Spatio-Temporal Analysis of Influenza-Like Illness and Prediction of Incidence in High-Risk Regions in the United States from 2011 to 2020

Abstract

About 8% of the Americans contract influenza during an average season according to the Centers for Disease Control and Prevention in the United States. It is necessary to strengthen the early warning for influenza and the prediction of public health. In this study, Spatial autocorrelation analysis and spatial scanning analysis were used to identify the spatiotemporal patterns of influenza-like illness (ILI) prevalence in the United States, during the 2011–2020 transmission seasons. A seasonal autoregressive integrated moving average (SARIMA) model was constructed to predict the influenza incidence of high-risk states. We found the highest incidence of ILI was mainly concentrated in the states of Louisiana, District of Columbia and Virginia. Mississippi was a high-risk state with a higher influenza incidence, and exhibited a high-high cluster with neighboring states. A SARIMA (1, 0, 0) (1, 1, 0)52 model was suitable for forecasting the ILI incidence of Mississippi. The relative errors between actual values and predicted values indicated that the predicted values matched the actual values well. Influenza is still an important health problem in the United States. The spread of ILI varies by season and geographical region. The peak season of influenza was the winter and spring, and the states with higher influenza rates are concentrated in the southeast. Increased surveillance in high-risk states could help control the spread of the influenza.

The prediction of influenza-like illness using national influenza surveillance data and Baidu query data

“…To predict respiratory illness trends, the ILI% was used to predict trends in influenza virus or respiratory illness incidence. To predict the trend precisely, several researchers have used the ILI% and search indices to predict respiratory illness incidence via different methods, such as the seasonal autoregressive integrated moving average (SARIMA) model and linear regression models [ 20 – 23 ]. However, the results have shown that the prediction accuracy is not high [ 16 , 24 ].…”

Spatiotemporally comparative analysis of three common infectious diseases in China during 2013–2015

“…The clusters are ranked by the LLR value and their statistical significance is evaluated with Monte Carlo simulations. The window with the largest LLR value is deemed as the first cluster (or primary cluster) when the P-value is significant, i.e., less than 0.05, and the other significant windows are considered the secondary clusters [ 27 , 33 ]. Meanwhile, the Observed/Expected (ODE) and Relative Risk (RR) are used to quantify the risk of clusters.…”

Epidemiological Trends of Malaria in Five Years and under Children of Nsanje District in Malawi, 2015–2019

“…Time series analysis was performed using a Seasonal Autoregressive Integrated Moving Average (SARIMA) model, which was applied to the malaria incidence data. SARIMA model can be expressed as SARIMA (p,d,q) (P,D,Q) s where letters (p,d,q) are orders of autoregression, the order of difference and order of moving average, respectively; letters (P,Q,D) s are the order of seasonal autoregression, the order of difference and the order of moving average, respectively, and s is the specific value of the cycle, which in this case is 12 [ 13 ]. Monthly time series plot of incidence (per 1000 population) was drawn to check for stationarity.…”

“…The incidence of Malaria displays a cyclic pattern, time series models are the most widely used models for forecasting diseases that show a cyclic pattern [ 5 ]. Time series analysis has the advantage of predicting incidence and it is characterized by the number of patients in the past and responds by predicting the number of patients in the future [ 13 ]. Malaria has high transmissibility and seasonality, and this makes SARIMA models the best fit for the data because of the high predictive power the model has [ 4 ].…”