 In this study, we suggest a framework to measure both public awareness and preparedness of specialized actors integrating digital and field surveillance. In particular, we use African Swain fever as a case study. First of all, have you ever heard of African Swain fever? So suppose you are reading your daily news and you find that there is a new disease in town. You probably want to know more about it to be prepared. So you go and search for it in your free search of information, which is Wikipedia generally. So what is African Swain fever? African Swain fever is a transponderous wine disease. It's non-zoonotic, so it's not transmissible to humans, but it has high fatality rate for the pig and population. It's responsible for massive losses in pig population and drastic economic consequences, and it has become a major crisis for the pork industry in recent years. According to the Food and Agriculture Organization, this disease is not only impending animal health and welfare, but also has a detrimental impact on biodiversity and livelihood of farmers. The problem with the African Swain fever is that there is no treatment or vaccine, and mass culling is the only solution for containment. So let's see how the spread of the disease works. The spread of African Swain fever has two main factors. The biological path includes infected pig, biological vectors like pigs or infected boars. And then there is the human factor, which is multifactorial and involves both specialized population and the general population. So focusing on the human factor, as said before, both specialized actors like farmers and the food chain play a role in this step, in particular with infringement or low compliance with biosecurity and preventive measures, movement of contaminated formites or underreporting of African Swain fever-suspected cases. But also the general population can play a role by, for example, feeding animals with contaminated food. So here we propose an integrated approach that combines survey data and digital data to monitor awareness and preparedness of both specialized actors and the general public. In particular, from digital sources we can measure public awareness and information-seeking behavior, while from traditional field surveillance we can assess the level of preparedness of local authorities and farmers. This can help to give insight on a tension level to improve communication strategy and improve risk management of emerging diseases. Focusing now on the data sources, we consider three main sources of data. On the digital side we use Wikipedia page views for 12 countries as a proxy of information-seeking behavior. We also consider a news volume about African Swain fever as a proxy of information exposure. We also compare the digital data with time series with the cases reports for African Swain fever. And we use on-site surveys, so on the traditional surveillance data side, we use on-site surveys focusing on Estonia to farmers and authorities. To analyze the correlation between African Swain fever, media coverage in each country and online users' collective response we introduced to regression model. Here YFT is the number of country-specific Wikipedia page views and independent variables are either news volume or the news volume plus a memory term. The first model is a simple regression model and the second includes the memory effect which accounts for loss of interest in the public response to media coverage. We compare the two models using R2-Configent and F-Test showing that adding the memory effect improves the performance of the model. This means that after a peak of attention there is a loss of interest in the general population that should be accounted for when we design a communication campaign. To qualitatively explore the content of the digital news, we analyzed the prevalent topic in the news using an LDA model focusing in particular on the news for Estonia. The most relevant one referred to control measure. Now focusing on the specialized actor, we assess also levered information and preparedness of specialized actors such as farmers and authorities. In particular we see that information sources spawn from social media to news magazine and colleagues and friends. And also specialized actors are well aware and mostly prepared for the disease too for the management of the disease. Of course the study faces several limitations with the field activities that were complicated by the concurrent COVID-19 pandemic on multiple fronts. In fact this problem includes a small sample size of both Estonian farmers and veterinary authorities and also the disruption in data collection due to lockdowns and the pandemic in general. So in conclusion we propose a framework to assess preparedness and awareness in both specialized and general population. The integration of the digital data can support traditional surveillance to design better information campaign and also measuring real time decrease of information seeking behavior can help to adjust communication campaign. Thank you for your attention.