Nothing, nothing whatsoever, could have prepared us for what has happened since the beginning of this year. I know that some researchers and public health professionals who have been modelling pandemics have argued that they were always aware of the risks but that no one has ever taken them seriously enough. Even Bill Gates (2015) has been preaching the dangers of a pandemic for years as reflected in his TED talk. But for over 100 years since the so-called Spanish flu at the end of the First World War, we have not really taken to heart the idea that everyone and everywhere might be infected by a disease that we were unable to control.
Rion Brattig Correia, Ian B. Wood, Johan Bollen, and Luis M. Rocha
Annual Review of Biomedical Data Science Vol. 3
Social media data have been increasingly used to study biomedical and health-related phenomena. From cohort-level discussions of a condition to population-level analyses of sentiment, social media have provided scientists with unprecedented amounts of data to study human behavior associated with a variety of health conditions and medical treatments. Here we review recent work in mining social media for biomedical, epidemiological, and social phenomena information relevant to the multilevel complexity of human health. We pay particular attention to topics where social media data analysis has shown the most progress, including pharmacovigilance and sentiment analysis, especially for mental health. We also discuss a variety of innovative uses of social media data for health-related applications as well as important limitations of social media data access and use.
Naoki Masuda and Petter Holme
Phys. Rev. Research 2, 023163
Many aspects of human and animal interaction, such as the frequency of contacts of an individual, the number of interaction partners, and the time between the contacts of two individuals, are characterized by heavy-tailed distributions. These distributions affect the spreading of, e.g., infectious diseases or rumors, often because of impacts of the right tail of the distributions (i.e., the large values). In this paper we show that when it comes to inter-event time distributions, it is not the tail but the small values that control spreading dynamics. We investigate this effect both analytically and numerically for different versions of the susceptible-infected-recovered model on different types of networks.
Emanuele Del Fava, Jorge Cimentada, Daniela Perrotta, André Grow, Francesco Rampazzo, Sofia Gil-Clavel, Emilio Zagheni
Physical distancing measures are intended to mitigate the spread of COVID-19. However, the impact these measures have on social contact and disease transmission patterns remains unclear. We ran the first comparative contact survey (N=53,708) across eight countries (Belgium, France, Germany, Italy, Netherlands, Spain, United Kingdom, United States) for the period March 13 – April 13, 2020. Our results show that social contact numbers mainly decreased after governments issued physical distancing guidelines rather than after announcing national lockdown measures. Compared to pre-COVID levels, social contact numbers decreased by 48% – 85% across countries. Except in Italy, these reductions were smaller than those observed in Wuhan (China). However, they sufficed to bring the R0 below one in almost every context considered. Finally, in all countries studied, the numbers of contacts decreased more rapidly among older people than among younger people, indicating higher levels of protection for groups at greater risk.
Marco Tulio Angulo, Fernando Castaños, Jorge X. Velasco-Hernandez, Jaime A. Moreno
To mitigate the COVID-19 pandemic, much emphasis exists on implementing non-pharmaceutical interventions to keep the reproduction number below one. But using that objective ignores that some of these interventions, like bans of public events or lockdowns, must be transitory and as short as possible because of their significative economic and societal costs. Here we derive a simple and mathematically rigorous criterion for designing optimal transitory non-pharmaceutical interventions. We find that reducing the reproduction number below one is sufficient but not necessary. Instead, our criterion prescribes the required reduction in the reproduction number according to the maximum health services’ capacity. To explore the implications of our theoretical results, we study the non-pharmaceutical interventions implemented in 16 cities during the COVID-19 pandemic. In particular, we estimate the minimal reduction of the contact rate in each city that is necessary to control the epidemic optimally. We also compare the optimal start of the intervention with the start of the actual interventions applied in each city. Our results contribute to establishing a rigorous methodology to guide the design of non- pharmaceutical intervention policies.