Ecological niche modeling based on ensemble algorithms to predicting current and future potential distribution of African swine fever virus in China

Abstract

African swine fever (ASF) is a tick-borne infectious disease initially described in Shenyang province China in 2018 but is now currently present nationwide. ASF has high infectivity and mortality rates, which often results in transportation and trade bans, and high expenses to prevent and control the, hence causing huge economic losses and a huge negative impact on the Chinese pig farming industry. Ecological niche modeling has long been adopted in the epidemiology of infectious diseases, in particular vector-borne diseases. This study aimed to establish an ecological niche model combined with data from ASF incidence rates in China from August 2018 to December 2021 in order to predict areas for African swine fever virus (ASFV) distribution in China. The model was developed in R software using the biomod2 package and ensemble modeling techniques. Environmental and topographic variables included were mean diurnal range (°C), isothermality, mean temperature of wettest quarter (°C), precipitation seasonality (cv), mean precipitation of warmest quarter(mm), mean precipitation of coldest quarter (mm), normalized difference vegetation index, wind speed (m/s), solar radiation (kJ /day), and elevation/altitude (m). Contribution rates of the variables normalized difference vegetation index, mean temperature of wettest quarter, mean precipitation of coldest quarter, and mean precipitation of warmest quarter were, respectively, 47.61%, 28.85%, 10.85%, and 7.27% (according to CA), which accounted for over 80% of contribution rates related to variables. According to model prediction, most of areas revealed as suitable for ASF distribution are located in the southeast coast or central region of China, wherein environmental conditions are suitable for soft ticks’ survival. In contrast, areas unsuitable for ASFV distribution in China are associated with arid climate and poor vegetation, which are less conducive to soft ticks’ survival, hence to ASFV transmission. In addition, prediction spatial suitability for future ASFV distribution suggests narrower areas for ASFV spread. Thus, the ensemble model designed herein could be used to conceive more efficient prevention and control measure against ASF according to different geographical locations in China.

Preparation of Monoclonal Antibodies against the Viral p54 Protein and a Blocking ELISA for Detection of the Antibody against African Swine Fever Virus

“…Furthermore, this assay was used to detect ASFV antibodies in a total of 330 field serum samples collected from different regions of China. In addition, 28 out of the 330 field sera were found to be ASFV-positive, which is consistent with the results of another report by an ecological niche model based on ensemble algorithms [33].…”

Ecological Niche Modelling Approaches: Challenges and Applications in Vector-Borne Diseases

“…As far as we know, this is what occurs with a large amount of the studies dealing with VBDs. As these kinds of studies are usually performed with public health purposes at regional or local scales, they often rely on regional/national datasets collected by administrations or NGOs whose range of action is defined by restricted geographical or political borders (i.e., country or even provinces' administrations) (e.g., [67][68][69]80,[83][84][85][86][87][88][89][90][91][92][93][94][95][96]98,99,101,[104][105][106][107][108][112][113][114][115][116][117]). The concern resides in the fact that ENM built with occurrence data restricted to artificial boundaries might consider only a subset of the environmental conditions experienced by a species across its entire range (i.e., "spatial niche truncation" [118]); therefore, providing an incomplete description of the environmental limits [109] and underestimating the environmental conditions that the species can withstand [111] (see an example in Figure 2).…”

“…However, if many occurrence records are available, geographical filtering and balancing of occurrence data should be preferred relative to background manipulation [145]. Indeed, this method seems to be by far the most frequently used (e.g., [69,70,81,83,87,92,93,97,100,103,[105][106][107]112,116,117,119]). However, if only few spatially clustered occurrence records are available, the manipulation of the background dataset seems to be the second-best option [145].…”

Expounding the role of tick in Africa swine fever virus transmission and seeking effective prevention measures: A review

“…Although there is no evidence to link the outbreak of ASF in China to ticks, soft ticks are still classified as a low risk of ASF outbreak (1). In particular, according to the model prediction of Li et al, the southeast coast or central region of China is suitable for ASF distribution, and its environment is suitable for soft ticks, indicating that the tick-pig cycle may promote the outbreak of ASF in China (44).…”