Publications by authors named "Nutcha Wattanachit"

Article Synopsis
  • Accurate forecasts improve public health responses to seasonal influenza, with 26 teams providing predictions for hospital admissions in 2021-22 and 2022-23.
  • Six out of 23 models performed better than the baseline in 2021-22, while 12 out of 18 models did so in 2022-23, with the FluSight ensemble being highly ranked in both seasons.
  • Despite its accuracy, the FluSight ensemble and other models struggled with longer forecast periods, especially during times of rapid change in influenza patterns.
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During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org).

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Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. Forecasting teams were asked to provide national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one through four weeks ahead for the 2021-22 and 2022-23 influenza seasons. Across both seasons, 26 teams submitted forecasts, with the submitting teams varying between seasons.

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Article Synopsis
  • Influenza seasons vary greatly each year, complicating public health efforts to prepare and respond to outbreaks, which is why influenza forecasting is crucial in mitigating epidemic impacts.
  • The CDC runs the FluSight challenge, an annual exercise that utilizes both theoretical and practical forecasting methods to optimize predictions for U.S. influenza seasons.
  • Recent findings show that advanced ensemble forecasting methods, particularly those using beta transformations, outperform traditional models in accuracy and calibration, highlighting the need for improved techniques to enhance forecasting for outbreak preparedness.
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Background: Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022.

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Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.

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Forecasts support decision making in a variety of applications. Statistical models can produce accurate forecasts given abundant training data, but when data is sparse or rapidly changing, statistical models may not be able to make accurate predictions. Expert judgmental forecasts-models that combine expert-generated predictions into a single forecast-can make predictions when training data is limited by relying on human intuition.

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