Use of geographically weighted logistic regression to quantify spatial variation in the environmental and sociodemographic drivers of leptospirosis in Fiji: a modelling study.

BACKGROUND: Leptospirosis is a globally important zoonotic disease, with complex exposure pathways that depend on interactions between human beings, animals, and the environment. Major drivers of outbreaks include flooding, urbanisation, poverty, and agricultural intensification. The intensity of these drivers and their relative importance vary between geographical areas; however, non-spatial regression methods are incapable of capturing the spatial variations. This study aimed to explore the use of geographically weighted logistic regression (GWLR) to provide insights into the ecoepidemiology of human leptospirosis in Fiji. METHODS: We obtained field data from a cross-sectional community survey done in 2013 in the three main islands of Fiji. A blood sample obtained from each participant (aged 1-90 years) was tested for anti-Leptospira antibodies and household locations were recorded using GPS receivers. We used GWLR to quantify the spatial variation in the relative importance of five environmental and sociodemographic covariates (cattle density, distance to river, poverty rate, residential setting [urban or rural], and maximum rainfall in the wettest month) on leptospirosis transmission in Fiji. We developed two models, one using GWLR and one with standard logistic regression; for each model, the dependent variable was the presence or absence of anti-Leptospira antibodies. GWLR results were compared with results obtained with standard logistic regression, and used to produce a predictive risk map and maps showing the spatial variation in odds ratios (OR) for each covariate. FINDINGS: The dataset contained location information for 2046 participants from 1922 households representing 81 communities. The Aikaike information criterion value of the GWLR model was 1935·2 compared with 1254·2 for the standard logistic regression model, indicating that the GWLR model was more efficient. Both models produced similar OR for the covariates, but GWLR also detected spatial variation in the effect of each covariate. Maximum rainfall had the least variation across space (median OR 1·30, IQR 1·27-1·35), and distance to river varied the most (1·45, 1·35-2·05). The predictive risk map indicated that the highest risk was in the interior of Viti Levu, and the agricultural region and southern end of Vanua Levu. INTERPRETATION: GWLR provided a valuable method for modelling spatial heterogeneity of covariates for leptospirosis infection and their relative importance over space. Results of GWLR could be used to inform more place-specific interventions, particularly for diseases with strong environmental or sociodemographic drivers of transmission. FUNDING: WHO, Australian National Health & Medical Research Council, University of Queensland, UK Medical Research Council, Chadwick Trust

Human Leptospirosis Infection in Fiji: An Eco-epidemiological Approach to Identifying Risk Factors and Environmental Drivers for Transmission.

Leptospirosis is an important zoonotic disease in the Pacific Islands. In Fiji, two successive cyclones and severe flooding in 2012 resulted in outbreaks with 576 reported cases and 7% case-fatality. We conducted a cross-sectional seroprevalence study and used an eco-epidemiological approach to characterize risk factors and drivers for human leptospirosis infection in Fiji, and aimed to provide an evidence base for improving the effectiveness of public health mitigation and intervention strategies. Antibodies indicative of previous or recent infection were found in 19.4% of 2152 participants (81 communities on the 3 main islands). Questionnaires and geographic information systems data were used to assess variables related to demographics, individual behaviour, contact with animals, socioeconomics, living conditions, land use, and the natural environment. On multivariable logistic regression analysis, variables associated with the presence of Leptospira antibodies included male gender (OR 1.55), iTaukei ethnicity (OR 3.51), living in villages (OR 1.64), lack of treated water at home (OR 1.52), working outdoors (1.64), living in rural areas (OR 1.43), high poverty rate (OR 1.74), living <100m from a major river (OR 1.41), pigs in the community (OR 1.54), high cattle density in the district (OR 1.04 per head/sqkm), and high maximum rainfall in the wettest month (OR 1.003 per mm). Risk factors and drivers for human leptospirosis infection in Fiji are complex and multifactorial, with environmental factors playing crucial roles. With global climate change, severe weather events and flooding are expected to intensify in the South Pacific. Population growth could also lead to more intensive livestock farming; and urbanization in developing countries is often associated with urban and peri-urban slums where diseases of poverty proliferate. Climate change, flooding, population growth, urbanization, poverty and agricultural intensification are important drivers of zoonotic disease transmission; these factors may independently, or potentially synergistically, lead to enhanced leptospirosis transmission in Fiji and other similar settings

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

Introduction 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. Methods 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. Results 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. Conclusions 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

Use of geographically weighted logistic regression to quantify spatial variation in the environmental and sociodemographic drivers of leptospirosis in Fiji: a modelling study

Background Leptospirosis is a globally important zoonotic disease, with complex exposure pathways that depend on interactions between human beings, animals, and the environment. Major drivers of outbreaks include flooding, urbanisation, poverty, and agricultural intensification. The intensity of these drivers and their relative importance vary between geographical areas; however, non-spatial regression methods are incapable of capturing the spatial variations. This study aimed to explore the use of geographically weighted logistic regression (GWLR) to provide insights into the ecoepidemiology of human leptospirosis in Fiji. Methods We obtained field data from a cross-sectional community survey done in 2013 in the three main islands of Fiji. A blood sample obtained from each participant (aged 1–90 years) was tested for anti-Leptospira antibodies and household locations were recorded using GPS receivers. We used GWLR to quantify the spatial variation in the relative importance of five environmental and sociodemographic covariates (cattle density, distance to river, poverty rate, residential setting [urban or rural], and maximum rainfall in the wettest month) on leptospirosis transmission in Fiji. We developed two models, one using GWLR and one with standard logistic regression; for each model, the dependent variable was the presence or absence of anti-Leptospira antibodies. GWLR results were compared with results obtained with standard logistic regression, and used to produce a predictive risk map and maps showing the spatial variation in odds ratios (OR) for each covariate. Findings The dataset contained location information for 2046 participants from 1922 households representing 81 communities. The Aikaike information criterion value of the GWLR model was 1935·2 compared with 1254·2 for the standard logistic regression model, indicating that the GWLR model was more efficient. Both models produced similar OR for the covariates, but GWLR also detected spatial variation in the effect of each covariate. Maximum rainfall had the least variation across space (median OR 1·30, IQR 1·27–1·35), and distance to river varied the most (1·45, 1·35–2·05). The predictive risk map indicated that the highest risk was in the interior of Viti Levu, and the agricultural region and southern end of Vanua Levu. Interpretation GWLR provided a valuable method for modelling spatial heterogeneity of covariates for leptospirosis infection and their relative importance over space. Results of GWLR could be used to inform more place-specific interventions, particularly for diseases with strong environmental or sociodemographic drivers of transmission.a researchgrant from the Global Change Institute (607562) at The University of Queensland (QLD, Australia). CHW was supported by the UK Medical Research Council (grant MR/J003999/1) and the Chadwick Trust.

Unravelling infectious disease eco-epidemiology using Bayesian networks and scenario analysis: A case study of leptospirosis in Fiji

Regression models are the standard approaches used in infectious disease epidemiology, but have limited ability to represent causality or complexity. We explore Bayesian networks (BNs) as an alternative approach for modelling infectious disease transmission, using leptospirosis as an example. Data were obtained from a leptospirosis study in Fiji in 2013. We compared the performance of naïve versus expert-structured BNs for modelling the relative importance of animal species in disease transmission in different ethnic groups and residential settings. For BNs of animal exposures at the individual/household level, R2 for predicted versus observed infection rates were 0.59 for naïve and 0.75–0.93 for structured models of ethnic groups; and 0.54 for naïve and 0.93–1.00 for structured models of residential settings. BNs provide a promising approach for modelling infectious disease transmission under complex scenarios. The relative importance of animal species varied between subgroups, with important implications for more targeted public health control strategies

Gauge Symmetry Breaking through Soft Masses in Supersymmetric Gauge Theories

Effects of soft breaking in N=1 supersymmetric gauge theories are studied. For N_f < N_c, we include the dynamics of the non-perturbative superpotential and use the original (s)quark and gauge fields. For N_f > N_c +1, we formulate the dynamics in terms of dual (s)quarks and a dual gauge group SU(N_f-N_c). The mass squared of the squarks can be negative triggering spontaneous breakdown of flavor and color symmetry. The general condition for stability of the vacuum is derived. We determine the breaking pattern, determine the spectrum and argue that the masses vary smoothly as one crosses from the Higgs phase into the confining phase, thus exhibiting complementarity.Comment: Contribution to Inauguration Conferference of Asia Pacific Center for Theoretical Physics, 4-10 June, 1996, Seoul National University; LaTeX, no macros neede

Ross River virus antibody prevalence in the Fiji Islands, 2013-2015

A unique outbreak of Ross River virus (RRV) infection was reported in Fiji in 1979. In 2013, 29 RRV seroprevalence among residents was 46.5%. Of those born after 1982, 37.4% had anti-RRV 30 antibodies. Between 2013-2015, 10.9% of residents had seroconverted to RRV suggesting 31 ongoing endemic circulation of RRV in Fiji

Ross River virus antibody prevalence in the Fiji Islands, 2013-2015

A unique outbreak of Ross River virus (RRV) infection was reported in Fiji in 1979. In 2013, 29 RRV seroprevalence among residents was 46.5%. Of those born after 1982, 37.4% had anti-RRV 30 antibodies. Between 2013-2015, 10.9% of residents had seroconverted to RRV suggesting 31 ongoing endemic circulation of RRV in Fiji

Using paired serology and surveillance data to quantify dengue transmission and control during a large outbreak in Fiji.

Dengue is a major health burden, but it can be challenging to examine transmission and evaluate control measures because outbreaks depend on multiple factors, including human population structure, prior immunity and climate. We combined population-representative paired sera collected before and after the 2013/14 dengue-3 outbreak in Fiji with surveillance data to determine how such factors influence transmission and control in island settings. Our results suggested the 10-19 year-old age group had the highest risk of infection, but we did not find strong evidence that other demographic or environmental risk factors were linked to seroconversion. A mathematical model jointly fitted to surveillance and serological data suggested that herd immunity and seasonally varying transmission could not explain observed dynamics. However, the model showed evidence of an additional reduction in transmission coinciding with a vector clean-up campaign, which may have contributed to the decline in cases in the later stages of the outbreak

Supersymmetric top and bottom squark production at hadron colliders

The scalar partners of top and bottom quarks are expected to be the lightest squarks in supersymmetric theories, with potentially large cross sections at hadron colliders. We present predictions for the production of top and bottom squarks at the Tevatron and the LHC, including next-to-leading order corrections in supersymmetric QCD and the resummation of soft gluon emission at next-to-leading-logarithmic accuracy. We discuss the impact of the higher-order corrections on total cross sections and transverse-momentum distributions, and provide an estimate of the theoretical uncertainty due to scale variation and the parton distribution functions.Comment: 29 pages, 6 figure