Faculty

Professor Ghosh’s research focuses on infectious disease modelling and forecasting. He has worked as a senior project research scientist at the National Disease Modelling Consortium, Indian Institute of Technology Bombay. His research work at the Indian Institute of Technology Bombay included data-driven modelling of lymphatic filariasis and measles. Before this, he was a postdoctoral research associate at the University of Georgia, Athens, USA, working with Professor Amy K. Winter and Professor Matthew J. Ferrari. He did another postdoc at the Indian Institute of Science, Bangalore, under the guidance of Professor Debnath Pal. Professor Ghosh worked on modelling respiratory diseases like COVID-19 and MERS-CoV during his postdoc at IISc, Bangalore.

He completed PhD in applied mathematics from Indian Statistical Institute, Kolkata, under the supervision of Professor Joydev Chattopadhyay. He studied various emerging and re-emerging diseases in his doctoral thesis using mathematical and statistical modelling. During his BSc, MSc, PhD, and postdoc, he received several scholarships and fellowships, including Innovation in Science Pursuit for Inspired Research, National Eligibility Test - Junior Research Fellowship (AIR-8), Graduate Aptitude Test in Engineering - Junior Research Fellowship (AIR-38), and National Board for Higher Mathematics postdoc fellowship from the Government of India. Professor Ghosh received the best paper prize in “Medical Statistics: Disease Modelling” from NIMS-ICMR in September 2023.

Effect of climate variables on mosquito-borne diseases: This research aims to investigate the impact of climate variables, such as temperature, precipitation, and humidity, on the transmission dynamics of mosquito-borne diseases, including but not limited to malaria, dengue fever, Zika virus, and chikungunya. Understanding the interplay between climate variables and mosquito-borne diseases can enhance preparedness and resilience in the face of climate change.

Modelling and forecasting infectious diseases: Mathematical and computational modelling helps understand and combat diseases. Communicable diseases, such as measles, Japanese encephalitis, lymphatic filariasis, dengue fever, etc. affect marginalised communities and pose significant public health challenges. In India, for instance, collaborations between researchers, public health agencies, and policymakers are still lacking in developing, validating, and implementing mathematical and computational models to combat deadly diseases effectively. These models provide valuable insights into the complex dynamics of epidemic transmission and aid in designing targeted interventions to alleviate the burden of these diseases on vulnerable populations. Moreover, prediction and forecasting results can expedite disease elimination from the endemic regions.

Artificial Intelligence (AI) in public health research: Applying AI tools can help understand disease dynamics and predict future outbreaks. The results inform policy and further advancements in AI research applied to healthcare. We can take up the following questions in particular: (a) Review of the current applications of AI in public health, both globally and within specific regions. (b) How do AI tools contribute to improving tracking elimination status and outbreak prediction of various diseases? (c) What challenges are associated with implementing AI in public health, and how can these challenges be mitigated?

• Ghosh, S., Ghosh, I. and Mukhopadhyay, S., (2025). An age-stratified mathematical model to inform optimal measles vaccination strategies. medRxiv. https://doi.org/10.1101/2025.04.01.25325066​​​​​​​  
• Ghosh, I., Nath-Sain, S., Sen Gupta, S., Pant Joshi, C., Jain, T., Subramanian, S., Banerjee, S., and Mitra, M. K. (2024). COMET-LF: A compartmental model of dynamics of infection, disease, and elimination strategies for lymphatic filariasis. medRxiv. https://doi.org/10.1101/2024.09
• Ghosh, I., Gupta, S., and Rana, S. (2023). Anticipating dengue outbreaks using a novel hybrid ARIMA-ARNN model with exogenous variables. arXiv Preprint. arXiv:2312.15270.
• Maity, B., Saha, B., Ghosh, I., and Chattopadhyay, J. (2023). Model-based estimation of expected time to cholera extinction in Lusaka, Zambia. Bulletin of Mathematical Biology, 85(7), 55. https://doi.org/10.1007/s11538-023-01114-4
• Pal, S., and Ghosh, I. (2023). Dynamics of a coupled socio-environmental model: An application to global CO2 emissions. Ecological Modelling, 478, 110279. https://doi.org/10.1016/j.ecolmodel.2023.110279
• Panja, M., Chakraborty, T., Nadim, S. S., Ghosh, I., Kumar, U., and Liu, N. (2023). An ensemble neural network approach to forecast dengue outbreak based on climatic conditions. Chaos, Solitons & Fractals, 167, 113124. https://doi.org/10.1016/j.chaos.2023.113124
• Ghosh, I., Nadim, S. S., Raha, S., and Pal, D. (2023). Dynamics of a single-strain and two-strain respiratory infection driven by travel on a metapopulation network. Nonlinear Dynamics, 111(22), 21371–21389. https://doi.org/10.1007/s11071-023-08202-9
• Pal, S., and Ghosh, I. (2022). A mechanistic model for airborne and direct human-to-human transmission of COVID-19: Effect of mitigation strategies and immigration of infectious persons. The European Physical Journal Special Topics, 231(18), 3371–3389. https://doi.org/10.1140/epjs/s11734-022-00570-3
• Reja, S., Ghosh, S., Ghosh, I., Paul, A., and Bhattacharya, S. (2022). Investigation and control strategy for canine distemper disease on endangered wild dog species: A model-based approach. SN Applied Sciences, 4(6), 176. https://doi.org/10.1007/s42452-022-05001-8
• Chakraborty, T., Ghosh, I., Mahajan, T., and Arora, T. (2022). Nowcasting of COVID-19 confirmed cases: Foundations, trends, and challenges. In Modeling, Control and Drug Development for COVID-19 Outbreak Prevention (pp. 1023-1064). Elsevier. https://doi.org/10.1016/B978-0-323-91172-2.00018-2
• Ghosh, I. (2021). Within host dynamics of SARS-CoV-2 in humans: Modeling immune responses and antiviral treatments. SN Computer Science, 2(6), 482. https://doi.org/10.1007/s42979-021-00750-w
• Ghosh, I., and Martcheva, M. (2021). Modeling the effects of prosocial awareness on COVID-19 dynamics: Case studies on Colombia and India. Nonlinear Dynamics, 104(4), 4681–4700. https://doi.org/10.1007/s11071-021-06492-6
• Ghosh, I., Nadim, S. S., and Chattopadhyay, J. (2021). Zoonotic MERS-CoV transmission: Modeling, backward bifurcation and optimal control analysis. Nonlinear Dynamics, 103(3), 2973–2992. https://doi.org/10.1007/s11071-020-06008-8
• Nadim, S. S., Ghosh, I., and Chattopadhyay, J. (2021). Short-term predictions and prevention strategies for COVID-19: A model-based study. Applied Mathematics and Computation, 404, 126251. https://doi.org/10.1016/j.amc.2021.126251
• Nadim, S. S., Ghosh, I., Martcheva, M., and Chattopadhyay, J. (2020). Impact of venereal transmission on the dynamics of vertically transmitted viral diseases among mosquitoes. Mathematical Biosciences, 325, 108366. https://doi.org/10.1016/j.mbs.2020.108366
• Chakraborty, T., and Ghosh, I. (2020). Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis. Chaos, Solitons & Fractals, 135, 109850. https://doi.org/10.1016/j.chaos.2020.109850
• Ghosh, I., and Chakraborty, T. (2021). An integrated deterministic–stochastic approach for forecasting the long-term trajectories of COVID-19. International Journal of Modeling, Simulation, and Scientific Computing, 12(03), 2141001. https://doi.org/10.1142/S1793962321410019
• Sardar, T., Ghosh, I., Rodó, X., and Chattopadhyay, J. (2020). A realistic two-strain model for MERS-CoV infection uncovers the high risk for epidemic propagation. PLoS Neglected Tropical Diseases, 14(2), e0008065. https://doi.org/10.1371/journal.pntd.0008065
• Nadim, S. S., Ghosh, I., and Chattopadhyay, J. (2020). Global dynamics of a vector-borne disease model with two transmission routes. International Journal of Bifurcation and Chaos, 30(06), 2050083. https://doi.org/10.1142/S0218127420500836
• Chakraborty, T., Chattopadhyay, S., and Ghosh, I. (2019). Forecasting dengue epidemics using a hybrid methodology. Physica A: Statistical Mechanics and its Applications, 527, 121266. https://doi.org/10.1016/j.physa.2019.121266
• Ghosh, I., Tiwari, P. K., and Chattopadhyay, J. (2019). Effect of active case finding on dengue control: Implications from a mathematical model. Journal of Theoretical Biology, 464, 50-62. https://doi.org/10.1016/j.jtbi.2018.12.033
• Ghosh, I., Tiwari, P. K., Samanta, S., Elmojtaba, I. M., Al-Salti, N., and Chattopadhyay, J. (2018). A simple SI-type model for HIV/AIDS with media and self-imposed psychological fear. Mathematical Biosciences, 306, 160-169. https://doi.org/10.1016/j.mbs.2018.09.010
• Ghosh, I., Tiwari, P. K., Mandal, S., Martcheva, M., and Chattopadhyay, J. (2018). A mathematical study to control Guinea worm disease: A case study on Chad. Journal of Biological Dynamics, 12(1), 846-871. https://doi.org/10.1080/17513758.2018.1525544
• Sasmal, S. K., Ghosh, I., Huppert, A., and Chattopadhyay, J. (2018). Modeling the spread of Zika virus in a stage-structured population: Effect of sexual transmission. Bulletin of Mathematical Biology, 80(11), 3038-3067. https://doi.org/10.1007/s11538-018-0492-9
• Ghosh, I., Sardar, T., and Chattopadhyay, J. (2017). A mathematical study to control visceral leishmaniasis: An application to South Sudan. Bulletin of Mathematical Biology, 79(5), 1100-1134. https://doi.org/10.1007/s11538-017-0277-6

Google scholar link

ResearchGate link

Papers presented in conferences

• Ghosh, I. (2024, July 22-26). COMET-LF: A compartmental model of dynamics of infection, disease, and elimination strategies for lymphatic filariasis. Paper presented at the 13th European Conference on Mathematical and Theoretical Biology (ECMTB), Toledo, Spain.
• Ghosh, I. (2023, November 28 - December 1). Tracking progress to measles elimination using routinely collected subnational surveillance data: a case study from Nigeria. Paper presented at the 9th EPIDEMICS Conference, Bologna, Italy.
• Ghosh, I. (2023, September 26). Short-term predictions and prevention strategies for COVID-19: A model-based study. Best paper award in medical statistics: Mathematical modelling, presented on the 48th Foundation Day of ICMR-NIMS, New Delhi, India.
• Ghosh, I. (2017, August 23-26). A mathematical study to control visceral leishmaniasis: An application to South Sudan. Paper presented at the 6th China-Indian-Japan-Korea Mathematical Biology Colloquium, IIT Kanpur, India.
• Ghosh, I. (2020, February 4-7). Modeling the spread of Zika virus in a stage-structured population: Effect of sexual transmission. Paper presented at the 11th DSABNS, University of Trento, Italy.
• Ghosh, I. (2019, November 19-21). A simple SI-type model for HIV/AIDS with media and self-imposed psychological fear. Paper presented at the 7th India Biodiversity Meet, ISI Kolkata, India.

• INSPIRE (Department of Science and Technology, India) scholar from 2009-2014. 
• Qualified CSIR-UGC National Eligibility Test (Mathematical Sciences) Dec-2014 with all India rank 8.
• Qualified GATE 2015 in Mathematics with all India rank 38.
• International Travel Support from SERB (February, 2020).
• Selected for NBHM Postdoctoral fellowship (2019 - Part 2).
• Offered postdoctoral position at University of Georgia, USA.
• Best paper award in medical statistics: Mathematical modelling - Presented the awarded
  best paper on the 48th Foundation day of ICMR-NIMS, Sep. 26, 2023, New Delhi, India.

• Member, Center for Ecology of Infectious Diseases, University of Georgia, USA (Link)
• Member, Measles Analytics Hub, Imperial College London (Link)
• Editorial board member, PLoS Neglected Tropical Diseases (Link)