Time-Specific Ecologic Niche Models Forecast the Risk of Hemorrhagic Fever with Renal Syndrome in Dongting Lake District, China, 2005–2010

Abstract

BackgroundHemorrhagic fever with renal syndrome (HFRS), a rodent-borne infectious disease, is one of the most serious public health threats in China. Increasing our understanding of the spatial and temporal patterns of HFRS infections could guide local prevention and control strategies.Methodology/Principal FindingsWe employed statistical models to analyze HFRS case data together with environmental data from the Dongting Lake district during 2005–2010. Specifically, time-specific ecologic niche models (ENMs) were used to quantify and identify risk factors associated with HFRS transmission as well as forecast seasonal variation in risk across geographic areas. Results showed that the Maximum Entropy model provided the best predictive ability (AUC = 0.755). Time-specific Maximum Entropy models showed that the potential risk areas of HFRS significantly varied across seasons. High-risk areas were mainly found in the southeastern and southwestern areas of the Dongting Lake district. Our findings based on models focused on the spring and winter seasons showed particularly good performance. The potential risk areas were smaller in March, May and August compared with those identified for June, July and October to December. Both normalized difference vegetation index (NDVI) and land use types were found to be the dominant risk factors.Conclusions/SignificanceOur findings indicate that time-specific ENMs provide a useful tool to forecast the spatial and temporal risk of HFRS.

Spatiotemporal Heterogeneity Analysis of Hemorrhagic Fever with Renal Syndrome in China Using Geographically Weighted Regression Models

“…The correlation analysis indicated the HFRS incidences were significantly correlated with temperature, NDVI of January, grain yield, and land use (Land50/Land100/Land110/Land120), but not significantly with others, which meant the HFRS incidences were globally associated with the significant variables, but not globally associated with the others. The other factors were affecting factors chosen in many studies [ 5 , 9 , 20 , 36 , 37 ] and were significantly associated with HFRS incidence at some local scales. Exclusion of these factors might lead to losing potential information affecting HFRS epidemics.…”

“…Data regarding the HFRS incidence, population, environmental factors, and social-economic factors at the province level of China in 2005–2012 were collected. The environmental factors [ 22 ] included temperature [ 6 , 7 ], precipitation [ 6 , 23 ], humidity [ 7 ], NDVI [ 10 ], and elevation [ 9 , 24 ]. The social-economic factors included land use [ 6 ], cultivated land area and grain yield.…”

“…Studies in different areas of China and other countries have suggested that external environmental factors, including natural factors (such as temperature [ 6 , 7 , 8 ], precipitation [ 4 , 5 , 9 ], humidity [ 7 ], normalized difference vegetation index (NDVI) [ 10 , 11 ] and elevation [ 6 ]) and social-economic factors (such as land use [ 6 , 12 ]), may affect the incidence of HFRS. These factors may differentially influence the incidence of HFRS in different regions because of the spatiotemporal heterogeneity in climate types, ecological characteristics, population immunity, public health intervention measures, and socioeconomic factors within different regions [ 13 ].…”

Spatiotemporal patterns and determinants of dengue at county level in China from 2005–2017

“…ENMs include various algorithms such as maximum entropy and genetic algorithms in a rule-set production (GARP) (Ma and Sun, 2018;Liu et al, 2014). Maximum entropy can exploit the existing meteorological factors and environmental data to calculate their relations with certain biological phenomena and predict the suitable habitat of species and high risk areas for biological phenomena.…”

Numerical solution of a spatio-temporal gender-structured model for hantavirus infection in rodents

“…Hantavirus (family Bunyaviridae) is a rodent-borne infectious disease of significant concern as it can generate high case fatality rates in the human population [55]. Transmission of hantavirus from infected rodents, the main known reservoir of the virus, to humans, typically occurs via inhalation of aerosols contaminated by virus shed in excreta, saliva, and urine [36]. Risk of infection with hantavirus in the human population is facilitated by crowding conditions and close proximity to rodent populations.…”