Spatiotemporal patterns of clinical bovine dermatophilosis in Zimbabwe 1995–2014

Ndhlovu, Felistas; Ndhlovu, Daud Nyosi; Chikerema, Silvester M.; Masocha, Mhosisi; Nyagura, Mudavanhu; Pfukenyi, Davies M.

doi:10.4102/ojvr.v84i1.1386

Cited by 6 publications

(4 citation statements)

References 31 publications

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“…The spatial clustering of ticks in specific districts corroborate and expand previous studies on the spatial distribution of ticks and tick-borne diseases in Zimbabwe. In a previous study, [ 17 ] modelled spatio-temporal patterns of bovine dermatophilosis (a disease associated with A variegatum) over a 19 year period in Zimbabwe and observed a directional spread of the disease from the identified clusters over time. Similarly, [ 19 ] showed that there was a shift in the distribution of ixodid ticks in Zimbabwe.…”

Section: Discussionmentioning

confidence: 99%

“…This is despite the fact that location-specific information on different tick species forms the basis for effective management of tick-borne diseases. In fact, spatial patterns of tick prevalence have not been fully explored in Zimbabwe [ 17 ]. The limited focus on spatial patterns of tick prevalence may partly explain why, to date, there is no consistent strategy to effectively prevent or control tick-borne diseases in the country [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

“…Spatial statistics were applied to detect transmission hotspots of human anthrax in Georgia in a study by [ 20 ]. Furthermore, [ 17 ] used a retrospective space–time scan statistics to detect temporal and spatial clusters of dermatophilosis, a tick-borne disease. Put together, these studies provide important insights into the importance of applying geospatial tools and spatial analytical approaches in improving tick and tick-borne disease hotspot detection.…”

Section: Introductionmentioning

confidence: 99%

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Spatial clustering of fourteen tick species across districts of Zimbabwe

Shekede

Chikerema

Spargo³

et al. 2021

BMC Vet Res

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Background Ticks transmit several diseases that result in high morbidity and mortality in livestock. Tick-borne diseases are an economic burden that negatively affect livestock production, cost countries billions of dollars through vaccine procurement and other disease management efforts. Thus, understanding the spatial distribution of tick hotspots is critical for identifying potential areas of high tick-borne disease transmission and setting up priority areas for targeted tick disease management. In this study, optimised hotspot analysis was applied to detect hotspots and coldspots of 14 common tick species in Zimbabwe. Data on the spatial distribution of tick species were obtained from the Epidemiology Unit of the Division of Veterinary Field Services of Zimbabwe. Results A total of 55,133 ticks were collected with Rhipicephalus decoloratus being the most common species (28.7%), followed by Amblyomma hebraeum (20.6%), and Rhipicephalus sanguineus sensu lato (0.06%) being the least common species. Results also showed that tick hotspots are species-specific with particular tick species occupying defined localities in the country. For instance, Amblyomma variegatum, Rhipicephalus appendiculatus, Rhipicephalus decoloratus, Rhipicephalus compostus, Rhipicephalus microplus, Rhipicephalus pravus, and Rhipicephalus simus were concentrated in the north and north eastern districts of the country. In contrast, Amblyomma hebraeum, Hyalomma rufipes, Hyalomma trancatum and Rhipicephalus evertsi evertsi were prevalent in the southern districts of Zimbabwe. Conclusion The occurrence of broadly similar hotspots of several tick species in different districts suggests presence of spatial overlaps in the niche of the tick species. As ticks are vectors of several tick-borne diseases, there is high likelihood of multiple disease transmission in the same geographic region. This study is the first in Zimbabwe to demonstrate unique spatial patterns in the distribution of several tick species across the country. The results of this study provide an important opportunity for the development of spatially-targeted tick-borne disease management strategies.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatial clustering of fourteen tick species across districts of Zimbabwe

Shekede

Chikerema

Spargo³

et al. 2021

BMC Vet Res

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“…Mean monthly temperatures vary from 15 °C in July to 24 °C in November, while the mean annual temperature varies from 18 °C in the high-altitude areas to 23 °C in the low altitude areas. The study area is characterised by a subtropical climate with three recognisable seasons which are the hot wet season or summer stretching from mid-November to March; the cold dry season or winter stretching from April to July; and the hot dry season or spring from August to mid-November [ 43 ]. Zimbabwe experiences seasonal and spatial variation in malaria transmission that is related to the country’s climate especially rainfall pattern [ 13 , 44 ].…”

Section: Methodsmentioning

confidence: 99%

Spatial and spatio-temporal analysis of malaria cases in Zimbabwe

et al. 2020

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Background Although effective treatment for malaria is now available, approximately half of the global population remain at risk of the disease particularly in developing countries. To design effective malaria control strategies there is need to understand the pattern of malaria heterogeneity in an area. Therefore, the main objective of this study was to explore the spatial and spatio-temporal pattern of malaria cases in Zimbabwe based on malaria data aggregated at district level from 2011 to 2016. Methods Geographical information system (GIS) and spatial scan statistic were applied on passive malaria data collected from health facilities and aggregated at district level to detect existence of spatial clusters. The global Moran’s I test was used to infer the presence of spatial autocorrelation while the purely spatial retrospective analyses were performed to detect the spatial clusters of malaria cases with high rates based on the discrete Poisson model. Furthermore, space-time clusters with high rates were detected through the retrospective space-time analysis based on the discrete Poisson model. Results Results showed that there is significant positive spatial autocorrelation in malaria cases in the study area. In addition, malaria exhibits spatial heterogeneity as evidenced by the existence of statistically significant (P < 0.05) spatial and space-time clusters of malaria in specific geographic regions. The detected primary clusters persisted in the eastern region of the study area over the six year study period while the temporal pattern of malaria reflected the seasonality of the disease where clusters were detected within particular months of the year. Conclusions Geographic regions characterised by clusters of high rates were identified as malaria high risk areas. The results of this study could be useful in prioritizing resource allocation in high-risk areas for malaria control and elimination particularly in resource limited settings such as Zimbabwe. The results of this study are also useful to guide further investigation into the possible determinants of persistence of high clusters of malaria cases in particular geographic regions which is useful in reducing malaria burden in such areas.

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