Analysis of peste des petits ruminants virus spread and the risk of its introduction into the territory of the Russian Federation

Abstract

Aim: This study examines methods to effectively control peste des petits ruminants (PPR), an emerging, highly contagious, transboundary disease that has been designated as a highly dangerous disease by the World Organization for Animal Health. Mathematical modeling was used as a predictive and preventive tool to assess the risk of PPR virus spread in the model area and the probability of its introduction into the territory of the Russian Federation. Materials and Methods: PPR risk assessment was performed by modeling the pathogen's ecological niche by performing linear regression analysis in the geographic information system ESRI ArcGIS Desktop and maximum entropy methods using MaxEnt software. The territories of Bangladesh, China, and Algeria were used as model countries because they have the highest number of confirmed PPR outbreaks, as reported by the Food and Agriculture Organization of the United Nations from 2009 to 2020. The prepared global model of the PPR pathogen's ecological niche was extrapolated onto the territory of the Far Eastern regions of the Russian Federation to assess the probability of virus introduction in that region. Results: Global model analysis showed that two factors exerted the highest influence on the spread of the PPR pathogen on a global scale: The minimum temperature of the coldest month of the year and the density of roads per unit area, which reflect the overall economic activity within a region. The highest risk of PPR spread was observed in areas with a minimum annual temperature of 16°C and road density of 5000 m/km2. Conclusion: According to the model, areas with a dominant subtropical climate, where small livestock breeding is performed and where the average daily air temperature is >0°C throughout the year, are at the highest risk of PPR outbreaks. The risk of PPR spreading outside these areas is significantly reduced. Local extrapolation of the PPR ecological niche model demonstrates that the probability of epizootic development does not exceed 3–4% within the territories of the constituent entities of the Russian Federation adjacent to Mongolia and China.

Analysis of environmental factors influencing lumpy skin disease outbreak seasonality and assessment of its spread risk in the Saratovskaya oblast of Russia

“…The selected factors should be relevant in the time the outbreak was recorded, correspond to the spatial scale of the research, and be ecologically relevant to the pathogen, its vectors, and hosts [9][10][11].…”

“…A combination of linear (L), quadratic (Q), their products (P), and hinge (H) values was used as feature types. The models were tested using cross-validation in 10 replications [9,10,30].…”

Analysis of spatial and temporal risk of Peste des Petits Ruminants Virus (PPRV) outbreaks in endemic settings: A scoping review

“…(45,60). Either linear (15,37,48,49) or logistic regression (50) models were used to explore or test hypotheses related to temporal trends. GeoDa 1.14.0 was used to perform Geographically Weighted Regression (GWR), with climate and geographical variables acting as predictors and log-transformed PPR cumulative incidence serving as the dependent variable.…”

“…Landscape was also assessed by five studies (40,43,49,69,71). Seasonality and landscape were highly linked to PPR epidemics (5/13) in each feature.…”

“…Most studies (71.2%, n = 30) described the temporal trends of PPR epidemics aggregated per day, week, month or year depending on the temporal aggregation of the original data. Generalized Linear Negative Binomial Regression Model (GLNBRM), (11), Generalized Linear Mixed Models (GLMMs) (47), Negative Binomial (45), linear (15,37,48,49) or logistic regression (50,51) models were used to explore or test hypothesis related to temporal trends. Other studies resorted to Bayesian approaches (3,52,53).…”