Identification of common spatial and temporal trends in the epidemiology of cattle bovine tuberculosis and human extrapulmonary and drug-resistant tuberculosis in Malawi

Abstract

Background: Identification of common spatial disease trends between cattle bovine tuberculosis (BTB) and human extrapulmonary tuberculosis (EPTB) and drug-resistant tuberculosis (DRTB) can support integrated disease control and monitoring programmes. We employed the recently developed multivariate disease mapping methods to examine whether the diseases exhibited any spatial correlation.

Methods: A retrospective study of cattle BTB and human EPTB and DRTB cases from 2018 to 2022 was conducted. Bivariate shared spatiotemporal components models were fitted to a) cattle BTB and human EPTB and b) cattle BTB and human DRTB at the district level in Malawi, with cattle density, human density and climatic variables as independent variables.

Results: Disease specific spatial effects were higher in the southern half of the country, while the shared spatial effects were more dominant in both the south and western parts of the country. The shared temporal effects showed constant trends, while disease specific temporal effects showed an increasing pattern for cattle BTB and a constant pattern for human EPTB and DRTB. The predicted disease incidence pattern for all forms of TB in the period without data showed a constant pattern over the years. Cattle density was positively associated with cattle BTB ( $\beta$ : 0.022; 95% Credible Interval (CI): 0.004, 0.042). Human density was positively associated with human EPTB ( $\beta$ : 0.005; 95% CI: 0.001, 0.009).

Conclusion: Cattle BTB and human EPTB and DRTB have a common spatial pattern in the west and southern parts of Malawi. Integrated interventions targeting high-density areas for cattle and human may have positive impacts on cattle BTB and human EPTB and DRTB.

Keywords: Common animal and human disease spatial effects; Log of count data; One Health; Zoonotic TB.

Fig. 1

Map of Malawi land mass showing districts and cities.

Fig. 2

Annual trends in cattle BTB and human EPTB and DRTB cases.

Fig. 3

Spatial trends in cattle BTB and human EPTB and DRTB cases, 2018 to 2022.

Fig. 4

Mean bias and root mean square error from count data models fitted to negative binomial simulated data based on parameters of the empirical cattle BTB data.

Fig. 5

Disease specific and shared spatial effect on cattle BTB and human EPTB (a) and their posterior probability (b).

Fig. 6

Disease specific and shared spatial effect on cattle BTB and human DRTB (a) and their posterior probability (b).

Fig. 7

Disease specific and shared temporal effect on cattle BTB and human EPTB.

Fig. 8

Disease specific and shared temporal effect on cattle BTB and human DRTB.

Fig. 9

Predicted cattle BTB and human EPTB and DRTB, 2018 to 2022.

Fig. 10

Predicted cattle BTB and human EPTB and DRTB in 2023 and 2024.