we review the derivation of transmission terms, explain the basis of confusion, and provide clarification. The root of the problem has been a failure to include explicit consideration of the area occupied by a host population, alongside both the number of infectious hosts and their density within the population. We argue that the terms ‘density-dependent transmission’ and ‘frequency-dependent transmission’ remain valid and useful (though a ‘fuller’ transmission term for the former is identified), but that the terms ‘mass action’, ‘true mass action’ and ‘pseudo mass action’ are all unhelpful and should be dropped. Also, contrary to what has often been assumed, the distinction between homogeneous and heterogeneous mixing in a host population is orthogonal to the distinction between density- and frequency-dependent transmission modes.

21 September 2005 in American Journal of Epidemiology
Verified
Journal Article
Open access
PDF file
RCRobert Christley
Robert Christley
GPG. L. Pinchbeck
G. L. Pinchbeck
RBR. G. Bowers
R. G. Bowers
DCD. Clancy
D. Clancy
NFN. P. French
N. P. French
JTJ. Turner
J. Turner
Simulation studies using susceptible-infectious-recovered models were conducted to estimate individuals' risk of infection and time to infection in small-world and randomly mixing networks. Infection transmitted more rapidly but ultimately resulted in fewer infected individuals in the small-world, compared with the random, network. The ability of measures of network centrality to identify high-risk individuals was also assessed. “Centrality” describes an individual's position in a population; numerous parameters are available to assess this attribute. Here, the authors use the centrality measures degree (number of contacts), random-walk betweenness (a measure of the proportion of times an individual lies on the path between other individuals), shortest-path betweenness (the proportion of times an individual lies on the shortest path between other individuals), and farness (the sum of the number of steps between an individual and all other individuals). Each was associated with time to infection and risk of infection in the simulated outbreaks. In the networks examined, degree (which is the most readily measured) was at least as good as other network parameters in predicting risk of infection. Identification of more central individuals in populations may be used to inform surveillance and infection control strategies.

28 July 2003 in Ecology Letters
Verified
Journal Article
Closed access
PDF file
RHRobert D. Holt
Robert D. Holt
ADAndrew P. Dobson
Andrew P. Dobson
MBMichael Begon
Michael Begon
RBRoger G. Bowers
Roger G. Bowers
ESEric M. Schauber
Eric M. Schauber

1 September 1999 in Journal of Theoretical Biology
Verified
Journal Article
Closed access
RNR. Norman
R. Norman
RBR.G. Bowers
R.G. Bowers
MBM. Begon
M. Begon
PHPeter Hudson
Peter Hudson


7 April 2004 in Proceedings Of The Royal Society B-Biological Sciences
Verified
Journal Article
Closed access
PDF file
MBMichael Boots
Michael Boots
RBRoger G. Bowers
Roger G. Bowers
We examine the evolutionary dynamics of resistance to parasites through acquired immunity. Resistance can be achieved through the innate mechanisms of avoidance of infection and reduced pathogenicity once infected, through recovery from infection and through remaining immune to infection: acquired immunity. We assume that each of these mechanisms is costly to the host and find that the evolutionary dynamics of innate immunity in hosts that also have acquired immunity are quantitatively the same as in hosts that possess only innate immunity. However, compared with resistance through avoidance or recovery, there is less likely to be polymorphism in the length of acquired immunity within populations. Long–lived organisms that can recover at intermediate rates faced with fast–transmitting pathogens that cause intermediate pathogenicity (mortality of infected individuals) are most likely to evolve long–lived acquired immunity. Our work emphasizes that because whether or not acquired immunity is beneficial depends on the characteristics of the disease, organisms may be selected to only develop acquired immunity to some of the diseases that they encounter.