Table 2.
Study characteristics, methodology and main results.
| Ref, Author, Date | Dis | Location | Period | Spatial (S) vs Spatio-Temporal (ST) |
Spatial Resolution | # of Year | # of Ecological Unit | Exposures of interest for the current Review | Statistical Methods | Multivariable Analysis | Other covariates in the model | Response | Adjusted for Spatial/Temporal Correlation | # of Cases | Pop at risk | Main Results |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Linard et al. [38] | NE (PUUV) | Belgium | 1994–2004 | S | Municipality Level | 11 | 581 | Forest cover, Built Areas |
Negative Binomial Regression | Yes | Average Income, % Pop Hunting |
Incidence | Yes | >1200 | ~10 million | NE incidence is positively associated with forest cover (especially broad-leaved forests), exposure risk is higher in forested and remote areas. |
| Schwarz et al. [40] | NE (PUUV) | Baden-Wuttenburg,Germany | 2001–2007 | ST | District Level | 8 | 44 | Forest cover | Poisson Regression | Yes | Temperatures, Population density, Year |
Incidence | No | 1,540 | ~10 million | NE incidence is associated with forest cover, especially for beech forest and seed plants forest. |
| Viel et al. [41] | NE (PUUV) | Franche-Comtè, France | 1999–2008 | S | District Level | 9 | 116 | Forest Cover | Poisson Regression | Yes | NDVI | Incidence | Yes | 113 | ~1 million | No evidence of association between NE and forest land cover, however an increased NDVI was associated with NE incidence. |
| Zeimes et al. [21] | NE (PUUV) | Sweden | 1991–1998 | S | Point pattern | 7 | / | Forest Cover | Logistic Regression, Ecological Niche Modelling | Yes | Distance to Forests, Distance to sea, Population density, Snow depth |
Presence/ Absence |
No | 1,726 | ~10 million | Presence of NE cases was associated with the presence of forest cover |
| Barrios et al. (2013) |
NE (PUUV) |
Belgium | 2000–2010 | S | Municipality Level | 11 | 581 | Croplands, Forest Cover, Built Areas, Other Land Use |
Regression Trees | Yes | Land Cover | Presence/ Absence |
No | / | ~10 million | Broad Leaf Forest cover was the most important landscape features determining the spatial spread of NE |
| Zeimes et al. [22] | NE (PUUV) |
Belgium, Finland, France, Netherlands, Norway, Sweden | 2003–2012 | S | Mixture ofPoint Pattern, Municipality and District Level | 10 | / | Forest Cover, Built Areas |
LogisticRegression, Boosted Regression Trees | Yes | Precipitations, Temperature, Contiguity of forests,Population, Enhanced Vegetation Index, Soil Water Index |
Presence/ Absence |
No | / | / | NE occurrence is positively associated with forest cover and built-up areas in forest ecotones. |
| Cunze et al. [39] | NE (PUUV) |
Germany | 2001–2015 | S | District Level | 15 | 402 | Forest Cover, Built areas |
Poisson Regression | Yes | Temperature, Precipitation |
Incidence | No | 573 | ~82 million | The percentage of forest area and the number of recorded PUUV infections is positively associated. |
| Busch et al. [18] | HPS (Andes virus) | Buenos Aires Province, Argentina | 1998–2001 | ST | District Level | 4 | 127 | Croplands | Poisson Regression | Yes | Population density, Rodent Abundance, Evapotranspiration |
Presence/ Absence |
Yes | 85 | ~12 million | No evidence of association between HPS presence and croplands. |
| Prist et al. [16] | HPS | Brazil | 1993–2012 | S | Municipality Level | 20 | 647 | Croplands, Forest Cover | Poisson Regression | Yes | Precipitations, Temperatures, % Rural workers, Human Development Index |
Incidence | Yes | 207 | ~17 million | HPS risk is associated with sugarcane crops, especially in areas covered by the Atlantic Forest. |
| Muylaert et al. (2019) | HPS | Brazil | 1993–2016 | ST | Municipality Level | 24 | 5570 | Forest Cover Change, Croplands Change |
Poisson Regression | Yes | Rodent diversity, Rainfall, Temperature, % Rural Workers | Incidence | Yes | 1795 | ~200 million | HPS risk is associated with maize sugarcane crops, and with forest cover. |
| Vadell et al. [19] | HPS (Andes virus) | Entre Rios, Argentina | 2004–2015 | S | District Level | 12 | 13 | Croplands, Forest Cover, Other Land Use |
Quasi-Poisson Regression, Logistic Regression |
Yes | Distance from Rivers, Rodent Composition |
Incidence & Presence/ Absence |
No | 60 | ~1.3 million | Theprobability of occurrence of HPS was higher in sites with a high percentage of tree cover. |
| Yan et al. [28] | HFRS | China | 1994–1998 | S | County Level | 5 | 1355 | Forest Cover, Built areas, Croplands, Other Land use |
Logistic regression | Yes | Temperature, Precipitation Soil types, Elevation |
Presence/ Absence |
No | 5–30 per year | ~1.2 billion | The logistic regression analysis showed that land for agriculture use, including paddy land, irrigated farmland, non-irrigated farmland, and orchard land, were the landscape elements with high probability of HFRS. |
| Fang et al. [13] | HFRS | BeijingMetropolitan Area, China |
1997–2006 | ST | Townships | 10 | 220 | Forest Cover, Built areas, Croplands, Other Land use |
Poisson Regression | No | / | Incidence | No | 852 | ~13.6 million | Inverse association between built-up land and HFRS incidence; Positive association between orchards, irrigable land, and rice paddies and HFRS incidence. No association between forest cover and HFRS incidence |
| Xiao et al. [23] | HFRS | Changsha, China | 2006–2015 | ST | Point Pattern | 10 | / | Forest Cover, Built Areas, Croplands |
Ecological Niche Modelling | Yes | Temperature, Precipitation, Elevation, Slope, Aspect, Human Footprint index, Population, NDVI | Presence/ Absence |
/ | 327 | ~6 million | The risk level of HFRS is correlated with an increase in area of cultivated and urban land, and a decrease in forested areas. |
| Li et al. [31] | HFRS | China | 2005–2012 | S | Province Level | 8 | 31 | Forest cover, Croplands |
Geographically Weighted Regression | Yes | Temperature, Precipitation, Grain Yield, NDVI, Elevation |
Incidence | / | / | 1.2 billion | HFRS was positively correlated with mosaic forest, shrub-land, grassland |
| Liu et al. (2014) | HFRS | Dongting Lake District, China |
2005–2010 | ST | Point Pattern | 6 | / | Croplands, Other Land Use |
Ecological Niche Modellling | Yes | Temperature, Precipitation, NDVI, Human Footprint Index, Slope, Distance to water sources, | Presence/ Absence |
/ | 296 | / | Cultivated land use and shrublands are associated with HFRS occurrence. |
| Liang et al. [30] | HFRS | Shaanxi Province, China |
2005–2016 | S | County Level | 12 | 80 | Built Area, Croplands, Forest cover, Other land use |
Boosted Regression Trees | Yes | Temperature, Precipitation, Humidity, Wind Speed, Elevation, GDP, Domestic Animal Density, Population Density | Incidence | / | 20,142 | ~37million | HFRS increased with increase of built areas, and croplands. No association with cover forest |
| Xiao et al. [14] | HFRS | Chenzhou, Hunan, China | 2006–2015 | S | Point Pattern | 10 | / | Forest cover, Built areas, Croplands, Other Land Use |
Predictive risk model | Yes | Temperature, Precipitation, Humidity, NDVI, TDVI, Rodent composition | Presence/absence | / | 723 | ~4.7 million | HFRS is positively associated with cultivated land, built-on land, and grassland. Fewer cases are found in forested areas and water-covered areas. |
| Xiao et al. [15] | HFRS | Loudi and Shaoyang, China | 2006–2013 | ST | Point Pattern | 8 | / | Forest cover, Built areas, Croplands, Other Land Use |
Predictive risk model | Yes | Rodent Composition | Presence/ Absence |
/ | 742 | ~10 million | Highest predicted risk of HFRS was found for cultivated land, following by forest and urban land. |
| Tian et al. [29] | HFRS | Hunan, China |
1993–2010 | ST | Province Level | 48 | 13 | Built Area | Poisson regression model | Yes | Rural-urban immigrants, GDP, Elevation, Epidemic Duration | Incidence | Yes | ~110,000 | ~60 million | HFRS incidence and urbanization followed an inverted U-shaped. At the beginning of urbanization, HFRS incidence increases, whereas in the second phase, HFRS incidence decreases. |
| She et al. [34] | HFRS | Shandong, China |
2010–2018 | S | District level | 9 | 137 | Croplands, Forest Cover, Built Areas |
Boosted Regression Trees | Yes | Elevation, GDP, Climatic Factors | Incidence | No | 11,432 | ~100 million | Proportion of cultivated land was positively associated with HFRS incidence. Forest cover shown a inverted U-shaped relationship with HFRS incidence. |
| Shen et al. [37] | HFRS | Xi’an, China | 2005–2018 | ST | Point Pattern | 14 | / | Built Area | Statistical Regression | No | / | Incidence | No | NA | ~10 million | HFRS incidence and urbanization followed an inverted U-shaped. At the beginning of urbanization, HFRS incidence increases, whereas in the second phase, HFRS incidence decreases. |
| Zhu et al. [35] | HFRS | Shaanxi Province, China | 2014–2016 | S | Point Pattern | 3 | / | Croplands, Forest Cover, Built Areas, Other Land Use |
Ecological Niche Modelling | Yes | Climatic Factors, NDVI, Elevation, Topography, Population Density |
Presence/ Absence |
No | 605 | ~1 million | Construction and cultivated were positively influencing HFRS occurrence; forests had low risk transmission. |
| Teng et al. [33] | HFRS | China | 2015–2018 | ST | Region | 4 | 31 | Forest Cover, Built Area | Poisson Regression | Yes | Climatic Factors, Economic Level | Incidence | Yes | NA | 1.2 billion | HFRS incidence was positively associated with Forest land cover and not with the urban indicators. |
| Zhu et al. [36] | HFRS | Weihe Basin, China |
2005–2020 | S | Point Patter | 16 | / | Croplands, Forest Cover, Built Areas, Other Land Use |
Ecological Niche Modelling | Yes | Climatic Factors, NDVI, Elevation, Topography, Population Density |
Presence/ Absence |
No | 26,307 | NA | Construction land was strongly influencing HFRS occurrence; cultivated land, had medium influence, woodland had the lowest transmission |