Previous research has demonstrated that various properties of infectious diseases can be inferred from online search behaviour. In this work we use time series of online search query frequencies to ...gain insights about the prevalence of COVID-19 in multiple countries. We first develop unsupervised modelling techniques based on associated symptom categories identified by the United Kingdom's National Health Service and Public Health England. We then attempt to minimise an expected bias in these signals caused by public interest -- as opposed to infections -- using the proportion of news media coverage devoted to COVID-19 as a proxy indicator. Our analysis indicates that models based on online searches precede the reported confirmed cases and deaths by 16.7 (10.2 - 23.2) and 22.1 (17.4 - 26.9) days, respectively. We also investigate transfer learning techniques for mapping supervised models from countries where the spread of disease has progressed extensively to countries that are in earlier phases of their respective epidemic curves. Furthermore, we compare time series of online search activity against confirmed COVID-19 cases or deaths jointly across multiple countries, uncovering interesting querying patterns, including the finding that rarer symptoms are better predictors than common ones. Finally, we show that web searches improve the short-term forecasting accuracy of autoregressive models for COVID-19 deaths. Our work provides evidence that online search data can be used to develop complementary public health surveillance methods to help inform the COVID-19 response in conjunction with more established approaches.
Carbon dioxide concentration in soil air was observed using gas detection device under coniferous (Red pine, ERC site) and deciduous (Oak, KEB site) forests from late May, 1993 to early December, ...1994. Soil temperature and soil water content were also measured to discuss the relationships between carbon dioxide concentration in soil air and these environmental factors.Results and conclusion of the study are summarized as follows:1) Carbon dioxide concentration in soil air was always higher than in the atmosphere and ranged from 0.1% at the minimum to 0.75-0.85% at the maximum.2) Carbon dioxide concentration in soil air increased from spring to summer and decreased from autumn to winter. In the summer of 1994, however, increase of carbon dioxide concentration was inhibited or even decreased because of an extremely low suction condition of soil. This seasonal change of the concentration reflects a similar seasonal change of carbon dioxide production in the soil which is mainly controlled by soil temperature and soil water content.3) Carbon dioxide concentration in soil air was relatively low in shallow depth and increased with depths. In the summer of 1993, a peak of carbon dioxide concentration was observed at the depths of 50-70cm at KEB site. These concentration profiles are not consistent with general profiles of carbon dioxide production, that indicates the importance of a process of carbon dioxide transport.4) Carbon dioxide concentration in soil air increased exponentially with soil temperature of the same depth. In a very dry condition (pF >2.4) at ERC site, however, this correlation reduced and carbon dioxide concentrations became lower than in a normal moisture condition. Carbon dioxide concentration in soil air was estimated using the relationship and soil temperature data. The estimated value agreed well with the observed value except when the soil was under extremely wet or dry condition.
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