Bovine anaplasmosis in Zimbabwe: spatio-temporal distribution and environmental drivers

Understanding the spatial and temporal distribution of Bovine anaplasmosis is crucial for identifying areas of high prevalence for targeted disease control. This research was aimed at modelling and mapping the B. anaplasmosis potential distribution, and identify hotspots as well as significant variables explaining the occurrence of the disease. The Getis Ord Gi* statistic for Hotspot analysis was used as well as MaxEnt ecological niche modelling. The effects of time, land-use, and agro-ecological regions on B. anaplasmosis occurrence were tested using Analysis of Variance (ANOVA). Results showed that several districts in Zimbabwe are suitable for the occurence of the disease for example Binga, Seke, Buhera, Kwekwe, Gweru, Mhondoro, Chegutu, Sanyati, and in the North: Mbire, Muzarabani, Mt Darwin, Shamva, Bindura, Zvimba and Makonde. Morbidity and mortality hotspots were detected in Gokwe-south, Kwekwe, and Chirumhanzu districts. Binga, Gokwe-south, Gutu, Hurungwe, Mazoe, Nkayi, Shamva, and Kwekwe districts also experienced high disease incidences. Temperature seasonality, precipitation seasonality, mean diurnal range, and isothermality were the most important variables in explaining 93% of B. anaplasmosis distribution. Unlike land-use and agro-ecological regions, time (months) had a significant effect on B. anaplasmosis occurrence with July and September having significantly (p < 0.05) higher cases and deaths than the rest of the months. The results of this study provide insights into the management strategies and control of B. anaplasmosis in Zimbabwe. It is thus concluded that geo-spatial techniques, combined with ecological niche modelling can provide useful insights into disease prevalence and distribution and hence can contribute to effective management and control of B. anaplasmosis in Zimbabwe.

Modelling climate change impacts on the spatial distribution of anthrax in Zimbabwe

BACKGROUND

In Zimbabwe, anthrax is endemic with outbreaks being reported almost annually in livestock, wildlife, and humans over the past 40 years. Accurate modelling of its spatial distribution is key in formulating effective control strategies. In this study, an Ensemble Species Distribution Model was used to model the current and future distribution of anthrax occurrence in Zimbabwe.

METHODS

Bioclimatic variables derived from the Beijing Climate Centre Climate System Model were used to model the disease. Collinearity testing was conducted on the 19 bioclimatic variables and elevation to remove redundancy. Variables that had no collinearity were used for anthrax habitat suitability modelling. Two future climate change scenarios for different Representative Concentration Pathways (RCP), RCP4.5 and RCP8.5 were used. Model evaluation was done using true skill, Kappa statistics and receiver operating characteristics.

RESULTS

The results showed that under current bioclimatic conditions, eastern and western districts of Zimbabwe were modelled as highly suitable, central districts moderately suitable and southern parts marginally suitable for anthrax occurrence. Future predictions demonstrated that the suitable (8%) and highly suitable (7%) areas for anthrax occurrence would increase under RCP4.5 scenario. In contrast, a respective decrease (11%) and marginal increase (0.6%) of suitable and highly suitable areas for anthrax occurrence were predicted under the RCP8.5 scenario. The percentage contribution of the predictors varied for the different scenarios; Bio6 and Bio18 for the current scenario, Bio2, Bio4 and Bio9 for the RCP4.5 and Bio3 and Bio15 for the RCP8.5 scenarios.

CONCLUSIONS

The study revealed that areas currently suitable for anthrax should be targeted for surveillance and prevention. The predicted future anthrax distribution can be used to guide and prioritise surveillance and control activities and optimise allocation of limited resources. In the marginally to moderately suitable areas, effective disease surveillance systems and awareness need to be put in place for early detection of outbreaks. Targeted vaccinations and other control measures including collaborative 'One Health' strategies need to be implemented in the predicted highly suitable areas. In the southern part where a high decrease in suitability was predicted, continued monitoring would be necessary to detect incursions early.

Spatial patterns of pulmonary tuberculosis (TB) cases in Zimbabwe from 2015 to 2018

INTRODUCTION

Accurate mapping of spatial heterogeneity in tuberculosis (TB) cases is critical for achieving high impact control as well as guide resource allocation in most developing countries. The main aim of this study was to explore the spatial patterns of TB occurrence at district level in Zimbabwe from 2015 to 2018 using GIS and spatial statistics as a preamble to identifying areas with elevated risk for prioritisation of control and intervention measures.

METHODS

In this study Getis-Ord Gi* statistics together with SaTscan were used to characterise TB hotspots and clusters in Zimbabwe at district level from 2015 to 2018. GIS software was used to map and visualise the results of cluster analysis.

RESULTS

Results show that TB occurrence exhibits spatial heterogeneity across the country. The TB hotspots were detected in the central, western and southern part of the country. These areas are characterised by artisanal mining activities as well as high poverty levels.

CONCLUSIONS AND RECOMMENDATIONS

Results of this study are useful to guide TB control programs and design effective strategies which are important in achieving the United Nations Sustainable Development goals (UNSDGs).

Spatial clustering of fourteen tick species across districts of Zimbabwe

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.

Spatial and spatio-temporal analysis of malaria cases in Zimbabwe

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.

Spatial distribution of Mycobacterium Tuberculosis in metropolitan Harare, Zimbabwe

INTRODUCTION

The contribution of high tuberculosis (TB) transmission pockets in propagating area-wide transmission has not been adequately described in Zimbabwe. This study aimed to describe the presence of hotspot transmission of TB cases in Harare city from 2011 to 2012 using geospatial techniques.

METHODS

Anonymised TB patient data stored in an electronic database at Harare City Health department was analysed using geospatial methods. Confirmed TB cases were mapped using geographic information system (GIS). Global Moran's I and Anselin Local Moran's I (LISA) were used to assess clustering and the local Getis-Ord Gi* was used to estimate hotspot phenomenon of TB cases in Harare City for the period between 2011 and 2012.

RESULTS

A total of 12,702 TB cases were accessed and mapped on the Harare City map. In both 2011 and 2012, ninety (90%) of cases were new and had a high human immunodeficiency virus (HIV)/TB co-infection rate of 72% across all suburbs. Tuberculosis prevalence was highest in the Southern district in both 2011 and 2012. There were pockets of spatial distribution of TB prevalence across West South West, Southern, Western, South Western and Eastern health districts. TB hot spot occurrence was restricted to the West South West, parts of South Western, Western health districts. West South West district had an increased peri-urban population with inadequate social services including health facilities. These conditions were conducive for increased intensity of TB occurrence, a probable indication of high transmission especially in the presence of high HIV co-infection.

CONCLUSIONS AND RECOMMENDATIONS

Increased TB transmission was limited to a health district with high informal internal migrants with limited health services in Harare City. To minimise spread of TB into greater Harare, there is need to improve access to TB services in the peri-urban areas.

Spatial overlaps in the distribution of HIV/AIDS and malaria in Zimbabwe

BACKGROUND

In most developing economies particularly in Africa, more people are likely to die of HIV/AIDS and malaria compared to other diseases. HIV/AIDS tends to be superimposed on the long standing malaria burden particularly in sub-Saharan Africa. The detection and understanding of spatial overlaps in disease occurrence is important for integrated and targeted disease control. Integrated disease control can enhance efficiency and cost-effectiveness through the development of drugs targeting multiple infections in the same geographic space.

METHODS

Using Zimbabwe as a case study, this study tests the hypothesis that malaria clusters coincide with HIV/AIDS clusters in space. Case data for the two diseases were obtained from the Ministry of Health and Child Care in Zimbabwe at district level via the District Health Information System (DHIS). Kulldorff's spatial scan statistic was used to test for spatial overlaps in clusters of high cases of HIV/AIDS and malaria at district level. The spatial scan test was used to identify areas with higher cases of HIV/AIDS and malaria than would be expected under spatial randomness.

RESULTS

Results of this study indicate that primary clusters of HIV/AIDS and malaria were not spatially coincident in Zimbabwe. While no spatial overlaps were detected between primary clusters of the two diseases, spatial overlaps were detected among statistically significant secondary clusters of HIV/AIDS and malaria. Spatial overlaps between HIV/AIDS and malaria occurred in five districts in the northern and eastern regions of Zimbabwe. In addition, findings of this study indicate that HIV/AIDS is more widespread in Zimbabwe compared to malaria.

CONCLUSIONS

The results of this study may therefore be used as a basis for spatially-targeted control of HIV/AIDS and malaria particularly in high disease burden areas. This is important as previous interventions have targeted the two diseases separately. Thus, targeted control could assist in resource allocation through prioritising areas in greatest need hence maximising the impact of disease control.