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This paper develops and analyses a population density-dependent mathematical model to study the transmission dynamics of COVID-19 in crowded settlements such as refugee camps, schools, markets and churches. The model quantifies the potential impact of physical/social distancing and population density on the disease burden. It further determines the number of secondary infections that may arise from having one infectious individual in a high population-activity area. Results reveal that with no fatalities, the reproduction numbers associated with asymptomatic and symptomatic cases are inversely proportional to; the habitat area size, and the efforts employed in tracing and hospitalising these cases. The critical habitat area below which the disease dies out is directly proportion to the time taken to identify and hospitalise infected individuals. Results also show that disease persistence in the community is guaranteed even with minimal admission of infected individuals. In a high-activity area with 70,000 individuals, results indicate that if 10% of these individuals do not adhere to standard operating procedures (SOPs) when allocated an area of 1m2 per individual, then every infectious seeder will cause approximately one new infection within 12 hours of activity. On the other hand, if the area occupied by the 70,000 individuals is increased to 1km2 then even at the lowest explored level of adherence to SOPs, the arising number of new cases within 12 hours would be less than one per infectious seeder. We conclude that proper use of masks and physical distancing measures should be highly enforced in crowded settings.

Limiting spread of low pathogenicity avian influenza (LPAI) during an outbreak is critical to reduce the negative impact on poultry producers and local economies. Mathematical models of disease transmission can support outbreak control efforts by estimating relevant epidemiological parameters. In this article, diagnostic testing data from each house on a premises infected during a LPAI H5N2 outbreak in the state of Minnesota in the United States in 2018 was used to estimate the time of virus introduction and adequate contact rate, which determines the rate of disease spread. A well-defined most likely time of virus introduction, and upper and lower 95% credibility intervals were estimated for each house. The length of the 95% credibility intervals ranged from 11 to 22 with a mean of 17 days. In some houses the contact rate estimates were also well-defined; however, the estimated upper 95% credibility interval bound for the contact rate was occasionally dependent on the upper bound of the prior distribution. The estimated modes ranged from 0.5 to 6.0 with a mean of 2.8 contacts per day. These estimates can be improved with early detection, increased testing of monitored premises, and combining the results of multiple barns that possess similar production systems.

Background: The SARS-CoV-2 virus that causes COVID-19 has severely impacted the health, economic and social status of Ugandans and people globally. Its mutations result into numerous deadly variants which are constantly emerging in short time intervals. The rise of these variants has led to increased virus transmissibility, disease severity and reduction in vaccine effectiveness. To-date, it is unclear which direction the pandemic is likely to take in the next few years.Methods: This study developed a deterministic two strain Susceptible-Exposed-Infectious-Recovered-Vaccinated (SEIR-V) mathematical model to describe the transmission dynamics of these SARS-CoV-2 variants. The model considers 10 compartments to deal with both strain one and strain two (emerging strain). Basic reproductive number R0 is computed and both local and global stability analysis of the disease free equilibrium investigated qualitatively. The model's bifurcation analysis revealed a forward type.Results: Simulation results indicate a 17.17 % increase in peak infections for strain two dominance as compared to strain one dominance. Further, a 10-fold increase in vaccination rate would lead to 16.98 % reduction in strain two peak infections. Furthermore, periodically administering booster doses within 18-24 months is recommended. Compulsory face mask usage and early hospitalization/isolation of symptomatic individuals would lead to 14.2 % and 16.98 % decrease in symptomatic infections respectively.Conclusions: Our results suggest that continuous vaccination (at a higher rate) of populations is still vital in the fight against COVID-19. Besides, results on face mask usage and hospitalization of symptomatic individuals provides valuable information that can be used to support emergency preparedness for future SARS-CoV-2 outbreaks.

Thanks to the ongoing expansion of internet access and, most recently, the movement restrictions that were put in place globally to stop COVID-19 spread, many small and medium enterprises (SMEs) are prepared to use social media platforms to market their products as a way to improve their business performance in emerging economies. Businesses at all levels that use social media marketing (SMM) see a considerable increase in their output. This study’s objective is to identify the factors that significantly help Ugandan SMEs implement SMM techniques to enhance their commercial performance. Here, statistical models are utilized to analyze how the age and gender of SMEs owners as moderating variables affect the adoption and performance of SMEs in Uganda. A theoretical model that is based on the Technology Acceptance Model (TAM) and Innovation Diffusion Theory (IDT) theories has been put out as part of a specific conceptual framework. The following indicators are used to evaluate the performance of SMEs: sales, customer engagement, awareness of customers’ needs, low operation costs, and brand modification by staff. Empirical model validation has been performed using 152 business units (observation units) corresponding to the number of respondents (units of analysis), and the ensuing analyses have been done using structural equation modelling (SEM). The results indicate that compatibility and perceived ease of use have a positive impact on SMEs to adopt SMM, while perceived usefulness has a negative impact on SMEs to adopt SMM. Age and gender as moderating variables all have a positive moderating effect. With limited studies available on the subject, this research contributes to already existing literature by combining two components of the TAM model and one component of the IDT to explain the impact of SMM on SMEs when moderated by both age and gender in a developing economy.

The 2015 highly pathogenic avian influenza (HPAI) H5N2 outbreak affected more than 200 Midwestern U.S. poultry premises. Although each affected poultry operation incurred substantial losses, some operations of the same production type and of similar scale had differences between one another in their ability to recognize evidence of the disease before formal diagnoses and in their ability to make proactive, farm-level disease containment decisions. In this case comparison study, we examine the effect of HPAI infection on two large egg production facilities and the epidemiologic and financial implications resulting from differences in detection and decision-making processes. Each egg laying facility had more than 1 million caged birds distributed among 18 barns on one premises (Farm A) and 17 barns on the other premises (Farm B). We examine how farm workers' awareness of disease signs, as well as how management's immediate or delayed decisions to engage in depopulation procedures, affected flock mortality, levels of environmental contamination, time intervals for re population, and farm profits on each farm. By predictive mathematical modeling, we estimated the time of virus introduction to examine how quickly infection was identified on the farms and then estimated associated contact rates within barns. We found that the farm that implemented depopulation immediately after detection of abnormal mortality (Farm A) was able to begin repopulation of barns 37 days sooner than the farm that began depopulation well after the detection of abnormally elevated mortality (Farm B). From average industry economic data, we determined that the loss associated with delayed detection using lost profit per day in relation to down time was an additional $3.3 million for Farm B when compared with Farm A.

The 2015 highly pathogenic avian influenza (HPAI) H5N2 outbreak affected more than 200 Midwestern U.S. poultry premises. Although each affected poultry operation incurred substantial losses, some operations of the same production type and of similar scale had differences between one another in their ability to recognize evidence of the disease before formal diagnoses and in their ability to make proactive, farm-level disease containment decisions. In this case comparison study, we examine the effect of HPAI infection on two large egg production facilities and the epidemiologic and financial implications resulting from differences in detection and decision-making processes. Each egg laying facility had more than 1 million caged birds distributed among 18 barns on one premises (Farm A) and 17 barns on the other premises (Farm B). We examine how farm workers' awareness of disease signs, as well as how management's immediate or delayed decisions to engage in depopulation procedures, affected flock mortality, levels of environmental contamination, time intervals for re population, and farm profits on each farm. By predictive mathematical modeling, we estimated the time of virus introduction to examine how quickly infection was identified on the farms and then estimated associated contact rates within barns. We found that the farm that implemented depopulation immediately after detection of abnormal mortality (Farm A) was able to begin repopulation of barns 37 days sooner than the farm that began depopulation well after the detection of abnormally elevated mortality (Farm B). From average industry economic data, we determined that the loss associated with delayed detection using lost profit per day in relation to down time was an additional $3.3 million for Farm B when compared with Farm A.Estudio retrospectivo de detección viral temprana y tardía y despoblación en granjas de gallinas de postura infectadas con el virus de la influenza aviar altamente patógeno durante el brote de H5N2 del año 2015 en los Estados Unidos. El brote de influenza aviar altamente patógena (HPAI) H5N2 del año 2015 afectó a más de 200 granjas avícolas del medio oeste de los Estados Unidos. Aunque cada operación avícola afectada incurrió en pérdidas sustanciales, algunas operaciones del mismo tipo de producción y de escala similar tuvieron diferencias entre sí en su capacidad para reconocer evidencias de la enfermedad antes de los diagnósticos formales y en su capacidad para realizar decisiones proactivas para la contención de la enfermedad a nivel de granja. En este estudio de caso, se examinó el efecto de la infección por influenza aviar altamente patógena en dos instalaciones grandes de producción de huevo y las implicaciones epidemiológicas y financieras que fueron resultado de los diferentes procesos de detección y toma de decisiones. Cada instalación de postura de huevo tenía más de un millón de aves enjauladas distribuidas en 18 casetas en una granja (Granja A) y 17 casetas en las otras instalaciones (Granja B). Se examinó cómo el conocimiento de los trabajadores agrícolas sobre los signos de la enfermedad, así como cómo las decisiones de manejo inmediatas o tardías para establecer procedimientos de despoblación, afectaron la mortalidad de las parvadas, los niveles de contaminación ambiental, los intervalos de tiempo para la repoblación y las ganancias en cada granja. Mediante un modelo matemático predictivo, se estimó el tiempo de introducción del virus para examinar la rapidez con la que se identificó la infección en las granjas y luego se estimaron las tasas de contacto asociadas dentro de las casetas. Se encontró que la granja que implementó la despoblación inmediatamente después de la detección de mortalidad anormal (Granja A) pudo comenzar la repoblación de las casetas 37 días antes que la granja que comenzó la despoblación mucho después de la detección de mortalidad anormalmente elevada (Granja B). A partir de los datos económicos promedio de la industria, se determinó que la pérdida asociada con la detección tardía utilizando las pérdidas de ganancias por día en relación con el tiempo de inactividad fue de $3.3 millones adicionales para la Granja B en comparación con la Granja A.

Embryonating chicken eggs (ECEs) are among the most sensitive laboratory host systems for avian influenza virus (AIV) titration, but ECEs are expensive and require space for storage and incubation. Therefore, reducing ECE use would conserve resources. We utilized statistical modeling to evaluate the accuracy and precision of AIV titration with 3 instead of 5 ECEs for each dilution by the Reed–Muench method for 50% endpoint calculation. Beta-Poisson and exponential dose-response models were used in a simulation study to evaluate observations from actual titration data from 18 AIV isolates. The reproducibility among replicates of a titration was evaluated with one AIV isolate titrated in 3 replicates with the beta-Poisson, exponential, and Weibull dose-response models. The standard deviation (SD) of the error between input and estimated virus titers was estimated with Monte Carlo simulations using the fitted dose-response models. Good fit was observed with all models that were utilized. Reducing the number of ECEs per dilution from 5 to 3 resulted in the width of the 95% confidence interval increasing from ±0.64 to ±0.75 log10 50% ECE infectious doses (EID50) and the SD of the error increased by 0.03 log10 EID50. Our study suggests that using fewer ECEs per dilution is a viable approach that will allow laboratories to reduce costs and improve efficiency.

Background Avian influenza (AI) is an infectious viral disease that affects several species and has zoonotic potential. Due to its associated health and economic repercussions, minimizing AI outbreaks is important. However, most control measures are generic and mostly target pathways important for the conventional poultry farms producing chickens, turkeys, and eggs and may not target other pathways that may be specific to the upland game bird sector. The goal of this study is to provide evidence to support the development of novel strategies for sector-specific AI control by comparing and contrasting practices and potential pathways for spread in upland game bird farms with those for conventional poultry farms in the United States. Farm practices and processes, seasonality of activities, geographic location and inter-farm distance were analyzed across the sectors. All the identified differences were framed and discussed in the context of their associated pathways for virus introduction into the farm and subsequent between-farm spread. Results Differences stemming from production systems and seasonality, inter-farm distance and farm densities were evident and these could influence both fomite-mediated and local-area spread risks. Upland game bird farms operate under a single, independent owner rather than being contracted with or owned by a company with other farms as is the case with conventional poultry. The seasonal marketing of upland game birds, largely driven by hunting seasons, implies that movements are seasonal and customer-vendor dynamics vary between industry groups. Farm location analysis revealed that, on average, an upland game bird premises was 15.42 km away from the nearest neighboring premises with birds compared to 3.74 km for turkey premises. Compared to turkey premises, the average poultry farm density in a radius of 10 km of an upland game bird premises was less than a half, and turkey premises were 3.8 times (43.5% compared with 11.5%) more likely to fall within a control area during the 2015 Minnesota outbreak. Conclusions We conclude that the existing differences in the seasonality of production, isolated geographic location and epidemiological seclusion of farms influence AI spread dynamics and therefore disease control measures should be informed by these and other factors to achieve success. Electronic supplementary material The online version of this article (10.1186/s12917-019-1876-y) contains supplementary material, which is available to authorized users.

Artificial insemination is a routine practice for turkeys that can introduce pathogens into breeder flocks in a variety of ways. In this manuscript, a risk analysis on the potential transmission of highly pathogenic avian influenza (HPAI) to naive hens through artificial insemination is presented. A case of HPAI on a stud farm where the potential transmission of the virus to susceptible hens in the 2015 H5N2 HPAI outbreak in Minnesota is described along with documentation of known and potential transmission pathways from the case. The pathways by which artificial insemination might result in the spread of HPAI to susceptible hens were determined by considering which could result in the 1) entry of HPAI virus onto a premises through semen movement; and 2) exposure of susceptible hens to HPAI as a result of this movement. In the reported case, HPAI virus was detected in semen from infected toms, however, transmission of HPAI to naive hens through semen is unclear since the in utero infectious dose is not known. This means that the early detection of infection might limit but not eliminate the risk of hen exposure. Because of the numerous potential pathways of spread and the close contact with the birds, it is highly likely that if semen from an HPAI-infected tom flock is used, there will be spread of the virus to naive hens through insemination. If insemination occurs with semen from stud farms in an HPAI control area, receiving hen farms should have restricted movements to prevent outbreak spread in the event that they become infected.

Better control of highly pathogenic avian influenza (HPAI) outbreaks requires deeper understanding of within-flock virus transmission dynamics. For such fatal diseases, daily mortality provides a proxy for disease incidence. We used the daily mortality data collected during the 2015 H5N2 HPAI outbreak in Minnesota turkey flocks to estimate the within-flock transmission rate parameter (β). The number of birds in Susceptible, Exposed, Infectious and Recovered compartments was inferred from the data and used in a generalised linear mixed model (GLMM) to estimate the parameters. Novel here was the correction of these data for normal mortality before use in the fitting process. We also used mortality threshold to determine HPAI-like mortality to improve the accuracy of estimates from the back-calculation approach. The estimated β was 3.2 (95% confidence interval (CI) 2.3–4.3) per day with a basic reproduction number of 12.8 (95% CI 9.2–17.2). Although flock-level estimates varied, the overall estimate was comparable to those from other studies. Sensitivity analyses demonstrated that the estimated β was highly sensitive to the bird-level latent period, emphasizing the need for its precise estimation. In all, for fatal poultry diseases, the back-calculation approach provides a computationally efficient means to obtain reasonable transmission parameter estimates from mortality data.

Producing a smaller yield of higher-value birds compared to conventional poultry production, the U.S. commercial upland game bird industry deals primarily in the sale of live birds for recreational hunting. In this study, our aims were to gain insights into the occurrence of avian influenza (AI) in the U.S. commercial upland game bird industry in comparison to other poultry sectors, to identify the presence of the specific AI risk factors in the practices of raising ducks on site and having connections to live bird markets (LBMs), and to assess how AI surveillance systems may have played a role in the reporting of the presence of exposure pathway-related information. We found that 23 AI epizootics involving upland game bird premises were reported, compared to 485 epizootics in the other poultry industries, and 86% of epizootics involving upland game birds were limited to only one premises. Regarding specific AI risk factors, 70% of upland game bird epizootics involved one of the two examined practices. In assessing the impact of surveillance systems, data framed around the implementation of surveillance systems revealed that the introduction of active surveillance coincided with the more thorough reporting of both the raising of ducks on site and premises having connections to LBMs. Our results suggest the need for more thorough data collection during epizootics and the need to assess additional exposure pathways specific to the commercial raise-for-release upland game bird industry.

IntroductionForeign animal disease (FAD) outbreaks can have devastating impacts, but they occur infrequently in any specific sector anywhere in the United States (US). Training to proactively discuss implementation of control and prevention strategies are beneficial in that they provide stakeholders with the practical information and educational experience they will need to respond effectively to an FAD. Such proactive approaches are the mission of the Secure Food System (SFS; University of Minnesota; St. Paul, Minnesota USA).MethodsThe SFS exercises were designed as educational activities based on avian influenza (AI) outbreaks in commercial poultry scenarios. These scenarios were created by subject matter experts and were based on epidemiology reports, risk pathway analyses, local industry practices, and site-specific circumstances. Target audiences of an exercise were the groups involved in FAD control: animal agriculture industry members; animal health regulators; and diagnosticians. Groups of industry participants seated together at tables represented fictional poultry premises and were guided by a moderator to respond to an on-farm situation within a simulated outbreak. The impact of SFS exercises was evaluated through interviews with randomized industry participants and selected table moderators. Descriptive statistics and qualitative analyses were performed on interview feedback.ResultsEleven SFS exercises occurred from December 2016 through October 2017 in multiple regions of the US. Exercises were conducted as company-wide, state-wide, or regional trainings. Nine were based on highly pathogenic avian influenza (HPAI) outbreaks and two focused on outbreaks of co-circulating HPAI and low pathogenicity avian influenza (LPAI). Poultry industry participants interviewed generally found attending an SFS exercise to be useful. The most commonly identified benefits of participation were its value to people without prior outbreak experience and knowledge gained about Continuity of Business (COB)-permitted movement. After completing an exercise, most participants evaluated their preparedness to respond to an outbreak as somewhat to very ready, and more than one-half reported their respective company or farms had discussions or changed actions due to participation.Conclusion:Evaluation feedback suggests the SFS exercises were an effective training method to supplement preparedness efforts for an AI outbreak. The concept of using multi-faceted scenarios and multiple education strategies during a tabletop exercise may be translatable to other emergency preparedness needs.LinskensEJ, NeuAE, WalzEJ, St. CharlesKM, CulhaneMR, SsematimbaA, GoldsmithTJ, HalvorsonDA, CardonaCJ. Preparing for a foreign animal disease outbreak using a novel tabletop exercise. Prehosp Disaster Med. 2018;33(6):640–646.

The spatial spread of highly pathogenic avian influenza (HPAI) H5N2 during the 2015 outbreak in the U.S. state of Minnesota was analyzed through the estimation of a spatial transmission kernel, which quantifies the infection hazard an infectious premises poses to an uninfected premises some given distance away. Parameters were estimated using a maximum likelihood method for the entire outbreak as well as for two phases defined by the daily number of newly detected HPAI-positive premises. The results indicate both a strong dependence of the likelihood of transmission on distance and a significant distance-independent component of outbreak spread for the overall outbreak. The results further suggest that HPAI spread differed during the later phase of the outbreak. The estimated spatial transmission kernel was used to compare the Minnesota outbreak with previous HPAI outbreaks in the Netherlands and Italy to contextualize the Minnesota transmission kernel results and make additional inferences about HPAI transmission during the Minnesota outbreak. Lastly, the spatial transmission kernel was used to identify high risk areas for HPAI spread in Minnesota. Risk maps were also used to evaluate the potential impact of an early marketing strategy implemented by poultry producers in a county in Minnesota during the outbreak, with results providing evidence that the strategy was successful in reducing the potential for HPAI spread.

Highly Pathogenic Avian Influenza (HPAI) is classified by the World Organization for Animal Health as one of the notifiable diseases. Its occurrence is associated with severe socio-economic impacts and is also zoonotic. Bangladesh HPAI epidemic data for the period between 2007 and 2013 were obtained and split into epidemic waves based on the time lag between outbreaks. By assuming the number of newly infected farms to be binomially distributed, we fit a Generalized Linear Model to the data to estimate between-farm transmission rates (β). These parameters are then used together with the calculated infectious periods to estimate the respective basic reproduction numbers (R0 ). The change in β and R0 with time during the course of each epidemic wave was explored. Finally, sensitivity analyses of the effects of reducing the delay in detecting infection on a farm as well as extended infectiousness of a farm beyond the day of culling were assessed. The point estimates obtained for β ranged from 0.08 (95% CI: 0.06-0.10) to 0.11 (95% CI: 0.08-0.20) per infectious farm per day while R0 ranged from 0.85 (95% CI: 0.77-1.02) to 0.96 (95% CI: 0.72-1.20). Sensitivity analyses reveal that the estimates are quite robust to changes in the assumptions about the day in reporting infection and extended infectiousness. In the analysis allowing for time-varying transmission parameters, the rising and declining phases observed in the epidemic data were synchronized with the moments when R0 was greater and less than one, respectively. From an epidemiological perspective, the consistency of these estimates and their magnitude (R0 ≈ 1) indicate that the effectiveness of the deployed control measures was largely invariant between epidemic waves and the trend of the time-varying R0 supports the hypothesis of sustained farm-to-farm transmission that is possibly initiated by a few unique introductions.

African swine fever (ASF) is a highly contagious, lethal and economically devastating haemorrhagic disease of domestic pigs. Insights into the dynamics and scale of virus transmission can be obtained from estimates of the basic reproduction number (R 0). We estimate R 0 for ASF virus in small holder, free-range pig production system in Gulu, Uganda. The estimation was based on data collected from outbreaks that affected 43 villages (out of the 289 villages with an overall pig population of 26,570) between April 2010 and November 2011. A total of 211 outbreaks met the criteria for inclusion in the study. Three methods were used, specifically; (i) GIS- based identification of the nearest infectious neighbour based on the Euclidean distance between outbreaks, (ii) epidemic doubling time, and (iii) a compartmental susceptible-infectious (SI) model. For implementation of the SI model, three approaches were used namely; curve fitting (CF), a linear regression model (LRM) and the SI/N proportion. The R 0 estimates from the nearest infectious neighbour and epidemic doubling time methods were 3.24 and 1.63 respectively. Estimates from the SI-based method were 1.58 for the CF approach, 1.90 for the LRM, and 1.77 for the SI/N proportion. Since all these values were above one, they predict the observed persistence of the virus in the population. We hypothesize that the observed variation in the estimates is a consequence of the data used. Higher resolution and temporally better defined data would likely reduce this variation. This is the first estimate of R0 for ASFV in a free range smallholder pig keeping system in sub-Saharan Africa and highlights the requirement for more efficient application of available disease control measures.

Contagious bovine pleuropneumonia (CBPP) is a cattle disease that has hampered the development of the livestock sector in sub-Saharan Africa. Currently, vaccination with a live vaccine strain is its recommended control measure although unofficial antimicrobial use is widely practiced. Here, modelling techniques are used to assess the potential impact of early elimination of infected cattle via accurate diagnosis on CBPP dynamics. A herd-level stochastic epidemiological model explicitly incorporating test sensitivity and specificity is developed. Interventions by annual vaccination, annual testing and elimination and a combination of both are implemented in a stepwise manner and their effectiveness compared by running 1000 simulations per intervention over ten years. The model predicts that among the simulated interventions, the ones likely to eliminate the disease from an isolated herd all involved annual vaccination of more than 75% of the animals with a vaccine that protects for at least 18 months combined with annual testing (and elimination of positive reactors) of 75% of the animals every six months after vaccination. The highest probability of disease elimination was 97.5% and this could occur within a median of 2.3 years. Generally, our model predicts that regular testing and elimination of positive reactors using improved tests will play a significant role in minimizing CBPP burden especially in the current situation where improved vaccines are yet to be developed.

A stochastic model designed to simulate transmission dynamics of African swine fever virus (ASFV) in a free-ranging pig population under various intervention scenarios is presented. The model was used to assess the relative impact of the timing of the implementation of different control strategies on disease-related mortality. The implementation of biosecurity measures was simulated through incorporation of a decay function on the transmission rate. The model predicts that biosecurity measures implemented within 14 days of the onset of an epidemic can avert up to 74% of pig deaths due to ASF while hypothetical vaccines that confer 70% immunity when deployed prior to day 14 of the epidemic could avert 65% of pig deaths. When the two control measures are combined, the model predicts that 91% of the pigs that would have otherwise succumbed to the disease if no intervention was implemented would be saved. However, if the combined interventions are delayed (defined as implementation from > 60 days) only 30% of ASF-related deaths would be averted. In the absence of vaccines against ASF, we recommend early implementation of enhanced biosecurity measures. Active surveillance and use of pen-side diagnostic assays, preferably linked to rapid dissemination of this data to veterinary authorities through mobile phone technology platforms are essential for rapid detection and confirmation of ASF outbreaks. This prediction, although it may seem intuitive, rationally confirms the importance of early intervention in managing ASF epidemics. The modelling approach is particularly valuable in that it determines an optimal timing for implementation of interventions in controlling ASF outbreaks.

Estimates of the per-contact probability of transmission between farms of Highly Pathogenic Avian Influenza virus of H7N7 subtype during the 2003 epidemic in the Netherlands are important for the design of better control and biosecurity strategies. We used standardized data collected during the epidemic and a model to extract data for untraced contacts based on the daily number of infectious farms within a given distance of a susceptible farm. With these data, we used a maximum likelihood estimation approach to estimate the transmission probabilities by the individual contact types, both traced and untraced. The estimated conditional probabilities, conditional on the contact originating from an infectious farm, of virus transmission were: 0.000057 per infectious farm within 1 km per day, 0.000413 per infectious farm between 1 and 3 km per day, 0.0000895 per infectious farm between 3 and 10 km per day, 0.0011 per crisis organisation contact, 0.0414 per feed delivery contact, 0.308 per egg transport contact, 0.133 per other-professional contact and, 0.246 per rendering contact. We validate these outcomes against literature data on virus genetic sequences for outbreak farms. These estimates can be used to inform further studies on the role that improved biosecurity between contacts and/or contact frequency reduction can play in eliminating between-farm spread of the virus during future epidemics. The findings also highlight the need to; 1) understand the routes underlying the infections without traced contacts and, 2) to review whether the contact-tracing protocol is exhaustive in relation to all the farm’s day-to-day activities and practices.