Another type of approach used epidemiological data (such as incidence) to calibrate diseases and behavioral models. Data from social media have been used to estimate the spread of awareness in the population during the H1N1 2009 pandemic. Others have used surveys to measure risk perception during the H1N1 2009 pandemic, , to estimate the perceived severity of the SARS outbreak, or characterize behavioral changes induced by the seasonal flu. Researchers have designed games, , surveys, and used datasets (e.g., television viewing) to infer social-distancing as well as attitudes, altruism and self-interest in the context of vaccinations, ,,. In the small set of papers informed, at least partially, by empirical data we find interesting approaches. One of the reviews on the subject noted a key challenge: only 15% of the papers are based on empirical data, most models being “purely theoretical and lack representative data and a validation process”. Arguably, it can be considered as the hard problem of epidemiology.Įven before the COVID-19 pandemic, the literature tackling this issue was vast. Capturing the feedback loop between human behavior and infectious diseases is one of the key challenges in epidemiology. Although obvious, particularly during a pandemic, we still do not have a well understood and developed theory or even an accepted standardized approach to account for this observation. The unfolding of such illnesses, in turn, might drastically affect our actions. Our interactions, movements, and behavior affect the spreading of infectious diseases. I summarize the methodology, data used, findings of the articles in each category and provide an outlook highlighting future challenges as well as opportunities. Considering the focus, and methodology I have classified the sample into seven main categories: epidemic models, surveys, comments/perspectives, papers aiming to quantify the effects of NPIs, reviews, articles using data proxies to measure NPIs, and publicly available datasets describing NPIs. While the large majority of the sample was obtained by querying PubMed, it includes also a hand-curated list. In doing so, I analyze 348 articles written by more than 2518 authors in the first 12 months of the emergency. Here, I review some of the vast literature written on the subject of NPIs during the COVID-19 pandemic. The scale of the emergency, the ease of survey as well as crowdsourcing deployment guaranteed by the latest technology, several Data for Good programs developed by tech giants, major mobile phone providers, and other companies have allowed unprecedented access to data describing behavioral changes induced by the pandemic. Travel bans, events cancellation, social distancing, curfews, and lockdowns have become unfortunately very familiar. Non-pharmaceutical interventions (NPIs) have been the key weapon against the SARS-CoV-2 virus and affected virtually any societal process. Things have dramatically changed in 2020. The main issue was the lack of empirical data capturing behavioral change induced by diseases. Before COVID-19 the literature on the subject was mainly theoretical and largely missed validation. While intuitive, our understanding of such feedback loop is still limited. On the other, the unfolding of viruses might induce changes to our daily activities.
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On one side, our movements and interactions are the engines of transmission. Infectious diseases and human behavior are intertwined.