| 1. |
- Bett, Bernard, et al.
(författare)
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Climate Change and Infectious Livestock Diseases : The Case of Rift Valley Fever and Tick-Borne Diseases
- 2019
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Ingår i: The Climate-Smart Agriculture Papers. - Cham : Springer. - 9783319927978 - 9783319927985 ; , s. 29-37
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Bokkapitel (refereegranskat)abstract
- Climate change influences the occurrence and transmission of a wide range of livestock diseases through multiple pathways. Diseases caused by pathogens that spent part of their life cycle outside the host (e.g. in vectors or the environment) are more sensitive in this regard, compared to those caused by obligate pathogens. In this chapter, we use two well-studied vector-borne diseases—Rift Valley fever (RVF) and tick-borne diseases (TBDs)—as case studies to describe direct pathways through which climate change influences infectious disease-risk in East and southern Africa. The first case study demonstrates that changes in the distribution and frequency of above-normal precipitation increases the frequency of RVF epidemics. The second case study suggests that an increase in temperature would cause shifts in the spatial distribution of TBDs, with cooler and wetter areas expected to experience heightened risk with climate change. These diseases already cause severe losses in agricultural productivity, food security and socio-economic development wherever they occur, and an increase in their incidence or geographical coverage would intensify these losses. We further illustrate some of the control measures that can be used to manage these diseases and recommend that more research should be done to better understand the impacts of climate change on livestock diseases as well as on the effectiveness of the available intervention measures.
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| 2. |
- Bett, Bernard, et al.
(författare)
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Spatiotemporal analysis of historical records (2001-2012) on dengue fever in Vietnam and development of a statistical model for forecasting risk
- 2019
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 14:11
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Dengue fever is the most widespread infectious disease of humans transmitted by Aedes mosquitoes. It is the leading cause of hospitalization and death in children in the Southeast Asia and western Pacific regions. We analyzed surveillance records from health centers in Vietnam collected between 2001-2012 to determine seasonal trends, develop risk maps and an incidence forecasting model.METHODS: The data were analyzed using a hierarchical spatial Bayesian model that approximates its posterior parameter distributions using the integrated Laplace approximation algorithm (INLA). Meteorological, altitude and land cover (LC) data were used as predictors. The data were grouped by province (n = 63) and month (n = 144) and divided into training (2001-2009) and validation (2010-2012) sets. Thirteen meteorological variables, 7 land cover data and altitude were considered as predictors. Only significant predictors were kept in the final multivariable model. Eleven dummy variables representing month were also fitted to account for seasonal effects. Spatial and temporal effects were accounted for using Besag-York-Mollie (BYM) and autoregressive (1) models. Their levels of significance were analyzed using deviance information criterion (DIC). The model was validated based on the Theil's coefficient which compared predicted and observed incidence estimated using the validation data. Dengue incidence predictions for 2010-2012 were also used to generate risk maps.RESULTS: The mean monthly dengue incidence during the period was 6.94 cases (SD 14.49) per 100,000 people. Analyses on the temporal trends of the disease showed regular seasonal epidemics that were interrupted every 3 years (specifically in July 2004, July 2007 and September 2010) by major fluctuations in incidence. Monthly mean minimum temperature, rainfall, area under urban settlement/build-up areas and altitude were significant in the final model. Minimum temperature and rainfall had non-linear effects and lagging them by two months provided a better fitting model compared to using unlagged variables. Forecasts for the validation period closely mirrored the observed data and accurately captured the troughs and peaks of dengue incidence trajectories. A favorable Theil's coefficient of inequality of 0.22 was generated.CONCLUSIONS: The study identified temperature, rainfall, altitude and area under urban settlement as being significant predictors of dengue incidence. The statistical model fitted the data well based on Theil's coefficient of inequality, and risk maps generated from its predictions identified most of the high-risk provinces throughout the country.
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| 3. |
- Kairu-Wanyoike, Salome, et al.
(författare)
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Positive association between Brucella spp. seroprevalences in livestock and humans from a cross-sectional study in Garissa and Tana River Counties, Kenya.
- 2019
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Ingår i: PLoS Neglected Tropical Diseases. - : Public Library of Science (PLoS). - 1935-2727 .- 1935-2735. ; 13:10
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Brucella spp. is a zoonotic bacterial agent of high public health and socio-economic importance. It infects many species of animals including wildlife, and people may get exposed through direct contact with an infected animal or consumption of raw or undercooked animal products. A linked livestock-human cross-sectional study to determine seroprevalences and risk factors of brucellosis in livestock and humans was designed. Estimates were made for intra-cluster correlation coefficients (ICCs) for these observations at the household and village levels.METHODOLOGY: The study was implemented in Garissa (specifically Ijara and Sangailu areas) and Tana River (Bura and Hola) counties. A household was the unit of analysis and the sample size was derived using the standard procedures. Serum samples were obtained from selected livestock and people from randomly selected households. Humans were sampled in both counties, while livestock could be sampled only in Tana River County. Samples obtained were screened for anti-Brucella IgG antibodies using ELISA kits. Data were analyzed using generalized linear mixed effects logistic regression models with the household (herd) and village being used as random effects.RESULTS: The overall Brucella spp. seroprevalences were 3.47% (95% confidence interval [CI]: 2.72-4.36%) and 35.81% (95% CI: 32.87-38.84) in livestock and humans, respectively. In livestock, older animals and those sampled in Hola had significantly higher seroprevalences than younger ones or those sampled in Bura. Herd and village random effects were significant and ICC estimates associated with these variables were 0.40 (95% CI: 0.22-0.60) and 0.24 (95% CI: 0.08-0.52), respectively. In humans, Brucella spp. seroprevalence was significantly higher in older people, males, and people who lived in pastoral areas than younger ones, females or those who lived in irrigated or riverine areas. People from households that had at least one seropositive animal were 3.35 (95% CI: 1.51-7.41) times more likely to be seropositive compared to those that did not. Human exposures significantly clustered at the household level; the ICC estimate obtained was 0.21 (95% CI: 0.06-0.52).CONCLUSION: The presence of a Brucella spp.-seropositive animal in a household significantly increased the odds of Brucella spp. seropositivity in humans in that household. Exposure to Brucella spp. of both livestock and humans clustered significantly at the household level. This suggests that risk-based surveillance measures, guided by locations of primary cases reported, either in humans or livestock, can be used to detect Brucella spp. infections in livestock or humans, respectively.
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