Infectious disease epidemics remain one of the great threats to humanity, with increased disease emergence and the threat of a global pandemic, there are renewed efforts to forecast and predict these events. We propose to develop tools to measure the effectiveness and impact of epidemic nowcasting, forecasting and intervention. In order to do this, we will compare outcomes across interventions to determine the cost-effectiveness of interventions and optimize resource allocation. The goal of improving the precision of outbreak predictions is to increase the timeliness and accuracy of the response and, ultimately, to reduce costs in terms of mortality, morbidity and economies. We can measure the accuracy of epidemic predictions post-hoc by comparing predicted timing, extent, region and affected populations to actual epidemic parameters as they occur. We propose this as one method of validating and informing predictive models and tools. New tools for epidemic forecasting are under continuous development, facilitated by cutting-edge digital technologies including AI and machine learning. We will compare the accuracy of these innovative approaches against current gold standards in epidemic forecasting, where they exist, e.g., the Swiss influenza Sentinella network. We will also assess the uptake of forecasts by Ministries of Health (e.g., policy reform, resource allocation, emergency declarations), hospital management (e.g., extra staffing, vaccine stockpiles, PPE) lay press (e.g., televised coverage of warnings, newspaper articles, public information campaigns), pharmaceutical companies (e.g., vaccine sales, stockpiling) and public response (e.g., vaccine uptake, precautionary measures, sentiment analysis on social media).
Princeton and Geneva Universities are internationally recognized as centers of excellence in mathematical physics. The proposal is to open a channel for fostering collaboration in research and teaching in this cross disciplinary field. The proposal builds on the successful interaction between Professors Aizenman (Princeton) and Duminil-Copin (Geneva). They have already a record of successful interaction, having jointly produced a number of results which were well received by the professional community and accepted for publication in leading journals. Professor Duminil-Copin has visited Princeton several times and very successfully delivered the Math Department’s honorific Minerva Lecture Series. The aim of the proposed grant is to build on this collaboration and expand the opportunities of mutual visits involving the PI’s, qualified students, postdocs and faculty members. The purpose of these would be interactive research, exchange lectures, students’ formative visits, and jointly organized annual workshops on research topics of joint interest.
The expected specific outcomes are the following: