Peste des Petits Ruminants Risk Factors and Space-Time Clusters in Bangladesh

Abstract

Peste des Petits Ruminants (PPR) is endemic in Bangladesh, but its spatial distribution and risk factors have not yet been reported. Using four years of national-level, passive surveillance data (2014 to 2017), in this study we aimed to identify risk factors, create PPR risk maps and describe PPR time-space clusters. We selected PPR case records—mainly based on presumptive diagnosis of small ruminants in subdistrict veterinary hospitals—and sheep and goat population data from all 64 districts of Bangladesh. Peste des Petits Ruminants cumulative incidence per 10,000 animals at risk per district was used to conduct cluster and hotspot analysis and create predictive maps for each year and all 4 years combined. The association between PPR cumulative incidence and hypothesized risk factors—including climatic variables, elevation, road length, river length, railroad length, land cover, and water bodies—was analyzed using a geographically weighted regression model. The total number of PPR cases reported during the study period was 5.2 million. We found that most PPR cases (27.6%) were reported in the monsoon season. The highest and lowest proportions of cases were reported from Rajshahi (36.1%) and Barisal divisions (2.1%), respectively. We identified five space-time clusters, 9 high–high clusters, and 9 hotspots. The predicted cumulative incidences of PPR were persistently higher in north-east, north-west, and south-east parts of Bangladesh. Road length (P = 0.03) was positively associated with PPR incidence in Bangladesh. Results suggest that movement of animals (road length) plays an important role in the epidemiology of PPR in Bangladesh. Along with restriction of animal movement, hotspots and high–high clusters should be targeted first for immunization coverage in Bangladesh and similar PPR endemic countries to achieve eradication.

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

“…We tested risk factors for transmission of PPRV reported in the literature against the outcomes of interest (total number of outbreaks per district or whether a district has reported an outbreak) (A. K. M. A. Rahman et al., 2021; Ruget et al., 2019). As previously reported (Ma et al., 2019), our regression models indicated a strong negative association between annual precipitation with the likelihood of PPR outbreaks implying that lower rainfall increases the chance of PPR outbreaks.…”

“…Although spatio-temporal and broader epidemiological studies are necessary primers for designing and implementing PPR surveillance and risk-targeted control programs, for example vaccination (Abdrakhmanov et al, 2021;Ma et al, 2019;A. K. M. A. Rahman et al, 2021;Ruget et al, 2019), such studies have not been undertaken for Uganda.…”

Epidemic Analysis of Peste Des Petits Ruminants in India

“…According to FAO data, PPR has been reported in 23 provinces in China, and a total of 29 incidents of PPR epidemics have been reported in China from 2016 to 2021. Rahman et al found that the median of PPR infection per 10000 small ruminants in Bangladesh was 198 [27]. Abubakar et al…”

Spatiotemporal characteristics analysis and potential distribution prediction of peste des petits ruminants (PPR) in China from 2007–2018

“…Spatial autocorrelation analysis has been applied in the surveillance of epidemic diseases, such as HIV (Agegnehu & Tesema, 2020), PPR (Rahman et al, 2021) and bluetongue (Ma et al, 2019a). Global spatial autocorrelation is used to analyse the overall spatial characteristics of geographical elements in a research area (Si et al, 2008).…”

“…Spatial autocorrelation analysis often uses global spatial autocorrelation and local indicators of spatial association to explore the spatial correlation of geographical elements (Rossi & Queneherve, 1998). Spatial autocorrelation analysis has been applied in the surveillance of epidemic diseases, such as HIV (Agegnehu & Tesema, 2020), PPR (Rahman et al., 2021) and bluetongue (Ma et al., 2019a). Global spatial autocorrelation is used to analyse the overall spatial characteristics of geographical elements in a research area (Si et al., 2008).…”