In March 2020, the world awoke to a new reality defined by lockdowns, overwhelmed hospitals, and the omnipresence of COVID-19. As I immersed myself in the book What If by UHasselt biostatisticians Niel Hens and Christel Faes, I found myself holding a backstage pass to the tumultuous and uncertain times of the pandemic, unraveling the intricate dance between data, models, and decisions.
Reading this book on a Sunday afternoon by the fireplace, the authors took me back to a period that now seems distant. Bit by bit, they peeled back the layers of the decision-making process behind the abstract daily COVID-19 figures. It is fascinating to see that mathematical models used by biostatisticians, virologists, and epidemiologists influenced important political and health decisions.
While I’ve had many a statistics course during my studies, I was still afraid to be showered with unknown terminology and mathematical reasoning. Yet, the authors manage the delicate balance of explaining the reasoning behind complex models without overwhelming the reader. The narrative simplifies the subject without patronizing, allowing readers to feel they understand the decisions even if the intricate models remain complex. You should still keep a clear head, though - I would discourage reading it at said fireplace while your father-in-law watches the local cycling on full volume.
Despite the efforts to make the subject accessible, it may not appeal to everyone. The narrative might benefit from a bit more focus on the bigger picture to help the reader better understand the broader implications of the presented facts. It would be interesting to see more concrete examples of decisions or changes that were implemented as a result of their work. Or even better, to see when they were ignored.
Indeed, it’s painful to read how Niel and Christel write about the contrast between their dedicated work and the occasional dismissal or divergence from their advice by policymakers. This dynamic paints a vivid picture of the challenges at the intersection of science and politics during a crisis. However, the researchers skillfully refrain from making value judgments as they present the facts without imposing opinions.
What if really gives an insight into the complexities of pandemic research. I was glad to see mental health data emerge as a crucial factor in the models as well as in decision-making, broadening the perspective beyond infection rates. This holistic approach adds depth to the narrative, recognizing the profound impact of the pandemic on individuals’ well-being. Using data to capture the richness of human lives can only go so far, though. Rightfully, the authors candidly recognize the limitations of their models. This transparency adds credibility to their work, acknowledging the challenging nature of conducting research in the throes of a pandemic.
The story concludes with the authors looking forward and contemplating solutions for the future. The book’s exploration of post-factum analyses on vaccine impact, including unexpected conclusions like the potential effectiveness of vaccinating children, provides valuable insights for any future outbreak. Ultimately, it left me hopeful that the lessons learned can guide us to navigate a new—and inevitable—health crisis more effectively.
In conclusion, What if? is a thought-provoking journey into COVID-19 modeling, shedding light on the intricate dance between data, models, and decisions. It serves as a valuable testament to the essential role of data in preventing worst-case scenarios. As we emerge from the pandemic, the book leaves us with a hopeful outlook on the future impact of data collection in shaping better crisis models and, consequently, a better outcome for us all.
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