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

Assessing and Prioritizing Zoonotic Diseases in Punjab, India: A One Health Approach

Zoonotic diseases have a significant impact on both human and animal health globally. The present study was planned to prioritize the zoonoses in Punjab state of India. To develop a zoonotic disease prioritization scoring system, a comprehensive approach has been taken, including literature review, key person interviews with animal health experts (n = 12) and medical professionals (n = 7), and nine focus group discussions (FGDs) with veterinary academicians, medical professionals, and field veterinary doctors. The scoring system comprises of seven major criteria, each assigned a weightage score (ws): prevalence/incidence of the disease (ws = 0.20), severity of illnesses in humans (ws = 0.18), epidemic potential (ws = 0.16), socio-economic burden (ws = 0.16), availability of effective control and prevention measures (ws = 0.15), inter-sectoral collaborations (ws = 0.1), and bioterrorism potential (ws = 0.05). The finalized scoring system, accompanied by a list of 15 selected zoonotic diseases, was implemented among a group of 23 professionals engaged in zoonoses research (n = 7), animal health (n = 10), and medical health (n = 6) to determine their prioritization. The zoonotic diseases prioritized for the Punjab (India) included, Brucellosis (0.70) > Rabies (0.69) > Anthrax (0.64) > Leptospirosis (0.62) = Toxoplasmosis (0.62) = Highly Pathogenic Avian Influenza (HPAI) (0.62) > Bovine tuberculosis (0.61) > Q fever (0.60) > Cysticercosis (0.59) > Listeriosis (0.58) > Crimean-Congo haemorrhagic fever (CCHF) (0.57) > Japanese encephalitis (0.56) = Echinococcosis (0.56) > Dermatophytosis (0.53) > and Scrub typhus (0.48), respectively. Higher priority is suggested for endemic zoonoses (e.g., brucellosis and rabies) as compared to those with epidemic potential (e.g., CCHF, HPAI etc.) in Punjab. Results of the current study will help in the development of targeted control and prevention strategies for zoonotic diseases in Punjab and other geographical regions facing similar challenges.

Global prevalence and species diversity of tick-borne pathogens in buffaloes worldwide: a systematic review and meta-analysis

BACKGROUND

Buffaloes are important contributors to the livestock economy in many countries, particularly in Asia, and tick-borne pathogens (TBPs) commonly infect buffaloes, giving rise to serious pathologies other than their zoonotic potential.

METHODS

The present investigation focuses on the prevalence of TBPs infecting buffaloes worldwide. All published global data on TBPs in buffaloes were collected from different databases (e.g., PubMed, Scopus, ScienceDirect, and Google Scholar) and subjected to various meta-analyses using OpenMeta[Analyst] software, and all analyses were conducted based on a 95% confidence interval.

RESULTS

Over 100 articles discussing the prevalence and species diversity of TBPs in buffaloes were retrieved. Most of these reports focused on water buffaloes (Bubalus bubalis), whereas a few reports on TBPs in African buffaloes (Syncerus caffer) had been published. The pooled global prevalence of the apicomplexan parasites Babesia and Theileria, as well as the bacterial pathogens Anaplasma, Coxiella burnetii, Borrelia, Bartonella, and Ehrlichia in addition to Crimean-Congo hemorrhagic fever virus, were all evaluated based on the detection methods and 95% confidence intervals. Interestingly, no Rickettsia spp. were detected in buffaloes with scarce data. TBPs of buffaloes displayed a fairly high species diversity, which underlines the high infection risk to other animals, especially cattle. Babesia bovis, B. bigemina, B. orientalis, B. occultans and B. naoakii, Theileria annulata, T. orientalis complex (orientalis/sergenti/buffeli), T. parva, T. mutans, T. sinensis, T. velifera, T. lestoquardi-like, T. taurotragi, T. sp. (buffalo) and T. ovis, and Anaplasma marginale, A. centrale, A. platys, A. platys-like and "Candidatus Anaplasma boleense" were all were identified from naturally infected buffaloes.

CONCLUSIONS

Several important aspects were highlighted for the status of TBPs, which have serious economic implications for the buffalo as well as cattle industries, particularly in Asian and African countries, which should aid in the development and implementation of prevention and control methods for veterinary care practitioners, and animal owners.

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

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 diagnostic sensitivity (DSe) and diagnostic specificity (DSp) 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, using a Bayesian latent class analysis. 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 DSe of 0.97 (95% Probability Intervals (PIs): 0.93; 0.99) compared to the trans-PCR method applied in milk samples 0.76 (0.63; 0.87) and genital swabs 0.73 (0.58; 0.85). The DSps of all tests were high, with trans-PCR in genital swabs recording the highest DSp of 0.99 (0.98; 1), while the DSp of trans-PCR in milk samples and ELISA in serum samples were 0.97 (0.95; 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.

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

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.

Current perspectives on the occurrence of Q fever: highlighting the need for systematic surveillance for a neglected zoonotic disease in Indian subcontinent

Coxiellosis or Q fever is an important global occupational zoonotic disease caused by one of the most contagious bacterial pathogens - Coxiella burnetii, which ranks one among the 13 global priority zoonoses. The detection of C. burnetii infection is exhibiting an increasing trend in high-risk personnel around the globe. It has increasingly been detected from foods of animal origin (including bulk milk, eggs, and meat) as well as tick vectors in many parts of the world. Coxiellosis is reported to be an important public health threat causing spontaneous abortions in humans and potential reproductive failure, which would result in production losses among livestock. Further, comprehensive coverage of the reports and trends of Q fever in developing countries, where this infection is supposed to be widely prevalent appears scarce. Also, the pathogen remains grossly neglected and underreported. Moreover, policymakers and funding agencies do not view it as a priority problem, especially in the Indian subcontinent, including Sri Lanka, Bhutan, Pakistan, Nepal, Bangladesh and Maldives. Here, we review the occurrence and epidemiology of the disease in a global context with special emphasis on its status in the Indian subcontinent.