Students and postdocs are welcome to bring in their own project or ideas for us to create a project together. The students and postdocs working on these projects will have the opportunity to collaborate with our partners across the Global South, including members of the Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC)  and the Global South Artificial Intelligence for Pandemic and Epidemic Preparedness and Response Network (AI4PEP) 

Objective:

The overarching goal of this project is to develop and deploy AI, data science, and mathematical methodologies and technologies for disease detection, data management and processing, and real-time model validation and calibration using advanced computational methods.

Some of the infectious diseases that we are currently developing and deploying mathematical and artificial intelligence models for include:

  • All emerging and re-emerging infectious Diseases
  •  COVID-19 
  • Flu
  •  RSV
  • Malaria
  • Mpox
  • Cholera
  •  HIV
  • Lyme Disease

Data: Through our NSERC-funded program on “Modelling ecological dynamics in changing environments: novel theories, policy suggestions and operational tools for mitigating the impact of anthropogenic disturbances”, We have made significant progress in collecting a substantial amount of locally relevant, conventional, and unconventional data (e.g. Africa infectious disease data portal, COVID-19 Dashboards, Monkeypox Stigmatization, Jane and Finch in Data, COVID-19 Sentiment Dashboard, COVID-19 in Canada, COVID-19 Vaccination in Canada) that will be needed for this program. The data is sourced from a wide array of public (e.g social media, News) and private sources. Additionally, we have advanced in the creation of interactive AI-powered data visualization frameworks (dashboards) to visualize the data and the state of disease outbreaks (COVID-19 Dashboards, Monkeypox Stigmatization, Jane and Finch in Data, COVID-19 Sentiment Dashboard, COVID-19 in Canada, COVID-19 Vaccination in Canada). Through our collaborations with governments in 10 African countries, we have also have access to the District Health Information System 2 (DHIS2) data portals of the various governments . Furthermore, we have have access to a variety of Canadian data (such as Acute Care Enhanced Surveillance (ACES), wastewater-based surveillance data etc.) through collaborations with local, provincial, and federal government agencies, hospitals, and academic research institutes. In addition, we have also been developing techniques to identify potential signals from unconventional sources.

Diverse data, ranging from animal (livestock, wildlife, companion animals) to human Health (public health, internet, social media, surveys), environmental (weather, air quality, satellite), demographic (population, vegetation, social vulnerability), and financial, is currently being collected and made publicly available. Our  data curation  follows current practices, including the use of the Findability, Accessibility, Interoperability, and Reuse (FAIR) Principles. Indigenous community data collected in Canada will adhere to the First Nations principles of ownership, control, access, and possession (OCAP®). Addressing the challenge of aggregating and aligning data from various multi-system multi-species sources leverages prior solutions.

On going subprojects
  1. Integrative Infectious Disease Modeling: Incorporating Human Behavioral Dynamics for Enhanced Control Strategies: This project aim to design, analyze, and deploy novel integrative infectious disease models that incorporate the influence of human behavioral dynamics on the spread of infectious diseases.
  2. Advancing Long-COVID-19 Research with Mathematical, Geospatial, and Machine Learning Approaches: This project aims to advance research on Long-COVID-19 by leveraging state-of-the-art machine learning, geospatial analysis, and mathematical modeling.
  3. Integrative Modelling: The objective of this project is to create a comprehensive framework that examines the effects of public health interventions on a wide range of societal and economic factors. This involves integrating different individual models to assess the overall impact of the virus on society.
  4. Early Warning System (EWS) for emerging and re-emerging infectious disease: This project aim to leverage Artificial Intelligence (AI) and Deep-Learning (DL) models to build an EWS for emerging and re-emerging infectious diseases. We will automate a framework that collects and integrates data from multiple online web-based sources (such as Google Trends, Google Trends Rate, social media, satellite data, drug consumption in pharmacies, economic activity data and outdoor containers identified from Google Street View images) processes them and uses them to train and test a forecasting model, stores the results into the cloud to subsequently visualize them on publicly available dashboards.
  5. Modelling, predicting, and risk assessment of mpox (monkeypox) and other (re)emerging zoonotic threats to inform decision-making and public health actions: mathematical, geospatial, and machine learning approaches: This project aim to design and analyze epidemiological and geospatial models including artificial intelligence-based and mathematical models to study the epidemiology, transmission dynamics, and immunology and intervention strategies of mpox, and other zoonotic threats. These models will be used to generate valuable insights; predict the effectiveness of control strategies; identify modifiable risk factors which could be targeted for intervention, as well as other possible intervention strategies and inform public health decision-making; analyze public health interventions and risk management strategies; investigate the effectiveness of public health interventions, and thus provide rapid evidence to inform clinical and health system management and public health response, and/or decision-making in Canada and/or globally

6.  Hybrid Artificial Intelligence and Mathematical Models: The project aims to create a hybrid models framework that combines artificial intelligence with traditional mathematical epidemic models. This framework will facilitate the quick identification of relationships among various time series data from multiple sources and the time series produced by mathematical epidemic models.

7. Forecasting Scenarios and Optimizing Interventions:  This project aim develop a comprehensive template of transmission dynamics models incorporating the demographical structure of the population, the epidemiological/immunological characteristics of infections, and details of pharmaceutical and non-pharmaceutical interventions. 

8.  Modelling, predicting, and risk assessment of climate sensitive infectious diseases to inform decision-making and public health actions: mathematical, geospatial, and machine learning approaches: This project aim to design and analyze epidemiological and geospatial models including artificial intelligence-based and mathematical models to study the epidemiology, transmission dynamics, and immunology and intervention strategies of  climate sensitive infectious diseases . These models will be used to generate valuable insights; predict the effectiveness of control strategies; identify modifiable risk factors which could be targeted for intervention, as well as other possible intervention strategies and inform public health decision-making; analyze public health interventions and risk management strategies; investigate the effectiveness of public health interventions, and thus provide rapid evidence to inform clinical and health system management and public health response, and/or decision-making in Canada and/or globally.

 9. A disease outbreak detection and response tool supported by AI and a multi-source real-time data collection platform: The aim is to design and deploy an AI-powered, climate-responsive, integrated, and user-friendly platform for predicting, managing, and combating respiratory diseases while democratizing access to data science and machine learning techniques for non-experts. The framework utilizes state-of-the-art AI and mathematical models to integrate and model both conventional (historical data, animal data, virus sequencing etc.) and unconventional data (such as Google Trends, Google Trends Rate, social media, satellite data, drug consumption in pharmacies, economic activity data and outdoor containers identified from Google Street View images) to detect possible diseases scenarios and corresponding interventions to suppress disease spread safely with minimal social impact.

10. Impact of COVID on  Antimicrobial resistance: We aim to use a mathematical model combined with potential wastewater surveillance of Antimicrobial resistance (AR)  to study the impact of COVID-19 on AR: would the pandemic shift the stability of the dynamics of the system from non-AR to AR in the long run? And what would be the critical threshold for certain actions to combat AR, in order to maintain non-AR infection’s stability and thus keep AR infection under control?

11. Agent-Based and Network Infectious Disease Models: The aim is to develop, expand and refine the agent-based modeling framework for infectious diseases.

12. Immune response, immune memory and cross-immunity: This project aims to design and analyze models of an immune response to an emergent infectious pathogen incorporating immune memory generated by prior infection by related pathogens.  Such pre-existing immunity has a large influence on the potential for disease spread.

13. Infectious Diseases Data Curation/Data Scraping and forecasting: The project aims to;

  • Curate data typically needed to model spillover, spread, and control of infectious diseases from publicly available sources. This data includes:
  • Animal data: Information about livestock and wildlife (health surveillance, demographic, mobility, performance for livestock, and biosecurity) and companion animals.
  • Human health data: Surveillance from public health agencies and the internet/social media (web scraping); quality of life and mental health; surveys about perceptions and attitudes towards infectious diseases, medical (e.g., vaccines), and non-medical interventions (e.g., mask wearing), including data from specific populations (e.g., Black and Indigenous communities).
  • Environmental and hydro-climatic data: Weather and climatic conditions; pollution indices; pathogens and hydrological data, including discharges, water temperature, and wastewater surveillance.
  • Demographic and socio-economic data: Population and vegetation concentrations, social vulnerability indices.
  • Financial data: Cost of interventions and economic consequences during and after  an outbreak.
  • Design dashboards for the data.
  • Integrate models that we have designed into dashboards to forecast the data in real-time.

14.  Mathematical Model of Malaria Dynamics: Integrating Human Behavior, Vaccination Strategies, and Climate Velocity: This project aims to develop and analyze a sophisticated mathematical model of malaria dynamics. The model will incorporate human behavioral dynamics, vaccination strategies, climate velocity effects, and seasonal variations in mosquito biting rates to comprehensively study the complex interplay of factors influencing the spread of malaria. Relevant data for this research can be accessed here. 

15.  A Comparative Study: Analyzing Sentiment and Emotion with ChatGPT, Gemini, and Transformers: ChatGPT and Gemini have garnered significant interest for their ability to produce articulate and top-notch responses to human queries. This research delves into comparing the effectiveness of ChatGPT and Gemini against Transformers in conducting sentiment and emotional analysis.

16. Modelling vulnerable communities resilience: pandemic lessons, post- pandemic recovery, and data gaps: Understanding missing gaps in understanding the impact of Covid-19 on  community’s health, livelihoods and income. 

17. Exploring disruption in mental health and substance treatment services during the pandemic  in Jane and Finch community

18. A comparative study of community, neighbourhood and street-level care during covid. 

19. Novel Approaches to Sustainability, Governance, Climate Resilience, and Equity: supporting recovery and renewal in a post-pandemic world.

Some of our Refereed Journal Publications so far under this Theme

  1. Sharma, Y., Laison, E. K., Philippsen, T., Ma, J., Kong, J., Ghaemi, S., … & 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.
  2. Yuh, M. N., Ndum Okwen, G. A., Miong, R. H. P., Bragazzi, N. L., Kong JD., Movahedi Nia, Z., … Patrick Mbah, O. (2024). Using an innovative family-centered evidence toolkit to improve the livelihood of people with disabilities in Bamenda (Cameroon): a mixed-method study. Frontiers in Public Health, 11, 1190722.
  3. Nunes, M. C., Thommes, E., Fr ̈ohlich, H., Flahault, A., Arino, J., Baguelin, M., Kong JD … Coudeville, L. (2024). Redefining pandemic preparedness: Multidisciplinary insights from the CERP modelling workshop in infectious diseases, workshop report. Infectious Disease Modelling.
  4. Kaur, M., Cargill, T., Hui, K., Vu, M., Bragazzi, N. L., Kong JD (2024). A Novel Approach for the Early Detection of Medical Resource Demand Surges During Health Care Emergencies: Infodemiology Study of Tweets. JMIR Formative Research, 8, e46087.
  5. Bain, L. E., Yankam, B. M., Kong, J. D., Nkfusai, N. C., Badru, O. A., Ebuenyi, I. D., … & Adeagbo, O. (2023). Global Health Mentorship: Challenges and Opportunities for Equitable Partnership. BMJ global health, 8(11), e013751.

 

  1. Kaur, M., Bragazzi, N. L., Heffernan, J., Tsasis, P., Kong JD (2023). COVID-19 in Ontario Long-term Care Facilities Project, a manually curated and validated database. Frontiers in Public Health, 11, 1133419.
  2. Nia ZM, Ahmadi A, Mellado B, Wu J, Orbinski J, Asgary A, Kong JD. Twitter-based gender recognition using transformers. Math Biosci Eng. 2023 Aug 3;20(9):15962-15981. doi: 10.3934/mbe.2023711. PMID: 37919997.
  3. Movahedi Nia, Z., Bragazzi, N., Asgary, A., Orbinski, J., Wu, J., Kong JD (2023). Mpox Panic,Infodemic, and Stigmatization of the Two-Spirit, Lesbian, Gay, Bisexual, Transgender, Queer or Questioning, Intersex, Asexual Community: Geospatial Analysis, Topic Modeling, and Sentiment Analysis of a Large, Multilingual Social Media Data. Journal of Medical Inter- net Research. 25, e45108.
  4. Fevrier, K., Effoduh, J. O., Kong JD, Bragazzi, N. L. (2023). Artificial Intelligence, Law, and Vulnerabilities. In AI and Society. : 179-196.
  5. Ji J, Wang H, Wang L, Ramazi P, Kong JD, Watmough J. Climate-dependent effectiveness of nonpharmaceutical interventions on COVID-19 mitigation. Mathematical Biosciences. 2023 Dec 1;366:109087.
  6. Zahra Movahedi Nia, Ali Ahmadi, Bruce Mellado, Jianhong Wu, James Orbinski, Ali As- gary, Jude D. Kong. Twitter-based gender recognition using transformers[J]. Mathematical Biosciences and Engineering, 2023, 20(9): 15962-15981. doi: 10.3934/mbe.2023711
  7. Avusuglo WS, Bragazzi N, Asgary A, Orbinski J, Wu J, Kong JD. Leveraging an epi- demic–economic mathematical model to assess human responses to COVID-19 policies and disease progression. Scientific Reports. 2023 Aug 8;13(1):12842.
  8. Movahedi Nia Z, Bragazzi NL, Ahamadi A, Asgary A, Mellado B, Orbinski J, Seyyed-Kalantari L, Woldegerima WA, Wu J, Kong JD. 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. Journal of the Royal Society Interface. 2023 Sep 13;20(206):20230200.
  9. Sekkak I, Nasri BR, R ́emillard BN, Kong JD, El Fatini M. A stochastic analysis of a SIQR epidemic model with short and long-term prophylaxis. Communications in Nonlinear Science and Numerical Simulation. 2023 Sep 15:107523.
  10. Han Q, Bragazzi N, Asgary A, Orbinski J, Wu J, Kong JD. Estimation of epidemiological parameters and ascertainment rate from early transmission of COVID-19 across Africa. Royal Society Open Science. 2023 Sep 20;10(9):230316.
  11. Nia, Z. M., Bragazzi, N. L., Wu, J., & Kong, J. D. (2023). A Twitter Dataset for Monkey- pox, May 2022. Data in Brief, 109118.
  12. Movahedi Nia, Z., Bragazzi, N., Asgary, A., Orbinski, J., Wu, J., & Kong, J. (2023). Mpox Panic, Infodemic, and Stigmatization of the Two-Spirit, Lesbian, Gay, Bisexual, Transgender, Queer or Questioning, Intersex, Asexual Community: Geospatial Analysis, Topic Modeling, and Sentiment Analysis of a Large, Multilingual Social Media Database. Journal of Medical Internet Research, 25, e45108.
  13. Puce, L., Okwen, P., Yuh, M. N., Akah, G., Pambe Miong, R. H., Kong, J., & Bragazzi, N. L. (2023). Well-being and quality of life in people with disabilities practicing sports, athletes with disabilities, and para-athletes: Insights from a critical review of the literature. Frontiers in Psychology, 14, 242.
  14. Wu, T., Imrit, M. A., Movahedinia, Z., Kong, J., Woolway, R. I., & Sharma, S. (2023). Climate tracking by freshwater fishes suggests that fish diversity in temperate lakes may be increasingly threatened by climate warming. Diversity and Distributions, 29(2), 300-315.
  15. Kong, J. D., Akpudo, U. E., Effoduh, J. O., & Bragazzi, N. L. (2023, February). Leveraging Responsible, Explainable, and Local Artificial Intelligence Solutions for Clinical Public Health in the Global South. In Healthcare (Vol. 11, No. 4, p. 457). MDPI.
  16. Puce, L., Okwen, P., Yuh, M. N., Akah, G., Pambe Miong, R. H., Kong, J., & Bragazzi, N. L. (2023). Well-being and quality of life in people with disabilities practicing sports, athletes with disabilities, and para-athletes: Insights from a critical review of the literature. Frontiers in Psychology, 14, 242.

 

  1. Bragazzi NL, Han Q, Iyaniwura SA, Omame A, Shausan A, Wang X, Woldegerima WA, Wu J, Kong JD. Adaptive changes in sexual behavior in the high-risk population in re- sponse to human monkeypox transmission in Canada can help control the outbreak: Insights from a two-group, two-route epidemic model. J Med Virol. 2023 Apr;95(4):e28575. doi: 10.1002/jmv.28575. PMID: 36772860.
  2. Kaur M, Bragazzi NL, Heffernan J, Tsasis P, Wu J, Kong JD. COVID-19 in Ontario Long-term Care Facilities Project, a manually curated and validated database. Front Pubic Health. 2023 Feb 10;11:1133419. doi: 10.3389/fpubh.2023.1133419. PMID: 36844842; PMCID: PMC9950626.
  3. Bragazzi NL, Kong JD, Mahroum N, Tsigalou C, Khamisy-Farah R, Converti M, Wu J. Epidemiological trends and clinical features of the ongoing monkeypox epidemic: A preliminary pooled data analysis and literature review. Journal of medical virology. 2023 Jan;95(1):e27931.
  4. Bragazzi NL, Kong JD, Wu J. Is monkeypox a new, emerging sexually transmitted disease? A rapid review of the literature. Journal of Medical Virology. 2023 Jan;95(1):e28145.
  5. Ogbuokiri B, Ahmadi A, Nia ZM, Mellado B, Wu J, Orbinski J, Asgary A, Kong J. Vaccine hesitancy hotspots in africa: An insight from geotagged twitter posts.IEEE Transactions on Computational Social Systems. 2023 Jan 19. doi: 10.1109/TCSS.2023.3236368.
  6. Iyaniwura SA, Musa R, Kong JD. A generalized distributed delay model of COVID-19: An en- demic model with immunity waning. Mathematical Biosciences and Engineering. 2023;20(3):5379- 412.
  7. Lieberman B, Kong JD, Gusinow R, Asgary A, Bragazzi NL, Choma J, Dahbi SE, Hayashi K, Kar D, Kawonga M, Mbada M. Big data-and artificial intelligence-based hot-spot analysis of COVID-19: Gauteng, South Africa, as a case study. BMC Medical Informatics and Decision Making. 2023 Dec;23(1):1-5.
  8. Ahmed, H., Cargill, T., Bragazzi, N. L., & Kong, J. Dataset of Non-pharmaceutical inter- ventions and community support measures across Canadian universities and colleges during COVID-19 in 2020. Frontiers in Public Health, 4512.
  9. Alavinejad, M., Mellado, B., Asgary, A., Mbada, M., Mathaha, T., Lieberman∗, B., … & 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.
  10. Ogbuokiri, B., Ahmadi, A., Bragazzi, N. L., Nia, Z. M., Mellado, B., Wu, J., … & Kong, J. (2022). Public sentiments toward COVID-19 vaccines in South African cities: An analysis of Twitter posts. Frontiers in Public Health, 10.
  11. Nia, Z. M., Ahmadi, A., Bragazzi, N. L., Woldegerima, W. A., Mellado, B., Wu, J., … & Kong, J. D. (2022). A cross-country analysis of macroeconomic responses to COVID-19 pandemic using Twitter sentiments. PloS one, 17(8), e0272208.
  12. Dai, H., Younis, A., Kong, J., Puce, L., Jabbour, G., & Bragazzi, N. L. (2022). Big Data in Cardiology: State-of-Art and Future Prospects. Towards a new cardiology: more predictive, personalized, participatory, digital, smarter, and bigger. Frontiers in Cardiovascular Medicine, 606.
  13. Wang X, Han Q, Kong JD. Studying the mixed transmission in a community with age heterogeneity: COVID-19 as a case study. Infectious Disease Modelling. 2022 Jun 1;7(2):250-60.
  14. Kong J, Mellado B, Wu J (2022). Harnessing the power of data: Artificial Intelligence -based pandemic support. UNESCO: https: // unesdoc. unesco. org/ ark: /48223/ pf0000380883. locale= en .
  15. Yuan P, Aruffo E, Li Q, Li J, Tan Y, Zheng T, David J, Ogden N, Gatov E, Gournis E, Collier S, Sander B, Fan G, Heffernan JM, Li J, Kong JD, Arino J, B ́elair J, Watmough J, & Zhu, H. Evaluating the Risk of Reopening the Border: A Case Study of Ontario (Canada) to New York (USA) Using Mathematical Modeling. In Mathematics of Public Health 2022 (pp. 287-301). Springer, Cham.
  16. Yuan P, Li J, Aruffo E, Gatov E, Li Q, Zheng T, Ogden NH, Sander B, Heffernan J, Collier S, Tan Y, Li J, Arino J, Belair J, Watmough J, Kong JD, Moyles I, Zhu H. (2022). Efficacy of a “stay-at-home” policy on SARS-CoV-2 transmission in Toronto, Canada: a mathematical modelling study. Canadian Medical Association Open Access Journal, 10(2), E367-E378.
  17. Behzadifar M, Aalipour A, Kehsvari M, Darvishi Teli B, Ghanbari MK, Gorji HA, Sheikhi A, Azari S, Heydarian M, Ehsanzadeh SJ, Kong JD. The effect of COVID-19 on pub- lic hospital revenues in Iran: An interrupted time-series analysis. PLoS One. 2022 Mar 31;17(3):e0266343.
  18. Bragazzi NL, Bridgewood C, Watad A, Damiani G, Kong JD, McGonagle D. Harnessing Big Data, Smart and Digital Technologies and Artificial Intelligence for Preventing, Early Intercepting, Managing, and Treating Psoriatic Arthritis: Insights From a Systematic Review of the Literature. Front. Immunol. 13: 847312. doi: 10.3389/fimmu. 2022 Mar 10.
  19. Tao S, Bragazzi NL, Wu J, Mellado B, Kong JD. 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. 2022 Jan 18;12(1):1-9.
  20. Kazemi M, Bragazzi NL, Kong JD. Assessing Inequities in COVID-19 Vaccine Roll-Out Strategy Programs: A Cross-Country Study Using a Machine Learning Approach. Vaccines. 2022 Feb;10(2):194.
  21. Iyaniwura SA, Rabiu M, David JF, Kong JD. The basic reproduction number of COVID-19 across Africa. Plos one. 2022 Feb 25;17(2):e0264455.
  22. Habees AA, Aldabbas E, Bragazzi NL, Kong JD. Bacteria–bacteriophage cycles facilitate Cholera outbreak cycles: an indirect Susceptible-Infected-Recovered-Bacteria-Phage (iSIRBP) model-based mathematical study. Journal of Biological Dynamics. 2022 Dec 31;16(1):29-43.
  23. Guelmami N, Tannoubi A, Chalghaf N, Saidane M, Kong J, Puce L, Fairouz A, Bragazzi NL, Alroobaea R. Latent Profile Analysis to Survey Positive Mental Health and Well-Being: A Pilot Investigation Insight Tunisian Facebook Users. Front. Psychiatry. 2022 Apr 7;13:824134.
  24. Chen R, Safiri S, Behzadifar M, Kong JD, Zguira MS, Bragazzi NL, Zhong W, Zhang W. Health effects of metabolic risks in the United States from 1990 to 2019. Frontiers in Public Health. 2022 Jan 31;10:751126.
  25. Yan C, Law M, Nguyen S, Cheung J, Kong J. Comparing Public Sentiment Toward COVID- 19 Vaccines Across Canadian Cities: Analysis of Comments on Reddit. Journal of medical Internet research. 2021 Sep 24;23(9):e32685.
  26. Dai H, Tang B, Younis A, Kong JD, Zhong W, Bragazzi NL. Regional and socioeconomic disparities in cardiovascular disease in Canada during 2005–2016: evidence from repeated nationwide cross-sectional surveys. BMJ Global Health. 2021 Nov 1;6(11):e006809.
  27. Cheong Q, Au-Yeung M, Quon S, Concepcion K, Kong JD. Predictive Modeling of Vacci- nation Uptake in US Counties: A Machine Learning–Based Approach. Journal of medical Internet research. 2021 Nov 25;23(11):e33231.
  28. Bragazzi NL, Kolahi A, Nejadghaderi SA, Lochner P, Brigo F, Naldi A, Lanteri P, Garbarino S, Sullman MJM, Dai H, Wu , Kong JD, Jahrami H, Sohrabi M, & Safiri S. Global, regional, and national burden of Guillain–Barr ́e syndrome and its underlying causes from 1990 to 2019. Journal of neuroinflammation. 2021 Dec;18(1):1-1.
  29. Kong JD, Tekwa EW, Gignoux-Wolfsohn SA. Social, economic, and environmental factors influencing the basic reproduction number of COVID-19 across countries. PloS one. 2021 Jun 9;16(6):e0252373.

 

  1. Duhon J, Bragazzi N, Kong JD. The impact of non-pharmaceutical interventions, demo- graphic, social, and climatic factors on the initial growth rate of COVID-19: A cross-country study. Science of The Total Environment. 2021 Mar 15;760:144325.
  2. Dai H, Younis A, Kong JD, Bragazzi NL, Wu J. Trends and Regional Variation in Prevalence of Cardiovascular Risk Factors and Association With Socioeconomic Status in Canada, 2005- 2016. JAMA network open. 2021 Aug 2;4(8):e2121443
  3. Bouba Y, Tsinda EK, Fonkou MD, Mmbando GS, Bragazzi NL, Kong JD. The determinants of the low COVID-19 transmission and mortality rates in Africa: a cross-country analysis. Frontiers in public health. 2021 Oct 21;9:751197.
  4. Mahroum N, Damiani G, Watad A, Amital H, Bragazzi NL, Farah R, Wu JH, Kong JD, Bridgewood C, McGonagle D, Khamisy-Farah R. Higher rates of COVID-19 but less severe infections reported for patients on Dupilumab: a Big Data analysis of the World Health Or- ganization VigiBase. European Review for Medical and Pharmacological Sciences. 2021 Sep 1;25(18):5865-70.
  5. Guelmami N, Khalifa MB, Chalghaf N, Kong JD, Amayra T, Wu J, Azaiez F, Bragazzi NL. Development of the 12-Item Social Media Disinformation Scale and its Association With Social Media Addiction and Mental Health Related to COVID-19 in Tunisia: Survey-Based Pilot Case Study. JMIR Formative Research. 2021 Jun 9;5(6):e27280.
  6. Betti M, Bragazzi N, Heffernan J, Kong J, Raad A. Could a New COVID-19 Mutant Strain Undermine Vaccination Efforts? A Mathematical Modelling Approach for Estimating the Spread of B. 1.1. 7 Using Ontario, Canada, as a Case Study. Vaccines. 2021 Jun;9(6):592.
  7. Guelmami N, ben Khalifa M, Chalghaf N, Kong JD, Amayra T, Wu J, Azaiez F, Bragazzi NL. Preliminary development of the social media disinformation scale (SMDS-12) and its association with social media addiction and mental health: COVID-19 as a pilot case study. JMIR Formative Research. 2021;5(6):e27280.
  8. Bragazzi NL, Mahroum N, Damiani G, Kong JD, Wu J. Effectiveness of community face mask use on COVID-19 epidemiological trends and patterns in Italy: evidence from a” trans- lational” study. Infectious Diseases (London, England). 2021 Mar 9:1-3.

 

  1. Khamisy-Farah R, Damiani G, Kong JD, Wu JH, Bragazzi NL. Safety profile of Dupilumab during pregnancy: a data mining and disproportionality analysis of over 37,000 reports from the WHO individual case safety reporting database (VigiBaseTM). Eur Rev Med Pharmacol Sci. 2021 Sep 1;25(17):5448-51.
  2. Stevenson F, Hayasi K, Bragazzi NL, Kong JD, Asgary A, Lieberman B, Ruan X, Mathaha T, Dahbi SE, Choma J, Kawonga M. Development of an early alert system for an additional wave of covid-19 cases using a recurrent neural network with long short-term memory. International Journal of Environmental Research and Public Health. 2021 Jul 9;18(14):7376.
  3. Botelho, C., Kong, J. D., Lucien, M. A., Shuai, Z., & Wang, H. (2021). A mathematical model for Vibrio-phage interactions. Mathematical Biosciences and Engineering, 18(3).
  4. Moyles IR, Heffernan JM, Kong JD. Cost and social distancing dynamics in a mathematical model of COVID-19 with application to Ontario, Canada. Royal Society open science. 2021 Feb 24;8(2):201770.
  5. Mellado B, Wu J, Kong JD, Bragazzi NL, Asgary A, Kawonga M, Choma N, Hayasi K, Lieberman B, Mathaha T, Mbada M. Leveraging artificial intelligence and big data to optimize COVID-19 clinical public health and vaccination roll-out strategies in Africa. International Journal of Environmental Research and Public Health. 2021 Jul 26;18(15):7890.
  6. Mahroum N, Watad A, Bridgewood C, Mansour M, Nasr A, Hussein A, Khamisy-Farah A, Farah R, Gendelman O, Lidar M, Shoenfeld Y, Amital H, Kong JD, Wu J, Bragazzi NL, & McGonagle D. Systematic Review and Meta-Analysis of Tocilizumab Therapy Versus Standard of Care in over 15,000 COVID-19 Pneumonia Patients during the First Eight Months of the Pandemic. International Journal of Environmental Research and Public Health. 2021 Jan;18(17):9149.
  7. Khamisy-Farah R, Gilbey P, Furstenau LB, Sott MK, Farah R, Viviani M, Bisogni M, Kong JD, Ciliberti R, Bragazzi NL. Big Data for Biomedical Education with a Focus on the COVID- 19 Era: An Integrative Review of the Literature. International Journal of Environmental Research and Public Health. 2021 Jan;18(17):8989.
  8. Mbogning Fonkou MD, Bragazzi NL, Tsinda EK, Bouba Y, Mmbando GS, Kong JD. Covid- 19 pandemic related research in africa: Bibliometric analysis of scholarly output, collaborations and scientific leadership. International journal of environmental research and public health. 2021 Jan;18(14):7273.
  9. Zhong W, Bragazzi NL, Kong JD, Safiri S, Behzadifar M, Liu J, Liu X, Wang W. Bur- den of Respiratory Infection and Tuberculosis Among US States from 1990 to 2019. Clinical Epidemiology. 2021;13:503.
  10. Bragazzi NL, Beamish D, Kong JD, Wu J. Illicit Drug Use in Canada and Implications for Suicidal Behaviors, and Household Food Insecurity: Findings from a Large, Nationally Representative Survey. International Journal of Environmental Research and Public Health. 2021 Jan;18(12):6425.
  11. Sott MK, Nascimento LD, Foguesatto CR, Furstenau LB, Faccin K, Zawislak PA, Mellado B, Kong JD, BragazziNL. A Bibliometric Network Analysis of Recent Publications on Digital Agriculture to Depict Strategic Themes and Evolution Structure. Sensors. 2021 Jan;21(23):7889.
  12. Khamisy-Farah R, Furstenau LB, Kong JD, Wu J, Bragazzi NL. Gynecology meets big data in the disruptive innovation medical era: State-of-art and future prospects. International Journal of Environmental Research and Public Health. 2021 Jan;18(10):5058.
  13. Kong JD, Tchuendom RF, Adeleye SA, David JF, Admasu FS, Bakare EA, Siewe N. SARS- CoV-2 and self-medication in Cameroon: a mathematical model. Journal of Biological Dy- namics. 2021 Jan 1;15(1):137-50.
  14. Betti M, Bragazzi NL, Heffernan JM, Kong J, Raad A. Integrated vaccination and non- pharmaceutical interventions based strategies in Ontario, Canada, as a case study: a mathe- matical modelling study. J R Soc Interface. 2021 Jul;18(180):20210009. doi: 10.1098/rsif.2021.0009. Epub 2021 Jul 14. PMID: 34255985; PMCID: PMC8277469.
  15. Li J, Yuan P, Heffernan J, Zheng T, Ogden N, Sander B, Li J, Li Q, B ́elair J, Kong JD, Aruffo E. Fangcang shelter hospitals during the COVID-19 epidemic, Wuhan, China. Bulletin of the World Health Organization. 2020 Dec 1;98(12):830.
  16. McCarthy Z, Athar S, Alavinejad M, Chow C, Moyles I, Nah K, Kong JD, Agrawal N, Jaber A, Keane L, Liu S. Quantifying the annual incidence and underestimation of seasonal influenza: A modelling approach. Theoretical Biology and Medical Modelling. 2020 Dec;17(1):1- 6.
  17. Kong JD, Wang H, Siddique T, Foght J, Semple K, Burkus Z, Lewis MA. Second-generation stoichiometric mathematical model to predict methane emissions from oil sands tailings. Sci- ence of the Total Environment. 2019 Dec 1;694:133645.
  18. Tadiri CP, Kong JD, Fussmann GF, Scott ME, Wang H. A Data-Validated Host-Parasite Model for Infectious Disease Outbreaks. Frontiers in Ecology and Evolution. 2019 Aug 21;7:307.
  19. Kong JD, Salceanu P, Wang H. A stoichiometric organic matter decomposition model in a chemostat culture. Journal of mathematical biology. 2018 Feb;76(3):609-44.

 

  1. Kong JD, Jin C, Wang H. The inverse method for a childhood infectious disease model with its application to pre-vaccination and post-vaccination measles data. Bulletin of mathematical biology. 2015 Dec;77(12):2231-63.
  2. Kong JD, Davis W, Wang H. Dynamics of a cholera transmission model with immunological threshold and natural phage control in reservoir. Bulletin of mathematical biology. 2014 Aug 1;76(8):2025-51.
  3. Kong JD, Davis W, Li X, Wang H. Stability and sensitivity analysis of the iSIR model for indirectly transmitted infectious diseases with immunological threshold. SIAM Journal on Applied Mathematics. 2014;74(5):1418-41.



Selected Refereed Policy Briefs:

  1. Kong J, Mellado B, Wu J (2022). Harnessing the power of data: Artificial Intelligence -based

pandemic support. UNESCO: https: // unesdoc. unesco. org/ ark: /48223/ pf0000380883. locale= en .

Refereed book publication:

  1. Kong JD, Kumar SS, Palumbo P. DDE models of the glucose-insulin system: a useful tool

for the artificial pancreas. In Managing complexity, reducing perplexity 2014 (pp. 109-117). Springer, Cham

  1. Kong, J. D., Fevrier, K., Effoduh, J. O., & Bragazzi, N. L. (2022). Artificial Intelligence, Law, and Vulnerabilities. In AI and Society (pp. 179-196). Chapman and Hall/CRC.

Conference publication:

  1. Palumbo P, Pepe P, Kong JD, Kumar SS, Panunzi S, De Gaetano A. Regulation of the human

plasma glycemia by means of glucose measurements and subcutaneous insulin administration. IFAC Proceedings Volumes. 2013 Jan 1;46(20):524-9.

Block publication

  1. The Power of Collaboration, Artificial Intelligence and Big Data in the fight against COVID-19 in Africa. https: // tinyurl. com/ 5cwjwjws

Other Publications

  1. Global South Artificial Intelligence and Data Innovation Newsletter https://ai4pep.org/news-letter-first-edition/
  2. Global South Artificial Intelligence and Data Innovation podcast; https://ai4pep.org/podcast-series/

Links to preprints of manuscript in press or currently being reviewed on this Theme

  1. Nilgiriwala,K.S., Mahajan, U., Ahmad, R.A., de Castro, R., Lazo, L., Kong, J., Lee, A.S.H., Veerakumarasivam, A., Sharef, N.M., Demidenko, S. (2024). Navigating the Governance of Artificial Intelligence (AI) in Asian Nations: A Focus on India, Indonesia, Malaysia and the Philippines.
  2. Trigui, H., Guerfali, F., Harigua, E., Atri, C., Hammami, K., Souiai, O., Kong, J.D., Sokhn, E., Qasrawi, R., Wu, J., El Morr, C., Znaidi, S. (2024). AI Governance in the MENA Region.
  3. de Carvalho, A., Bonidia, R., Rocha, U., Kong, J.D., Dauhajre, M., Struchiner, C., Goedert, G., Stadler, P.F., Walter, M.E., Sanches, D., Day, T., Castro, M., Edmunds, J., Colom ́e- Hidalgo, M., Morban, D.A.H., Franco, E.F., Ugarte-Gil, C., Espinoza-Lopez, P., Carrasco- Escobar, G. (2024). Democratising Artificial Intelligence for Pandemic Preparedness and Global Governance in Latin American and Caribbean Countries.

 

  1. Fonkou, M. D. M., & Kong, J. D. (2024). Phage Therapy: Leveraging Machine Learning and Big Data techniques to Unveil the Evolution, Innovations, and Global Landscape in the Fight Against Antibiotic Resistance.
  2. Ayana, G., Dese, K., Daba, H., Mellado, B., Badu, K., Yamba, E. I., … & Kong, J. D. (2023). Decolonizing global AI governance: Assessment of the state of decolonized AI governance in Sub-Saharan Africa. Available at SSRN 4652444.]
  3. Iyaniwura, S. A., Han, Q., Yong, N. B., Rutayisire, G., Adom-Konadu, A., Mbah, O. P., . . . & Kong, J. D. (2023). Regional variation and epidemiological insights in malaria underesti- mation in Cameroon. medRxiv, 2023-11.
  4. Perikli, N., Bhattacharya, S., Ogbuokiri, B., Nia, Z. M., Lieberman, B., Jude K., Tripathi, N., … & Mellado, B. (2023). COVID-19 South African Vaccine Hesitancy Models Show Boost in Performance Upon Fine-Tuning on M-pox Tweets. arXiv preprint arXiv:2310.04453.
  5. Vasconcelos VV, Marquitti F, Ong T, McManus LC, Aguiar M, Campos AB, Dutta PS, Jo- vanelly K, Junquera V, Kong J, Krueger EH. Rate-Induced Transitions in Networked Com- plex Adaptive Systems: Exploring Dynamics and Management Implications Across Ecological, Social, and Socioecological Systems. arXiv preprint arXiv:2309.07449. 2023 Sep 14.
  6. Qin H, Kong J, Ding W, Ahluwalia R, Morr CE, Engin Z, Effoduh JO, Hwa R, Guo SJ, Seyyed-Kalantari L, Muyingo SK. Towards Trustworthy Artificial Intelligence for Equitable Global Health. arXiv preprint arXiv:2309.05088. 2023 Sep 10.
  7. Perikli N, Bhattacharya S, Ogbuokiri B, Nia ZM, Lieberman B, Tripathi N, Dahbi SE, Steven- son F, Bragazzi N, Kong J, Mellado B. Detecting the Presence of COVID-19 Vaccina- tion Hesitancy from South African Twitter Data Using Machine Learning. arXiv preprint arXiv:2307.15072. 2023 Jul 12.
  8. Avusuglo WS, Han Q, Woldegerima WA, Bragazzi N, Asgary A, Ahmadi A, Orbinski J, Wu J, Mellado B, Kong JD. Impact assessment of self-medication on COVID-19 prevalence in Gauteng, South Africa, using an age-structured disease transmission modelling framework.
  9. Avusuglo W, Han Q, Woldegerima WA, Asgary A, Wu J, Orbinski J, Bragazzi NL, Ahmadi A, Kong JD. COVID-19 and Malaria Co-Infection: Do Stigmatization and Self-Medication Matter? A Case for Nigeria. A Case for Nigeria.
  10. Bragazzi NL, Kong JD, Mahroum N, Tsigalou C, Khamisy-Farah R, Converti M. The ongoing monkeypox epidemic urges the systematic collection of sexual orientation and gender identity data in clinical settings and in electronic health records to monitor and end LGBTQI+ health- related disparities and inequities.
  11. Bragazzi NL, Kong JD, Mahroum N, Tsigalou C, Khamisy-Farah R, Converti M, Wu J. Epidemiological trends and clinical features of the ongoing monkeypox epidemic: A preliminary pooled data analysis and.
  12. Bragazzi NL, Iyaniwura SA, Han Q, Woldegerima WA, Kong JD. Quantifying the Basic Reproduction Number and the Under-Estimated Fraction of Mpox Cases Around the World at the Onset of the Outbreak: A Mathematical Modeling and Machine Learning-Based Study. Available at SSRN 4533567.
  13. Effoduh, J. O., Akpudo, U. E., & Kong, J. D. (2023). Towards an Inclusive Data Governance Policy for the Use of Artificial Intelligence in Africa. Available at SSRN.
  14. Bragazzi, N. L., Kong, J. D., & Wu, J. (2023). Integrated epidemiological, clinical, and molecular evidence points to an earlier origin of the current monkeypox outbreak and a complex route of exposure. Available at SSRN. (Submitted to Journal of medical virology).
  15. Bragazzi, N. L., Kong, J. D., & Wu, J. (2023). A tale of two (and more) stories: smallpox- monkeypox viruses (HIV, and other sexually transmitted disesases) interaction dynamics. Re- searchGate Project: 2022 Monkeypox Epidemic. (Submitted to Journal of medical virol- ogy).
  16. Bragazzi, N. L., Kong, J. D., & Wu, J. (2023). Monkeypox and laboratory medicine: more data are urgenty needed. ResearchGate Preprint. (Submitted to Journal of medical vi- rology).
  17. Avusuglo, W., Han, Q., Woldegerima, W. A., Bragazzi, N. L., Ahmadi, A., Asgary, A., … & Kong, J. D. (2023). COVID-19 and malaria co-infection: do stigmatization and self- medication matter? A mathematical modelling study for Nigeria. A mathematical modelling study for Nigeria (April 21, 2022). (Submitted to Scientific Reports).
  18. Nia, Z. M., Ahmadi, A., Mellado, B., Wu, J., Orbinski, J., Agary, A., & Kong, J. D. (2023). Twitter-Based Gender Recognition Using Transformers. arXiv preprint arXiv:2205.06801. (Submitted to Pattern Recognition Letters).
  19. Sekkak I, Kong JD, El Fatini M. Containing and Managing an Emerging Disease Outbreak: A Stochastic Modelling Approach. Available at SSRN. 2023 Feb 17. (Submitted to SIAM Journal on Applied Mathematics).
  20. Naderi PT, Asgary A, Kong J, Wu J, Taghiyareh F. COVID-19 Vaccine Hesitancy and Information Diffusion: An Agent-based Modeling Approach. arXiv preprint arXiv:2109.01182. (Submitted to Scientific Reports).
  21. Sott MK, da Silva Nascimento L, Foguesatto CR, Furstenau LB, Faccin K, Zawislak PA, Mel- lado B, Kong JD, Bragazzi NL. Agriculture 4.0 and Smart Sensors. The Scientific Evolution of Digital Agriculture: Challenges and Opportunities. (Submitted to Sensors).
  22. Guelmami N, Chalghaf N, Wu J, Kong JD, Mellado B, Jahrami H, Ben Khalifa M, Amayra T, Azaiez F, Bragazzi NL. Social Media COVID-19 Information and Vaccine Decision: A Latent Class Analysis. Available at SSRN 3841301. (Submitted to BMC Medical Infor- matics and Decision Making).
  23. David JF, Iyaniwura SA, Yuan P, Tan Y, Kong JD, Zhu H. Modeling the potential impact of indirect transmission on COVID-19 epidemic. medRxiv. (Submitted to Bulletin of Mathematical Biology).