Predictive risk mapping of an environmentally-driven infectious disease using spatial Bayesian networks: A case study of leptospirosis in Fiji

• Published in: PLoS neglected tropical diseases 2018-10, Vol.12 (10), p.e0006857
• Main Authors: Mayfield, Helen J, Smith, Carl S, Lowry, John H, Watson, Conall H, Baker, Michael G, Kama, Mike, Nilles, Eric J, Lau, Colleen L
• Format: Article
• Language: English
• Subjects: Agglutination tests; Animal Husbandry; Animals; Artificial intelligence; Bayes Theorem; Bayesian analysis; Biology and Life Sciences; Censuses; Climate change; Commercial farms; Complexity; Dairy farms; Decision support systems; Demographics; Developing countries; Earth Sciences; Environmental Exposure; Environmental factors; Epidemiology; Ethics; Exposure; Fiji - epidemiology; Geographic information systems; Health risks; Humans; Infections; Infectious diseases; LDCs; Leptospirosis; Leptospirosis - epidemiology; Leptospirosis - transmission; Livestock; Machine learning; Mapping; Mathematical models; Medicine and Health Sciences; People and Places; Population; Poverty; Probability theory; Regression analysis; Risk; Risk Assessment; Rural areas; Rural environments; Rural Population; Seroepidemiologic Studies; Serology; Social factors; Sociology; Spatial Analysis; Statistical analysis; Subgroups; Swine; Transmission; Tropical diseases; Urban areas; Urban Population; Zoonoses; Zoonoses - epidemiology; Zoonoses - transmission
• ISSN: 1935-2735; 1935-2727; 1935-2735
• DOI: 10.1371/journal.pntd.0006857

Leptospirosis is a zoonotic disease responsible for over 1 million severe cases and 60,000 deaths annually. The wide range of animal hosts and complex environmental drivers of transmission make targeted interventions challenging, particularly when restricted to regression-based analyses which have limited ability to deal with complexity. In Fiji, important environmental and socio-demographic factors include living in rural areas, poverty, and livestock exposure. This study aims to examine drivers of transmission under different scenarios of environmental and livestock exposures. Spatial Bayesian networks (SBN) were used to analyse the influence of livestock and poverty on the risk of leptospirosis infection in urban compared to rural areas. The SBN models used a combination of spatially-explicit field data from previous work and publically available census information. Predictive risk maps were produced for overall risk, and for scenarios related to poverty, livestock, and urban/rural setting. While high, rather than low, commercial dairy farm density similarly increased the risk of infection in both urban (12% to 18%) and rural areas (70% to 79%), the presence of pigs in a village had different impact in rural (43% to 84%) compared with urban areas (4% to 24%). Areas with high poverty rates were predicted to have 26.6% and 18.0% higher probability of above average seroprevalence in rural and urban areas, respectively. In urban areas, this represents >300% difference between areas of low and high poverty, compared to 43% difference in rural areas. Our study demonstrates the use of SBN to provide valuable insights into the drivers of leptospirosis transmission under complex scenarios. By estimating the risk of leptospirosis infection under different scenarios, such as urban versus rural areas, these subgroups or areas can be targeted with more precise interventions that focus on the most relevant key drivers of infection.