Julien Arino (University of Manitoba)

Ali Asgary (York University)

Jacques Bélair (University of Montreal, CRM)

Adalsteinn Brown (University of Toronto)

Jason Brown (Dalhousie University)

Arthur Charpentier (Université du Québec à Montréal)

James Colliander (University of British Columbia, PIMS)

Dongmei Chen (Queen’s University)

Morgan Craig (University of Montreal)

Shengyuan (Michael) Chen (York University)

Charmaine Dean (University of Waterloo)

Jonathan Dushoff (McMaster University)

Ida Ferrara (York University)

David Fisman (University of Toronto)

Ed Furman (York University)

Amy Greer (University of Guelph)

Hélène Guérin (Université du Québec à Montréal)

Jane Heffernan (York University)

Joan Hu (Simon Fraser University)

Amy Hurford (Memorial University of Newfoundland)

Jeanette Jansen (Dalhousie University)

Theodore Kolokolnikov (Dalhousie University)

Jude D. Kong (York University)

Manisha Kulkarni (University of Ottawa)

Patrick Leighton (University of Montreal)

Michael Li (University of Alberta)

Juxin Liu (University of Saskatchewan)

Felicia Magpantay (Queen’s University)

Doug Manuel (Ottawa Hospital Research Instititute)

Sharmistha Mishra (University of Toronto)

Swetaprovo Chaudhuri (University of Toronto)

Manuel Morales (University of Montreal, CRM)

Junling Ma (University of Victoria)

Sajjad Mohammad (National Research Council of Canada)

Seyed Moghadas (York University)

Bouchra Nasri (University of Montreal)

Nick Ogden (Public Health Agency of Canada)

James Ooi (National Research Council of Canada)

Nathaniel Osgood (University of Saskatchewan)

Peter Park (York University)

Lin Wang (University of New Brunswick)

Luc Vinet (University of Montreal, CRM)

Javier Sanchez (University of Prince Edward Island)

Sanjeev Seahra (University of New Brunswick, AARMS) 

Luis Seco (University of Toronto)

Chris Soteros (University of Saskatchewan)

Ashleigh Tuite (University of Toronto)

Edward Thommes (Sanofi Pasteur, University of Guelph)

James Watmough (University of New Brunswick)

Cheryl Waldner (University of Saskatchewan)

Michael Wolfson (University of Ottawa)

Mamadou Yauck (Université du Québec à Montréal)

Xiaoqiang Zhao (Memorial University of Newfoundland) 

Xingfu Zou (Western University)

Hao Wang (University of Alberta)

Monica Cojocaru (University of Guelph)

Venkata Duvvuri (University of Toronto)

Woldegebriel Assefa Woldegerima (York University)

Suzanna Shoush (University of Toronto)

Ed Furman (York University)

Mariah Ahmad (York University)

Mortaza Baky-Haskuee ( York University & The Fields Institute) 

Hudson Blue (York University)

Korryn Bodner (University of Toronto)

Gabrielle Brankston (University of Guelph)

Jummy David (York University)

Felix Foutel-Roudier (Université du Québec à Montréal (UQÀM))

Martin Grunnill (York University)

Donglin Han (University of Alberta)

Francis Hu (University of Montreal)

Sana Jahedi (McMaster University)

Patrick Leighton (University of Montreal)

Ao Li (York University)

Xiaoyan Li (University of Saskatchewan)

Ankai Liu (Queen’s University)

Wade McDonald (University of Saskatchewan)

Zahra Mohammadi (University of Guelph)

Mohsen Mousavi (York University)

Dinh Toan Nguyen (Gustave Eiffel University)

Chelsea Nyarko (University of Waterloo)

Ken Peng (University of Waterloo)

Tanya Philippsen (University of Victoria)

Weston Roda (University of Alberta)

Idriss Sekkak (University of Montreal)

Yogita Sharma (University of Victoria)

Manting Wang (University of Victoria) 

Richard Zhao (Queen’s University)

Korryn Bodner (University of Toronto)

Leila Amiri (York University)

Shelly Dixit (York University)

Bushra Majeed (York University)

Zach McCarthy (York University)

Tanjima Akhter (University of Alberta)

Xuyuan Wang (University of Alberta)

Russell   Milne (University of Alberta)

Brandon Bellows (University of Saskatchewan)

Mohammadali Tofighi (York University)

Arnab Mukherjee (University of Toronto)

Sasha van Katwyk (University of Ottawa)

Oskar Laverny (York University)

Nushrat Nazia (University of Waterloo)

Isam Al-Darabsah (University of Manitoba)

Sungju Moon (McMaster University)

Qiuyi Su (York University)

Zeinab Jamali (University of Saskatchewan)

Yujie Pei (University of Saskatchewan)

Somoyeh Sepahrom (University of Saskatchewan)

Dinh Toan Nguyen (Université du Québec à Montréal)

Kumar Murty, Chair, Director Fields Institute

Jianhong Wu, Co-PI, LIAM Director, York University

Adalsteinn Brown, Dean, Dalla Lana School of Publich Health, University of Toronto

Jame Colliander, Director, PIMS

Octav Cornea, Director, CRM

Deirdre Haskell, Deputy Director, Fields Institute

Sanjeev Seahra, Director, AARMS

Carolyn Tuohy, University of Toronto

Luc Vinet, Director, CRM

Deirdre Haskell, Chair, Deputy Director, Fields Institute

Marni Mishna, Deputy Director, PIMS

Morales Manuel, Deputy Director, CRM

Franklin Mendivil, Executive Committee, AARMS

France Gagnon, Dean of Research, Dalla Lana School of Public Health, University of Toronto

Total Elements: 221
Refereed Publication Count: 221-29=192
Book Chapters: 12 (within the Fields Communications)
Books (Fields Communications): 2
Preprints: 13
Submissions: 1
Manuscripts in Preparation: 3 (not listed in this document)
Reports (Ontario Science Table): 1

Abdeslami, M. M., Basri, L., El Fatini, M., Sekkak, I., & Taki, R. (2023). Vaccination effect on a stochastic epidemic model with healing and relapse. International Journal of Biomathematics. https://doi.org/10.1142/s1793524523500031

Abdollahi E, Keynan Y, Foucault P, Brophy J, Sheffield H, Moghadas SM. (2022).  Evaluation of TB elimination strategies in Canadian Inuit populations: Nunavut as a case study. Infectious Disease Modelling, ISSN 2468-0427, https://doi.org/10.1016/j.idm.2022.07.005

Acharya, K. R., Romero-Leiton, J. P., Parmley, E. J., & Nasri, B. (2023). Identification of the elements of models of antimicrobial resistance of bacteria for assessing their usefulness and usability in One Health decision making: a protocol for scoping review. BMJ open, 13(3), e069022. https://doi.org/10.1136/bmjopen-2022-069022

Ahmed, H., Cargill, T., Nicola Luigi Bragazzi, & Jude Dzevela Kong. (2022). Dataset of non-pharmaceutical interventions and community support measures across Canadian universities and colleges during COVID-19 in 2020. Frontiers in Public Health, 10. https://doi.org/10.3389/fpubh.2022.1066654

Akanteva, A., Dick, D.W., Amiraslani, S, Heffernan J.M. (2023). Canadian Covid-19 pandemic public health mitigation measures at the province level. Sci Data 10, 882. https://doi.org/10.1038/s41597-023-02759-y

Alavinejad, M., Mellado, B., Asgary, A., Mbada, M., Mathaha, T., Lieberman, B., Stevenson, F., Tripathi, N., Swain, A. K., Orbinski, J., Wu, J., & Kong, J. D. (2022). Management of hospital beds and ventilators in the Gauteng province, South Africa, during the COVID-19 pandemic. PLOS Global Public Health, 2(11), e0001113. https://doi.org/10.1371/journal.pgph.0001113

Alavinejad, M., Mellado, B., Asgary, A., Mbada, M., Mathaha, T., Lieberman, B., Stevenson, F., Tripathi, N., Swain, A. K., Orbinski, J., Wu, J., & Kong, J. D. (2022). Management of Healthcare Resources in the Gauteng Province, South Africa, During the COVID-19 Pandemic. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4049177

Alavinejad, M., Tosato, M., Bragazzi, NL., McCarthy, Z., Wu, J., and Bourouiba, L. (2024). Markers of community outbreak and facility type for mitigation of COVID-19 in long-term care homes in Ontario, Canada: Insights and implications from a time-series analysis. Annals of Epidemiology, 90, 9-20. https://doi.org/10.1016/j.annepidem.2023.08.005

[Preprint]--Ali, S.R., Lacourse, E., Pelletier-Dumas, M., Lina, J.M., Belair, J., de la Sablonniere, R. (2024). Beyond What Meets the Eye: Unveiling Dynamics of Compliance with Preventive Measures in the COVID-19 Era. Preprint available at Research Square. https://doi.org/10.21203/rs.3.rs-4391822/v1

[Book Chapter]--Arino, J. (2022). Describing, Modelling and Forecasting the Spatial and Temporal Spread of COVID-19: A Short Review. In: Murty, V.K., Wu, J. (eds) Mathematics of Public Health. Fields Institute Communications, vol 85. Springer, Cham. https://doi.org/10.1007/978-3-030-85053-1_2

Arino, J., & Milliken, E. (2022). Bistability in deterministic and stochastic SLIAR-type models with imperfect and waning vaccine protection. Journal of Mathematical Biology, 84(7). https://doi.org/10.1007/s00285-022-01765-9

Arino, J., & Milliken, E. (2022). Effect of Movement on the Early Phase of an Epidemic. Bulletin of Mathematical Biology, 84(11). https://doi.org/10.1007/s11538-022-01077-5

Asgary, A., Aarabi, M., Dixit, S., Wen, H., Ahmed, M., & Wu, J. (2024). A Survey of the Use of Modeling, Simulation, Visualization, and Mapping in Public Health Emergency Operations Centers during the COVID-19 Pandemic. International Journal of Environmental Research and Public Health, 21(3), 295. https://doi.org/10.3390/ijerph21030295

Asgary, A., Blue, H., Cronemberger, F., & Ni, M. (2022). Simulating a Hockey Hub COVID-19 Mass Vaccination Facility. Healthcare, 10(5), 843. https://doi.org/10.3390/healthcare10050843

Asgary, A., Blue, H., Solis, A. O., McCarthy, Z., Najafabadi, M., Tofighi, M. A., & Wu, J. (2022). Modeling COVID-19 Outbreaks in Long-Term Care Facilities Using an Agent-Based Modeling and Simulation Approach. International Journal of Environmental Research and Public Health, 19(5), 2635. https://doi.org/10.3390/ijerph19052635

Asgary, A., MG Cojocaru, MM Najafabadi, J Wu (2021). Simulating preventative testing of SARS-CoV-2 in schools: policy implications. BMC Public Health, 21(1): 1-18. https://doi.org/10.1186/s12889-020-10153-1

Asgary, A., Najafabadi, M. M., Wendel, S. K., Resnick-Ault, D., Zane, R. D., & Wu, J. (2021). Optimizing planning and design of COVID-19 drive-through mass vaccination clinics by simulation. Health and technology, 11(6), 1359–1368. https://doi.org/10.1007/s12553-021-00594-y

Asgary, A., Solis, A. O., Khan, N., Janithra Wimaladasa, & Maryam Shafiei Sabet. (2023). Spatiotemporal Analysis of Emergency Calls during the COVID-19 Pandemic: Case of the City of Vaughan. Urban Science, 7(2), 62–62. https://doi.org/10.3390/urbansci7020062

Asgary, A., Valtchev, S., Chen, M., Najafabadi, M., & Wu, J. (2021). Artificial intelligence model of drive-through vaccination simulation. International journal of environmental research and public health, 18(1), 268.https://doi.org/10.3390/ijerph18010268

Avusuglo, W.S., Bragazzi, N., Asgary, A., Orbinski, J., Wu, J., and Kong, J.D. (2023). Leveraging an epidemic–economic mathematical model to assess human responses to COVID-19 policies and disease progression. Sci Rep 13, 12842 (2023). https://doi.org/10.1038/s41598-023-39723-0

Avusuglo, W.S., Han, Q., Woldegerima, W.A., Bragazzi, N.L., Asgary, A., Ahmadi, A., Orbinski, J., Wu, J., Bellado, B., & Kong, J.D. (2024). Impact assessment of self-medication on COVID-19 prevalence in Gauteng, South Africa, using an age-structured disease transmission modelling framework. BMC Public Health 24, 1540. https://doi.org/10.1186/s12889-024-18984-y

Avusuglo, W., Han, Q., Woldegerima, W. A., Asgary, A., Wu, J., Orbinski, J., Bragazzi, N. L., Ahmadi, A., & Kong, J. D. (2024). Assessment of bidirectional impact of stigmatization induced self-medication on COVID-19 and malaria transmissions using mathematical modelling: Nigeria as a case study. Math Biosci. 376, 109249. https://doi.org/10.1016/j.mbs.2024.109249

Avusuglo, W. S., Mosleh, R., Tedi Ramaj, Li, A., Sileshi Sintayehu Sharbayta, Abdoul Aziz Fall, Srijana Ghimire, Shi, F., Lee, J. K. H., Thommes, E. W., Shin, T., & Wu, J. (2023). Workplace absenteeism due to COVID-19 and influenza across Canada: A mathematical model. Journal of Theoretical Biology, 111559–111559. https://doi.org/10.1016/j.jtbi.2023.111559

[Book Chapter]--Baez, J.C., Li, X., Libkind, S., Osgood, N.D., Redekopp, E. (2023). A Categorical Framework for Modeling with Stock and Flow Diagrams. In: David, J., Wu, J. (eds) Mathematics of Public Health. Fields Institute Communications, vol 88. Springer, Cham. https://doi.org/10.1007/978-3-031-40805-2_8

Baky Haskuee, M., Efendiev, M., Murty, VK. (2024). Containment Policies, Behaviour and Dynamics of the Pandemic. Advances in Mathematical Sciences and Applications, 33(2), pp. 419-457. https://mcm-www.jwu.ac.jp/~aikit/AMSA/pdf/abstract/2024/Top_2024_026.pdf

Baligh Jahromi, A., Attarian, K., Asgary, A., & Wu, J. (2024). Advancing Indoor Epidemiological Surveillance: Integrating Real-Time Object Detection and Spatial Analysis for Precise Contact Rate Analysis and Enhanced Public Health Strategies. International Journal of Environmental Research and Public Health, 21(11), 1502. https://doi.org/10.3390/ijerph21111502

Bednarski, S., Cowen, L., Ma, J., Philippsen, T., van, & Wang, M. (2022). A contact tracing SIR model for randomly mixed populations. Journal of Biological Dynamics, 16(1), 859–879. https://doi.org/10.1080/17513758.2022.2153938

Behzadifar, M., Aalipour, A., Kehsvari, M., Darvishi Teli, B., Ghanbari, M. K., Gorji, H. A., Sheikhi, A., Azari, S., Heydarian, M., Ehsanzadeh, S. J., Kong, J. D., Ahadi, M., & Bragazzi, N. L. (2022). The effect of COVID-19 on public hospital revenues in Iran: An interrupted time-series analysis. PLOS ONE, 17(3), e0266343. https://doi.org/10.1371/journal.pone.0266343

Bodner, K., Wang, L., Kustra, R., Kwong J., Sander B., Sbihi H., Irvine M.A., Mishra S. (2024). Impact of unequal testing on vaccine effectiveness estimates across two study designs: a simulation study. Nat Commun 16, 4849 (2025). https://doi.org/10.1038/s41467-025-59768-1

Boligarla, S., Laison, E.K.E., Li, J. et al. Leveraging machine learning approaches for predicting potential Lyme disease cases and incidence rates in the United States using Twitter. BMC Med Inform Decis Mak 23, 217 (2023). https://doi.org/10.1186/s12911-023-02315-z

Bouggar, D., El Fatini, M., El Hichamy, I., Nasri, B. R., & Sekkak, I. (2022). Near-optimal stochastic control for radiotherapy treatment in a random cancer model. Systems & Control Letters, 170, 105400. https://doi.org/10.1016/j.sysconle.2022.105400

Bouggar, D., El Fatini, M., Nasri, B., Petersson, R., & Sekkak, I. (2024). Stochastic near-optimal controls for treatment and vaccination in a COVID-19 model with transmission incorporating Lévy jumps. Stochastics, 96(1), 887–920. https://doi.org/10.1080/17442508.2024.2320846

Bragazzi, N. L., Garbarino, S., Puce, L., Trompetto, C., Marinelli, L., Currà, A., Jahrami, H., Trabelsi, K., Mellado, B., Asgary, A., Wu, J., & Kong, J. D. (2022). Planetary sleep medicine: Studying sleep at the individual, population, and planetary level. Frontiers in Public Health, 10. https://doi.org/10.3389/fpubh.2022.1005100

Bragazzi, N. L., Kong, J. D., & Wu, J. (2022). Integrated epidemiological, clinical, and molecular evidence points to an earlier origin of the current monkeypox outbreak and a complex route of exposure. Journal of Medical Virology. https://doi.org/10.1002/jmv.28244

Bragazzi, N. L., Kong, J. D., & Wu, J. (2022). Is monkeypox a new, emerging sexually transmitted disease? A rapid review of the literature. Journal of Medical Virology. https://doi.org/10.1002/jmv.28145

Bragazzi, N. L., Kong, J. D., Mahroum, N., Tsigalou, C., Khamisy-Farah, R., Converti, M., & Wu, J. (2022). Epidemiological trends and clinical features of the ongoing monkeypox epidemic: a preliminary pooled data analysis and literature review. Journal of Medical Virology. https://doi.org/10.1002/jmv.27931

Bragazzi, N. L., Woldegerima, W. A., Iyaniwura, S. A., Han, Q., Wang, X., Shausan, A., Badu, K., Okwen, P., Prescod, C., Westin, M., Omame, A., Converti, M., Mellado, B., Wu, J., & Kong, J. D. (2022). Knowing the unknown: The underestimation of monkeypox cases. Insights and implications from an integrative review of the literature. Frontiers in Microbiology, 13. https://doi.org/10.3389/fmicb.2022.1011049

Brankston, G., Fisman, D.N., Poljak, Z., Tuite, A.R., Greer, A.L. (2024). Examining the effects of voluntary avoidance behaviour and policy-mediated behaviour change on the dynamics of SARS-CoV-2: A mathematical model. Infectious Disease Modelling, 9(3), 701-712. https://doi.org/10.1016/j.idm.2024.04.001

Brankston, G., Merkley, E., Fisman, D., Tuite, A., Poljak, Z., Loewen, P., & Greer, A. (2021). Quantifying Contact Patterns in Response to COVID-19 Public Health Measures in Canada. BMC Public Health, 21(1),2040. https://doi.org/10.1186/s12889-021-12080-1

Brankston, G., Merkley, E., Loewen, P.J., Avery, B.P., Carson, C.A., Dougherty, B.P., Fisman D.N., Tuite, A.R., Poljak, Z., Greer, A.L. (2022). Pandemic fatigue or enduring precautionary behaviours? Canadians’ long-term response to COVID-19 public health measures. Preventive Medicine Reports, 30, 101993. https://doi.org/10.1016/j.pmedr.2022.101993

[Book Chapter]--Bucyibaruta, G., Dean, C.B., Renouf, E.M. (2022). A Logistic Growth Model with Logistically Varying Carrying Capacity for Covid-19 Deaths Using Data from Ontario, Canada. In: Murty, V.K., Wu, J. (eds) Mathematics of Public Health. Fields Institute Communications, vol 85. Springer, Cham. https://doi.org/10.1007/978-3-030-85053-1_3

Chakraborty AK, Gao S, Miry R, Ramazi P, Greiner R, Lewis MA, Wang H. (2024). An early warning indicator trained on stochastic disease-spreading models with different noises. Journal of the Royal Society Interface, Vol. 21: 20240199. https://doi.org/10.1098/rsif.2024.0199

Chakraborty A, Wang H, Ramazi P. (2024). From policy to prediction: assessing forecasting accuracy in an integrated framework with machine learning and disease models. Journal of Computational Biology, Vol. 31: 1104–1117. https://doi.org/10.1089/cmb.2023.037

Chaudhuri, S., Kasibhatla, P., Mukherjee, A., Pan, W., Morrison, G., Mishra, S., & Murty, V. K. (2022). Analysis of overdispersion in airborne transmission of COVID-19. Physics of Fluids, 34(5), 051914. https://doi.org/10.1063/5.0089347

Chen, A. A., Renouf, E. M., Dean, C. B., & Hu, X. J. (2025). The effects of deprivation, age, and regional differences in COVID-19 mortality from 2020 to 2022: a retrospective analysis of public provincial data. BMC public health, 25(1), 148. https://doi.org/10.1186/s12889-024-21031-5

Cheng, T., & Zou, X. (2022). A new perspective on infection forces with demonstration by a DDE infectious disease model. Mathematical Biosciences and Engineering, 19(5), 4856–4880. https://doi.org/10.3934/mbe.2022227

Cheng T and Zou X. (2024). Modelling the impact of precaution on disease dynamics and its evolution. J Math. Biol. 89(2024):1,22 pages. https://doi.org/10.1007/s00285-024-02100-0 

Ciupeanu, A.S., Varughese, M., Roda, W. C., Han, D., Cheng, Q., & Li, M. Y. (2022). Mathematical modeling of the dynamics of COVID-19 variants of concern: Asymptotic and finite-time perspectives. Infectious Disease Modelling, 7(4), 581–596. https://doi.org/10.1016/j.idm.2022.08.004

Colijn, C., Earn, D. J., Dushoff, J., Ogden, N. H., Li, M., Knox, N., Van Domselaar, G., Franklin, K., Jolly, G., & Otto, S. P. (2022). The need for linked genomic surveillance of SARS-CoV-2. Canada Communicable Disease Report, 48(4), 131–139. https://doi.org/10.14745/ccdr.v48i04a03

Coudeville, L., Amiche, A., Rahman, A., Arino, J., Tang, B., Jollivet, O., Dogu, A., Thommes, E., & Wu, J. (2022). Disease transmission and mass gatherings: a case study on meningococcal infection during Hajj. BMC Infectious Diseases, 22(1). https://doi.org/10.1186/s12879-022-07234-4

Csörgő, M., Dawson, D., Nasri, B., & Rémillard, B. (2022). A random walk through Canadian contributions on empirical processes and their applications in probability and statistics. Canadian Journal of Statistics. 50(4), 1116-1142. https://doi.org/10.1002/cjs.11730

David, J., Bragazzi, N. L., Scarabel, F., McCarthy, Z., & Wu, J. (2022). Non-pharmaceutical intervention levels to reduce the COVID-19 attack ratio among children. Royal Society Open Science, 9(3). https://doi.org/10.1098/rsos.211863

[Book Chapter]--David, J., Brankston, G., Sekkak, I., Moon, S., Li, X., Jahedi, S., Mohammadi, Z., Li, A., Grunnil, M., Song, P., Woldergema, WA., Bragazzi, N., & Wu, J. (2023). Mathematical Models: Perspectives of Mathematical Modelers and Public Health Professionals. In: David, J., Wu, J. (eds) Mathematics of Public Health. Fields Institute Communications, vol 88. Springer, Cham. https://doi.org/10.1007/978-3-031-40805-2_1

David, J.F., Iyaniwura, S.A. (2022). Effect of Human Mobility on the Spatial Spread of Airborne Diseases: An Epidemic Model with Indirect Transmission. Bull Math Biol 84, 63. https://doi.org/10.1007/s11538-022-01020-8

[Book]--David, J., & Wu, J. (2023). Mathematical Modelling from the Next Generation. (eds) Mathematics of Public Health. Fields Institute Communications, vol 88. Springer, Cham.

Delory, T., Arino, J., Haÿ, P. E., Klotz, V., & Boëlle, P. Y. (2023). SARS-CoV-2 in Nursing Homes: Analysis of Routine Surveillance Data in Four European Countries. Aging and disease, 14(2), 325–330. https://doi.org/10.14336/AD.2022.0820

Deng J, Shu H, Wang L and Zou X. (2024). Modeling virus-stimulated proliferation of CD4+ T-cell, cell-to-cell transmission and viral loss in HIV infection dynamics. Math. Biosci. 377: 109302.https://doi.org/10.1016/j.mbs.2024.109302

[Preprint]--Divol V., Guérin H., Nguyen D.T. & Tran V.C. (2024). Measure estimation on a manifold explored by a diffusion process. Arxiv:2410.11777 (2024) https://doi.org/10.48550/arXiv.2410.11777

Duvvuri VR, Hicks JT, Damodaran L, Grunnill M, Braukmann T, Wu J, Gubbay JB, Patel SN, Bahl J. (2023). Comparing the transmission potential from sequence and surveillance data of 2009 North American influenza pandemic waves. Infect Dis Model. Feb 16;8(1):240-252. https://doi.org/10.1016/j.idm.2023.02.003

El-Mousawi F, Ortiz AM, Berkat R, Nasri B. (2024). The Impact of “Soft” and “Hard” Flood Adaptation Measures on Affected Population’s Mental Health: A Mixed Method Scoping Review. Disaster Medicine and Public Health Preparedness, 18, e118. https://doi.org/10.1017/dmp.2024.128

Fan, G., Li, J., Bélair, J., & Zhu, H. (2023). Delayed Model for the Transmission and Control of COVID-19 with Fangcang Shelter Hospitals. Siam Journal on Applied Mathematics, 83(1), 276–301. https://doi.org/10.1137/21m146154x

[Preprint]--Ferrara I & Furman E. (2024). To Vaccinate or Not to Vaccinate in a Model with Social Pressure, Morality and Cognitive Dissonance. Morality and Cognitive Dissonance.  SSRN. http://dx.doi.org/10.2139/ssrn.4591079

Fitzpatrick MC, Moghadas SM, Vilches TN, Shah A, Pandey A, Galvani AP. (2023). Estimated US Pediatric Hospitalizations and School Absenteeism Associated with Accelerated COVID-19 Bivalent Booster Vaccination. JAMA Network Open 2023; 6(5): e2313586. https://doi.org/10.1001/jamanetworkopen.2023.13586

Flynn-Primrose, D., Hoover, N., Mohammadi, Z., Hung, A. , Lee, J. , Tomovici, M. , Thommes, E. , Neame, D. and Cojocaru, M. (2022) Meaningful Contact Estimates among Children in a Childcare Centre with Applications to Contact Matrices in Infectious Disease Modelling. Journal of Applied Mathematics and Physics, 10, 1525-1546. https://doi.org/10.4236/jamp.2022.105107

Foutel-Rodier, F., Charpentier, A. & Guérin, H. (2025). Optimal vaccination policy to prevent endemicity: a stochastic model. J. Math. Biol. 90, 10. https://doi.org/10.1007/s00285-024-02171-z

Ganser I, Buckeridge DL, Heffernan JM, Prague M, Thiébaut R. (2024). Estimating the population effectiveness of interventions against COVID-19 in France: A modelling study. Epidemics, 46, 100744. https://doi.org/10.1016/j.epidem.2024.100744

Gao S, Chakraborty AK, Greiner R, Lewis MA, Wang H. (2025). Early detection of disease outbreaks and non-outbreaks using incidence data: A framework using feature-based time series classification and machine learning. PLOS Computational Biology, Vol. 21: e1012782. https://doi.org/10.1371/journal.pcbi.1012782

[Preprint]--Gazeau S, Deng X, Brunet-Ratnasingham E, Kaufmann DE, Larochelle C, Morel PA, Heffernan JM, Davis CL, Smith AM, Jenner AL, Craig M. (2024). Using virtual patient cohorts to uncover immune response differences in cancer and immunosuppressed COVID-19 patients. bioRxiv https://doi.org/10.1101/2024.08.01.605860

Gholami, S., Korosec, C. S., Farhang‐Sardroodi, S., Dick, D. W., Craig, M., Ghaemi, M. S., Ooi, H. K., & Heffernan, J. M. (2023). A mathematical model of protein subunits COVID-19 vaccines. Mathematical Biosciences, 358, 108970. https://doi.org/10.1016/j.mbs.2023.108970

Grunnill M, Arino J, Ghasemi A, Thommes EW, Wu J. (2024). MetaCast: A package for broadCASTing epidemiological and ecological models over META-populations. Journal of Open Source Software, 9(99), 6851. https://doi.org/10.21105/joss.06851

Grunnill, M., Arino, J., McCarthy, Z., Bragazzi, NL., Coudeville, L., Thommes, E. W., Amiche, A., Ghasemi, A., Bourouiba, L., Tofighi, M., Asgary, A., Baky-Haskuee, M., & Wu, J. (2024). Modelling Disease Mitigation at Mass Gatherings: A Case Study of COVID-19 at the 2022 FIFA World Cup. PLOS Comp. Biol. Jan 18;20(1) e1011018. https://doi.org/10.1371/journal.pcbi.1011018

Grunnill, M., Eshaghi, A., Damodaran, L. et al. Inferring enterovirus D68 transmission dynamics from the genomic data of two 2022 North American outbreaks. npj Viruses 2, 34 (2024). https://doi.org/10.1038/s44298-024-00047-z

[Preprint]--Guérin, H., Nguyen, D.-T., & Tran, V.-C. (2022). Strong uniform convergence of Laplacians of random geometric and directed kNN graphs on compact manifolds. ArXiv.org. https://doi.org/10.48550/arXiv.2212.10287

Han Z, Wang Y, Gao S, Sun G, Wang H. (2024). Final epidemic size of a two-community SIR model with asymmetric coupling, Journal of Mathematical Biology, Vol. 88: 51. https://doi.org/10.1007/s00285-024-02073-0

Hegazy, N., Peng, K.K., D’Aoust, P.M. et al. (2025). Variability of Clinical Metrics in Small Population Communities Drive Perceived Wastewater and Environmental Surveillance Data Quality: Ontario, Canada-Wide Study. ACS ES&T Water 5(4), 1605-1619. https://doi.org/10.1021/acsestwater.4c00958

Hegazy, N., Peng, K.K., Hu, J., Dean, C.M., Renouf, E., Delatolla, R. (2025). Wastewater-Based Surveillance More Accurately Describes Disease Burden Of COVID-19 In Communities with Less Than 60,000 Inhabitants – An Ontario-Wide Study. Open Forum Infectious Diseases, 12(1), 1954. https://doi.org/10.1093/ofid/ofae631.2113

Hempel, K., McDonald, W., Osgood, N. D., Fisman, D., Halperin, S. A., Crowcroft, N., Klein, N. P., Rohani, P., & Doroshenko, A. (2023). Evaluation of the effectiveness of maternal immunization against pertussis in Alberta using agent-based modeling: A Canadian immunization research network study. Vaccine, 15(41), 2430-2438. https://doi.org/10.1016/j.vaccine.2022.12.071

Hillmer, M. P., Feng, P., McLaughlin, J. R., Murty, V. K., Sander, B., Greenberg, A., & Brown, A. D. (2021). Ontario’s COVID-19 Modelling Consensus Table: mobilizing scientific expertise to support pandemic response. Canadian Journal of Public Health, 112, 799-806. https://doi.org/10.17269/s41997-021-00559-8

Hu, L., Shi, M., Li, M., & Ma, J. (2023). The effectiveness of control measures during the 2022 COVID-19 outbreak in Shanghai, China. PLOS ONE, 18(5), e0285937–e0285937. https://doi.org/10.1371/journal.pone.0285937

Hurford, A., Martignoni, M. M., Loredo-Osti, J. C., Anokye, F., Arino, J., Husain, B. S., Gaas, B., & Watmough, J. (2023). Pandemic modelling for regions implementing an elimination strategy. Journal of Theoretical Biology, 561, 111378. https://doi.org/10.1016/j.jtbi.2022.111378

[Preprint]--Idriss Sekkak, Jude Dzevela Kong, & Mohamed El Fatini. (2022). Containing and Managing an Emerging Disease Outbreak: A Stochastic Modelling Approach. Social Science Research Network. https://doi.org/10.2139/ssrn.4040246

Idriss Sekkak, Nasri, B., Rémillard, B., Jude Dzevela Kong, & Mohamed El Fatini. (2023). A Stochastic Analysis of a SIQR Epidemic Model with Short and Long-term Prophylaxis. Communications in Nonlinear Science and Numerical Simulation, V127, 2023, 107523, ISSN 1007-5704. https://doi.org/10.1016/j.cnsns.2023.107523

Iyaniwura, S. A., Musa Rabiu, & Jude Dzevela Kong. (2023). A generalized distributed delay model of COVID-19: An endemic model with immunity waning. Mathematical Biosciences and Engineering, 20(3), 5379–5412. https://doi.org/10.3934/mbe.2023249

Iyaniwura, S. A., Rabiu, M., David, J. F., & Kong, J. D. (2022). The basic reproduction number of COVID-19 across Africa. PLOS ONE, 17(2), e0264455. https://doi.org/10.1371/journal.pone.0264455

Jahedi, S., Wang, L., Yorke, J.A., Watmough, J. (2024). Finding Hopf bifurcation islands and identifying thresholds for success or failure in oncolytic viral therapy. Mathematical Biosciences, 376, 109275. https://doi.org/10.1016/j.mbs.2024.109275

Jalal Possik, Asgari, A., Solis, A. O., Zacharewicz, G., Shafiee, M., Najafabadi, M. M., Nazanin Nadri, Guimaraes, A. S., Hossein Iranfar, Ma, P., Lee, C. M., Mohammadali Tofighi, Mahmoud Aarabi, Gorecki, S., & Wu, J. (2023). An Agent-Based Modeling and Virtual Reality Application Using Distributed Simulation: Case of a COVID-19 Intensive Care Unit. IEEE Transactions on Engineering Management, 70(8), 2931–2943. https://doi.org/10.1109/tem.2022.3195813

Jalal Possik, Azar, D., Solis, A. O., Asgary, A., Zacharewicz, G., Karami, A., Mohammadali Tofighi, Najafabadi, M. M., Shafiee, M., Merchant, A. A., Mahmoud Aarabi, & Wu, J. (2022). A distributed digital twin implementation of a hemodialysis unit aimed at helping prevent the spread of the Omicron COVID-19 variant. 2022 IEEE/ACM 26th International Symposium on Distributed Simulation and Real Time Applications (DS-RT), Alès, France, 168-174. https://doi.org/10.1109/ds-rt55542.2022.9932047

Ji J, Ahmed S, Wang H. (2025). A hybrid approach to study and forecast climate-sensitive norovirus infections in the USA. Journal of Theoretical Biology, Vol. 598: 112007. https://doi.org/10.1016/j.jtbi.2024.112007

Jing, S., Milne, R., Wang, H., & Xue, L. (2023). Vaccine hesitancy promotes emergence of new SARS-CoV-2 variants. 570, 111522–111522. https://doi.org/10.1016/j.jtbi.2023.111522

Kaur, M., Nicola Luigi Bragazzi, Heffernan, J. M., Tsasis, P., Wu, J., & Jude Dzevela Kong. (2023). COVID-19 in Ontario Long-term Care Facilities Project, a manually curated and validated database. Frontiers in Public Health, 11. https://doi.org/10.3389/fpubh.2023.1133419

Kim, S., Athar, S., LI, Y., S. Koumarianos, Cheng, T., Amiri, L., W. Avusuglo, W.A. Woldegerima, Fall, A., John-Baptiste, A., Diener, A., & Wu, J. (2022). Assessing the epidemiological and economic impact of alternative vaccination strategies: a modeling study. International Journal of Infectious Diseases, 116, S60–S60. https://doi.org/10.1016/j.ijid.2021.12.142

[Book Chapter]--Knight, J., Mishra, S. (2023). Contact Matrices in Compartmental Disease Transmission Models. In: David, J., Wu, J. (eds) Mathematics of Public Health. Fields Institute Communications, vol 88. Springer, Cham. https://doi.org/10.1007/978-3-031-40805-2_4

Kolokolnikov, T., & Iron, D. (2021). Law of mass action and saturation in SIR model with application to Coronavirus modelling. Infectious Disease Modelling, 6, 91–97. https://doi.org/10.1016/j.idm.2020.11.002

Kong, Jude & Bragazzi, Nicola & Movhedi, Zahra & Wu, Jianhong. (2022). The monkeypox outbreak and the risk of a racist media coverage. Journal of Medical Virology.

[Preprint]--Korosec CS, Conway JM, Matveev VA, Ostrowski M, Heffernan JM, & Ghaemi MS. (2025). Machine Learning Reveals Distinct Immunogenic Signatures of Th1 Imprinting in ART-Treated Individuals with HIV Following Repeated SARS-CoV-2 Vaccination. BioRxiv: https://doi.org/10.1101/2025.03.18.643769

Korosec CS, Wahl LM, Heffernan JM. (2024). Within-host evolution of SARS-CoV-2: how often are de novo mutations transmitted from symptomatic infections? Virus Evolution, 10(1), veae006.
https://doi.org/10.1093/ve/veae006

Laison E, Hamza Ibrahim M, Boligarla S, Li J, Mahadevan R, Ng A, Muthuramalingam V, Lee W, Yin Y, Nasri B. (2023). Identifying Potential Lyme Disease Cases Using Self-Reported Worldwide Tweets: Deep Learning Modeling Approach Enhanced with Sentimental Words Through Emojis. J Med Internet Res, 25:e47014. https://doi.org/10.2196/47014

Li, A., Wang, Y., Cong, P., & Zou, X. (2021). Re-examination of the impact of some non-pharmaceutical interventions and media coverage on the COVID-19 outbreak in Wuhan. Infectious Disease Modelling, 6, 975–987. https://doi.org/10.1016/j.idm.2021.07.001

Li A, Wang Z, Moghadas SM. (2023). Modelling the impact of timelines of testing and isolation on disease control. Infectious Disease Modelling 8: 58-71. https://doi.org/10.1016/j.idm.2022.11.008

Li A, Wu J, Moghadas SM. (2023). Epidemic dynamics with time-varying transmission risk reveal the role of disease stage-dependent infectiousness. J Theor Biol 573, 111594. https://doi.org/10.1016/j.jtbi.2023.111594

A. Li and X. Zou. (2024). R0 may not tell us everything: transient disease dynamics of some SIR models over patchy environments. Bull. Math. Biol. 86(2024):41. https://doi.org/10.1007/s11538-024-01271-7

Li, M., Dushoff, J., David, & Bolker, B. M. (2023). Evaluating undercounts in epidemics: response to Maruotti et al. 2022. J. Med. Virol: 95(2) e28474. https://doi.org/10.1002/jmv.28474

Li, M., Ling, Y., & Ma, J. (2023). The dynamics of the risk perception on a social network and its effect on disease dynamics. Infectious Disease Modelling, 8(3), 632–644. https://doi.org/10.1016/j.idm.2023.05.006

Li, M., Zhai, R., & Ma, J. (2023). The effects of disease control measures on the reproduction number of COVID-19 in British Columbia, Canada. Mathematical Biosciences and Engineering, 20(8), 13849–13863. https://doi.org/10.3934/mbe.2023616

Li X, Patel V, Duan L, Mikuliak J, Basran J, Osgood ND. (2024). Real-Time Epidemiology and Acute Care Need Monitoring and Forecasting for COVID-19 via Bayesian Sequential Monte Carlo-Leveraged Transmission Models. Int J Environ Res Public Health, 21(2):193. https://doi.org/10.3390/ijerph21020193

Lieberman, B., Kong, J.D., Gusinow, R. et al. Big data- and artificial intelligence-based hot-spot analysis of COVID-19: Gauteng, South Africa, as a case study. BMC Med Inform Decis Mak 23, 19 (2023). https://doi.org/10.1186/s12911-023-02098-3

Liu, A., & Magpantay, G. (2022). A Quantification of Long Transient Dynamics. SIAM Journal on Applied Mathematics, 82(2), 381–407. https://doi.org/10.1137/20m1367131

Liu, A., Magpantay, G., & Abdella, K. (2023). A framework for long-lasting, slowly varying transient dynamics. Mathematical Biosciences and Engineering, 20(7), 12130–12153. https://doi.org/10.3934/mbe.2023540

Liu, J., Bellows, B., Hu, X.J. et al. A new time-varying coefficient regression approach for analyzing infectious disease data. Sci Rep 13, 14687 (2023). https://doi.org/10.1038/s41598-023-41551-1

Magpantay, F.M.G., Mao, J., Ren, S., Zhao, S., Meadows, T. (2023). The reinfection threshold, revisited. Mathematical Biosciences, 363, 109045. https://doi.org/10.1016/j.mbs.2023.109045

Majeed, B., Jummy Funke David, Nicola Luigi Bragazzi, McCarthy, Z., Grunnill, M., Heffernan, J. M., Wu, J., & Woldegebriel Assefa Woldegerima. (2023). Mitigating co-circulation of seasonal influenza and COVID-19 pandemic in the presence of vaccination: A mathematical modeling approach. Frontiers in Public Health, 10. https://doi.org/10.3389/fpubh.2022.1086849

Martignoni, M. M., Mohammadi, Z., J. Concepción Loredo-Osti, & Hurford, A. (2023). Extensive SARS-CoV-2 testing reveals BA.1/BA.2 asymptomatic rates and underreporting in school children. Can Commun Dis Rep. 49(4):155-165. https://doi.org/10.14745/ccdr.v49i04a08

Martignoni, M. M., Rahman, P., & Hurford, A. (2022). Rotational worker vaccination provides indirect protection to vulnerable groups in regions with low COVID-19 prevalence. AIMS Mathematics, 7(3): 3988-4003. https://doi.org/10.3934/math.2022220

Martignoni, M. M., Renault, J., Baafi, J., & Hurford, A. (2022). Downsizing of COVID-19 contact tracing in highly immune populations. PLOS ONE, 17(6), e0268586–e0268586. https://doi.org/10.1371/journal.pone.0268586

Matveev VA, Mihelic EZ, Benko E, et. al. (2023). Immunogenicity of COVID-19 vaccines and their effect on HIV reservoir in older people with HIV. iScience, 26(10), 107915. https://www.sciencedirect.com/science/article/pii/S2589004223019922

McDonald, G. W., Bradford, L., Neapetung, M., Osgood, N. D., Strickert, G., Waldner, C. L., Belcher, K., McLeod, L., & Bharadwaj, L. (2022). Case Study of Collaborative Modeling in an Indigenous Community. Water, 14(17), 2601. https://doi.org/10.3390/w14172601

[Book Chapter]--McDonald, G.W., Osgood, N.D. (2023). Agent-Based Modeling and Its Trade-Offs: An Introduction and Examples. In: David, J., Wu, J. (eds) Mathematics of Public Health. Fields Institute Communications, vol 88. Springer, Cham. https://doi.org/10.1007/978-3-031-40805-2_9

Mistry, A., Zhang, X., Ray, S., Seahra, S. (2024). SCORE: Scalable Contact Tracing over Uncertain Trajectories. In: Zaslavsky, A., Ning, Z., Kalogeraki, V., Georgakopoulos, D., Chrysanthis, P.K. (eds) Mobile and Ubiquitous Systems: Computing, Networking and Services. MobiQuitous 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 593. Springer, Cham. https://doi.org/10.1007/978-3-031-63989-0_4

Moghadas SM, Thomas N Vilches, Kevin Zhang, Chad R Wells, Affan Shoukat, Burton H Singer, Lauren Ancel Meyers, Kathleen M Neuzil, Joanne M Langley, Meagan C Fitzpatrick, Alison P Galvani. (2021). The Impact of Vaccination on Coronavirus Disease 2019 (COVID-19) Outbreaks in the United States. Clinical Infectious Diseases, 73(12), 2257–2264. https://doi.org/10.1093/cid/ciab079

[Preprint]--Mohammadi, Z., Cojocaru M, Arino, J., & Hurford, A. (2023). Importation models for travel-related SARS-CoV-2 cases reported in Newfoundland and Labrador during the COVID-19 pandemic. medRxiv preprint. https://doi.org/10.1101/2023.06.08.23291136

Mohammadi, Z., Cojocaru, M. G., & Thommes, E. W. (2022). Human behaviour, NPI and mobility reduction effects on COVID-19 transmission in different countries of the world. BMC Public Health, 22(1). https://doi.org/10.1186/s12889-022-13921-3

Molla, J., Sekkak, I., Ortiz, A. M., Moyles, I., & Nasri, B. (2023). Mathematical modeling of mpox: a scoping review. One Health, 16, 100540. https://doi.org/10.1016/j.onehlt.2023.100540

Moon, S., Wolfson, M. (2024). Exploring the Chaotic Dynamics of Cocirculating Disease Strains: Toward Agent-Based Modeling. In: Skiadas, C.H., Dimotikalis, Y. (eds) 16th Chaotic Modeling and Simulation International Conference. CHAOS 2023. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-031-60907-7_31

Moran EJ, Martignoni MM, Lecomte N, Leighton P, & Hurford A. (2023). When host populations move north, but disease moves south: Counter-intuitive impacts of climate change on disease spread. Theoretical Ecology, 16(1), 13–19. https://doi.org/10.1007/s12080-022-00551-z

Mosleh R, Baky-Haskuee M, Ghasemi A, Grunnill M, Arino J, Tofighi M, Thommes EW, Wu J. (2024). Evaluating infectious disease outbreak potential and mitigation effectiveness on cruise ships. Journal of Theoretical Biology, 592, 111875. https://doi.org/10.1016/j.jtbi.2024.111875

Moyles, I.R., Korosec, C.S. & Heffernan, J.M. (2023). Determination of significant immunological timescales from mRNA-LNP-based vaccines in humans. J. Math. Biol. 86. https://doi.org/10.1007/s00285-023-01919-3

[Preprint]--Mukherjee A, Mishra S, Murty VK, Chaudhuri S. (2023). Analysing the distribution of SARS-CoV-2 infections in schools: integrating model predictions with real world observations. bioRxiv. https://doi.org/10.1101/2023.12.21.572736

[Book]--Murty, V. K., & Wu, J. (2022). Mathematics of Public Health: Proceedings of the Seminar on the Mathematical Modelling of COVID-19. (eds) Mathematics of Public Health. Fields Institute Communications, vol 85. Springer, Cham.

Naeemi, P., Asgary, A., Arabi, M., Taghi-Molla, A., Wu, J. (2024). Pandemic resilience serious game: crafting an educational strategy board game. J Public Health (Berl.)  https://doi.org/10.1007/s10389-024-02305-z

Nasri BR, Rémillard BN, Bahraoui T. (2022). Change-point problems for multivariate time series using pseudo-observations. Journal of Multivariate Analysis, 187,104857.  https://doi.org/10.1016/j.jmva.2021.104857

Nasri, BR. (2022). Tests of serial dependence for multivariate time series with arbitrary distributions.  Journal of Multivariate Analysis. 192, 105102. https://doi.org/10.1016/j.jmva.2022.105102

[Submission]--Nazia, N., & Dean, C. (2025). Joint spatial modelling of COVID-19 severity and geographic inequality among seniors: An analysis of spatial, socioeconomic and demographic risk in Ontario, Canada. Manuscript submitted for publication.

Nazia N, Law J, Butt ZA. (2023). Modelling the spatiotemporal spread of COVID-19 outbreaks and prioritization of the risk areas in Toronto, Canada. Health Place, 80:102988.
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[Preprint]--Nguyen, D.-T. (2022). Scaling limit of the collision measures of multiple random walks. ArXiv.org. https://doi.org/10.48550/arXiv.2203.08523

Nia, Z. M., Ahmadi, A., Bragazzi, N. L., Woldegerima, W. A., Mellado, B., Wu, J., Orbinski, J., Asgary, A., & Kong, J. D. (2022). A cross-country analysis of macroeconomic responses to COVID-19 pandemic using Twitter sentiments. PLOS ONE, 17(8), e0272208. https://doi.org/10.1371/journal.pone.0272208

Nia, Z. M., Asgary, A., Bragazzi, N., Mellado, B., Orbinski, J., Wu, J., & Kong, J. (2022). Nowcasting unemployment rate during the COVID-19 pandemic using Twitter data: The case of South Africa. Frontiers in Public Health, 10. https://doi.org/10.3389/fpubh.2022.952363

Nia ZM, Bragazzi NL, Ahamadi A, Asgary A, Mellado B, Orbinski J, Seyyed-Kalantari L, Woldegerima WA, Wu J and Kong JD. (2023). Off-label drug use during the COVID-19 pandemic in Africa: topic modelling and sentiment analysis of ivermectin in South Africa and Nigeria as a case study. J. R. Soc. Interface. 20: 20230200. http://doi.org/10.1098/rsif.2023.0200

Nia ZM, Bragazzi NL, Asgary A, Orbinski J, Wu J, & Kong JD. (2023). Mpox panic, infodemic, and stigmatization of the 2SLGBTQIAP+ community: geospatial analysis, topic modeling, and sentiment analysis of a large, multilingual social media database. J Med Internet Res. 25, e45108. https://doi.org/10.2196/45108

Nia, Z. M., Bragazzi, N. L., Wu, J., & Kong, J. D. (2023). A Twitter dataset for Monkeypox, May 2022. Data in Brief, 48, 109118. https://doi.org/10.1016/j.dib.2023.109118

Nia ZM, Seyyed-Kalantari L, Goitom M, Mellado B, Ahmadi A, Asgary A, Orbinski J, Wu J, Kong JD. (2025). Leveraging deep-learning and unconventional data for real-time surveillance, forecasting, and early warning of respiratory pathogens outbreak. Artificial Intelligence in Medicine, 161, 103076. https://doi.org/10.1016/j.artmed.2025.103076

Nicola Luigi Bragazzi, Qing Kai Han, Iyaniwura, S. A., Omame, A., Aminath Shausan, Wang, X., Woldegebriel Assefa Woldegerima, Wu, J., & Jude Dzevela Kong. (2023). Adaptive changes in sexual behavior in the high‐risk population in response to human monkeypox transmission in Canada can help control the outbreak: insights from a two‐group, two‐route epidemic model. Journal of Medical Virology, 95(4). https://doi.org/10.1002/jmv.28575

Nourbakhsh, S., Fazil, A., Li, M., Mangat, C. S., Peterson, S. W., Daigle, J., Langner, S., Shurgold, J., D’Aoust, P., Delatolla, R., Mercier, E., Pang, X., Lee, B. E., Stuart, R., Wijayasri, S., & Champredon, D. (2022). A wastewater-based epidemic model for SARS-CoV-2 with application to three Canadian cities. Epidemics, 39, 100560. https://doi.org/10.1016/j.epidem.2022.100560

Nunes MC, Thommes E, Holger Fröhlich, et. al. (2024). Redefining pandemic preparedness: Multidisciplinary insights from the CERP modelling workshop in infectious diseases, workshop report. Infectious Disease Modelling, 9(2), 501-518. https://doi.org/10.1016/j.idm.2024.02.008

Ortiz, A.M. & Nasri, B. (2024). Socio-demographic determinants of COVID-19 vaccine uptake in Ontario: Exploring differences across the Health Region model. Vaccine 42(8), 2106-2114. https://doi.org/10.1016/j.vaccine.2024.02.045

Pandey, A., Fitzpatrick, M.C., Moghadas, S.M., Vilches, T.N., Ko, C., Vasan, A., Galvani, A.P. (2023). Modelling the impact of a high-uptake bivalent booster scenario on the COVID-19 burden and healthcare costs in New York City. The Lancet Regional Health - Americas, 24, 100555. https://doi.org/10.1016/j.lana.2023.100555

Pandey A, Wells CR, StadnytskyiV, Sah P, Moghadas SM, Marathe M, Singer BH, Nesterova O, Galvani AP, Disease burden among Ukrainians forcibly displaced by the 2022 Russian invasion, Proceedings of the National Academy of Sciences USA 120(8): e2215424120. https://doi.org/10.1073/pnas.2215424120

Parker, M. R. P., Li, Y., Elliott, L. T., Ma, J., & Cowen, L. L. E. (2021). Under‐reporting of COVID‐19 in the Northern Health Authority region of British Columbia. Canadian Journal of Statistics, 49(4), 1018–1038. https://doi.org/10.1002/cjs.11664

[Preprint]--Peng, K. K., Dean, C. B., Delatolla, R., & Hu, X. J. (2024). Learning associations of COVID-19 hospitalizations with wastewater viral signals by Markov modulated models. arXiv preprint https://doi.org/10.48550/arXiv.2410.07487

Peng, K., Renouf, E., Dean, C. B., Hu, X., Delatolla, R., & Manuel, D. (2023). An exploration of the relationship between wastewater viral signals and COVID-19 hospitalizations in Ottawa, Canada. Infectious Disease Modelling, 8(3), 617–631. https://doi.org/10.1016/j.idm.2023.05.011

Puce, L., Trabelsi, K., Ammar, A., Jabbour, G., Marinelli, L., Mori, L., Kong, J. D., Tsigalou, C., Cotellessa, F., Schenone, C., Samanipour, M. H., Biz, C., Ruggieri, P., Trompetto, C., & Bragazzi, N. L. (2022). A tale of two stories: COVID-19 and disability. A critical scoping review of the literature on the effects of the pandemic among athletes with disabilities and para-athletes. Frontiers in Physiology, 13, 967661. https://doi.org/10.3389/fphys.2022.967661

Qesmi R, Heffernan J and Wu J. (2023). Stability Switches, Hopf Bifurcation and Chaotic Dynamics in Simple Epidemic Model with State Dependent Delay. International Journal of Bifurcation and Chaos, 33(11), 2330028. https://doi.org/10.1142/S0218127423300288

Qian, W., Stanley, K. G., & Osgood, N. D. (2023). Impacts of observation frequency on proximity contact data and modeled transmission dynamics. PLOS Computational Biology, 19(2), e1010917–e1010917. https://doi.org/10.1371/journal.pcbi.1010917

[Preprint]--Qing Kai Han, Nicola Luigi Bragazzi, Asgary, A., Orbinski, J., Wu, J., & Jude Dzevela Kong. (2022). Estimation of epidemiological parameters and ascertainment rate from early transmission of COVID-19 across Africa. Research Square (Research Square). https://doi.org/10.21203/rs.3.rs-1708820/v1

Ramaj, T. and Zou, X. (2023). On the treatment of melanoma: A mathematical model of oncolytic virotherapy, Math. Biosci. 365 (2023): 109073. https://doi.org/10.1016/j.mbs.2023.109073

Ramsay D, McDonald W, Thompson M, Erickson N, Gow S, Osgood ND, Waldner C. (2025). Contagious acquisition of antimicrobial resistance is critical for explaining emergence in western Canadian feedlots—insights from an agent-based modelling tool. Frontiers in Veterinary Science, 11. https://doi.org/10.3389/fvets.2024.1466986 

Rees EE, Avery BP, Carabin H, Carson CA, Champredon D, de Montigny S, Dougherty B, Nasri BR, Ogden NH. Effectiveness of non-pharmaceutical interventions to reduce SARS-CoV-2 transmission in Canada and their association with COVID-19 hospitalization rates. Can Commun Dis Rep. 2022 Oct 1;48(10):438-448. PMID: 38162959; PMCID: PMC10756332. https://pmc.ncbi.nlm.nih.gov/articles/PMC10756332/

Romero-Leiton, J. P., Acharya, K. R., Parmley, J. E., Arino, J., & Nasri, B. (2023). Modelling the transmission of dengue, zika and chikungunya: a scoping review protocol. BMJ open, 13(9), e074385. https://doi.org/10.1136/bmjopen-2023-074385

Romero-Leiton, J.P., Peterson, A., Aguirre, P., Acharya, K., Nasri B. (2025). Assessing the impact of mutations and horizontal gene transfer on the antimicrobial resistance and its control: a mathematical model. Comp. Appl. Math. 44, 82. https://doi.org/10.1007/s40314-024-03043-4

Romero-Leiton JP, Laison E, Alfaro R, Parmley EJ, Arino J, Acharya KR, Nasri B. (2025). Exploring Zika's dynamics: A scoping review journey from epidemic to equations through mathematical modelling. Infectious Disease Modelling, 10(2), 536-558. https://doi.org/10.1016/j.idm.2024.12.016

Romero-Leiton, J.P., Sekkak, I., Arino, J., Moyles, I. & Nasri, B. (2025). Mathematical Modelling of the First HIV/ZIKV Co-infection Cases in Colombia and Brazil. Bull Math Biol 87, 54 (2025). https://doi.org/10.1007/s11538-025-01429-x

[Book Chapter]--Röst, G., Wang, Z., Moghadas, S.M. (2024). Waiting for the Perfect Vaccine. In: Mondaini, R.P. (eds) Trends in Biomathematics: Exploring Epidemics, Eco-Epidemiological Systems, and Optimal Control Strategies. Springer, Cham. https://doi.org/10.1007/978-3-031-59072-6_10

Sah P, Fitzpatrick MC, Zimmer CF, Abdollahi E, Juden-Kelly L, Moghadas SM, Singer BH, Galvani AP. (2021). Asymptomatic COVID-19 infection: a systematic review and meta-analysis, Proceedings of the National Academy of Sciences USA 118; 34. https://doi.org/10.1073/pnas.2109229118

Sah P, Moghadas SM, Vilches TN, ShoukatA, SingerBH, HotezPJ, SchneiderEC, Galvani AP. (2021). Implications of suboptimal COVID-19 vaccination coverage in Florida and Texas, Lancet Infectious Diseases 21(11), 1493-1494. https://doi.org/10.1016/S1473-3099(21)00620-4 

Sah P, VilchesTN, Moghadas SM, Gondi S, SchneiderEC, SingerJ, Chokshi DA, Galvani AP. (2022). Return on investment of the COVID-19 vaccination campaign in New York City. JAMA Network Open 5(11). https://doi.org/10.1001/jamanetworkopen.2022.43127

Sah P, Vilches TN, Pandey A, SchneiderEC, MoghadasSM, Galvani AP. (2022). Estimating the impact of vaccination on reducing COVID-19 burden in the United States: December 2020 to March 2022. Journal of Global Health 12:03062. https://doi.org/10.7189/jogh.12.03062

Saldaña F, Wang H, Camacho-Gutiérrez J. (2025). Unraveling the influence of the objective functional on epidemic optimal control: Insights from the SIR model. Mathematical Biosciences, Vol. 381: 109395.
https://doi.org/10.1016/j.mbs.2025.109395

[Book Chapter]--Schanzer, D.L. et al. (2023). Beyond Translation: An Overview of Best Practices for Evidence-Informed Decision Making for Public Health Practice. In: Woolford, D.G., Kotsopoulos, D., Samuels, B. (eds) Applied Data Science. Studies in Big Data, vol 125. Springer, Cham. https://doi.org/10.1007/978-3-031-29937-7_3

[Book Chapter]--Sekkak, I., Nasri, B.R. (2023). An Optimal Control Approach for Public Health Interventions on an Epidemic-Viral Model in Deterministic and Stochastic Environments. In: David, J., Wu, J. (eds) Mathematics of Public Health. Fields Institute Communications, vol 88. Springer, Cham. https://doi.org/10.1007/978-3-031-40805-2_5

Sharma Y, Liason E, Philippsen T, Ma J, Kong J, Ghaemi S, Liu J, Hu F, Nasri B. (2024). Models and data used to predict the abundance and distribution of Ixodes scapularis (blacklegged tick) in North America: a scoping review.  The Lancet Regional Health – Americas, 32, 100706. https://doi.org/10.1016/j.lana.2024.100706

[Book Chapter]--Shi, C., Vilches, T.N., Li, A., Wu, J., Moghadas, S.M. (2023). Modeling Mutation-Driven Emergence of Drug-Resistance: A Case Study of SARS-CoV-2. In: David, J., Wu, J. (eds) Mathematics of Public Health. Fields Institute Communications, vol 88. Springer, Cham. https://doi.org/10.1007/978-3-031-40805-2_7

Shoukat, A., Abdollahi, E., Galvani, A.P., Halperin, S.A., Langley, J.M., Moghadas, S.M. (2023). Cost-effectiveness analysis of nirsevimab and maternal RSVpreF vaccine strategies for prevention of Respiratory Syncytial Virus disease among infants in Canada: a simulation study. The Lancet Regional Health Americas, 28, 100629.  https://doi.org/10.1016/j.lana.2023.100629

Solis, A. O., Asgary, A., Khan, N., Janithra Wimaladasa, & Maryam Shafiei Sabet. (2022). Emergency Calls in the City of Vaughan (Canada) During the COVID-19 Pandemic: A Spatiotemporal Analysis. RiuNet (Universitat Politècnica de València).  https://doi.org/10.4995/carma2022.2022.15087

Song, H., Liu, F., Li, F., Cao, X., Wang, H., Jia, Z., Zhu, H., Li, M. Y., Lin, W., Yang, H., Hu, J., & Jin, Z. (2022). Modeling the second outbreak of COVID-19 with isolation and contact tracing. Discrete & Continuous Dynamical Systems - B, 27(10), 5757-5777. https://doi.org/10.3934/dcdsb.2021294

Su, Y., Zheng, B., & Zou, X. (2022). Wolbachia Dynamics in Mosquitoes with Incomplete CI and Imperfect Maternal Transmission by a DDE System. Bulletin of Mathematical Biology, 84(9). https://doi.org/10.1007/s11538-022-01042-2

Sumsuzzman, D.M., Ye, Y., Wang, Z. et al. (2025). Impact of disease severity, age, sex, comorbidity, and vaccination on secondary attack rates of SARS-CoV-2: a global systematic review and meta-analysis. BMC Infect Dis 25, 215 (2025). https://doi.org/10.1186/s12879-025-10610-5

Svetozar Zarko Valtchev, Asgary, A., Chen, M., Felippe Cronemberger, Najafabadi, M. M., Cojocaru, M., & Wu, J. (2021). Managing SARS-CoV-2 Testing in Schools with an Artificial Intelligence Model and Application Developed by Simulation Data. Electronics, 10(14), 1626–1626. https://doi.org/10.3390/electronics10141626

Tan, Y., Yuan, P., Moyles, I., Heffernan, J. M., Watmough, J., Tang, S., & Zhu, H. (2023). The stochasticity in adherence to nonpharmaceutical interventions and booster doses and the mitigation of COVID-19. 16(3&4), 602–626. https://doi.org/10.3934/dcdss.2023044

Tang, B., Zhang, X., Li, Q., Bragazzi, N. L., Golemi-Kotra, D., & Wu, J. (2022). The minimal COVID-19 vaccination coverage and efficacy to compensate for a potential increase of transmission contacts, and increased transmission probability of the emerging strains. BMC Public Health, 22(1). https://doi.org/10.1186/s12889-022-13429-w

Tao, S., Bragazzi, N. L., Wu, J., Mellado, B., & Kong, J. D. (2022). Harnessing Artificial Intelligence to assess the impact of nonpharmaceutical interventions on the second wave of the Coronavirus Disease 2019 pandemic across the world. Scientific Reports, 12(1), 944. https://doi.org/10.1038/s41598-021-04731-5

Tian, Y., Basran, J., McDonald, W., & Osgood, N. D. (2025). Early COVID-19 Pandemic Preparedness: Informing Public Health Interventions and Hospital Capacity Planning Through Participatory Hybrid Simulation Modeling. International Journal of Environmental Research and Public Health, 22(1), 39. https://doi.org/10.3390/ijerph22010039

Tian, Y., Basran, J., Stempien, J., Danyliw, A., Fast, G., Falastein, P., & Osgood, N. D. (2023). Participatory Modeling with Discrete-Event Simulation: A Hybrid Approach to Inform Policy Development to Reduce Emergency Department Wait Times. Systems, 11(7), 362–362. https://doi.org/10.3390/systems11070362

Tian, Y., Zhang, W., Duan, L., McDonald, W., & Osgood, N. D. (2023). Comparison of pretrained transformer-based models for influenza and COVID-19 detection using social media text data in Saskatchewan, Canada. Frontiers in Digital Health, 5. https://doi.org/10.3389/fdgth.2023.1203874

Tofighi, M., Asgary, A., Merchant, A. A., Shafiee, M. A., Najafabadi, M. M., Nadri, N., Aarabi, M., Heffernan, J., & Wu, J. (2021). Modelling COVID-19 transmission in a hemodialysis centre using simulation generated contacts matrices. PLOS ONE, 16(11) e0259970.
https://doi.org/10.1371/journal.pone.0259970

Tofighi, M., Asgary, A., Tofighi, G., Najafabadi, M. M., Arino, J., Amiche, A., Rahman, A., McCarthy, Z., Bragazzi, N. L., Thommes, E., Coudeville, L., Grunnill, M. D., Bourouiba, L., & Wu, J. (2022). Estimating social contacts in mass gatherings for disease outbreak prevention and management: case of Hajj pilgrimage. Tropical Diseases, Travel Medicine and Vaccines, 8(1).
https://doi.org/10.1186/s40794-022-00177-3

Tosato, M., Zhang, X., & Wu, J. (2022). A patchy model for tick population dynamics with patch-specific developmental delays. Mathematical Biosciences and Engineering, 19(5), 5329–5360.
https://doi.org/10.3934/mbe.2022250

[Report]--Tuite, A. R., Fisman, D. N., Odutayo, A., Bobos, P., Allen, V., Bogoch, I. I., Brown, A. D., Evans, G. A., Greenberg, A., Hopkins, J., Maltsev, A., Manuel, D. G., McGeer, A., Morris, A. M., Mubareka, S., Munshi, L., Murty, V. K., Patel, S. N., Razak, F., & Reid, R. J. (2021). COVID-19 Hospitalizations, ICU Admissions and Deaths Associated with the New Variants of Concern.Science Briefs of the Ontario COVID-19 Science Advisory Table. 2021;1(18)  https://doi.org/10.47326/ocsat.2021.02.18.1.0

Vaithyanathasarma, S., Caron, F., Bistodeau-Gagnon, G., & Belair, J. (2023). Bringing back the people in modelling epidemics. Int. Journal of Mathematical Education in Science and Technology, 55(2), 492-508. https://doi.org/10.1080/0020739X.2023.2249466

Vaziry, Kolokolnikov, T., & Kevrekidis, P. G. (2022). Modelling of spatial infection spread through heterogeneous population: from lattice to partial differential equation models. Royal Society Open Science, 9(10). https://doi.org/10.1098/rsos.220064

Vilches TN, Abdollahi E, Cipriano LE, Haworth-Brockman M, Keynan Y, Sheffield H, Langley JM, Moghadas SM. (2022) Impact of non-pharmaceutical interventions and vaccination on COVID-19 outbreaks in Nunavut, Canada: a Canadian Immunization Research Network (CIRN) study, BMC Public Health 22:1042.https://doi.org/10.1186/s12889-022-13432-1

Vilches TN, Moghadas SM, Sah P, Fitzpatrick MC, Shoukat A, Pandey A, Singer BH, Schneider EC, Galvani AP. (2022). Estimating COVID-19 infections, hospitalizations, and deaths following the US vaccination campaigns during the pandemic. JAMA Network Open 5(1):e2142725. https://doi.org/10.1001/jamanetworkopen.2021.42725

Vilches TN, Nourbakhsh S, Zhang K, Juden-Kelly L, Cipriano LE, Langley J, Sah P, Galvani AP, Moghadas SM.  Multifaceted strategies for the control of COVID-19 outbreaks in long-term care facilities in Ontario, Canada.  Preventive Medicine, Volume 148, 2021, 106564, ISSN 0091-7435, https://doi.org/10.1016/j.ypmed.2021.106564

Vilches TN, Rafferty E, Wells CR, Galvani AP, Moghadas SM. (2022). Economic evaluation of COVID-19 rapid antigen screening programs in the workplace. BMC Medicine 20(1):452. https://doi.org/10.1186/s12916-022-02641-5

Vilches TN, Sah P, Abdollahi E, Moghadas SM, Galvani AP. (2021). Importance of non-pharmaceutical interventions in the COVID-19 vaccination era: a case study of the Seychelles, Journal of Global Health 11: 03104. https://doi.org/10.7189/jogh.11.03104

Vilches TN, Sah P, Moghadas SM, Shoukat A, Fitzpatrick MC, Hotez PJ, Schneider EC, Galvani AP. (2022). COVID-19 hospitalizations and deaths averted under an accelerated vaccination program in northeastern and southern regions of the USA, The Lancet Regional Health - Americas 6: 100147. https://doi.org/10.1016/j.lana.2021.100147

Wang, X., Han, Q., & Kong, J. D. (2022). Studying the mixed transmission in a community with age heterogeneity: COVID-19 as a case study. Infectious Disease Modelling, 7(2), 250-260.
https://doi.org/10.1016/j.idm.2022.05.006

Wang Z, Röst G, Moghadas SM. 2024 Deviation from the recommended schedule: optimal dosing interval for a two-dose vaccination programme. R. Soc. Open Sci. 11: 231971. https://doi.org/10.1098/rsos.231971

Wells CR, Pandey A, Gokcebel S, Krieger G, Donoghue M, Singer BH, Moghadas SM, Galvani AP, Townsend JP. (2022). Quarantine and serial testing for variants of SARS-CoV-2 with benefits of vaccination and boosting on consequent control of COVID-19, Proceedings of the National Academy of Sciences USA - NEXUS 1(3):1-6.https://doi.org/10.1093/pnasnexus/pgac100

Wells CR, Pandey A, Moghadas SM, Singer BH, Krieger G, Heron RJL, Turner DE, Abshire JP, Phillips KM, Donoghue AM, Galvani AP, Townsend JP. (2022). Comparative analyses of eighteen rapid antigen tests and RT-PCR for COVID-19 quarantine and surveillance-based isolation, Communications Medicine 2(84):1-12. https://doi.org/10.1038/s43856-022-00147-y

Wells CR, Pandey A, Moghadas SM, Fitzpatrick MC, Singer BH, & Galvani AP. (2024). Evaluation of Strategies for Transitioning to Annual SARS-CoV-2 Vaccination Campaigns in the United States. Annals of Internal Medicine, 177(5), 609-617. https://doi.org/10.7326/M23-2451

Wells CR, Townsend JP, Pandey A, Fitzpatrick MC, Crystal WC, Moghadas SM, Galvani AP. (2022). Quarantine and testing strategies to ameliorate transmission due to travel during the COVID-19 pandemic: a modelling study. The Lancet Regional Health. 14(100304). https://doi.org/10.1016/j.lanepe.2021.100304

Wu P, Wang X, Wang H. (2024). Spatial heterogeneity analysis for the transmission of syphilis disease in China via a data-validated reaction-diffusion model. Mathematical Biosciences, Vol. 375: 109243. https://doi.org/10.1016/j.mbs.2024.109243

Wu P, Wang X, Wang H. (2025). Spatiotemporal dynamics of immune responses to viral infection and re-infection. Physica D: Nonlinear Phenomena, Vol. 472: 134519. https://doi.org/10.1016/j.physd.2024.134519
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[Book Chapter]--Xi, D.D.Z., Dean, C.B., Renouf, E.M. (2022). Joint Modeling of Hospitalization and Mortality of Ontario Covid-19 Cases. In: Murty, V.K., Wu, J. (eds) Mathematics of Public Health. Fields Institute Communications, vol 85. Springer, Cham. https://doi.org/10.1007/978-3-030-85053-1_13

Xia, Y., Flores Anato, J.L., Colijn, C. et al. Canada’s provincial COVID-19 pandemic modelling efforts: A review of mathematical models and their impacts on the responses. Can J Public Health 115, 541–557 (2024). https://doi.org/10.17269/s41997-024-00910-9

Xu, J., Wang, Z. & Moghadas, S.M. Modelling the effect of travel-related policies on disease control in a meta-population structure. J. Math. Biol. 87, 55 (2023). https://doi.org/10.1007/s00285-023-01990-w

Xu, W., Shu, H., Wang, L., Wang, X., & Watmough, J. (2023). The importance of quarantine: modelling the COVID-19 testing process. Journal of Mathematical Biology, 86(5). https://doi.org/10.1007/s00285-023-01916-6

Yi, W., Ma, J., & Cao, J. (2022). Basic reproduction number for the SIR epidemic in degree correlated networks. Physica D: Nonlinear Phenomena, 433, 133183–133183. https://doi.org/10.1016/j.physd.2022.133183

Yin F, Jiang X, Qian X, Xia X, Pan Y, Wu J. (2022). Modeling and quantifying the influence of rumor and counter-rumor on information propagation dynamics. Chaos, Solitons & Fractals, 162, 112392. https://doi.org/10.1016/j.chaos.2022.112392

Yin, F., She, Y., Yang, Q., Pang, H., Huang, Y., and Wu, J. (2024). Modeling and Analyzing Dynamic Information Propagation on Sina Weibo in a Semi-Directed Network. Journal of Biological Systems 32(1), 219-237. https://doi.org/10.1142/S0218339024500086

Yin, F., She, Y., Wang, J., Wu, Y., and Wu, J. (2023). Modeling and Analyzing Information Propagation Evolution Integrating Internal and External Influences. Advanced Theory and Simulations 7(3), 2300845. https://doi.org/10.1002/adts.202300845

Yin, F., Wang, J., Pang, H., Pei, X., Jin, Z., Wu, J. (2024). Modeling and analyzing network dynamics of COVID-19 vaccine information propagation in the Chinese Sina Microblog. Comput Math Organ Theory. https://doi.org/10.1007/s10588-024-09386-x

Yin, F., Wang, J., Jiang, X., Huang, Y., Yang, Q., and Wu, J. (2023). Modeling and analyzing an opinion network dynamics considering the environmental factor.  Mathematical Biosciences and Engineering, 20(9), 16866-16885. https://doi.org/10.3934/mbe.2023752

[Book Chapter]--Yuan, P. et al. (2022). Evaluating the Risk of Reopening the Border: A Case Study of Ontario (Canada) to New York (USA) Using Mathematical Modeling. In: Murty, V.K., Wu, J. (eds) Mathematics of Public Health. Fields Institute Communications, vol 85. Springer, Cham. https://doi.org/10.1007/978-3-030-85053-1_14 

Zhang, W., Liu, S., Osgood, N., Zhu, H., Qian, Y., & Jia, P. (2022). Using simulation modelling and systems science to help contain COVID‐19: A systematic review. Systems Research and Behavioral Science, 40(1), 207-234. https://doi.org/10.1002/sres.2897

Zhang X, Scarabel F, Murty K & Wu, J. (2023). Renewal equations for delayed population behaviour adaptation coupled with disease transmission dynamics: A mechanism for multiple waves of emerging infections. Mathematical Biosciences, 365, 109068. https://doi.org/10.1016/j.mbs.2023.109068

Zhang X and Wu J. (2024). Tick-Borne Pathogen Co-infection by Co-feeding on Incompetent Hosts: Global Convergence and Impact of Developmental Delay. SIAM Journal on App. Math. 84(3). https://doi.org/10.1137/23M1577419

Zhao S and Magpantay FMG. (2025) Disease Transmission on Random Graphs Using Edge-Based Percolation. Mathematical Methods in the Applied Sciences. https://doi.org/10.1002/mma.10963

[Preprint]--Zhao S, Saeed S, Carter M, Stoner B, Hoover M, Guan H and Magpantay FMG. (2024) Edge-based Modeling for Disease Transmission on Random Graphs: An Application to Mitigate a Syphilis Outbreak. https://doi.org/10.48550/arXiv.2410.13024

Zhou, S., Lin, W., & Wu, J. (2022). Generalized invariance principles for discrete-time stochastic dynamical systems. Automatica, 143, 110436. https://doi.org/10.1016/j.automatica.2022.110436

Zhou, S., Lin, W., Mao, X., & Wu, J. (2023). Generalized Invariance Principles for Stochastic Dynamical Systems and Their Applications. IEEE Transactions on Automatic Control, 1–15.
https://doi.org/10.1109/tac.2023.3274215

Zhu, H., Liu, S., Li, X., Zhang, W., Osgood, N. D., & Jia, P. (2023). Using a hybrid simulation model to assess the impacts of combined COVID-19 containment measures in a high-speed train station. Journal of Simulation, 1–25. https://doi.org/10.1080/17477778.2023.2189027