An interpretable hybrid predictive model of COVID-19 cases using autoregressive model and LSTM

Impact of COVID-19 Interventions on Respiratory and Intestinal Infectious Disease Notifications - Jiangsu Province, China, 2020-2023

Introduction

Many measures implemented to control the coronavirus disease 2019 (COVID-19) pandemic have reshaped the epidemic patterns of other infectious diseases. This study estimated the impact of the COVID-19 pandemic on respiratory and intestinal infectious diseases and potential changes following reopening.

Methods

The optimal intervention and counterfactual models were selected from the seasonal autoregressive integrated moving average (SARIMA), neural network autoregression (NNAR), and hybrid models based on the minimum mean absolute percentage error (MAPE) in the test set. The relative change rate between the actual notification rate and that predicted by the optimal model was calculated for the entire COVID-19 epidemic prevention period and the "reopening" period.

Results

Compared with the predicted notification rate based on the counterfactual model, the total relative change rates for the 9 infectious diseases were -44.24%, respiratory infections (-55.41%), and intestinal infections (-26.59%) during 2020-2022. Compared with the predicted notification rate based on the intervention model, the total relative change rates were +247.98%, respiratory infections (+389.59%), and intestinal infections (+50.46%) in 2023. Among them, the relative increases in influenza (+499.98%) and hand-foot-mouth disease (HFMD) (+70.97%) were significant.

Conclusions

Measures taken in Jiangsu Province in response to COVID-19 effectively constrained the spread of respiratory and intestinal infectious diseases. Influenza and HFMD rebounded significantly after the lifting of COVID-19 intervention restrictions.

Towards Improved XAI-Based Epidemiological Research into the Next Potential Pandemic

By applying AI techniques to a variety of pandemic-relevant data, artificial intelligence (AI) has substantially supported the control of the spread of the SARS-CoV-2 virus. Along with this, epidemiological machine learning studies of SARS-CoV-2 have been frequently published. While these models can be perceived as precise and policy-relevant to guide governments towards optimal containment policies, their black box nature can hamper building trust and relying confidently on the prescriptions proposed. This paper focuses on interpretable AI-based epidemiological models in the context of the recent SARS-CoV-2 pandemic. We systematically review existing studies, which jointly incorporate AI, SARS-CoV-2 epidemiology, and explainable AI approaches (XAI). First, we propose a conceptual framework by synthesizing the main methodological features of the existing AI pipelines of SARS-CoV-2. Upon the proposed conceptual framework and by analyzing the selected epidemiological studies, we reflect on current research gaps in epidemiological AI toolboxes and how to fill these gaps to generate enhanced policy support in the next potential pandemic.

Forecasting daily COVID-19 cases with gradient boosted regression trees and other methods: evidence from U.S. cities

Introduction

There is a vast literature on the performance of different short-term forecasting models for country specific COVID-19 cases, but much less research with respect to city level cases. This paper employs daily case counts for 25 Metropolitan Statistical Areas (MSAs) in the U.S. to evaluate the efficacy of a variety of statistical forecasting models with respect to 7 and 28-day ahead predictions.

Methods

This study employed Gradient Boosted Regression Trees (GBRT), Linear Mixed Effects (LME), Susceptible, Infectious, or Recovered (SIR), and Seasonal Autoregressive Integrated Moving Average (SARIMA) models to generate daily forecasts of COVID-19 cases from November 2020 to March 2021.

Results

Consistent with other research that have employed Machine Learning (ML) based methods, we find that Median Absolute Percentage Error (MAPE) values for both 7-day ahead and 28-day ahead predictions from GBRTs are lower than corresponding values from SIR, Linear Mixed Effects (LME), and Seasonal Autoregressive Integrated Moving Average (SARIMA) specifications for the majority of MSAs during November-December 2020 and January 2021. GBRT and SARIMA models do not offer high-quality predictions for February 2021. However, SARIMA generated MAPE values for 28-day ahead predictions are slightly lower than corresponding GBRT estimates for March 2021.

Discussion

The results of this research demonstrate that basic ML models can lead to relatively accurate forecasts at the local level, which is important for resource allocation decisions and epidemiological surveillance by policymakers.