Spatio‐temporal analysis of peste des petits ruminants outbreaks in PR China (2013–2018): Updates based on the newest data

Self Cite

Section: Discussionmentioning

confidence: 99%

Transmission of African swine fever in China Through Legal Trade of Live Pigs

Gao

Liu

et al. 2020

Self Cite

“…The following geographically distributed landscape, climate, and socio-economic characteristics for each administrative unit were selected as potential explanatory factors, based on an analysis of scientific publications on the spatial and temporal modelling of PPR (Cao et al, 2018;Gao et al, 2019;Lembo et al, 2013;Ma et al, 2017Ma et al, , 2019Mokhtari et al, 2017;Ruget et al, 2019): (1) Variables proxying an intensity of regional economic activity that may influence the disease spread by transport links-total road length, road density, average population density, and the Engel coefficient 1 ; (2) Average den- The performance of the obtained model was tested by predicting for the Chinese study region and comparing the observed and predicted number of PPR outbreaks. The regression residuals were tested for spatial autocorrelation using Moran's I test (Mitchell, 2005).…”

Section: Modelling Methodsmentioning

confidence: 99%

“…The following geographically distributed landscape, climate, and socio‐economic characteristics for each administrative unit were selected as potential explanatory factors, based on an analysis of scientific publications on the spatial and temporal modelling of PPR (Cao et al., 2018; Gao et al., 2019; Lembo et al., 2013; Ma et al., 2017, 2019; Mokhtari et al., 2017; Ruget et al., 2019): (1) Variables proxying an intensity of regional economic activity that may influence the disease spread by transport links— total road length, road density, average population density, and the Engel coefficient; (2) Average density of small ruminants and average cattle density as indicators of susceptible population's and other potential host's density; (3) Most general landscape and climatic factors that may shape a suitable habitat for small ruminants and provide favourable conditions for the virus spread— average elevation, annual mean temperature, annual precipitation, and maximum green vegetation fraction. To provide environmental and socio‐economic similarity between the study regions in China and Kazakhstan, we ensured that each variable's range overlapped for both countries.…”

Section: Methodsmentioning

confidence: 99%

Mapping the risks of the spread of peste des petits ruminants in the Republic of Kazakhstan

Abdrakhmanov

Mukhanbetkaliyev

Sultanov

et al. 2021

Peste des petits ruminants (PPR) is a viral transboundary disease seen in small ruminants, that causes significant damage to agriculture. This disease has not been previously registered in the Republic of Kazakhstan (RK). This paper presents an assessment of the susceptibility of the RK's territory to the spread of the disease in the event of its importation from infected countries. The negative binomial regression model that was trained on the PPR outbreaks in China, was used to rank municipal districts in the RK in terms of PPR spread risk. The outbreak count per administrative district was used as a risk indicator, while a number of socio-economic, landscape, and climatic factors were considered as explanatory variables. Summary road length, altitude, the density of small ruminants, the maximum green vegetation fraction, cattle density, and the Engel coefficient were the most significant factors. The model demonstrated a good performance in training data (R 2 = 0.69), and was transferred to the RK, suggesting a significantly lower susceptibility of this country to the spread of PPR. Hot spot analysis identified three clusters of districts at the highest risk, located in the western, eastern, and southern parts of Kazakhstan. As part of the study, a countrywide survey was conducted to collect data on the distribution of livestock populations, which resulted in the compilation of a complete geo-database of small ruminant holdings in the RK.The research results may be used to formulate a national strategy for preventing the importation and spread of PPR in Kazakhstan through targeted monitoring in high-risk areas.

“…Different sets of variables hypothesized to directly or indirectly facilitate/ support the PPR mode of transmission were considered in this study as supported by recent scholarly literature and specific epidemiological aspects of PPR virus transmission. PPR transmission and spread is usually facilitated by human socio-economic activities, bioclimatic conditions, topographic and environmental factors that tend to favour suitability of PPR disease occurrence (Gao et al, 2019). These factors, acting singly or in combination may contribute significantly to the transmission and spread of the PPR virus resulting into re-introduction or introduction of such infectious diseases into new areas.…”

Section: Preparation Of Potential Explanatory Variablesmentioning

confidence: 99%

Spatio‐temporal cluster analysis and transmission drivers for Peste des Petits Ruminants in Uganda

Nkamwesiga

Korennoy

Lumu

et al. 2022

Peste des Petits Ruminants (PPR) is a transboundary, highly contagious, and fatal disease of small ruminants. PPR causes global annual economic losses of between USD 1.5-2.0 billion across more than 70 affected countries. Despite the commercial availability of effective PPR vaccines, lack of financial and technical commitment to PPR control coupled with a dearth of refined PPR risk profiling data in different endemic countries has perpetuated PPR virus transmission. In Uganda, over the past five years, PPR has extended from north-eastern Uganda (Karamoja) with sporadic incursions in other districts /regions. To identify disease cluster hotspot trends that would facilitate the design and implementation of PPR risk-based control methods (including vaccination), we employed the space-time cube approach to identify trends in the clustering of outbreaks in neighbouring space-time cells using confirmed PPR outbreak report data (2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020). We also used negative binomial and logistic regression models and identified high small ruminant density, extended road length, low annual precipitation and high soil water index as the most important drivers of PPR in Uganda. The study identified (with 90 -99% confidence) five PPR disease hotspot trend categories across subregions of Uganda. Diminishing hotspots were identified in the Karamoja region whereas consecutive, sporadic, new, and emerging hotspots were identified in central and southwestern districts of Uganda. Inter-district and cross-border small ruminant movement facilitated by longer road stretches and animal comingling precipitate PPR outbreaks as well as PPR virus spread from its initial Karamoja focus to the central and south-western Uganda. There is therefore urgent need to prioritize considerable vaccination coverage to obtain the required herd immunity among small ruminants in the new hotspot areas to block transmission to further emerging hotspots. Findings of this study provide a basis for more robust timing and prioritization of control measures including vaccination.