Chimeric Forecasting: An experiment to leverage human judgment to improve forecasts of infectious disease using simulated surveillance data
Forecasts of infectious agents provide public health officials advanced warning about the intensity and timing of the spread of disease. Past work has found that accuracy and calibration of forecasts is weakest when attempting to predict an epidemic peak. Forecasts from a mechanistic model would be...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-06-01
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| Series: | Epidemics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1755436524000173 |
| _version_ | 1797271395363717120 |
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| author | Thomas McAndrew Graham C. Gibson David Braun Abhishek Srivastava Kate Brown |
| author_facet | Thomas McAndrew Graham C. Gibson David Braun Abhishek Srivastava Kate Brown |
| author_sort | Thomas McAndrew |
| collection | DOAJ |
| description | Forecasts of infectious agents provide public health officials advanced warning about the intensity and timing of the spread of disease. Past work has found that accuracy and calibration of forecasts is weakest when attempting to predict an epidemic peak. Forecasts from a mechanistic model would be improved if there existed accurate information about the timing and intensity of an epidemic. We presented 3000 humans with simulated surveillance data about the number of incident hospitalizations from a current and two past seasons, and asked that they predict the peak time and intensity of the underlying epidemic. We found that in comparison to two control models, a model including human judgment produced more accurate forecasts of peak time and intensity of hospitalizations during an epidemic. Chimeric models have the potential to improve our ability to predict targets of public health interest which may in turn reduce infectious disease burden. |
| first_indexed | 2024-03-07T14:02:06Z |
| format | Article |
| id | doaj.art-707305714f4545e0b6726eb95b8f1c97 |
| institution | Directory Open Access Journal |
| issn | 1755-4365 |
| language | English |
| last_indexed | 2024-03-07T14:02:06Z |
| publishDate | 2024-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Epidemics |
| spelling | doaj.art-707305714f4545e0b6726eb95b8f1c972024-03-07T05:27:11ZengElsevierEpidemics1755-43652024-06-0147100756Chimeric Forecasting: An experiment to leverage human judgment to improve forecasts of infectious disease using simulated surveillance dataThomas McAndrew0Graham C. Gibson1David Braun2Abhishek Srivastava3Kate Brown4Department of Community and Population Health, College of Health, Lehigh University, Bethlehem PA, United States of America; Corresponding author.Statistical Sciences, Los Alamos National Laboratory, Los Alamos, NM, United States of AmericaDepartment of Psychology College of Arts and Science, Lehigh University, Bethlehem PA, United States of AmericaP.C. Rossin College of Engineering & Applied Science, Lehigh University, Bethlehem PA, United States of AmericaDepartment of Community and Population Health, College of Health, Lehigh University, Bethlehem PA, United States of AmericaForecasts of infectious agents provide public health officials advanced warning about the intensity and timing of the spread of disease. Past work has found that accuracy and calibration of forecasts is weakest when attempting to predict an epidemic peak. Forecasts from a mechanistic model would be improved if there existed accurate information about the timing and intensity of an epidemic. We presented 3000 humans with simulated surveillance data about the number of incident hospitalizations from a current and two past seasons, and asked that they predict the peak time and intensity of the underlying epidemic. We found that in comparison to two control models, a model including human judgment produced more accurate forecasts of peak time and intensity of hospitalizations during an epidemic. Chimeric models have the potential to improve our ability to predict targets of public health interest which may in turn reduce infectious disease burden.http://www.sciencedirect.com/science/article/pii/S1755436524000173ForecastingCompartmental modelsHuman judgment |
| spellingShingle | Thomas McAndrew Graham C. Gibson David Braun Abhishek Srivastava Kate Brown Chimeric Forecasting: An experiment to leverage human judgment to improve forecasts of infectious disease using simulated surveillance data Epidemics Forecasting Compartmental models Human judgment |
| title | Chimeric Forecasting: An experiment to leverage human judgment to improve forecasts of infectious disease using simulated surveillance data |
| title_full | Chimeric Forecasting: An experiment to leverage human judgment to improve forecasts of infectious disease using simulated surveillance data |
| title_fullStr | Chimeric Forecasting: An experiment to leverage human judgment to improve forecasts of infectious disease using simulated surveillance data |
| title_full_unstemmed | Chimeric Forecasting: An experiment to leverage human judgment to improve forecasts of infectious disease using simulated surveillance data |
| title_short | Chimeric Forecasting: An experiment to leverage human judgment to improve forecasts of infectious disease using simulated surveillance data |
| title_sort | chimeric forecasting an experiment to leverage human judgment to improve forecasts of infectious disease using simulated surveillance data |
| topic | Forecasting Compartmental models Human judgment |
| url | http://www.sciencedirect.com/science/article/pii/S1755436524000173 |
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