Published Papers

1. Liton Chandra Deb*, Manuel Jara*, and Cristina Lanzas. 2023. Early evaluation of the Food and Drug Administration (FDA) guidance on antibiotic use in food animals on antimicrobial resistance trends reported by the National Antimicrobial Resistance Monitoring System (2012-2019). One Health. In press.

2. Farthing, Trevor S.*, Ashlan Jolley*, Katelin B. Nickel, Cherie Hill, Dustin Stwalley, Kimberly A. Reske, Jennie H. Kwon, Margaret A. Olsen, Jason P. Burnham, Erik R. Dubberke, and Cristina Lanzas. 2023. Early COVID-19 pandemic effects on individual-level risk for healthcare-associated infections in hospitalized patients. Infection Control and Hospital Epidemiology. In press.

3. Ashlan Jolley*, William Love, Erin Frey, Cristina Lanzas. 2023. Impacts of the COVID-19 pandemic on antimicrobial use in companion animals in a tertiary veterinary teaching hospital in North Carolina. Zoonoses and Public Health, 70; 393-402.

4. Davies, K.*, Lenhart, S., Day, J., Lloyd., A., Lanzas, C. 2023. Extensions of mean-field approximations for environmentally-transmitted pathogen networks. Mathematical Biosciences and Engineering, 20: 1637-1673.

5. Das, Praachi, Morganne Igoe, Suzanne Lenhart, Alun Lloyd, Lan Luong, Dajun Tian, Cristina Lanzas, Agricola Odoi. 2022. Geographic Disparities and Predictors of COVID-19 Incidence Risk in the St. Louis Area, Missouri (USA). PloS One, 17: e0274899.

6. Mahmud, Bejan, Meghan A. Wallace, Kimberly A. Reske, Kelly Alvarado, David A. Rasmussen, Carey-Ann D. Burnham, Cristina Lanzas, Erik R. Dubberke, Gautam Dantas. 2022. Epidemiology of Plasmid Lineages Mediating The Clinical Spread of Extended Spectrum Beta-Lactamases. mSystems, e00519-22.

7. Love, W.*, Wang. C.A.*, Lanzas, C. 2022. Identifying patient-level risk factors associated with non-β-lactam resistance outcomes in invasive methicillin-resistant Staphylococcus aureus infections in the United States using chain graphs. Journal of Antimicrobial Chemotherapy-Antimicrobial Resistance, 4: dlac068.

8. Lanzas, C., Jara, M.*, Tucker, R.*, Curtis, S.* 2022. A review of epidemiological models of Clostridioides difficile transmission and control (2009-2021). Anaerobe, 74: 102541. 

9. Igoe, Morganne Elizabeth, Praachi Das, Suzanne Lenhart, Alun Lloyd, Lan Luong, Dajun Tian, Cristina Lanzas, Agricola Odoi. 2022. Geographic Disparities and Predictors of COVID-19 Hospitalization Risk in the St. Louis Area, Missouri (USA). BMC Public Health, 22: 1-10.

10. Farthing, T.*, Lanzas, C. 2021. Assessing the efficacy of interventions to control indoor SARS-Cov-2 transmission: an agent-based modeling approach. Epidemics, 34: 100524.   

11. Machado, G., Farthing, T*, Andraud, M., Nunes Lopes, F., Lanzas, C. 2021. Modeling the role of mortality-based response triggers on the effectiveness of African swine fever control strategies. Transboundary and Emerging Diseases:1-15.

12. Farthing, T.*, Dawson, D*, Sanderson, M., Senger, H., Lanzas, C. 2021. Combining epidemiological and ecological methods to quantify social effects on Escherichia coli transmission. Royal Society Open Science, 8: 210328.

13. Sulyok, C, Fox, L., Ritchie, H., Lanzas, C, Lenhart, S., Day, J. 2021. Mathematically Modeling the Effect of Touch Frequency on the Environmental Transmission of Clostridioides difficile in Health-care Settings. Mathematical Biosciences. 340, 108666.

14. Dawson, D.*, Rasmussen, D., Peng, X., Lanzas, C. 2021. Inferring environmental transmission using phylodynamics: A case-study using simulated evolution of an enteric pathogen. Journal of the Royal Society Interface, 18: 20210041.

15. Stephenson, B., Lanzas, C., Lenhart, Ponce, E., Bintz, J., Dubberke, E., Day, J. 2020. Comparing intervention strategies for reducing Clostridiodes difficile transmission: An agent-based modeling study. BMC Infectious Diseases, 20: 1-17.

16. Farthing, T.*. Dawson, D*, Sanderson, M., Lanzas, C. 2020. Accounting for space and uncertainty in real-time-location-system derived contact networks. Ecology and Evolution, 10: 4702-4715. 

17. Erwin, S*, Foster, D., Jacob, M., Papich, M., Lanzas, C. 2020. The effect of enrofloxacin on enteric Escherichia coli: fitting a mathematical model to in vivo data. PLoS ONE, 15: e0228138. 

18. Garabed, R., Jolles, A., Garira, W., Lanzas, C., Gutierrez, J., Rempala, G. 2020. Multiscale dynamics of infectious diseases. Interface Focus. 10: 20190118.

19. Lanzas, C., Davis, K.*, Erwin, S.*, Dawson, D.* 2019. On modelling environmentally-transmitted pathogens. Interface Focus. 10: 20190056.

20. Lashnits, E.*, Dawson, D*., Breitschwerdt, E.B., Lanzas., C. 2019. Ecological and socioeconomic drivers of Bartonella henselae exposure in dogs in North Carolina. Vector-Borne and Zoonotic Diseases., 19:582-595. 

21. Cazer, C., Al-Mamun, M., Kaniyamattam, K., Love, W.*, Booth, J.G., Lanzas, C., Grohn, Y.T. 2019. Shared multidrug resistance patterns in chicken-associated Escherichia coli identified by association rule mining. Frontiers in Microbiology, 10: 687. 

22. Zawack, K., Love, W.*, Lanzas, C., Booth, J.G., Grohn, Y.T. 2019. Estimation of Multidrug Resistance Variability in the National Antimicrobial Monitoring System. Preventive Veterinary Medicine, 167: 137-145.

23. Chen, C., Lanzas, C., Lee, C., Zenarosa, G., Arif, A., Dulin, M. 2019. Metapopulation Model from Pathogen’s Perspective: A Versatile Framework to Quantify Pathogen Transfer and Circulation between Environment and Hosts. Nature Scientific Reports. 9: 1694.

24. Dawson, D.*, Farthing, T.*, Sanderson, M., Lanzas, C. 2019. Transmission on empirical dynamic contact networks is influenced by data processing decisions. Epidemics. 26: 32-42.

25. Dawson, D.*, Keung, J.H.*, Napoles, M.G.*, Vella, M.R.*, Chen, S.*, Sanderson, M., Lanzas, C. 2018. Investigating behavioral drivers of seasonal Shiga-toxigenic Escherichia coli (STEC) patterns in grazing cattle using an agent-based model. PLoS ONE, 13: e0205418.

26. Love, W.,* Zawack, K., Booth, J.G., Grohn, Y.T., Lanzas, C. 2018. Resistance correlation networks for 10 nontyphoidal Salmonella subpopulations in an active antimicrobial surveillance program. Epidemiology and Infection, 146: 991-1002.

27. Fletcher, J., Erwin, S.*, Lanzas, C. Theriot, C. 2018. Shifts in the gut metabolome and Clostridium difficile transcriptome throughout colonization and infection in a mouse model. mSphere, 3: e00089-18.

28. Zawack, K., Love, W.* Lanzas, C., Booth, J.G., Grohn, Y.T. 2018. Inferring the interaction structure of resistance to antimicrobials. Preventive Veterinary Medicine. 152: 81-88. 

29. Chen, S,* Lenhart, S., Day, J., Lee, C., Dulin, M., Lanzas, C. 2017. Pathogen transfer through environment-host contact: An agent-based queueing theoretic framework. Mathematical Medicine and Biology. 35: 409-425.

30. Grohn, Y.T., Carson, C., Lanzas, C., Pullum, L., Stanhope, M.J., and Volkova, V. 2017. A proposed analytic framework for determining the impact of an antimicrobial resistance intervention. Animal Health Reviews, 18: 1-25.

31. Stephenson, B., Lanzas, C., Lenhart, S., Day, J. 2017. Optimal control of vaccination rate in an epidemiological model of Clostridium difficile transmission. Journal of Mathematical Biology, 75: 1693-1713.

32. Bintz, J., Lenhart, S., Lanzas, C. 2017. Antimicrobial stewardship and environmental decontamination for the control of Clostridium difficile transmission in healthcare settings. Bulletin of Mathematical Biology, 79: 36-62.

33. Love, W.,* Zawack, K., Booth, J.G., Grohn, Y.T., Lanzas, C. 2016. Markov networks of collateral antibiotic resistance: National antimicrobial resistance monitoring system surveillance results from Escherichia coli isolates, 2004-2013. PLoS Computational Biology,12: e1005160.

34. Kwon, J., Lanzas, C., Reske, K., Hink, T., Seiler, S., Bommarito, K., Burnham, C., Dubberke, E. 2016. The role of food as a potential source of Clostridium difficile acquisition in hospitalized patients. Infection Control and Hospital Epidemiology, 37:1401-1407.

35.Chen, S* and Lanzas, C. 2016. Distinction and connection between contact network, social network, and disease transmission network. Preventive Veterinary Medicine, 131:8-11

36.Chen, S.*, Sanderson, M., Lee, C., Cernicchiaro, N., Renter, D., Lanzas., C. 2016. Basic reproduction number and transmission dynamics of common serogroups of enterohemorrhagic Escherichia coli. Applied and Environmental Microbiology, 82: 5612-5620

37. Zawack, K., Li, M. Booth, J.G., Love, W.*, Lanzas, C., Grohn, Y.T. 2016. Monitoring antimicrobial resistance in the food supply chain and its implications for FDA policy initiatives. Antimicrobial Agents and Chemotherapy, 602:5302-5311

38.Lanzas, C., and Chen, S.* 2015. Mathematical modeling tools to study pre-harvest food safety. Microbiology spectrum, 4:doi:10.1128/microbiolspec.PFS-0001-2013

39.Chen, S.*, Ilany, A, White, B.J., Sanderson, M.W., Lanzas, C. 2015. Spatial-temporal dynamics of high-resolution animal networks: What can we learn from domestic animals? PLoS ONE, 10: e0129253.

40. Lanzas, C., and Chen, S.* 2015. Complex system modeling for veterinary epidemiology. Preventive Veterinary Medicine, 118: 207-214

41. Aguilar-Bonavides, C.*, Sanchez-Arias, R., Lanzas, C. 2014. Major Histocompatibility Complex Class II Epitope Accurate Prediction by Sparse Representation. BioData Minding, 7:23

42.Lanzas, C and Dubberke, E. 2014. Effectiveness of screening hospital admissions for colonization in reducing Clostridium difficile transmission: a modeling evaluation. Infection Control and HospitalEpidemiology, 35: 1043-1050

43. Chen, S.*, White, B., Sanderson, M., Amrine, D., Ilany, A., Lanzas, C. 2014. A highly dynamic animal contact network and implications on disease transmission. Nature Scientific Reports, 4: 4472

44. Chen, S.*, Sanderson, M., White, B., Amrine, D., Lanzas, C. 2013. Temporal-spatial heterogeneity in animal-environment contact: implications for the exposure and transmission of pathogens. Nature Scientific Reports, 3:3112 45. Magombedze, G.*, Ngonghala, C.*, Lanzas, C. 2013. Evaluation of the Iceberg phenomenon in Johne’sdisease through mathematical modelling. PLoS ONE. 8: e76636 

46.Volkova, V. V., Lu. Z., Lanzas, C., Scott, H.M., Grohn, Y.T. 2013. Modelling dynamics of plasmid-gene mediated antimicrobial resistance in enteric bacteria using stochastic differential equations.Nature Scientific Reports, 3: 2463

47. Volkova, V. V., Lu., Z., Lanzas, C., Grohn, Y.T. 2013. Evaluating targets for control of plasmid-mediated antimicrobial resistance in enteric commensals of beef cattle: modeling approach. Epidemiology and Infection, 141: 2294-2312

48.Chen, S.*, Sanderson, M., Lanzas, C. 2013. Investigating effects of between-and within-host variabilityon Escherichia coli O157 shedding pattern and transmission. Preventive Veterinary Medicine. 109:47-57

49.Volkova, V. V., Lanzas, C., Lu, Z., Grohn, Y.T. 2012. Mathematical model of plasmid-mediated resistanceto ceftiofur in commensal enteric Escherichia coli of cattle. PLoS ONE. 7: e367 

50. Lanzas, C., Dubberke, E.R,, Lu, Z., Reske, K.A., Grohn, Y.T. 2011. Epidemiological model for Clostridiumdifficile transmission in health care settings. Infection Control and Hospital Epidemiology. 32: 553-561

51. Lanzas, C., Lu, Z., Grohn, Y.T. 2011. Mathematical modeling of the transmission and control of foodbornepathogens and antimicrobial resistance at preharvest. Foodborne Pathogens and Disease, 8: 1-10 

52. Dubberke, E. R., Haslam, D. B., Lanzas, C., Bobo, L. D., Burnham, C. D., Grohn, Y. T., Tarr. P. I. 2011.The ecology and pathobiology of Clostridium difficile infections: an interdisciplinary challenge. Zoonosesand Public Health, 58: 4-20

53.Lanzas, C., Warnick, L. D., James, K. L., Wright, E. M., Wiedmann, M. and Grohn, Y. T., 2010. Transmission dynamics of a multidrug-resistant Salmonella typhimurium outbreak in a dairy farm. Foodborne Pathogens and Disease, 7: 467-474

54. Lanzas, C., Ayscue, P., Ivanek, R., Grohn, Y.T. 2010. Model or meal? Farm animal populations as modelsfor infectious diseases of humans. Nature Reviews Microbiology, 8:139-148

55. Seo, S., Lanzas, C., Tedeschi, L.O., Pell, A., Fox, D.G., 2009. Development of a mechanistic model torepresent the dynamics of particle flow out of the rumen and to predict rate of passage of forage particlesin dairy cattle. Journal of Dairy Science, 92: 3981-4000

56.Ayscue, P., Lanzas, C., Ivanek, R., Grohn, Y.T., 2009. Modeling on-farm Escherichia coli O157:H7 population dynamics. Foodborne Pathogens and Disease, 6: 461-470

57.Lanzas, C., Broderick, G.A., Fox, D.G., 2008. Improved feed protein fractionation schemes for formulatingrations with the Cornell Net Carbohydrate and Protein System. Journal of Dairy Science, 91:4881-4891

58. Lanzas, C., Warnick, L.D., Ivanek, R., Ayscue, P., Nydam, D.V., Grohn, Y.T., 2008. The risk and control of Salmonella outbreaks in calf-raising operations: a mathematical modeling approach. VeterinaryResearch, 39:61

59. Lanzas, C., Brien, S., Ivanek, R., Lo, Y., Chapagain, P.P., Ray, K.A., Ayscue, P., Warnick, L.D., Grohn,Y.T., 2008. The effect of heterogeneous infectious period and contagiousness on the dynamics of Salmonellatransmission in dairy cows.Epidemiology and Infection, 136:1496-1510

60. Lanzas, C., Sniffen, C.J., Seo, S., Tedeschi, L.O., Fox, D.G. 2007. A revised CNCPS feed carbohydratefractionation scheme for formulating rations for ruminants. Animal Feed Science Technology, 136:167-190

61. Lanzas, C., Pell, A.N., Fox, D.G., 2007. Digestion kinetics of dried cereal grains. Animal Feed ScienceTechnology, 136: 265-280

62. Seo, S., Lanzas, C., Tedeschi, L.O., Fox, D.G., 2007. Development of a mechanistic model to represent the dynamics of liquid flow out of the rumen and to predict rate of passage of liquid in dairy cattle. Journal Dairy Science, 90: 840-855

63. Lanzas, C., Seo, S., Tedeschi, L.O., Fox, D.G., 2007. Evaluation of protein fractionation systems used informulating rations for dairy cattle. Journal Dairy Science, 90: 507-521

64. Seo, S., Tedeschi, L.O., Lanzas, C., Schwab, C.G., Fox, D.G., 2006. Development and evaluation of empiricalequations to predict feed passage rate in cattle. Animal Feed Science Technology, 128: 67-83