The Spatiotemporal Pattern and Its Determinants of Hemorrhagic Fever With Renal Syndrome in Northeastern China: Spatiotemporal Analysis

Abstract

Background Hemorrhagic fever with renal syndrome (HFRS) is a significant zoonotic disease mainly transmitted by rodents. However, the determinants of its spatiotemporal patterns in Northeast China remain unclear. Objective This study aimed to investigate the spatiotemporal dynamics and epidemiological characteristics of HFRS and detect the meteorological effect of the HFRS epidemic in Northeastern China. Methods The HFRS cases of Northeastern China were collected from the Chinese Center for Disease Control and Prevention, and meteorological data were collected from the National Basic Geographic Information Center. Times series analyses, wavelet analysis, Geodetector model, and SARIMA model were performed to identify the epidemiological characteristics, periodical fluctuation, and meteorological effect of HFRS in Northeastern China. Results A total of 52,655 HFRS cases were reported in Northeastern China from 2006 to 2020, and most patients with HFRS (n=36,558, 69.43%) were aged between 30-59 years. HFRS occurred most frequently in June and November and had a significant 4- to 6-month periodicity. The explanatory power of the meteorological factors to HFRS varies from 0.15 ≤ q ≤ 0.01. In Heilongjiang province, mean temperature with a 4-month lag, mean ground temperature with a 4-month lag, and mean pressure with a 5-month lag had the most explanatory power on HFRS. In Liaoning province, mean temperature with a 1-month lag, mean ground temperature with a 1-month lag, and mean wind speed with a 4-month lag were found to have an effect on HFRS, but in Jilin province, the most important meteorological factors for HFRS were precipitation with a 6-month lag and maximum evaporation with a 5-month lag. The interaction analysis of meteorological factors mostly showed nonlinear enhancement. The SARIMA model predicted that 8,343 cases of HFRS are expected to occur in Northeastern China. Conclusions HFRS showed significant inequality in epidemic and meteorological effects in Northeastern China, and eastern prefecture-level cities presented a high risk of epidemic. This study quantifies the hysteresis effects of different meteorological factors and prompts us to focus on the influence of ground temperature and precipitation on HFRS transmission in future studies, which could assist local health authorities in developing HFRS-climate surveillance, prevention, and control strategies targeting high-risk populations in China.

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

“…A long-term symmetric negative correlation between MAP and HFRS was observed (MAP = − 1.727, P = 0.006), inconsistent with recent studies 16 , 40 . Plausible explanations are that 16 , 28 , 40 , 41 : (1) changes in MAP might influence rodent behavior, potentially impacting their interaction with humans or with each other. This could affect the transmission dynamics of the hantaviruses responsible for HFRS.…”

“…Growing evidence associates climatic parameters with HFRS 11 , 15 , 16 . However, the asymmetric relationships in both long and short terms have been underexplored.…”

“…Studies have also linked climatic variables with HFRS 11 , 15 , 16 . For instance, Luo et al indicated that a 6-month lag in mean temperature (MT) (RR = 3.05) and no lag in aggregate precipitation (AP) (RR = 2.08) had the most significant impact on HFRS in China using a generalized additive model (GAM) 17 .…”

“…Chen et al 15 found that humidity and wind speed were correlated with the onset of HFRS, and there existed a non-linear exposure-lag-response relationship in Shenyang using a GAM. Wang et al 16 observed that the most influential meteorological factors for HFRS were mean temperature with a 4-month lag, mean ground temperature with a 4-month lag, and mean air pressure (MAP) with a 5-month lag in Heilongjiang using Geodetector and autoregressive integrated moving average (ARIMA) models. However, there are gaps: (1) most studies have concentrated on the effects of temperature, air pressure, rainfall, and humidity on HFRS 16 , 18 , with scant evidence concerning sunshine and wind’s impact.…”

Spatiotemporal variations and potential influencing factors of hemorrhagic fever with renal syndrome: A case study in Weihe Basin, China

“…The number of HFRS cases reached the highest in 2012 with 3,542 cases. The current HFRS epidemic in Weihe Basin was still on an upward path, which may be related to the 5-10-year epidemic cycle of HFRS [ 30 – 31 ]. At the same time, HFRS cases occurred in every month of each year, with a large peak in autumn and winter and a small peak in summer.…”