Using empirical dynamic modeling to identify the impact of meteorological factors on hemorrhagic fever with renal syndrome in Weifang, Northeastern China, from 2011 to 2020.

• Published in: PLOS Neglected Tropical Diseases
• Main Authors: Liang Zheng, Qi Gao, Shengnan Yu, Yijin Chen, Yuan Shi, Minghao Sun, Ying Liu, Zhiqiang Wang, Xiujun Li
• Format: Article in Journal/Newspaper
• Language: English
• Published: Public Library of Science (PLoS) 2024
• Subjects: Arctic medicine. Tropical medicine; RC955-962; Public aspects of medicine; RA1-1270; Arctic
• Online Access: https://doi.org/10.1371/journal.pntd.0012151; https://doaj.org/article/590daa0d940241a684dba0684c1ec368

Background Hemorrhagic Fever with Renal Syndrome (HFRS) continues to pose a significant public health threat to the well-being of the population. Given that the spread of HFRS is susceptible to meteorological factors, we aim to probe into the meteorological drivers of HFRS. Thus, novel techniques that can discern time-delayed non-linear relationships from nonlinear dynamical systems are compulsory. Methods We analyze the epidemiological features of HFRS in Weifang City, 2011-2020, via the employment of the Empirical Dynamic Modeling (EDM) method. Our analysis delves into the intricate web of time-delayed non-linear associations between meteorological factors and HFRS. Additionally, we investigate the repercussions of minor perturbations in meteorological variables on future HFRS incidence. Results A total of 2515 HFRS cases were reported in Weifang from 2011 to 2020. The number of cases per week was 4.81, and the average weekly incidence was 0.52 per 1,000,000. The propagation of HFRS is significantly impacted by the mean weekly temperature, relative humidity, cumulative rainfall, and wind speed, and the ρCCM converges to 0.55,0.48,0.38 and 0.39, respectively. The graphical representation of the relationship between temperature (lagged by 2 weeks) and the incidence of HFRS exhibits an inverted U-shaped curve, whereby the incidence of HFRS culminates as the temperature reaches 10 °C. Moreover, temperature, relative humidity, cumulative rainfall, and wind speed exhibit a positive correlation with HFRS incidence, with a time lag of 4-6 months. Conclusions Our discoveries suggest that meteorological factors can drive the transmission of HFRS both at a macroscopic and microscopic scale. Prospective alterations in meteorological conditions, for instance, elevations in temperature, relative humidity, and precipitation will instigate an upsurge in the incidence of HFRS after 4-6 months, and thus, timely public health measures should be taken to mitigate these changes.