%0 Journal Article %1 Bichara20152004 %A Bichara, D. %A Kang, Y. %A Castillo-Chavez, C. %A Horan, R. %A Perrings, C. %D 2015 %I Springer New York LLC %J Bulletin of Mathematical Biology %K Basic Biological; Communicable Concepts; Disease Diseases; Epidemics; Factors Humans; Infectious; Mathematical Models, Number; Reproduction Risk Statistical; Transmission, and basic biological data; disease epidemic; factor; human; mathematical model; number; numerical phenomena; reproduction risk statistical statistics transmission, transmission; %N 11 %P 2004-2034 %R http://dx.doi.org/10.1007/s11538-015-0113-5 %T SIS and SIR Epidemic Models Under Virtual Dispersal %U http://dx.doi.org/10.1007/s11538-015-0113-5 %V 77 %X We develop a multi-group epidemic framework via virtual dispersal where the risk of infection is a function of the residence time and local environmental risk. This novel approach eliminates the need to define and measure contact rates that are used in the traditional multi-group epidemic models with heterogeneous mixing. We apply this approach to a general n-patch SIS model whose basic reproduction number R0 is computed as a function of a patch residence-time matrix P. Our analysis implies that the resulting n-patch SIS model has robust dynamics when patches are strongly connected: There is a unique globally stable endemic equilibrium when R0>1, while the disease-free equilibrium is globally stable when R0≀1. Our further analysis indicates that the dispersal behavior described by the residence-time matrix P has profound effects on the disease dynamics at the single patch level with consequences that proper dispersal behavior along with the local environmental risk can either promote or eliminate the endemic in particular patches. Our work highlights the impact of residence-time matrix if the patches are not strongly connected. Our framework can be generalized in other endemic and disease outbreak models. As an illustration, we apply our framework to a two-patch SIR single-outbreak epidemic model where the process of disease invasion is connected to the final epidemic size relationship. We also explore the impact of disease-prevalence-driven decision using a phenomenological modeling approach in order to contrast the role of constant versus state-dependent P on disease dynamics. Â\copyright 2015, Society for Mathematical Biology.

@article{Bichara20152004, abstract = {We develop a multi-group epidemic framework via virtual dispersal where the risk of infection is a function of the residence time and local environmental risk. This novel approach eliminates the need to define and measure contact rates that are used in the traditional multi-group epidemic models with heterogeneous mixing. We apply this approach to a general n-patch SIS model whose basic reproduction number R0 is computed as a function of a patch residence-time matrix P. Our analysis implies that the resulting n-patch SIS model has robust dynamics when patches are strongly connected: There is a unique globally stable endemic equilibrium when R0>1, while the disease-free equilibrium is globally stable when R0{\^a}‰€1. Our further analysis indicates that the dispersal behavior described by the residence-time matrix P has profound effects on the disease dynamics at the single patch level with consequences that proper dispersal behavior along with the local environmental risk can either promote or eliminate the endemic in particular patches. Our work highlights the impact of residence-time matrix if the patches are not strongly connected. Our framework can be generalized in other endemic and disease outbreak models. As an illustration, we apply our framework to a two-patch SIR single-outbreak epidemic model where the process of disease invasion is connected to the final epidemic size relationship. We also explore the impact of disease-prevalence-driven decision using a phenomenological modeling approach in order to contrast the role of constant versus state-dependent P on disease dynamics. {\^A}{\copyright} 2015, Society for Mathematical Biology.}, added-at = {2017-11-10T22:48:29.000+0100}, affiliation = {SAL Mathematical, Computational and Modeling Science Center, Arizona State University, Tempe, AZ, United States; College of Letters and Sciences, Arizona State University, Mesa, AZ, United States; Department of Agricultural, Food and Resource Economics, Michigan State University, East Lansing, MI, United States; School of Life Sciences, Arizona State University, Tempe, AZ, United States}, author = {Bichara, D. and Kang, Y. and Castillo-Chavez, C. and Horan, R. and Perrings, C.}, author_keywords = {Adaptive behavior; Dispersal; Epidemiology; Final size relationship; Global stability; Residence times; SIS{\^a}€“SIR models}, biburl = {https://www.bibsonomy.org/bibtex/227cf99197df32c794b57eb8e03cd78ba/ccchavez}, coden = {BMTBA}, correspondence_address1 = {Kang, Y.; College of Letters and Sciences, Arizona State UniversityUnited States; email: yun.kang@asu.edu}, 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/10.1007/s11538-015-0113-5}, interhash = {35502d9767502b4af94b0cfebb2b32ed}, intrahash = {27cf99197df32c794b57eb8e03cd78ba}, issn = {00928240}, journal = {Bulletin of Mathematical Biology}, keywords = {Basic Biological; Communicable Concepts; Disease Diseases; Epidemics; Factors Humans; Infectious; Mathematical Models, Number; Reproduction Risk Statistical; Transmission, and basic biological data; disease epidemic; factor; human; mathematical model; number; numerical phenomena; reproduction risk statistical statistics transmission, transmission;}, language = {English}, number = 11, pages = {2004-2034}, publisher = {Springer New York LLC}, pubmed_id = {26489419}, timestamp = {2017-11-10T22:48:29.000+0100}, title = {SIS and SIR Epidemic Models Under Virtual Dispersal}, url = {http://dx.doi.org/10.1007/s11538-015-0113-5}, volume = 77, year = 2015 }