arXiv subjects

Juan Cambeiro

Publications and source records attributed to Juan Cambeiro.

3 recordsLinked to original sources

arXiv2024

Assessing Human Judgment Forecasts in the Rapid Spread of the Mpox Outbreak: Insights and Challenges for Pandemic Preparedness

In May 2022, mpox (formerly monkeypox) spread to non-endemic countries rapidly. Human judgment is a forecasting approach that has been sparsely evaluated during the beginning of an outbreak. We collected -- between May 19, 2022 and July 31, 2022 -- 1275 forecasts from 442 individuals of six questions about the mpox outbreak where ground truth data are now available. Individual human judgment forecasts and an equally weighted ensemble were evaluated, as well as compared to a random walk, autoregressive, and doubling time model. We found (1) individual human judgment forecasts underestimated outbreak size, (2) the ensemble forecast median moved closer to the ground truth over time but uncertainty around the median did not appreciably decrease, and (3) compared to computational models, for 2-8 week ahead forecasts, the human judgment ensemble outperformed all three models when using median absolute error and weighted interval score; for one week ahead forecasts a random walk outperformed human judgment. We propose two possible explanations: at the time a forecast was submitted, the mode was correlated with the most recent (and smaller) observation that would eventually determine ground truth. Several forecasts were solicited on a logarithmic scale which may have caused humans to generate forecasts with unintended, large uncertainty intervals. To aide in outbreak preparedness, platforms that solicit human judgment forecasts may wish to assess whether specifying a forecast on logarithmic scale matches an individual's intended forecast, support human judgment by finding cues that are typically used to build forecasts, and, to improve performance, tailor their platform to allow forecasters to assign zero probability to events.

q-bio.PE↗

arXiv2022

Aggregating human judgment probabilistic predictions of COVID-19 transmission, burden, and preventative measures

Aggregated human judgment forecasts for COVID-19 targets of public health importance are accurate, often outperforming computational models. Our work shows aggregated human judgment forecasts for infectious agents are timely, accurate, and adaptable, and can be used as tool to aid public health decision making during outbreaks.

stat.AP↗

arXiv2022

Chimeric forecasting: combining probabilistic predictions from computational models and human judgment

Forecasts of the trajectory of an infectious agent can help guide public health decision making. A traditional approach to forecasting fits a computational model to structured data and generates a predictive distribution. However, human judgment has access to the same data as computational models plus experience, intuition, and subjective data. We propose a chimeric ensemble -- a combination of computational and human judgment forecasts -- as a novel approach to predicting the trajectory of an infectious agent. Each month from January, 2021 to June, 2021 we asked two generalist crowds, using the same criteria as the COVID-19 Forecast Hub, to submit a predictive distribution over incident cases and deaths at the US national level either two or three weeks into the future and combined these human judgment forecasts with forecasts from computational models submitted to the COVID-19 Forecasthub into a chimeric ensemble. We find a chimeric ensemble compared to an ensemble including only computational models improves predictions of incident cases and shows similar performance for predictions of incident deaths. A chimeric ensemble is a flexible, supportive public health tool and shows promising results for predictions of the spread of an infectious agent.

stat.AP↗