A new Seasonal Difference Space-Time Autoregressive Integrated Moving Average (SD-STARIMA) model and spatiotemporal trend prediction analysis for Hemorrhagic Fever with Renal Syndrome (HFRS)

Abstract

Hemorrhagic fever with renal syndrome (HFRS) is a naturally-occurring, fecally transmitted disease caused by a Hantavirus (HV). It is extremely damaging to human health and results in many deaths annually, especially in Hubei Province, China. One of the primary characteristics of HFRS is the spatiotemporal heterogeneity of its occurrence, with notable seasonal differences. In view of this heterogeneity, the present study suggests that there is a need to focus on trend simulation and the spatiotemporal prediction of HFRS outbreaks. To facilitate this, we constructed a new Seasonal Difference Space-Time Autoregressive Integrated Moving Average (SD-STARIMA) model. The SD-STARIMA model is based on the spatial and temporal characteristics of the Space-Time Autoregressive Integrated Moving Average (STARMA) model first developed by Cliff and Ord in 1974, which has proven useful in modelling the temporal aspects of spatially located data. This model can simulate the trends in HFRS epidemics, taking into consideration both spatial and temporal variations. The SD-STARIMA model is also able to make seasonal difference calculations to eliminate temporally non-stationary problems that are present in the HFRS data. Experiments have demonstrated that the proposed SD-STARIMA model offers notably better prediction accuracy, especially for spatiotemporal series data with seasonal distribution characteristics.

An extended Weight Kernel Density Estimation model forecasts COVID-19 onset risk and identifies spatiotemporal variations of lockdown effects in China

“…In these models, the spatial relationships among cities typically have been downplayed. Facing new infectious diseases (e.g., COVID-19) with limited prior knowledge of their features and also limited associated comparability with previous diseases, there has been much uncertainty in setting the theoretical parameters and assumptions of mathematical prediction models 12 – 15 . Knowledge uncertainty such as this could possibly have led to the highly mixed and uncertain conclusions among the above, existing studies.…”

Improving the precision of modeling the incidence of hemorrhagic fever with renal syndrome in mainland China with an ensemble machine learning approach

“…Chang Qi et al [ 35 ] compared the differences in fitting and prediction accuracies of SARFIMA and SARIMA when applied to HFRS disease and found that the SARFIMA model outperforms the SARIMA model in terms of improving the forecast of monthly HFRS cases. Youlin Zhao et al [ 36 ] demonstrated that the SD-STARIMA model offers noticeably better prediction accuracy than the traditional approach, especially for spatiotemporal series data with seasonality characteristics.…”

Misdiagnosis of scrub typhus as hemorrhagic fever with renal syndrome and potential co-infection of both diseases in patients in Shandong Province, China, 2013–2014

“…The disease was thought to be transmitted to humans by inhaling aerosolized excreta of infected animals, or through direct contact with infected rodents and their feces, saliva or blood [ 7 ]. The major clinical manifestations of HFRS include fever, hemorrhage and renal failure [ 8 ]. HFRS is widely distributed in the world with China reporting the highest incidence [ 9 ].…”