Epidemic dreams: dreaming about health during the COVID-19 pandemic

The continuity hypothesis of dreams suggests that the content of dreams is continuous with the dreamer's waking experiences. Given the unprecedented nature of the experiences during COVID-19, we studied the continuity hypothesis in the context of the pandemic. We implemented a deep-learning algorithm that can extract mentions of medical conditions from text and applied it to two datasets collected during the pandemic: 2888 dream reports (dreaming life experiences), and 57 milion tweets (waking life experiences) mentioning the pandemic. The health expressions common to both sets were typical COVID-19 symptoms (e.g. cough, fever and anxiety), suggesting that dreams reflected people's real-world experiences. The health expressions that distinguished the two sets reflected differences in thought processes: expressions in waking life reflected a linear and logical thought process and, as such, described realistic symptoms or related disorders (e.g. nasal pain, SARS, H1N1); those in dreaming life reflected a thought process closer to the visual and emotional spheres and, as such, described either conditions unrelated to the virus (e.g. maggots, deformities, snake bites), or conditions of surreal nature (e.g. teeth falling out, body crumbling into sand). Our results confirm that dream reports represent an understudied yet valuable source of people's health experiences in the real world.

Major points 1. I understand that the authors are not sleep researchers but is simply unaccettable the equivalence between REM sleep and dreaming. The authors have to realize that empirical research on dreams has definitely clarified that dreams are not exclusive of REM sleep and that they can be collected practically in any stage of sleep. This means that all points in the manuscript in which they speak on REM sleep as a physiological scenario of dream production should be changed accordingly 2. Since a large body of cross-sectional and longitudinal studies on the pandemic has been published using surveys, these findings on dream activity should be discussed compared to the current descriptive data  Psychology. 2021, 12, 1907DOI=10.3389/fpsyg.2021 3. Concerning the "continuity hypothesis", it commonly refers to: (1) dreams are a continuation of our waking experiences; (2) neurobiological mechanisms underlying episodic memory in wakefulness are mostly similar to those of dream recall during sleep (e.g., Scarpelli et al. Investigation on Neurobiological Mechanisms of Dreaming in the New Decade. Brain Sci. 2021 Feb 11;11(2):220. doi: 10.3390/brainsci11020220). The article only mention the first meaning of this accepted meaning 4. Please, substantiate the sentence in the discussion (page 10, rows 21.22): " The phrases more frequent in the dream reports about bizarre body disfunctions represent a metaphoric manner of thinking about COVID-19 with more activation in visual and emotional areas and less in verbal and logical ones." The issue of greater activation in visual and emotional areas is still debated. Please, provide references. Still more, I think that there is no empirical study showing that there is smaller activation "in verbal and logical ones." 5. The dramatic increase of nightmares during the pandemic is one of the most consistent findings of empirical studies and have a relevant clinical implication in relation to PTSD symptoms. The current qualitative findings on nightmares should also be discussed with reference to this evidence.

Review form: Reviewer 2
Is the manuscript scientifically sound in its present form? Yes

Do you have any ethical concerns with this paper? No
Have you any concerns about statistical analyses in this paper? No

Recommendation?
Accept with minor revision (please list in comments)

Comments to the Author(s)
In this work, the authors present a novel work on dream reports, highlighting their importance, if interpreted correctly, to better understand and get insights about people's health experiences in the real world, specifically in the case of extremely challenging and distressing events, such as the COVID-19 pandemic. Data about dream reports regarding dreaming life experience, and tweets mentioning the pandemic, i.e., waking life experiences, have been collected and used in their study. The target has been that of extract mentions of virtually any medical condition from text by applying deep learning algorithms. Their findings demonstrate how dreams reflected people's real-world experiences and that the health expressions that distinguished the two sets of data reflected differences in thought processes. The idea of studying the dream reports along with social media posts and analyzing their impact on large-scale phenomena, such as the COVID-19 Pandemic, is extremely interesting. Moreover, this is the first study carrying out a systematic analysis of medical conditions featured in dream reports and compared them to those expressed in waking discussions, starting from the wellknown "continuity hypothesis of dreaming". The latter refers to the relationship between health concerns discussed during waking life and the representation of such concerns in dreams, which is relevant to the study of people's well-being. As the same authors underline, one of the main limitations is in terms of biases linked with the data collection process, but they used a rank-based method to compare the health mentions in the two datasets, by ranking conditions by their frequency in each dataset separately.
In terms of methodology, they used MedDL for extracting mentions of medical conditions from text, and they trained the method on social media data, and as the same authors underline, training on dream reports could be beneficial for the analysis results. Although the methodology is not novel in terms of NLP deep-learning tool used for extracting mentions of medical conditions, and the data collected and training process may produce biases in results, both the work and findings are novel. The main aspect on which the authors could focus is on comparing the tool used with other methods to better validate the model. Moreover, a figure showing the pipeline of the methodology would help in the readability. Finally, to strengthen the methodological approach and extending results, starting from the cooccurrence network of dream reports, which resembles the comorbidity networks (see the work: Moni, Mohammad Ali, and Pietro Liò. "Network-based analysis of comorbidities risk during an infection: SARS and HIV case studies." BMC bioinformatics 15.1 (2014): 1-23.), authors could explore some measures, such as the relative risk and ϕ-correlation. Respectively, these measures represent the relationship between the two conditions related to the datasets and the robustness of this association.

Decision letter (RSOS-211080.R0)
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Dear Dr Aiello
The Editors assigned to your paper RSOS-211080 "Epidemic Dreams: Dreaming about health during the COVID-19 pandemic" have now received comments from reviewers and would like you to revise the paper in accordance with the reviewer comments and any comments from the Editors. Please note this decision does not guarantee eventual acceptance.
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Within the intrinsic limits of a scarce originality and of some intrinsic bias in the used methods, the study provides a qualitative picture of dreams during the pandemic.
Major points 1. I understand that the authors are not sleep researchers but is simply unaccettable the equivalence between REM sleep and dreaming. The authors have to realize that empirical research on dreams has definitely clarified that dreams are not exclusive of REM sleep and that they can be collected practically in any stage of sleep. This means that all points in the manuscript in which they speak on REM sleep as a physiological scenario of dream production should be changed accordingly 2. Since a large body of cross-sectional and longitudinal studies on the pandemic has been published using surveys, these findings on dream activity should be discussed compared to the current descriptive data  (2):220. doi: 10.3390/brainsci11020220). The article only mention the first meaning of this accepted meaning 4. Please, substantiate the sentence in the discussion (page 10, rows 21.22): " The phrases more frequent in the dream reports about bizarre body disfunctions represent a metaphoric manner of thinking about COVID-19 with more activation in visual and emotional areas and less in verbal and logical ones." The issue of greater activation in visual and emotional areas is still debated. Please, provide references. Still more, I think that there is no empirical study showing that there is smaller activation "in verbal and logical ones." 5. The dramatic increase of nightmares during the pandemic is one of the most consistent findings of empirical studies and have a relevant clinical implication in relation to PTSD symptoms. The current qualitative findings on nightmares should also be discussed with reference to this evidence.
Reviewer: 2 Comments to the Author(s) In this work, the authors present a novel work on dream reports, highlighting their importance, if interpreted correctly, to better understand and get insights about people's health experiences in the real world, specifically in the case of extremely challenging and distressing events, such as the COVID-19 pandemic. Data about dream reports regarding dreaming life experience, and tweets mentioning the pandemic, i.e., waking life experiences, have been collected and used in their study. The target has been that of extract mentions of virtually any medical condition from text by applying deep learning algorithms. Their findings demonstrate how dreams reflected people's real-world experiences and that the health expressions that distinguished the two sets of data reflected differences in thought processes. The idea of studying the dream reports along with social media posts and analyzing their impact on large-scale phenomena, such as the COVID-19 Pandemic, is extremely interesting. Moreover, this is the first study carrying out a systematic analysis of medical conditions featured in dream reports and compared them to those expressed in waking discussions, starting from the well-known "continuity hypothesis of dreaming". The latter refers to the relationship between health concerns discussed during waking life and the representation of such concerns in dreams, which is relevant to the study of people's well-being. As the same authors underline, one of the main limitations is in terms of biases linked with the data collection process, but they used a rank-based method to compare the health mentions in the two datasets, by ranking conditions by their frequency in each dataset separately. In terms of methodology, they used MedDL for extracting mentions of medical conditions from text, and they trained the method on social media data, and as the same authors underline, training on dream reports could be beneficial for the analysis results. Although the methodology is not novel in terms of NLP deep-learning tool used for extracting mentions of medical conditions, and the data collected and training process may produce biases in results, both the work and findings are novel. The main aspect on which the authors could focus is on comparing the tool used with other methods to better validate the model. Moreover, a figure showing the pipeline of the methodology would help in the readability. Finally, to strengthen the methodological approach and extending results, starting from the cooccurrence network of dream reports, which resembles the comorbidity networks (see the work: Moni, Mohammad Ali, and Pietro Liò. "Network-based analysis of comorbidities risk during an infection: SARS and HIV case studies." BMC bioinformatics 15.1 (2014): 1-23.), authors could explore some measures, such as the relative risk and ϕ-correlation. Respectively, these measures represent the relationship between the two conditions related to the datasets and the robustness of this association.

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Author's Response to Decision Letter for (RSOS-211080.R0) See Appendix A.

Decision letter (RSOS-211080.R1)
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It is a pleasure to accept your manuscript entitled "Epidemic Dreams: Dreaming about health during the COVID-19 pandemic" in its current form for publication in Royal Society Open Science. The comments of the reviewer(s) who reviewed your manuscript are included at the foot of this letter.
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Thank you for your fine contribution. On behalf of the Editors of Royal Society Open Science, we look forward to your continued contributions to the Journal. Response to the reviews of paper RSOS-211080 "Epidemic Dreams: Dreaming about health during the COVID-19 pandemic" We would like to express our sincere thanks to the Associate Editor and the reviewers for their comments. We have worked to address all their concerns in the revised version of the manuscript. The main changes are highlighted with blue text. Below, we provide a detailed response to the reviewers' requests.
We hope they will find the new version of the paper improved.

Associate Editor's requests
While the reviewers found that the idea of studying the dream reports along with social media posts to conduct qualitative analysis of dreams during the pandemic is interesting, they raised a few major concerns that should be addressed in the revised manuscript. Examples include more accurate use of terminologies (such as REM sleep, continuity hypothesis), discussions of findings on dream activities in comparison to the existing descriptive data, more empirical studies to support some of the statements in the manuscript, additional measures to quantify the associations revealed by the co-occurrence network of dream reports, etc. Please refer to the reviews for the details of the major points raised We have thoroughly revised the paper according to the Associate Editor's and the Reviewer's suggestions. In summary: • We revised our terminology to define key concepts more accurately and unambiguously.
• We acknowledged prior survey-based work on pandemic dreams in the introduction, and discussed the similarities of those studies' results with our findings in the discussion.
• We rephrased the explanation of why dream reported contained methaphoric representations.
• We improved the visual summary of our analytics pipeline.
• We calculated two additional metrics to assess the robustness of the semantic associations between medical conditions in the network. We used those metrics to filter out spurious associations, and repeated the network analysis on the resulting network.

Requests from the Reviewer 1
The equivalence between REM sleep and dreaming is simply unacceptable. The authors have to realize that empirical research on dreams has definitely clarified that dreams are not exclusive of REM sleep and 1 Appendix A 2 that they can be collected practically in any stage of sleep. This means that all points in the manuscript in which they speak on REM sleep as a physiological scenario of dream production should be changed accordingly The reviewer is right in noting that dreaming can occur in both REM and non-REM sleep cycles. We changed all the parts in the manuscript to remove or rephrase all the sentences that hinted at an exclusive association between REM sleep and dreams.
Since a large body of cross-sectional and longitudinal studies on the pandemic has been published using surveys, these findings on dream activity should be discussed compared to the current descriptive data. [...] The dramatic increase of nightmares during the pandemic is one of the most consistent findings of empirical studies and have a relevant clinical implication in relation to PTSD symptoms. The current qualitative findings on nightmares should also be discussed with reference to this evidence.
We thank the reviewer for providing references relevant to our work. We were aware of some of these papers-in the introduction of the original manuscript, we had already referenced "Pandemic nightmares" by Scarpelli et al. (reference 25)-but indeed we did missed some key contributions that were published later in 2021. To better acknowledge prior research that used surveys to quantify sleep and dreams during the pandemic, we expanded the list of references in the introduction with those suggested by the reviewer, and clarified how our contribution can enrich the perspective of survey-based studies. Unlike survey studies, the text of the dream reports was available to us, which allowed us to study mentions of medical conditions at a high level. We expanded the discussion to highlight similarities with the findings from previous literature. In particular, similar to our results, survey participants reported increased emotional load and higher frequency of nightmares and bizarre images compared to pre-lockdown. Participants also reported a substantial increase in anxiety, in agreement with the themes of panic, anxiety, phobia, and fear that we identified in our thematic analysis of network clusters. Pandemic-related dreams were also more frequent, according to survey respondents. In this work, we thoroughly analyzed the set of medical conditions in "pandemic dreams".
Concerning the "continuity hypothesis", it commonly refers to: (1) dreams are a continuation of our waking experiences; (2) neurobiological mechanisms underlying episodic memory in wakefulness are mostly similar to those of dream recall during sleep (e.g., Scarpelli et al. Investigation on Neurobiological Mechanisms of Dreaming in the New Decade. Brain Sci. 2021 Feb 11;11(2):220. doi: 10.3390/brainsci11020220). The article only mention the first meaning of this accepted meaning In the revised version, we mention both meanings, and reference the suggested paper.
Please, substantiate the sentence in the discussion (page 10, rows 21.22): "The phrases more frequent in the dream reports about bizarre body disfunctions represent a metaphoric manner of thinking about COVID-19 with more activation in visual and emotional areas and less in verbal and logical ones." The issue of greater activation in visual and emotional areas is still debated. Please, provide references. Still more, I think that there is no empirical study showing that there is smaller activation "in verbal and logical ones." The issue of higher/lower activation of emotional vs. logical areas is indeed debated. On the conservative side, we removed the sentence referring to brain activation altogether.

Requests from the Reviewer 2
The main aspect on which the authors could focus is on comparing the tool used with other methods to better validate the model.
We thank the reviewer for this comment. In the original version of the manuscript, we failed to provide a measure of quality of the output of our medical entity extractor. In previous work (Šćepanović et al. "Extracting medical entities from social media" ACM CHIL, 2020), we tested MedDL on standard benchmarks against the state-of-the-art methods for medical entity extraction from unstructured text. When applied to tweets, MedDL achieved an F1-score of 0.74; the baselines ranged from 0.34 to 0.63. When applied to Reddit posts, whose length and structure are more similar to dream reports compared to tweets, MedDL fared an F1-score of 0.71, while the competing approaches scored between 0.17 and 0.38. In short, MedDL outperformed by a large margin the state-of-the-art approaches developed until 2020 (the year MedDL was developed and published). We reported these statistics in the revised version of the paper.
A figure showing the pipeline of the methodology would help in the readability.
We completely revised Figure 1 to add detail to the representation of the pipeline. We hope that the reviewer will find this illustration improved.
To strengthen the methodological approach and extending results, starting from the co-occurrence network of dream reports, which resembles the comorbidity networks (see the work: Moni, Mohammad Ali, and Pietro Lió. "Network-based analysis of comorbidities risk during an infection: SARS and HIV case studies." BMC bioinformatics 15.1 (2014): 1-23.), authors could explore some measures, such as the relative risk and fi-correlation. Respectively, these measures represent the relationship between the two conditions related to the datasets and the robustness of this association.
We took the suggestion of the reviewer onboard and we calculated both the relative risk (RR ij ) and the Phi-correlation (φ ij ) between pairs of co-occurring conditions, and used them to filter out weak associations using the procedure from Ali and Lió, which we summarized in Section 3.2.3. The filtering step reduced the network from 1,419 nodes and 4,084 edges to 1,416 nodes and 3,759 edges. We repeated the analysis using this filtered network and updated the statistics in Section 4.2. The results varied only slightly, and the overall conclusions remained unchanged.