%0 Journal Article %1 Aparicio2009209 %A Aparicio, J.P. %A Castillo-Chavez, C. %D 2009 %J Mathematical Biosciences and Engineering %K Age Analysis; Assessment; Biological; Biometry; Computer Data Disease Distribution; Dynamics; Equus Factors; Hazards Humans; Incidence; Interpretation, Models, Models; Outbreaks; Population Proportional Rate; Risk Simulation; Statistical; Survival Tuberculosis, age analysis; article; asinus assessment; biological biometry; computer distribution; dynamics; epidemic; factor; hazards human; incidence; methodology; model; mortality; population proportional rate; risk simulation; statistical statistics; survival survival; tuberculosis, %N 2 %P 209-237 %R http://dx.doi.org/10.3934/mbe.2009.6.209 %T Mathematical modelling of tuberculosis epidemics %U http://dx.doi.org/10.3934/mbe.2009.6.209 %V 6 %X The strengths and limitations of using homogeneous mixing and heterogeneous mixing epidemic models are explored in the context of the transmission dynamics of tuberculosis. The focus is on three types of models: a standard incidence homogeneous mixing model, a non-homogeneous mixing model that incorporates 'household' contacts, and an age-structured model. The models are parameterized using demographic and epidemiological data and the patterns generated from these models are compared. Furthermore, the effects of population growth, stochasticity, clustering of contacts, and age structure on disease dynamics are explored. This framework is used to asses the possible causes for the observed historical decline of tuberculosis notifications.

@article{Aparicio2009209, abstract = {The strengths and limitations of using homogeneous mixing and heterogeneous mixing epidemic models are explored in the context of the transmission dynamics of tuberculosis. The focus is on three types of models: a standard incidence homogeneous mixing model, a non-homogeneous mixing model that incorporates 'household' contacts, and an age-structured model. The models are parameterized using demographic and epidemiological data and the patterns generated from these models are compared. Furthermore, the effects of population growth, stochasticity, clustering of contacts, and age structure on disease dynamics are explored. This framework is used to asses the possible causes for the observed historical decline of tuberculosis notifications.}, added-at = {2017-11-10T22:48:29.000+0100}, affiliation = {School of Science and Technology, Universidad Metropolitan{\~A}{!'}, San Juan 00928-1150, Puerto Rico; Instituto de Investigati{\~A}³n en Energ{\~A}-as no Convencionales, Universidad Nacional de Salta, 4400 Salta, Argentina; School of Human Evolution and Social Change, PO Box 872402, Tempe, AZ 85287-2402, United States; Mathematical, Computational and Modeling Sciences Center, PO Box 871904, Tempe, AZ 85287-1904, United States; School of Mathematical and Statistical Sciences, Arizona State University, PO BOX 871804, Tempe, AZ 85287-1804, United States; Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, United States}, author = {Aparicio, J.P. and Castillo-Chavez, C.}, author_keywords = {Demography; Non-autonomous systems; Stochastic models; Tuberculosis}, biburl = {https://www.bibsonomy.org/bibtex/2e6efd3b42330a2bca3da41f0cc8328a4/ccchavez}, correspondence_address1 = {Aparicio, J. P.; School of Science and Technology, Universidad Metropolitan{\~A}{!'}, San Juan 00928-1150, Puerto Rico; email: juan.p.aparicio@gmail.com}, date-added = {2017-11-10 21:45:26 +0000}, date-modified = {2017-11-10 21:45:26 +0000}, document_type = {Article}, doi = {http://dx.doi.org/10.3934/mbe.2009.6.209}, interhash = {575520e2dd47810eeab3bf1c06d8040f}, intrahash = {e6efd3b42330a2bca3da41f0cc8328a4}, issn = {15471063}, journal = {Mathematical Biosciences and Engineering}, keywords = {Age Analysis; Assessment; Biological; Biometry; Computer Data Disease Distribution; Dynamics; Equus Factors; Hazards Humans; Incidence; Interpretation, Models, Models; Outbreaks; Population Proportional Rate; Risk Simulation; Statistical; Survival Tuberculosis, age analysis; article; asinus assessment; biological biometry; computer distribution; dynamics; epidemic; factor; hazards human; incidence; methodology; model; mortality; population proportional rate; risk simulation; statistical statistics; survival survival; tuberculosis,}, language = {English}, number = 2, pages = {209-237}, pubmed_id = {19364150}, timestamp = {2017-11-10T22:48:29.000+0100}, title = {Mathematical modelling of tuberculosis epidemics}, url = {http://dx.doi.org/10.3934/mbe.2009.6.209}, volume = 6, year = 2009 }