Mathematical models to study the biology of pathogens and the infectious diseases they cause

Joao B. Xavier; Jonathan M. Monk; Saugat Poudel; Charles J. Norsigian; Anand V. Sastry; Chen Liao; Jose Bento; Marc A. Suchard; Mario L. Arrieta-Ortiz; Eliza J.R. Peterson; Nitin S. Baliga; Thomas Stoeger; Felicia Ruffin; Reese A.K. Richardson; Catherine A. Gao; Thomas D. Horvath; Anthony M. Haag; Qinglong Wu; Tor Savidge; Michael R. Yeaman

doi:10.1016/j.isci.2022.104079

Mathematical models to study the biology of pathogens and the infectious diseases they cause

Joao B. Xavier, Jonathan M. Monk, Saugat Poudel, Charles J. Norsigian, Anand V. Sastry, Chen Liao, Jose Bento, Marc A. Suchard, Mario L. Arrieta-Ortiz, Eliza J.R. Peterson, Nitin S. Baliga, Thomas Stoeger, Felicia Ruffin, Reese A.K. Richardson, Catherine A. Gao, Thomas D. Horvath, Anthony M. Haag, Qinglong Wu, Tor Savidge, Michael R. Yeaman

Research output: Contribution to journal › Review article › peer-review

10 Scopus citations

Abstract

Mathematical models have many applications in infectious diseases: epidemiologists use them to forecast outbreaks and design containment strategies; systems biologists use them to study complex processes sustaining pathogens, from the metabolic networks empowering microbial cells to ecological networks in the microbiome that protects its host. Here, we (1) review important models relevant to infectious diseases, (2) draw parallels among models ranging widely in scale. We end by discussing a minimal set of information for a model to promote its use by others and to enable predictions that help us better fight pathogens and the diseases they cause.

Original language	English
Article number	104079
Journal	iScience
Volume	25
Issue number	4
DOIs	https://doi.org/10.1016/j.isci.2022.104079
State	Published - Apr 15 2022

Keywords

Computer modeling
Infection control in health technology
Microbiology

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10.1016/j.isci.2022.104079

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Xavier, J. B., Monk, J. M., Poudel, S., Norsigian, C. J., Sastry, A. V., Liao, C., Bento, J., Suchard, M. A., Arrieta-Ortiz, M. L., Peterson, E. J. R., Baliga, N. S., Stoeger, T., Ruffin, F., Richardson, R. A. K., Gao, C. A., Horvath, T. D., Haag, A. M., Wu, Q., Savidge, T., & Yeaman, M. R. (2022). Mathematical models to study the biology of pathogens and the infectious diseases they cause. iScience, 25(4), Article 104079. https://doi.org/10.1016/j.isci.2022.104079

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title = "Mathematical models to study the biology of pathogens and the infectious diseases they cause",

abstract = "Mathematical models have many applications in infectious diseases: epidemiologists use them to forecast outbreaks and design containment strategies; systems biologists use them to study complex processes sustaining pathogens, from the metabolic networks empowering microbial cells to ecological networks in the microbiome that protects its host. Here, we (1) review important models relevant to infectious diseases, (2) draw parallels among models ranging widely in scale. We end by discussing a minimal set of information for a model to promote its use by others and to enable predictions that help us better fight pathogens and the diseases they cause.",

keywords = "Computer modeling, Infection control in health technology, Microbiology",

author = "Xavier, {Joao B.} and Monk, {Jonathan M.} and Saugat Poudel and Norsigian, {Charles J.} and Sastry, {Anand V.} and Chen Liao and Jose Bento and Suchard, {Marc A.} and Arrieta-Ortiz, {Mario L.} and Peterson, {Eliza J.R.} and Baliga, {Nitin S.} and Thomas Stoeger and Felicia Ruffin and Richardson, {Reese A.K.} and Gao, {Catherine A.} and Horvath, {Thomas D.} and Haag, {Anthony M.} and Qinglong Wu and Tor Savidge and Yeaman, {Michael R.}",

note = "Publisher Copyright: {\textcopyright} 2022 The Author(s)",

year = "2022",

month = apr,

day = "15",

doi = "10.1016/j.isci.2022.104079",

language = "English",

volume = "25",

journal = "iScience",

issn = "2589-0042",

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Xavier, JB, Monk, JM, Poudel, S, Norsigian, CJ, Sastry, AV, Liao, C, Bento, J, Suchard, MA, Arrieta-Ortiz, ML, Peterson, EJR, Baliga, NS, Stoeger, T, Ruffin, F, Richardson, RAK, Gao, CA, Horvath, TD, Haag, AM, Wu, Q, Savidge, T & Yeaman, MR 2022, 'Mathematical models to study the biology of pathogens and the infectious diseases they cause', iScience, vol. 25, no. 4, 104079. https://doi.org/10.1016/j.isci.2022.104079

TY - JOUR

T1 - Mathematical models to study the biology of pathogens and the infectious diseases they cause

AU - Xavier, Joao B.

AU - Monk, Jonathan M.

AU - Poudel, Saugat

AU - Norsigian, Charles J.

AU - Sastry, Anand V.

AU - Liao, Chen

AU - Bento, Jose

AU - Suchard, Marc A.

AU - Arrieta-Ortiz, Mario L.

AU - Peterson, Eliza J.R.

AU - Baliga, Nitin S.

AU - Stoeger, Thomas

AU - Ruffin, Felicia

AU - Richardson, Reese A.K.

AU - Gao, Catherine A.

AU - Horvath, Thomas D.

AU - Haag, Anthony M.

AU - Wu, Qinglong

AU - Savidge, Tor

AU - Yeaman, Michael R.

PY - 2022/4/15

Y1 - 2022/4/15

N2 - Mathematical models have many applications in infectious diseases: epidemiologists use them to forecast outbreaks and design containment strategies; systems biologists use them to study complex processes sustaining pathogens, from the metabolic networks empowering microbial cells to ecological networks in the microbiome that protects its host. Here, we (1) review important models relevant to infectious diseases, (2) draw parallels among models ranging widely in scale. We end by discussing a minimal set of information for a model to promote its use by others and to enable predictions that help us better fight pathogens and the diseases they cause.

AB - Mathematical models have many applications in infectious diseases: epidemiologists use them to forecast outbreaks and design containment strategies; systems biologists use them to study complex processes sustaining pathogens, from the metabolic networks empowering microbial cells to ecological networks in the microbiome that protects its host. Here, we (1) review important models relevant to infectious diseases, (2) draw parallels among models ranging widely in scale. We end by discussing a minimal set of information for a model to promote its use by others and to enable predictions that help us better fight pathogens and the diseases they cause.

KW - Computer modeling

KW - Infection control in health technology

KW - Microbiology

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U2 - 10.1016/j.isci.2022.104079

DO - 10.1016/j.isci.2022.104079

M3 - Review article

C2 - 35359802

AN - SCOPUS:85127111254

SN - 2589-0042

VL - 25

JO - iScience

JF - iScience

IS - 4

M1 - 104079

ER -

Mathematical models to study the biology of pathogens and the infectious diseases they cause

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