# Initial Establishment of Warning Model for Epidemic Intensity of Norovirus GII Associated with Acute Gastroenteritis in Beijing Based on Synthetic Index Method

## TL;DR

This study creates a warning model to classify the epidemic intensity of Norovirus GII-related acute gastroenteritis in Beijing using case and environmental data.

## Contribution

The paper introduces a synthetic index method to classify Norovirus GII epidemic intensity using combined case and environmental surveillance data.

## Key findings

- Norovirus GII epidemic intensity was classified into five grades based on surveillance data.
- Early warnings can be issued if the synthetic index exceeds a threshold in two consecutive weeks.
- The model provides a reference for future studies but should be combined with expert judgment.

## Abstract

At present, there is no research that classifies the epidemic intensity of acute gastroenteritis (AGE) caused by Norovirus (NoV) GII combined cases and environmental surveillance data at the same time. With reference to the experience of the epidemiological-level classification of infectious disease and the actual epidemiological status of NoV AGE in Chaoyang District, Beijing, China, the epidemic intensity of NoV GII was divided into five grades with increasing intensity from grade 1 to grade 5, which corresponds to non-epidemic risk, general risk, moderate risk, high risk, and ultra-high risk, respectively. If the synthetic index of two consecutive monitoring weeks in the epidemic season of 2023–2024 exceeds a certain threshold, an early warning for the corresponding epidemic intensity will be issued and recommendations on the corresponding control measures will be given. This study established and quantified the criteria for the epidemic intensity of AGE caused by NoV GII based on case surveillance data and environmental surveillance data. It provides a reference for other methods to carry out relevant studies in the future. However, mathematical models cannot completely replace skilled experience. Therefore, when making decisions with early warning models in practice, it is necessary to refer to the opinions of professional and experienced experts to avoid the bias of the early warning model from affecting strategy judgment.