Identification of risk factors associated with Coxiella burnetii infection in cattle and buffaloes in India

Keshavamurthy, Ravikiran; Kalambhe, Deepali; Aulakh, Rabinder Singh; Dhand, Navneet K.

doi:10.1016/j.prevetmed.2020.105081

Cited by 10 publications

(8 citation statements)

References 36 publications

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“…Since C. burnetii is resistant in the environment (Marrie and Raoult, 1997;Tissot Dupont et al, 1999), common spaces to horses and small ruminants become an important source of contamination. Our findings were in agreement with those of Menadi et al (2020) and Keshavamurthy et al (2020) investigating cattle, Maurin and Raoult (1999) and Rizzo et al (2016) examining small ruminants. However, Benaissa et al (2017) and Selim and Ali (2020) reported no significant relationship between Q fever infection in dromedaries and contact with small ruminants.…”

Section: Discussionsupporting

confidence: 92%

Coxiella Burnetii in Horses of Algeria: Seroprevalence and Associated Risk Factors

Ansel¹,

Benfodil²,

Cherif³

et al. 2020

WVJ

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The Q fever is a worldwide zoonotic disease caused by Coxiella burnetii (an obligate intracellular bacterium). This pathogen affects humans, ruminants, equines, carnivores, rodents, and birds. A cross-sectional study was carried out from March 2017 to May 2018 to assess the seroprevalence and identify the risk factors of C. burnetii infection in horses (Equus Caballus) residing in three districts of Algerian, namely Tiaret, El-Bayadh, and Ghardaia. Serum samples collected from 182 horses were analyzed via enzyme-linked immunosorbent assay (ELISA). Association of seropositivity with potential risk factors related to animals (e.g., age, gender, breed, housing, and presence of ticks), breeding characteristics (e.g., geographical localization, contact with animals), and environmental characteristics (i.e., presence of water source) was analyzed by univariate and multivariate logistic regression. An overall seroprevalence of 9.9% (18/182) was obtained. The univariate analysis of risk factors for C. burnetii seroprevalence demonstrated higher seropositivity in horses that had contact with small ruminants (p=0.004) and dromedaries (p=0.002) as well as in those living near a water source (p=0.036) and in El-Bayadh district (p=0.005). The multivariate logistic regression analysis indicated that the risk of C. burnetii infection was significantly higher in horses that were in contact with small ruminants (RR: 15.6). Algeria is endemic for Q fever in horses and prophylactic measures must be taken to reduce /prevent its transmission to animals and humans.

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Section: Discussionsupporting

confidence: 92%

Coxiella Burnetii in Horses of Algeria: Seroprevalence and Associated Risk Factors

Ansel¹,

Benfodil²,

Cherif³

et al. 2020

WVJ

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“…However, only 25 buffaloes as compared to 719 cattle were included in this study (Shome et al, 2019). On the other hand, similar investigations in Punjab reported that cattle (adjusted Odds Ratio 3.37, 95% Confidence interval 1.23-9.20, p=0.02) were associated with larger odds of C. burnetii positive animal status than buffaloes (Keshavamurthy et al, 2020).…”

Section: Discussionmentioning

confidence: 83%

Evaluation of the sensitivity and specificity of three diagnostic tests for Coxiella burnetii infection in cattle and buffaloes in Punjab (India) using Bayesian latent class analysis

Meletis

Keshavamurthy

Aulakh

et al. 2021

Preprint

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Q Fever is a zoonotic disease of significant animal and public health concern, caused by Coxiella burnetii (C. burnetii) , an obligate intracellular bacterium. This study was done to evaluate the sensitivity ( Se ) and specificity ( Sp ) of three diagnostic methods to diagnose C. burnetii infection in cattle and buffaloes in Punjab, India: an indirect ELISA method applied in serum samples and a trans-Polymerase Chain Reaction (trans-PCR) technique applied in milk samples and genital swabs. Bayesian Latent Class Models were developed following the STARD-BLCM reporting guidelines. Conditional independence was assumed between the tests, given (i) the different biological principle of ELISA and trans-PCR and (ii) the fact that the trans-PCR was performed on different tissues. The ELISA method in the serum samples showed the highest Se of 0.97 (95% Probability Intervals (PIs): 0.93; 0.99) compared to the trans-PCR method applied in milk samples 0.76 (0.62; 0.87) and genital swabs 0.7 (0.55; 0.82). The Sps of all tests were high, with trans-PCR in genital swabs recording the highest Sp of 0.99 (0.98; 1), while the Sp of trans-PCR in milk samples and ELISA in serum samples were 0.98 (0.96; 0.99) and 0.95 (0.93; 0.97) respectively. The study results show that none of the applied tests are perfect, therefore, a testing regimen based on the diagnostic characteristic of the tests may be considered for diagnosis of C. burnetii .

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“…Q-fever has a complex disease cycle compared to other two diseases in our study and a range of risk factors associated with it. Additionally, the disease has been often neglected by health personnel, resulting in a poor understanding of its epidemiology [28,29]. Due to these reasons, Q-fever forecasting is a challenge, even in countries with good disease surveillance infrastructure.…”

Section: Discussionmentioning

confidence: 99%

A Comparison of Infectious Disease Forecasting Methods across Locations, Diseases, and Time

et al. 2022

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Accurate infectious disease forecasting can inform efforts to prevent outbreaks and mitigate adverse impacts. This study compares the performance of statistical, machine learning (ML), and deep learning (DL) approaches in forecasting infectious disease incidences across different countries and time intervals. We forecasted three diverse diseases: campylobacteriosis, typhoid, and Q-fever, using a wide variety of features (n = 46) from public datasets, e.g., landscape, climate, and socioeconomic factors. We compared autoregressive statistical models to two tree-based ML models (extreme gradient boosted trees [XGB] and random forest [RF]) and two DL models (multi-layer perceptron and encoder–decoder model). The disease models were trained on data from seven different countries at the region-level between 2009–2017. Forecasting performance of all models was assessed using mean absolute error, root mean square error, and Poisson deviance across Australia, Israel, and the United States for the months of January through August of 2018. The overall model results were compared across diseases as well as various data splits, including country, regions with highest and lowest cases, and the forecasted months out (i.e., nowcasting, short-term, and long-term forecasting). Overall, the XGB models performed the best for all diseases and, in general, tree-based ML models performed the best when looking at data splits. There were a few instances where the statistical or DL models had minutely smaller error metrics for specific subsets of typhoid, which is a disease with very low case counts. Feature importance per disease was measured by using four tree-based ML models (i.e., XGB and RF with and without region name as a feature). The most important feature groups included previous case counts, region name, population counts and density, mortality causes of neonatal to under 5 years of age, sanitation factors, and elevation. This study demonstrates the power of ML approaches to incorporate a wide range of factors to forecast various diseases, regardless of location, more accurately than traditional statistical approaches.

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Identification of risk factors associated with Coxiella burnetii infection in cattle and buffaloes in India

Cited by 10 publications

References 36 publications

Coxiella Burnetii in Horses of Algeria: Seroprevalence and Associated Risk Factors

Coxiella Burnetii in Horses of Algeria: Seroprevalence and Associated Risk Factors

Evaluation of the sensitivity and specificity of three diagnostic tests for Coxiella burnetii infection in cattle and buffaloes in Punjab (India) using Bayesian latent class analysis

A Comparison of Infectious Disease Forecasting Methods across Locations, Diseases, and Time

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