Gaps in Descriptive Epidemiology and Hepatitis C Virus Modeling Research

Estimating hepatitis C prevalence in the United States, 2017-2020

BACKGROUND AND AIMS

The National Health and Nutrition Examination Survey (NHANES) underestimates the true prevalence of HCV infection. By accounting for populations inadequately represented in NHANES, we created 2 models to estimate the national hepatitis C prevalence among US adults during 2017-2020.

APPROACH AND RESULTS

The first approach (NHANES+) replicated previous methodology by supplementing hepatitis C prevalence estimates among the US noninstitutionalized civilian population with a literature review and meta-analysis of hepatitis C prevalence among populations not included in the NHANES sampling frame. In the second approach (persons who injected drugs [PWID] adjustment), we developed a model to account for the underrepresentation of PWID in NHANES by incorporating the estimated number of adult PWID in the United States and applying PWID-specific hepatitis C prevalence estimates. Using the NHANES+ model, we estimated HCV RNA prevalence of 1.0% (95% CI: 0.5%-1.4%) among US adults in 2017-2020, corresponding to 2,463,700 (95% CI: 1,321,700-3,629,400) current HCV infections. Using the PWID adjustment model, we estimated HCV RNA prevalence of 1.6% (95% CI: 0.9%-2.2%), corresponding to 4,043,200 (95% CI: 2,401,800-5,607,100) current HCV infections.

CONCLUSIONS

Despite years of an effective cure, the estimated prevalence of hepatitis C in 2017-2020 remains unchanged from 2013 to 2016 when using a comparable methodology. When accounting for increased injection drug use, the estimated prevalence of hepatitis C is substantially higher than previously reported. National action is urgently needed to expand testing, increase access to treatment, and improve surveillance, especially among medically underserved populations, to support hepatitis C elimination goals.

Deep evolutionary fusion neural network: a new prediction standard for infectious disease incidence rates

BACKGROUND

Previously, many methods have been used to predict the incidence trends of infectious diseases. There are numerous methods for predicting the incidence trends of infectious diseases, and they have exhibited varying degrees of success. However, there are a lack of prediction benchmarks that integrate linear and nonlinear methods and effectively use internet data. The aim of this paper is to develop a prediction model of the incidence rate of infectious diseases that integrates multiple methods and multisource data, realizing ground-breaking research.

RESULTS

The infectious disease dataset is from an official release and includes four national and three regional datasets. The Baidu index platform provides internet data. We choose a single model (seasonal autoregressive integrated moving average (SARIMA), nonlinear autoregressive neural network (NAR), and long short-term memory (LSTM)) and a deep evolutionary fusion neural network (DEFNN). The DEFNN is built using the idea of neural evolution and fusion, and the DEFNN + is built using multisource data. We compare the model accuracy on reference group data and validate the model generalizability on external data. (1) The loss of SA-LSTM in the reference group dataset is 0.4919, which is significantly better than that of other single models. (2) The loss values of SA-LSTM on the national and regional external datasets are 0.9666, 1.2437, 0.2472, 0.7239, 1.4026, and 0.6868. (3) When multisource indices are added to the national dataset, the loss of the DEFNN + increases to 0.4212, 0.8218, 1.0331, and 0.8575.

CONCLUSIONS

We propose an SA-LSTM optimization model with good accuracy and generalizability based on the concept of multiple methods and multiple data fusion. DEFNN enriches and supplements infectious disease prediction methodologies, can serve as a new benchmark for future infectious disease predictions and provides a reference for the prediction of the incidence rates of various infectious diseases.