Abstract: Recent epidemics of pathogens such as influenza viruses, MERS coronavirus, chikungunya virus, Ebola virus, and Zika virus, highlight the importance of epidemics on local and global scales. Statistical and mathematical modeling have long been used as a conceptual tools to describe epidemic dynamics and assess possible interventions, yet their use to forecast the trajectory and spread of epidemics is relatively new to public health, and the use of forecasts in decision making remains limited. To help close this gap, the Centers for Disease Control and Prevention Epidemic Prediction Initiative (EPI) is building links between the research and decision-making communities to ensure that forecasts address specific public health needs, facilitate the sharing of data and knowledge about that data, establish standards for assessing and communicating forecast skill, compare different forecasting approaches, and identify effective communication strategies for forecasts. As part of this work, EPI hosted a forecasting challenge for dengue epidemics in 2015 and has hosted an annual prospective seasonal influenza forecasting challenge since 2013. In these open challenges participating groups submit forecasts for short-term activity and for longer-term seasonal targets (e.g. epidemic peak). The challenges have highlighted strengths and weaknesses in current forecasting approaches. For example, the accuracy of forecasts for short-term activity tends to be much higher than for seasonal targets, which often have lead times of several weeks or months. Current forecasts are therefore most useful for situational awareness and less so for key seasonal events more than a few weeks away. Nonetheless, for both kinds of targets simple ensemble forecasts outperformed expectations based on historical data alone, demonstrating that current forecasts add information beyond data alone. Through the forecasting challenges, EPI has opened a path for real-time forecasting of epidemics, allowing both researchers and decision makers to identify and address the challenges of making and disseminating forecasts during epidemics. Forecasting increasingly has an opportunity to contribute to evidence-based public health decision making, and as the science of epidemic forecasting continues to evolve, new opportunities for engagement are emerging.
Bio: Michael Johansson is a Biologist and the Modeling Unit Lead at the Centers for Disease Control and Prevention Dengue Branch in San Juan, Puerto Rico. He uses statistical and mathematical modeling to improve surveillance, prevention, and control of arboviral diseases including chikungunya, dengue, yellow fever, and Zika. He also works to improve the use of quantitative models to support decision making related to infectious disease outbreaks more broadly as co-founder of the CDC Epidemic Prediction initiative and co-chair of the US interagency Pandemic Prediction and Forecasting Science and Technology working group.
Biologist, CDC Dengue Branch And Visting Scientist at Harvard T.H. Chan School of Public Health