Epidemiologic models have played a key role during the COVID-19 pandemic, both in understanding how SARS-CoV-2 moves between people and through populations, predicts the spread of the pandemic and estimates the effects of different disease control measures. Over 350 people were in attendance as two experts in infectious disease dynamics Adam Kucharski (London School of Hygiene and Tropical Medicine) and Justin Lessler (Johns Hopkins Bloomberg School of Public Health) were brought together by session organizers Gary Chan (University of Washington) and Elizabeth Stuart (Johns Hopkins Bloomberg School of Public Health) to discuss the basics of infectious disease models, their assumptions, data requirements, and how they are used in practice to reveal the nature of disease spread and inform decision making. (see Event page)
Adam Kucharski started off the session by providing a brief overview of understanding disease dynamics. The examples that he provided helped to demonstrate some of the shortcomings of working with real-time case data and utility of incorporating multiple data sources into models as a means of providing more robust estimations of key epidemiological values. Adam demonstrated how situational awareness could be revealed as a valuable result of working with models and then moved on to demonstrate how synthesizing the available evidence of various controls into scenarios which allow for further exploration, for example, to help answer various “what if?” questions.
Justin Lessler provided comments that focused on the modeling involving two types of epidemiologic data, contact tracing and household data, and the implications for policy and control. After describing what contact tracing involves and walking through several examples of its analysis to provide an understanding of how these data can model transmission and spread of disease, he summarized the advantages and shortcomings of using these data. Whereas, contact tracing data loses its utility as an epidemic becomes more widespread, the second section of Justin’s remarks focused specifically on serologic data from households. These data become available towards the later stages or after an epidemic wave and Justin demonstrated how models of these data provide additional windows into understanding transmission both inside and outside of the home.
Moderator, biostatistician and public health expert Elizabeth Stuart (Johns Hopkins) left ample time for both panelists to discuss the issues that were presented and also answer questions posted by those in attendance. She started off with a very broad question that perhaps gets to a foundational aspect of the session, “How do we best combine our knowledge about statistics and data with our biological/epidemiological knowledge?” Other questions presented to the speakers by Elizabeth involved discussion about the following: “How do we validate our different models?”, “Social contact data, where does it come from and how is it handled?” and finally, knowing both of the speakers and the audience, “What would you want a room of 300 statisticians to hear?”
Unfortunately, as has become usual in COPSS-NISS COVID-19 Webinar Series, there were more questions than could be answered - even given the extra time provided and the fact that the panelists were clearly scrambling to type in answers when they weren’t speaking! Clearly this webinar series features topics that are of interest to many!
Want to learn more? Mark your calendar for every 1st and 3rd Thursday from noon to 1 pm ET. (See the NISS website for event details and to register for these sessions!)
Below, please find a recording of this session along with a link to the slides that the speakers used. The slides not only provide you with the key points that were offered but also include links to additional resources that should not be ignored!
Recording of the Session
Slides used by the Speakers
Adam Kucharski, (London School of Hygiene and Tropical Medicine)
Justin Lessler, (Johns Hopkins Bloomberg School of Public Health)
About this Webinar Series
The COPSS-NISS COVID-19 Data Science webinar series is co-organized by the Committee of the Presidents of Statistical Societies (COPSS) and its five charter member societies (ASA, ENAR, IMS, SSC, and WNAR), as well as NISS. This bi-monthly webinar features the latest research that is positioned on the cusp of new understanding and analysis of COVID-19 pandemic data, and promotes data-driven research and decision making to combat COVID-19. Find out more about this series and view all the previous sessions on the Webinar Series page.