Mathematical Modelling

OUCRU has a research programme dedicated to study of the transmission of infectious diseases in populations developing and using mathematical and computational models.

We use machine learning approaches in order to forecast the timing, intensity and duration of future epidemics. Mathematical models are developed and fitted to surveillance data in order to test hypotheses about mechanisms and drivers of transmission, and also to explore intervention strategies (in silico experiments) and look for optimal policies (optimal control theory). Our activities also involve theoretical and methodological developments.

We are generally interested in the seasonality of infectious diseases and its drivers (sociological, environmental, climatic), as well as in the spatial dynamics of infectious diseases, and its causes and consequences in terms of prevention and control. The research program is headed by Dr Marc Choisy and includes projects on influenza, dengue, measles (and other vaccine-preventable diseases), as well as COVID-19. The team has members of various backgrounds in biology, mathematics and computer science.

The team is also dedicated in running a national sero-surveillance system together with OUCRU Ha Noi, the Hospital of Tropical Diseases in Ho Chi Minh city and the National Institute of Hygiene and Epidemiology in Hanoi. The whole system is based on the exploitation of residual serum samples that are routinely collected in the hospitals. The analysis of such samples allows to assess – almost real-time – the population levels of protection against a panel of infectious diseases and identify gaps in vaccination. Such a system is other extremely useful to study any infectious diseases, even those that are not vaccine-preventable, and even those for which the serology is not correlation with protection. It can for example be used to assess the level of transmission of diseases that are fully (TB) or partially silently transmitted (e.g. dengue, SARS-CoV-2) and to understand better the determinants of population susceptibility to pathogens that are antigenically diverse such as dengue or influenza.


Fuhan Yang, Joseph L Servadio, Nguyen Thi Le Thanh, Ha Minh Lam, Marc Choisy, Pham Quang Thai, Tran Thi Nhu Thao, Nguyen Ha Thao Vy, Huynh Thi Phuong, Tran Dang Nguyen, Dong Thi Hoai Tam, Ephraim M Hanks, Ha Vinh, Ottar N Bjornstad, Nguyen Van Vinh Chau, Maciej F Boni
BMJ Glob Health
November 7, 2023
DOI: 10.1136/bmjgh-2023-013054
Lenny L. Ekawati, Ahmad Arif, Irma Hidayana, Ahmad Nurhasim, M. Zakiyuddin Munziri, Karina D. Lestari, Amanda Tan, Firdaus Ferdiansyah, Fikry Nashiruddin, Qorinah E.S. Adnani, Halik Malik, Tri Maharani, Andy Riza, Monalisa Pasaribu, Khairul Abidin, Adhi A. Andrianto, Nursalam, A.V. Sri Suhardiningsih, Ade Jubaedah, N.S. Widodo, Henry Surendra, Herawati Sudoyo, Adrian D. Smith, Philip Kreager, J. Kevin Baird, Iqbal R.F. Elyazar
Plos Global Public Health
December 9, 2022
DOI: 10.1371/journal.pgph.0000893
Jeffrey V Lazarus, Diana Romero, Christopher J Kopka, Salim Abdool Karim, Laith J Abu-Raddad, Gisele Almeida, Ricardo Baptista-Leite, Joshua A Barocas, Mauricio L Barreto, Yaneer Bar-Yam, Quique Bassat, Carolina Batista, Morgan Bazilian, Shu-Ti Chiou, Carlos Del Rio, Gregory J Dore, George F Gao, Lawrence O Gostin, Margaret Hellard, Jose L Jimenez, Gagandeep Kang, Nancy Lee, Mojca Maticic, Martin McKee, Sabin Nsanzimana, Miquel Oliu-Barton, Bary Pradelski, Oksana Pyzik, Kenneth Rabin, Sunil Raina, Sabina Faiz Rashid, Magdalena Rathe, Rocio Saenz, Sudhvir Singh, Malene Trock-Hempler, Sonia Villapol, Peiling Yap, Agnes Binagwaho, Adeeba Kamarulzaman, Ayman El-Mohandes, COVID-19 Consensus Statement Panel
November 1, 2022
DOI: 10.1038/s41586-022-05398-2
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