TRACE-TB Webinar: How to Use Data Science and Predictive Modelling in Response to Infectious Diseases

The first webinar in the two-part series will take place on 18 January 2022 at 4PM IST.
The TRACE-TB project is hosting a two-part webinar series that intends to demonstrate how data science, artificial intelligence, and machine learning can be utilised to bolster responses to infectious diseases in India.

Data science and predictive modelling have the potential to drive game-changing improvements in response to infectious diseases. They can enhance public health programs by helping to predict trends, forecast reported infections at a local level to inform capacity planning, model the effects of policy changes, and prepare for potential scenarios.

The ongoing COVID-19 pandemic has spurred an intense interest in predictive analytics and forecasting models. The need for robust solutions has been especially pressing in dense populations across the developing world, with their limited health resource availability, limited data to anticipate outbreaks, and long lead times for addressing shortfalls. It is paramount to ensure the availability of critical healthcare resources in order to reduce mortality.

TRACE-TB Webinar Series

The TRACE-TB project, supported by USAID and implemented by Wadhwani AI, aims to develop and deploy innovative AI solutions to combat tuberculosis and other widespread infectious diseases. The project will strengthen the delivery of healthcare services and accelerate efforts to eliminate infectious diseases in India effectively.

As part of this this initiative, the TRACE-TB project is hosting a two-part webinar series that intends to demonstrate how data science, artificial intelligence, and machine learning can be utilised to bolster responses to infectious diseases in India.

How to Use Data Science and Predictive Modelling in Response to Infectious Diseases

The first webinar in the series will take place on 18 January 2022 at 4PM IST. It will shed light on the ways in which public health responses to COVID-19 can be strengthened using predictive modelling. Furthermore, it will disseminate the learnings from the use of data science and AI/ML solutions in response to infectious diseases in the wake of the COVID pandemic.

Our panel of speakers will explore the way forward for applying these frameworks to estimate the case burdens experienced by other infectious diseases which are widespread in the developing world.


Ms. Sangita Patel
Director, Health Office at USAID India

Ms. Sangita Patel is a U.S. Senior Foreign Service Officer (Counsellor level) that assumed the duty of Health Office Director at USAID India since 2019.  Prior to joining the India team, she served with USAID in Pakistan, Zambia, Armenia, Namibia, and Washington D.C.  She has extensive experience of managing programs to advance the public health and social sector.  She has an M.P.H. in Maternal and Child Health from Tulane University, a B.A. in French, and a B.S. in Biology from Pennsylvania State University.

Ms. Ashwini Bhide, IAS Additional Commissioner at MCGM, Mumbai

Ms. Ashwini Bhide, an IAS officer from the batch of 1995 of Maharashtra cadre, is a postgraduate in English Literature from Pune University. Ms. Bhide has a rich experience of about two decades in IAS cadre as she has held key positions at district and state level in Maharashtra. Ms. Bhide currently serves as an Additional Municipal Commissioner at the Municipal Corporation of Greater Mumbai (M.C.G.M.). She headed M.C.G.M.’s COVID-19 War Room and is credited for bringing in many policy and technology initiatives to combat the ongoing COVID-19 pandemic.

Dr. Alpan Raval
Chief Scientist, AI/ML at Wadhwani AI

Dr. Alpan Raval has a Ph.D. in black hole physics and cosmology from the University of Maryland and served as a tenured faculty member at the Claremont Colleges in California in Mathematics and Biology. He worked at D. E. Shaw Research, Amazon, and LinkedIn, prior to taking up his current role. Additionally, he co-authored a book on network biology: Introduction to Biological Networks.

Shri Ravi Shankar Shukla IAS, DC – Dumka, Ex-MD at NHM, Jharkhand

Shri Ravi Shankar Shukla is an I.A.S. officer from the batch of 2012 of Jharkhand Cadre. Shri Shukla is currently serving as District Collector in Dumka district of Jharkhand. He served as Mission Director N.H.M., Jharkhand, during the first and second waves of the COVID pandemic and successfully led the response to COVID for the Government of Jharkhand.

Dr. Tavpritesh Sethi
Associate Professor of Computational Biology at IIT Delhi

Dr. Tavpritesh Sethi is an Associate Professor of Computational Biology at Indraprastha Institute of Information Technology Delhi, India and a fellow of the Wellcome Trust/D.B.T. India Alliance at A.I.I.M.S., New Delhi, India. Dr. Sethi is an editorial board member at PLOS One, Systems Medicine, and The Journal of Genetics. He is a member of the European Association of Systems Medicine and leads the Australasia region for International Association of Systems and Networks Medicine (IASyM).

Dr. Vineet Bhatia
Medical Officer MDR-TB at WHO-SERO

Dr. Vineet Bhatia works as a Medical Officer in the TB unit of the South-East Asia Regional Office of W.H.O. as a focal point for drug-resistant TB. He has earlier worked in different capacities at all three levels of WHO and has over 20 years of experience with tuberculosis programs and projects, in over 30 low- and middle-income countries across Asia and Africa.

Ms. Kachina Chawla
Health Office Senior Advisor, USAID India

Ms. Kachina Chawla is the Senior Strategic Advisor at USAID India, where she works on digital technology, inclusive development, and other emerging priorities such as COVID-19 and urban resilience. She is a public health professional who has spent the last 20 years working extensively across India in the areas of maternal and child health, family planning, and infectious diseases across three continents.

  • Wadhwani AI

    We are an independent and nonprofit institute developing multiple AI-based solutions in healthcare and agriculture, to bring about sustainable social impact at scale through the use of artificial intelligence.

ML Engineer


An ML Engineer at Wadhwani AI will be responsible for building robust machine learning solutions to problems of societal importance; usually under the guidance of senior ML scientists, and in collaboration with dedicated software engineers. To our partners, a Wadhwani AI solution is generally a decision making tool that requires some piece of data to engage. It will be your responsibility to ensure that the information provided using that piece of data is sound. This not only requires robust learned models, but pipelines over which those models can be built, tweaked, tested, and monitored. The following subsections provide details from the perspective of solution design:

Early stage of proof of concept (PoC)

  • Setup and structure code bases that support an interactive ML experimentation process, as well as quick initial deployments
  • Develop and maintain toolsets and processes for ensuring the reproducibility of results
  • Code reviews with other technical team members at various stages of the PoC
  • Develop, extend, adopt a reliable, colab-like environment for ML

Late PoC

This is early to mid-stage of AI product development

  • Develop ETL pipelines. These can also be shared and/or owned by data engineers
  • Setup and maintain feature stores, databases, and data catalogs. Ensuring data veracity and lineage of on-demand pulls
  • Develop and support model health metrics

Post PoC

Responsibilities during production deployment

  • Develop and support A/B testing. Setup continuous integration and development (CI/CD) processes and pipelines for models
  • Develop and support continuous model monitoring
  • Define and publish service-level agreements (SLAs) for model serving. Such agreements include model latency, throughput, and reliability
  • L1/L2/L3 support for model debugging
  • Develop and support model serving environments
  • Model compression and distillation

We realize this list is broad and extensive. While the ideal candidate has some exposure to each of these topics, we also envision great candidates being experts at some subset. If either of those cases happens to be you, please apply.


Master’s degree or above in a STEM field. Several years of experience getting their hands dirty applying their craft.


  • Expert level Python programmer
  • Hands-on experience with Python libraries
    • Popular neural network libraries
    • Popular data science libraries (Pandas, numpy)
  • Knowledge of systems-level programming. Under the hood knowledge of C or C++
  • Experience and knowledge of various tools that fit into the model building pipeline. There are several – you should be able to speak to the pluses and minuses of a variety of tools given some challenge within the ML development pipeline
  • Database concepts; SQL
  • Experience with cloud platforms is a plus

ML Scientist


As an ML Scientist at Wadhwani AI, you will be responsible for building robust machine learning solutions to problems of societal importance, usually under the guidance of senior ML scientists. You will participate in translating a problem in the social sector to a well-defined AI problem, in the development and execution of algorithms and solutions to the problem, in the successful and scaled deployment of the AI solution, and in defining appropriate metrics to evaluate the effectiveness of the deployed solution.

In order to apply machine learning for social good, you will need to understand user challenges and their context, curate and transform data, train and validate models, run simulations, and broadly derive insights from data. In doing so, you will work in cross-functional teams spanning ML modeling, engineering, product, and domain experts. You will also interface with social sector organizations as appropriate.  


Associate ML scientists will have a strong academic background in a quantitative field (see below) at the Bachelor’s or Master’s level, with project experience in applied machine learning. They will possess demonstrable skills in coding, data mining and analysis, and building and implementing ML or statistical models. Where needed, they will have to learn and adapt to the requirements imposed by real-life, scaled deployments. 

Candidates should have excellent communication skills and a willingness to adapt to the challenges of doing applied work for social good. 


  • B.Tech./B.E./B.S./M.Tech./M.E./M.S./M.Sc. or equivalent in Computer Science, Electrical Engineering, Statistics, Applied Mathematics, Physics, Economics, or a relevant quantitative field. Work experience beyond the terminal degree will determine the appropriate seniority level.
  • Solid software engineering skills across one or multiple languages including Python, C++, Java.
  • Interest in applying software engineering practices to ML projects.
  • Track record of project work in applied machine learning. Experience in applying AI models to concrete real-world problems is a plus.
  • Strong verbal and written communication skills in English.