BACKGROUND: When the first cases of a new infectious disease appear, questions arise about the further course of the epidemic and about the appropriate interventions to be taken to protect individuals and the public as a whole. Mathematical models can help answer these questions. In this article, the authors describe basic concepts in the mathematical modelling of infectious diseases, illustrate their use with a simple example, and present the results of influenza models. METHOD: Description of the mathematical modelling of infectious diseases and selective review of the literature. RESULTS: The two fundamental concepts of mathematical modelling of infectious diseases-the basic reproduction number and the generation time-allow a better understanding of the course of an epidemic. Modelling studies based on past influenza epidemics suggest that the rise of the epidemic curve can be slowed at the beginning of the epidemic by isolating ill persons and giving prophylactic medications to their contacts. Later on in the course of the epidemic, restricting the number of contacts (e.g., by closing schools) may mitigate the epidemic but will only have a limited effect on the total number of persons who contract the disease. CONCLUSION: Mathematical modelling is a valuable tool for understanding the dynamics of an epidemic and for planning and evaluating interventions.
BACKGROUND: When the first cases of a new infectious disease appear, questions arise about the further course of the epidemic and about the appropriate interventions to be taken to protect individuals and the public as a whole. Mathematical models can help answer these questions. In this article, the authors describe basic concepts in the mathematical modelling of infectious diseases, illustrate their use with a simple example, and present the results of influenza models. METHOD: Description of the mathematical modelling of infectious diseases and selective review of the literature. RESULTS: The two fundamental concepts of mathematical modelling of infectious diseases-the basic reproduction number and the generation time-allow a better understanding of the course of an epidemic. Modelling studies based on past influenza epidemics suggest that the rise of the epidemic curve can be slowed at the beginning of the epidemic by isolating ill persons and giving prophylactic medications to their contacts. Later on in the course of the epidemic, restricting the number of contacts (e.g., by closing schools) may mitigate the epidemic but will only have a limited effect on the total number of persons who contract the disease. CONCLUSION: Mathematical modelling is a valuable tool for understanding the dynamics of an epidemic and for planning and evaluating interventions.
Authors: Beatriz Vidondo; Jürgen Oberreich; Stefan O Brockmann; Hans-Peter Duerr; Markus Schwehm; Martin Eichner Journal: Swiss Med Wkly Date: 2009-09-05 Impact factor: 2.193
Authors: Raymond Gani; Helen Hughes; Douglas Fleming; Thomas Griffin; Jolyon Medlock; Steve Leach Journal: Emerg Infect Dis Date: 2005-09 Impact factor: 6.883
Authors: Neil M Ferguson; Derek A T Cummings; Christophe Fraser; James C Cajka; Philip C Cooley; Donald S Burke Journal: Nature Date: 2006-04-26 Impact factor: 49.962
Authors: David Bell; Angus Nicoll; Keiji Fukuda; Peter Horby; Arnold Monto; Frederick Hayden; Clare Wylks; Lance Sanders; Jonathan van Tam Journal: Emerg Infect Dis Date: 2006-01 Impact factor: 6.883
Authors: Ana L P Mateus; Harmony E Otete; Charles R Beck; Gayle P Dolan; Jonathan S Nguyen-Van-Tam Journal: Bull World Health Organ Date: 2014-09-29 Impact factor: 9.408
Authors: Rafael Mikolajczyk; André Karch; Nicole Rübsamen; Benno Garcia Voges; Stefanie Castell; Carolina Judith Klett-Tammen; Jérôme Oppliger; Pius Krütli; Timo Smieszek Journal: BMC Public Health Date: 2022-03-23 Impact factor: 3.295