Use of Network Analysis and Spread Models to Target Control Actions for Bovine Tuberculosis in a State from Brazil

Mapping Bovine Tuberculosis in Colombia, 2001-2019

INTRODUCTION

Bovine tuberculosis is a zoonotic disease of significant impact, particularly in countries where a pastoral economy is predominant. Despite its importance, few studies have analysed the disease's behaviour in Colombia, and none have developed maps using geographic information systems (GIS) to characterise it; as such, we developed this study to describe the temporal-spatial distribution of bovine tuberculosis in Colombia over a period of 19 years.

METHODS

A retrospective cross-sectional descriptive study, based on reports by the Colombian Agricultural Institute (ICA), surveillance of tuberculosis on cattle farms in Colombia from 2001 to 2019 was carried out. The data were converted into databases using Microsoft Access 365®, and multiple epidemiological maps were generated with the QGIS® version 3.36 software coupled to shape files of all the country's departments.

RESULTS

During the study period, 5273 bovine tuberculosis cases were identified in multiple different departments of Colombia (with a mean of 278 cases/year). Regarding its temporal distribution, the number of cases varied from a maximum of 903 cases (17.12% of the total) in 2015 to a minimum of 0 between 2001 and 2004 and between 2017 and 2019 (between 2005 and 2016, the minimum was 46 cases, 0.87%).

CONCLUSIONS

GIS are essential for understanding the temporospatial behaviour of zoonotic diseases in Colombia, as is the case for bovine tuberculosis, with its potential implications for the Human and One Health approaches.

Condemnation of bovine carcasses due to tuberculosis-gross lesions in Rio Grande do Sul, Brazil: Associated risk factors

Surveillance of bovine tuberculosis (bTB) lesions in animals at slaughterhouses is useful for controlling and eradicating the disease, besides providing epidemiological information. This study aimed to identify risk factors for bovine tuberculosis (bTB) condemnation in cattle at slaughterhouses in Rio Grande do Sul, Brazil. A logistic regression analysis was conducted using data on bTB-related condemnations. Variables examined included animal origin, number of slaughtered animals, season, inspection level (state or municipality), animal sex, and slaughterhouse location. A total of 297,817 Animal Transport Guides were evaluated, representing the transportation of 3497,521 animals. Among these, 6097 (2.05%) had at least one animal condemned due to bTB lesions. Risk factors for condemnation included larger batch sizes, female animals, slaughterhouses, and animal origin. The higher condemnation frequency in females and regions with dairy farms suggests links to milk production. Variation in condemnation rates by inspection level and slaughterhouse highlights the need for standardized procedures in identifying bTB lesions. Identifying these risk factors enables targeted interventions to enhance disease control and eradication efforts.

Coupling spatial statistics with social network analysis to estimate distinct risk areas of disease circulation to improve risk-based surveillance

Most animal disease surveillance systems concentrate efforts in blocking transmission pathways and tracing back infected contacts while not considering the risk of transporting animals into areas with elevated disease risk. Here, we use a suite of spatial statistics and social network analysis to characterize animal movement among areas with an estimated distinct risk of disease circulation to ultimately enhance surveillance activities. Our model utilized equine infectious anemia virus (EIAV) outbreaks, between-farm horse movements, and spatial landscape data from 2015 through 2017. We related EIAV occurrence and the movement of horses between farms with climate variables that foster conditions for local disease propagation. We then constructed a spatially explicit model that allows the effect of the climate variables on EIAV occurrence to vary through space (i.e., non-stationary). Our results identified important areas in which in-going movements were more likely to result in EIAV infections and disease propagation. Municipalities were then classified as having high 56 (11.3%), medium 48 (9.66%), and low 393 (79.1%) spatial risk. The majority of the movements were between low-risk areas, altogether representing 68.68% of all animal movements. Meanwhile, 9.48% were within high-risk areas, and 6.20% were within medium-risk areas. Only 5.37% of the animals entering low-risk areas came from high-risk areas. On the other hand, 4.91% of the animals in the high-risk areas came from low- and medium-risk areas. Our results demonstrate that animal movements and spatial risk mapping could be used to make informed decisions before issuing animal movement permits, thus potentially reducing the chances of reintroducing infection into areas of low risk.

Multiple species animal movements: network properties, disease dynamics and the impact of targeted control actions

Infectious diseases in livestock are well-known to infect multiple hosts and persist through a combination of within- and between-host transmission pathways. Uncertainty remains about the epidemic dynamics of diseases being introduced on farms with more than one susceptible host species. Here, we describe multi-host contact networks and elucidate the potential of disease spread through farms with multiple hosts. Four years of between-farm animal movement among all farms of a Brazilian state were described through a static and monthly snapshot of network representations. We developed a stochastic multilevel model to simulate scenarios in which infection was seeded into single host and multi-host farms to quantify disease spread potential, and simulate network-based control actions used to evaluate the reduction of secondarily infected farms. We showed that the swine network was more connected than cattle and small ruminants in both the static and monthly snapshots. The small ruminant network was highly fragmented, however, contributed to interconnecting farms, with other hosts acting as intermediaries throughout the networks. When a single host was initially infected, secondary infections were observed across farms with all other species. Our stochastic multi-host model demonstrated that targeting the top 3.25% of the farms ranked by degree reduced the number of secondarily infected farms. The results of the simulation highlight the importance of considering multi-host dynamics and contact networks while designing surveillance and preparedness control strategies against pathogens known to infect multiple species.