Visiting national parks during the COVID-19 pandemic - an example of social adaptation of tourists in the perspective of creating social innovations

Social needs arising from the occurrence of global threats prompt researchers from various fields to look for innovative solutions that are friendly to society. The COVID-19 pandemic was a global experience so strong that it influenced many social processes, enabling natural experiments to be conducted that provided new knowledge about human behavior. One of the greatest impacts of lockdowns was observed in the case of tourist activity. National parks are highly desirable destinations for tourists and are able to attract large numbers of visitors. Tourism inside national parks has shown systematic growth, driven not only by the desire to be close to nature and to seek aesthetically pleasing experiences, but also by the need for relaxation and for participation in outdoor sports. Even during the COVID-19 pandemic, visitor numbers to national parks did not decline, despite their temporary closure in 2020. The article presents the result of empirical research conducted in 2021 on tourists to selected national mountain parks. The research aimed to explore visitors' motives for visiting the parks, as well as types of behaviour and the opinions of tourists regarding the restrictions placed on tourism in certain national parks. Analysis was also conducted of tourists' attitudes towards restrictions on access to parks due to formal legal regulations, limitations caused by the COVID-19 pandemic, as well as the potential to recompense for these needs by replacing them with alternatives behaviors in tourists' place of residence in the form of open social innovations. It was found, based on the opinions of tourists, that they visited national parks during the pandemic mainly for recreational and health purposes. Motivation to explore and admire nature or local culture was ranked third. On the other hand, the most frequently mentioned substitute of limited access to the national park was visiting nearby forests, meadows and city parks. The research allowed to notice the need to develop innovative solutions conducive to the psychological comfort of a community deprived of the possibility of mutual contact.

Using multiagent modeling to forecast the spatiotemporal development of the COVID-19 pandemic in Poland.

In the article, the authors present a multi-agent model that simulates the development of the COVID-19 pandemic at the regional level. The developed what-if system is a multi-agent generalization of the SEIR epidemiological model, which enables predicting the pandemic's course in various regions of Poland, taking into account Poland's spatial and demographic diversity, the residents' level of mobility, and, primarily, the level of restrictions imposed and the associated compliance. The developed simulation system considers detailed topographic data and the residents' professional and private lifestyles specific to the community. A numerical agent represents each resident in the system, thus providing a highly detailed model of social interactions and the pandemic's development. The developed model, made publicly available as free software, was tested in three representative regions of Poland. As the obtained results indicate, implementing social distancing and limiting mobility is crucial for impeding a pandemic before the development of an effective vaccine. It is also essential to consider a given community's social, demographic, and topographic specificity and apply measures appropriate for a given region.

Long-term spatial and population-structured planning of non-pharmaceutical interventions to epidemic outbreaks.

In this paper, we consider the problem of planning non-pharmaceutical interventions to control the spread of infectious diseases. We propose a new model derived from classical compartmental models; however, we model spatial and population-structure heterogeneity of population mixing. The resulting model is a large-scale non-linear and non-convex optimisation problem. In order to solve it, we apply a special variant of covariance matrix adaptation evolution strategy. We show that results obtained for three different objectives are better than natural heuristics and, moreover, that the introduction of an individual's mobility to the model is significant for the quality of the decisions. We apply our approach to a six-compartmental model with detailed Poland and COVID-19 disease data. The obtained results are non-trivialand sometimes unexpected; therefore, we believe that our model could be applied to support policy-makers in fighting diseases at the long-term decision-making level.

Modeling the Spatial and Temporal Spread of COVID-19 in Poland Based on a Spatial Interaction Model

This article describes an original methodology for integrating global SIR-like epidemic models with spatial interaction models, which enables the forecasting of COVID-19 dynamics in Poland through time and space. Mobility level, estimated by the regional population density and distances among inhabitants, was the determining variable in the spatial interaction model. The spatiotemporal diffusion model, which allows the temporal prediction of case counts and the possibility of determining their spatial distribution, made it possible to forecast the dynamics of the COVID-19 pandemic at a regional level in Poland. This model was used to predict incidence in 380 counties in Poland, which represents a much more detailed modeling than NUTS 3 according to the widely used geocoding standard Nomenclature of Territorial Units for Statistics. The research covered the entire territory of Poland in seven weeks of early 2021, just before the start of vaccination in Poland. The results were verified using official epidemiological data collected by sanitary and epidemiological stations. As the conducted analyses show, the application of the approach proposed in the article, integrating epidemiological models with spatial interaction models, especially unconstrained gravity models and destination (attraction) constrained models, leads to obtaining almost 90% of the coefficient of determination, which reflects the quality of the model's fit with the spatiotemporal distribution of the validation data.