Global dynamics of highly pathogenic avian influenza outbreaks in poultry between 2005 and 2016-Focus on distance and rate of spread

Risk factors for avian influenza in Danish poultry and wild birds during the epidemic from June 2020 to May 2021

Exploring the risk factors of avian influenza (AI) occurrence helps us to monitor and control the disease. Since late 2020, the number of avian influenza outbreaks in domestic and wild birds has increased in most European countries, including Denmark. This study was conducted to identify potential risk factors for wild birds and poultry during the epidemic in 2020/2021 in Denmark. Using Danish AI surveillance data of actively surveyed poultry and passively surveyed wild birds from June 2020 to May 2021, we calculated geographical attributes for bird locations and assessed the potential risk factors of AI detections using logistic regression analyses. 4% of actively surveyed poultry and 39% of passively surveyed wild birds were detected with AI circulating or ongoing at the time. Of these, 10 and 99% tested positive for the H5/H7 AI subtypes, respectively. Our analyses did not find any statistically significant risk factors for actively surveyed poultry within the dataset. For passively surveyed wild birds, bird species belonging to the Anseriformes order had a higher risk of being AI virus positive than five other taxonomic bird orders, and Galliformes were of higher risk than two other taxonomic bird orders. Besides, every 1 km increase in the distance to wetlands was associated with a 5.18% decrease in the risk of being AI positive (OR (odds ratio) 0.95, 95% CI 0.91, 0.99), when all other variables were kept constant. Overall, bird orders and distance to wetlands were associated with the occurrence of AI. The findings may provide targets for surveillance strategies using limited resources and assist in risk-based surveillance during epidemics.

Environmental factors and spatiotemporal distribution characteristics of the global outbreaks of the highly pathogenic avian influenza H5N1

The spread of highly pathogenic avian influenza H5N1 has posed a major threat to global public health. Understanding the spatiotemporal outbreak characteristics and environmental factors of H5N1 outbreaks is of great significance for the establishment of effective prevention and control systems. The time and location of H5N1 outbreaks in poultry and wild birds officially confirmed by the World Organization for Animal Health from 2005 to 2019 were collected. Spatial autocorrelation analysis and multidistance spatial agglomeration analysis methods were used to analyze the global outbreak sites of H5N1. Combined with remote sensing data, the correlation between H5N1 outbreaks and environmental factors was analyzed using binary logistic regression methods. We analyzed the correlation between the H5N1 outbreak and environmental factors and finally made a risk prediction for the global H5N1 outbreaks. The results show that the peak of the H5N1 outbreaks occurs in winter and spring. H5N1 outbreaks exhibit aggregation, and a weak aggregation phenomenon is noted on the scale close to 5000 km. Water distance, road distance, railway distance, wind speed, leaf area index (LAI), and specific humidity were protective factors for the outbreak of H5N1, and the odds ratio (OR) were 0.985, 0.989, 0.995, 0.717, 0.832, and 0.935, respectively. Temperature was a risk factor with an OR of 1.073. The significance of these ORs was greater than 95%. The global risk prediction map was obtained. Given that the novel coronavirus (COVID-19) is spreading globally, the methods and results of this study can provide a reference for studying the spread of COVID-19.

Global formal live poultry and hatching egg trade network (2004-2016): description and association with poultry disease reporting and presence

As international trade constitutes one of the main spread pathways of diseases, a better understanding of the trade behaviors of countries will help identify strengths and areas for improvement in the approach of national authorities to controlling poultry diseases globally. Using data reported to the United Nations Comtrade and the World Organisation for Animal Health (OIE) between 2004 and 2016 by 193 countries, we used a network analysis on trade data of poultry hatching eggs, live poultry of less than 185 g and live poultry of 185 g or more to determine that: 1) quantities traded between countries are substantial, and tend to increase (average increase of 800,000 poultry heads and 21,000 tons of hatching eggs each year equivalent to an increase by 2-fold in 17 yr); 2) the stability of the networks was low (a quarter to half of trade relationships maintained between 2 consecutive years) and the subnetworks favorable to the spread of diseases were in general consistent with regional clustering, trade exchanges being equally at intracontinental and intercontinental levels; 3) countries with highest number of partners were located in the same world regions for the 3 poultry networks - Americas and Europe for export (up to 107 partners) and Africa, Asia and Europe for import (up to 36 partners); 4) for live poultry, biggest exporting countries shared more poultry disease surveillance data, and reported more disease presence than others, which did not stop them from trading. Biggest importers reported less poultry disease surveillance data and reported more disease presence than others; and 5) the main structural and trend characteristics of the international trade networks were in general similar for the 3 networks. The information derived from this work underlines the importance of applying the preventive measures advocated by the OIE and will support countries to reduce the risk of introduction of pathogens causing poultry diseases.

Scientific Opinion on the assessment of the control measures of the category A diseases of Animal Health Law: Highly Pathogenic Avian Influenza

EFSA received a mandate from the European Commission to assess the effectiveness of some of the control measures against diseases included in the Category A list according to Regulation (EU) 2016/429 on transmissible animal diseases ('Animal Health Law'). This opinion belongs to a series of opinions where these control measures will be assessed, with this opinion covering the assessment of control measures for Highly Pathogenic Avian Influenza (HPAI). In this opinion, EFSA and the AHAW Panel of experts review the effectiveness of: (i) clinical and laboratory sampling procedures, (ii) monitoring period and (iii) the minimum radius of the protection and surveillance zone, and the minimum length of time the measures should be applied in these zones. The general methodology used for this series of opinions has been published elsewhere; nonetheless, specific details of the model used for the assessment of the laboratory sampling procedures for HPAI are presented here. Here, also, the transmission kernels used for the assessment of the minimum radius of the protection and surveillance zones are shown. Several scenarios for which these control measures had to be assessed were designed and agreed prior to the start of the assessment. In summary, sampling procedures as described in the diagnostic manual for HPAI were considered efficient for gallinaceous poultry, whereas additional sampling is advised for Anseriformes. The monitoring period was assessed as effective, and it was demonstrated that the surveillance zone comprises 95% of the infections from an affected establishment. Recommendations provided for each of the scenarios assessed aim to support the European Commission in the drafting of further pieces of legislation, as well as for plausible ad hoc requests in relation to HPAI.

Autoregressive Models Applied to Time-Series Data in Veterinary Science

A time-series is any set of N time-ordered observations of a process. In veterinary epidemiology, our focus is generally on disease occurrence (the “process”) over time, but animal production, welfare or other traits might also be of interest. A common source of time-series datasets are animal disease monitoring and surveillance systems. Here, we scan the application of methods to analyse time-series data in the peer-reviewed, published literature. Based on this literature scan we focus on autocorrelation and illustrate the recommended steps using ARIMA (Autoregressive Integrated Moving Average Models) methods via analysis of a time-series of canine parvovirus (CPV) events in a pet dog population in Australia, 2009 to 2015. We conclude by identifying the barriers to the application of ARIMA methods in veterinary epidemiology and suggest some possible solutions. In the literature scan the selected 37 studies focused mostly on infectious and parasitic diseases, predominantly for analytical, rather than descriptive or predictive, purposes. Trends and seasonality were investigated, and autocorrelation analyzed, in most studies, most commonly using R software. An approach to analyzing autocorrelation using ARIMA methods was then illustrated using a time-series (week and month units) of CPV events in a pet dog population in Australia, reported to a national companion animal disease surveillance system. This time-series was derived by summing veterinarian reports of confirmed CPV diagnoses. We present data analysis output generated via the R statistical environment, and make this code available for the reader to apply to this or other time-series datasets. We also illustrate prediction of CPV events by rainfall as a covariate. Time-series analysis using ARIMA methods to understand and explore autocorrelation appears to be relatively uncommon in veterinary epidemiology. Some of the reasons might include limited availability of data of sufficient time unit length, lack of familiarity with analytical methods and available software, and how to best use the information generated. We recommend that wherever feasible, such time-series data be made available both for analysis and for methods development.

Type 1 diabetes incidence in children and adolescents in Mexico: Data from a nation-wide institutional register during 2000-2018

AIMS

To describe the annual incidence of type 1 diabetes in children and adolescents insured by the Mexican Institute of Social Security, the main health provider in Mexico, during 2000-2018.

METHODS

We conducted a secondary data analyses using the incidence registers from the Epidemiological Surveillance Coordination of the Mexican Institute of Social Security collected during 2000-2018. Incident type 1 diabetes cases (age 19 years old and below) were identified using ICD-10-CM E10 diagnostic codes. Age, sex, and geographical region and seasonal-specific incidence were calculated with their corresponding annual percentage change (APC) as well.

RESULTS

In the period 2000-2018, the number of incident cases with type 1 diabetes decreased from 3.4 to 2.8 per 100,000 in insured for subjects below 20 years old. We observed an increase in the 2000-2006, followed by a decrease for the 2006-2018 period (APC +16.1 and -8.7 respectively). Females and children <5 years old had a significant decrease in the incidence rate, while inhabitants in Central Mexico showed a significant increase. No difference was found in incidence between seasons.

CONCLUSIONS

Our study describes significant fluctuations of the incidence of type 1 diabetes during the period 2000-2018, which appeared to correspond to influenza outbreaks, among Mexican children and adolescents.