This paper defines the relative role of firms' characteristics on both their likelihood to innovate and the intensity of innovation (innovative outputs). Using Christensen's (1995; 1996) and Teece's (1986) proposals for analysing assets (resources and capabilities) and inter-asset linkages in order to produce and commercially exploit technological innovation, a conceptual framework is defined suitable to identify the variables affecting the probability of a firm to innovate and those affecting a greater count of innovations, once the firm has decided to innovate. Thus, a "double-hurdle" approach involving censored and truncated models was applied. The findings confirm that firms innovative assets investment in external technology, investment in R&D, and external alliances, alongside firm size are the significant variables in determining firms' likelihood to innovate, whereas external alliances, firm size and market orientation are related to intensity to innovate in the Brazilian Food Industry.

The flu season is caused by a combination of different pathogens, including influenza viruses (IVS), that cause the flu, and non-influenza respiratory viruses (NIRVs), that cause common colds or influenza-like illness. These viruses have similar circulation patterns, and weather has been considered a main driver of their dynamics, with peaks in the winter and almost no circulation during the summer in temperate regions. However, after the emergence of SARS-CoV2, in 2019, the dynamics of these respiratory viruses were strongly perturbed worldwide: some infections almost disappeared, others were delayed or occurred "off-season". This disruption raised questions regarding the dominant role of weather while also providing an unique opportunity to investigate the relevance of different driving factors on the epidemiological dynamics of IVs and NIRVs, including viral interactions, non-pharmacological individual measures (such as masking), or mobility. Here, we use epidemiological surveillance data on several respiratory viruses from Canada and the USA from 2016 to 2023, and tested the effects of weather and mobility in their dynamics before and after the COVID-19 pandemic. Using statistical modelling, we found evidence that whereas in the pre-COVID-19 pandemic period, weather had a strong effect and mobility a limited effect on dynamics; in the post-COVID-19 pandemic period the effect of weather was strongly reduced and mobility played a more relevant role. These results, together with previous studies, indicate that at least some of the behavioral changes resulting from the non-pharmacological interventions implemented during COVID-19 pandemic had a strong effect on the dynamics of respiratory viruses. Furthermore, our results support the idea that these seasonal dynamics are driven by a complex system of interactions between the different factors involved, which probably led to an equilibrium that was disturbed, and perhaps permanently altered, by the COVID-19 pandemic.

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Online searches have been used to study different health-related behaviours, including monitoring disease outbreaks. An obvious caveat is that several reasons can motivate individuals to seek online information and models that are blind to people's motivations are of limited use and can even mislead. This is particularly true during extraordinary public health crisis, such as the ongoing pandemic, when fear, curiosity and many other reasons can lead individuals to search for healthrelated information, masking the disease-driven searches. However, health crisis can also offer an opportunity to disentangle between different drivers and learn about human behavior. Here, we focus on the two pandemics of the 21st century (2009-H1N1 flu and Covid-19) and propose a methodology to discriminate between search patterns linked to general information seeking (media driven) and search patterns possibly more associated with actual infection (disease driven). We show that by learning from such pandemic periods, with high anxiety and media hype, it is possible to select online searches and improve model performance both in pandemic and seasonal settings. Moreover, and despite the common claim that more data is always better, our results indicate that lower volume of the right data can be better than including large volumes of apparently similar data, especially in the long run. Our work provides a general framework that can be applied beyond specific events and diseases, and argues that algorithms can be improved simply by using less (better) data. This has important consequences, for example, to solve the accuracy-explainability trade-off in machine-learning.However, if we could decouple searches mostly driven by media, anxiety, or curiosity, from the ones related with actual disease, we could not only improve disease monitoring, we could also deepen our understanding of online human behavior. In the case of Google search trends, identifying what terms are more correlated with media exposure and reducing their influence in the model is crucial to correct past errors.In this paper, we propose that the characteristics that make pandemics unique and hard to now-cast, such as media hype, can also be used as opportunities for two main reasons: 1) as pandemics tend to exacerbate behaviors, the noise (media) is of the same order of magnitude as the signal (cases), making it more visible, allowing us to discriminate between the two; and 2) because information seeking becomes less common as the pandemic progresses 18, 28 and these different dynamics can be used when selecting the search terms. In fact, instead of ignoring pandemic periods, studying what happens during the worst possible moment can help us understand which are the search-terms more associated with the disease and the ones that were prompted by media exposure. This solution might avoid over-fitting and enable the predictive model to be more robust over time, especially during seasonal events. Therefore, we focus on the only two XXI century WHO declared pandemics and aim ...