Spatial‐temporal dynamics and time series prediction of HFRS in mainland China: A long‐term retrospective study

Abstract

Hemorrhagic fever with renal syndrome (HFRS) is highly endemic in mainland China. The current study aims to characterize the spatial‐temporal dynamics of HFRS in mainland China during a long‐term period (1950−2018). A total of 1 665 431 cases of HFRS were reported with an average annual incidence of 54.22 cases/100 000 individuals during 1950−2018. The joint regression model was used to define the global trend of the HFRS cases with an increasing‐decreasing‐slightly increasing‐decreasing‐slightly increasing trend during the 68 years. Then spatial correlation analysis and wavelet cluster analysis were used to identify four types of clusters of HFRS cases located in central and northeastern China. Lastly, the prophet model outperforms auto‐regressive integrated moving average model in the HFRS modeling. Our findings will help reduce the knowledge gap on the transmission dynamics and distribution patterns of the HFRS in mainland China and facilitate to take effective preventive and control measures for the high‐risk epidemic area.

Asymmetric impact of climatic parameters on hemorrhagic fever with renal syndrome in Shandong using a nonlinear autoregressive distributed lag model

“…The disease is characterized by fever, hemorrhage, and renal failure, and can be fatal in severe cases. HFRS has been recognized as a significant public health concern in many parts of the world, particularly in Asia and Europe 1 – 3 , where it is endemic in certain regions 1 , 3 , 4 . While the disease is found in many countries, the prevalence of HFRS can vary widely from one region to another.…”

“…This variation can be attributed to a number of factors, including differences in rodent populations, climate, and human behavior 5 . In some areas 1 , 3 , 4 , such as China, Russia, and Korea, HFRS is endemic and outbreaks occur regularly, while in other regions, such as Europe and North America, the disease is relatively rare. There is also variation in the strains of hantaviruses that are prevalent in different regions 6 .…”

“…After the introduction of hantavirus into China, it adapted to various host animals, including a diverse range of rodents, shrews, and bats. Research indicates that there are over 8 suitable host species for the SEOV and 10 species of wild mouse hosts for the HTNV in China, which has currently constituted 90% of global HFRS cases 4 . Although great achievements have been made in implementing surveillance and control measures to mitigate the impact of HFRS in China 4 , most provinces, such as Shandong, Heilongjiang, Jilin, Liaoning, and Hubei, have reported endemic cases of over 20,000 4 , leading to an annualized death rate of approximately 5–10% in past years 7 , and a recent study indicated a recurring sign in HFRS morbidity owing to variants in circulating strains 8 .…”

“…Research indicates that there are over 8 suitable host species for the SEOV and 10 species of wild mouse hosts for the HTNV in China, which has currently constituted 90% of global HFRS cases 4 . Although great achievements have been made in implementing surveillance and control measures to mitigate the impact of HFRS in China 4 , most provinces, such as Shandong, Heilongjiang, Jilin, Liaoning, and Hubei, have reported endemic cases of over 20,000 4 , leading to an annualized death rate of approximately 5–10% in past years 7 , and a recent study indicated a recurring sign in HFRS morbidity owing to variants in circulating strains 8 . For this reason, recognizing the intrinsic association of potential factors with HFRS and developing an enhanced early warning system assist in comprehending the disease’s patterns and dynamics, ultimately aiding in the prevention and control of HFRS epidemic.…”

“…Given that Shandong (Geographical distribution can be seen in Fig. S1 ) holds the distinction of being the riskiest among all HFRS-endemic provinces in China 4 , our study aims: (1) to clarify both long- and short-term asymmetric correlations between climatic variables and HFRS in Shandong using the NARDL model; (2) to ascertain if the NARDL model offers a more precise estimation of HFRS epidemic compared to the ARDL model.…”