Nick Reich, one of the PLOS Disease Forecasting and Surveillance Channel Editors, is an Associate Professor of Biostatistics at the UMass-Amherst School of Public Health and Health Sciences. His research focuses on developing statistical methods for time-series forecasting, with a particular focus on ensemble methods and applications in infectious disease. Nick works collaboratively with public health officials from across the world, including the Ministry of Public Health in Thailand, the US Centers for Disease Control and Prevention, and the New York City Department of Health and Mental Hygiene.
Tell us about yourself and current research, and about how you came to be interested in this field.
My lab focuses on building forecast models for infectious disease outbreaks. On the one hand, we work on what you might call the “basic science” of forecasting, meaning the computational underpinnings of the field: developing new statistical methods, working towards standardized data structures, etcetera. We also work on deploying operationalized forecasts into real-time public health decision-making settings. For example, for the past three years, we have worked with the Thai Ministry of Public Health and the US Centers for Disease Control and Prevention, and are now delivering real-time forecasts of dengue fever and influenza every 1-2 weeks.
Why did you decide to join the editorial team at the PLOS Disease Forecasting and Surveillance Channel?
This was really a joint idea with Don Olson. We both pitched a similar idea at different times to folks at PLOS and then worked together to recruit others to the team. As scientific publishing becomes less localized in specialty journals, there is a need for new models for content curation. This seems like an inventive new method to aggregate content across journals.
Where do you see your field of research heading in the next few years? What are the next big questions the field will address?
The interest in surveillance and forecasting in particular has really increased over the last few years. But it feels a bit like the wild west out there. Everyone is doing forecasting but doing it in their own way. As a field, I think we really have to try to develop and cohere around some standards for data and reporting, to ensure that we can learn from each other and compare different approaches as we move forward.
What is your favorite piece on the Channel to date? What do you think is a must-read on the Channel?
I really like the piece by Sarah Cobey and Ed Baskerville in PLOS ONE called “Limits to Causal Inference with State-Space Reconstruction for Infectious Disease.” It’s such a cogent and carefully crafted refutation of a well-known and established method.
I also like the piece by Jason Asher et al., “Preliminary results of models to predict areas in the Americas with increased likelihood of Zika virus transmission in 2017,” which implements some forecasting with really novel links to public health decision-making and vaccine study design.
What is the importance of Open Access and Open Data in your field?
This is such a critical aspect of our field and of all of public health research. I think, on the whole, the forecasting and surveillance field has done a good job making Open Access and Open Data standards the accepted practice, but there still is a long way to go, especially as we try to incorporate new data sources.
Check out the PLOS Disease Forecasting and Surveillance Channel – channels.plos.org/dfs