Seroprevalence of infectious laryngotracheitis disease in backyard chickens in villages of Ada'a district, Oromia, Ethiopia: first report

Investigating the uses of machine learning algorithms to inform risk factor analyses: The example of avian infectious bronchitis virus (IBV) in broiler chickens

Infectious bronchitis virus (IBV) is a contagious coronavirus causing respiratory and urogenital disease in chickens and is responsible for significant economic losses for both the broiler and table egg layer industries. Despite IBV being regularly monitored using standard epidemiologic surveillance practices, knowledge and evidence of risk factors associated with IBV transmission remain limited. The study objective was to compare risk factor modeling outcomes between a traditional stepwise variable selection approach and a machine learning-based random forest Boruta algorithm using routinely collected IBV antibody titer data from broiler flocks. IBV antibody sampling events (n = 1111) from 166 broiler sites between 2016 and 2021 were accessed. Ninety-two geospatial-related and poultry-density variables were obtained using a geographic information system and data sets from publicly available sources. Seventeen and 27 candidate variables were screened to potentially have an association with elevated IBV antibody titers according to the manual selection and machine learning algorithm, respectively. Selected variables from both methods were further investigated by construction of multivariable generalized mixed logistic regression models. Six variables were shortlisted by both screening methods, which included year, distance to urban areas, main roads, landcover, density of layer sites and year, however, final models for both approaches only shared year as an important predictor. Despite limited significance of clinical outcomes, this work showcases the potential of a novel explorative modeling approach in combination with often unutilized resources such as publicly available geospatial data, surveillance health data and machine learning as potential supplementary tools to investigate risk factors related to infectious diseases.

Detection of Chicken Respiratory Pathogens in Live Markets of Addis Ababa, Ethiopia, and Epidemiological Implications

A moderate to high seroprevalence of exposure to Newcastle disease (NDV), avian metapneumovirus (aMPV), infectious laryngotracheitis virus (ILTV), infectious bronchitis virus (IBV) and Mycoplasma gallisepticum (MG) has recently been reported in Ethiopia, but it is unclear to what extent these contribute to clinical cases of respiratory disease. This study investigated the presence of these pathogens in chickens exhibiting respiratory disease in two live markets in Addis Ababa. Markets were visited weekly for three months, and 18 chickens displaying respiratory clinical signs were acquired. Swab samples were taken from the choana, trachea, air sac and larynx for bacteriology and PCR tests targeting these five pathogens. PCR-positive samples were sequenced. All 18 chickens were PCR-positive for aMPV, 50% for each of Mg and NDV, 39% for IBV and 11% for ILTV. Infections with >3 pathogens were detected in 17 of 18 chickens. Potentially pathogenic bacteria such as Escherichia coli, Klebsiella spp., Streptococcus spp. and Staphylococcus were found in 16 to 44% of chickens. IBV-positive samples were of the 793B genotype. The results associate the presence of these organisms with clinical respiratory disease and are consistent with recent serological investigations, indicating a high level of exposure to multiple respiratory pathogens.

Serological Evidence of Infectious Laryngotracheitis Infection and Associated Risk Factors in Chickens in Northwestern Ethiopia

Infectious laryngotracheitis (ILT) is a disease of high economic consequence to the poultry sector. Gallid herpesvirus 1 (GaHV-1), a.k.a infectious laryngotracheitis virus (ILTV), under the genus Iltovirus, and the family Herpesviridae, is the agent responsible for the disease. Despite the clinical signs on the field suggestive of ILT, it has long been considered nonexistent and a disease of no concern in Ethiopia. A cross-sectional study was conducted from November 2020 to June 2021 in three selected zones of the Amhara region (Central Gondar, South Gondar, and West Gojjam zones), Ethiopia, with the objective of estimating the seroprevalence of ILTV in chickens and identifying and quantifying associated risk factors. A total of 768 serum samples were collected using multistage cluster sampling and assayed for anti-ILTV antibodies using indirect ELISA. A questionnaire survey was used to identify the potential risk factors. Of the 768 samples, 454 (59.1%, 95% CI: 0.56–0.63) tested positive for anti-ILTV antibodies. Mixed-effect logistic regression analysis of potential risk factors showed that local breeds of chicken were less likely to be seropositive than exotic breeds (OR: 0.38, 95% CI: 0.24–0.61). In addition, factors such as using local feed source (OR: 6.53, 95% CI: 1.77–24.04), rearing chickens extensively (OR: 1.97, 95% CI: 0.78–5.02), mixing of different batches of chicken (OR: 14.51, 95% CI: 3.35–62.77), careless disposal of litter (OR: 1.62, 95% CI: 0.49–4.37), lack of house disinfection (OR: 11.05, 95% CI: 4.09–47.95), lack of farm protective footwear and clothing (OR: 20.85, 95% CI: 5.40–80.45), and careless disposal of dead chicken bodies had all been associated with increased seropositivity to ILTV. Therefore, implementation of biosecurity measures is highly recommended to control and prevent the spread of ILTV. Furthermore, molecular confirmation and characterization of the virus from ILT suggestive cases should be considered to justify the use of ILT vaccines.