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The COVID-19 pandemic represents a massive global health crisis. Because the crisis requires large-scale behaviour change and places significant psychological burdens on individuals, insights from the social and behavioural sciences can be used to help align human behavior with the recommendations of epidemiologists and public health experts. Here we discuss evidence from a selection of research topics relevant to pandemics, including work on navigating threats, social and cultural influences on behaviour, science communication, moral decision-making, leadership, and stress and coping. In each section, we note the nature and quality of prior research, including uncertainty and unsettled issues. We identify several insights for effective response to the COVID-19 pandemic, and also highlight important gaps researchers should move quickly to fill in the coming weeks and months.

Background Information and behaviour can spread through interpersonal ties. By targeting influential individuals, health interventions that harness the distributive properties of social networks may be made more effective and efficient than those that do not. Methods In this block-randomised trial of network targeting methods, we delivered two dissimilar public health interventions to 32 villages in rural Honduras (22–541 participants each; total study population of 5,773): chlorine for water purification, and multivitamins for micronutrient deficiencies. We blocked villages on the basis of network size, socioeconomic status, and baseline rates of water purification. We then randomised villages, separately for each intervention, to one of three targeting methods, introducing the interventions to 5% samples composed either of: (1) randomly selected villagers (n=9 villages for each intervention), (2) villagers with the most social ties (n=9), or (3) nominated friends of random villagers (n=9; the last strategy exploiting the “friendship paradox” of social networks). Primary endpoints were the proportion of available products redeemed by the entire population under each targeting method. Participants and data collectors were not aware of the targeting methods. The trial is registered with ClinicalTrials.gov (NCT01672580). Findings For each intervention, 9 villages (each with 1–20 initial target individuals) were randomised to each of the three targeting methods. Targeting the most highly connected individuals produced no greater adoption of the interventions than random targeting. Targeting nominated friends, however, increased adoption of the nutritional intervention by 12·2% compared to random targeting (95% CI, 6·9 to 17·9). Interpretation Introducing a health intervention to the nominated friends of random individuals can enhance that intervention’s diffusion by exploiting intrinsic properties of human social networks. This method has the additional advantage of scalability because it can be implemented without mapping the network. Deploying certain types of health interventions via network targeting, without increasing the number of individuals targeted or the resources used, may enhance the adoption and efficiency of those interventions, thereby improving population health. Funding NIH, Bill and Melinda Gates Foundation, Star Family Foundation, and the Canadian Institutes of Health Research. We thank The Clorox Company and Tishcon Corporation for their donations of supplies used in the study in Honduras.

Background Few weight-loss interventions are evaluated for longer than a year, and even fewer employ social and mobile technologies commonly used among young adults. We assessed the efficacy of a two-year, theory-based weight-loss intervention that was remotely and adaptively delivered via integrated user-experiences with 1) Facebook, 2) mobile apps, 3) text messaging, 4) emails, 5) a website, and 6) technology-mediated communication with a health coach. Methods From May 2011 to May 2012, 404 overweight or obese college students (aged 18 to 35 years) from three universities in San Diego, CA were randomized using a computer-based procedure to receive either the intervention (n=202) or general information about health and wellness (control group, n=202). The primary outcome was objectively measured weight in kg at 24 months, and differences between groups were evaluated using linear mixed-effects regression and an intention-to-treat framework. The trial was registered with ClinicalTrials.gov NCT01200459. Findings Participants’ mean (standard deviation (SD)) age was 22·7 (3.8) years. They were 70% female and 31% Hispanic. Mean (SD) body mass index was 29·0 (2.8) kg/m2. At 24 months, weight was assessed in 341 (84%) participants, but all 404 were included in analyses. Weight, adjusted for sex, ethnicity, and college, was significantly less in the intervention group compared to the control group at 6 months (−1·33 kg, 95% confidence interval (CI) = −2·36 to −0·30, p = 0·011) and 12 months (−1·33 kg, 95% CI =−2·30 to −0·35, p = 0·008). However, differences between groups at 18 months (−0·67 kg, 95% CI = −1·69 to 0·35, p = 0·200) and 24 months (−0·79 kg, 95% CI = −2·02 to 0·43, p = 0·204) were not significant. Interpretation Social and mobile technologies may facilitate limited short-term weight loss among young adults, but as utilized in this intervention, these approaches did not produce sustained reductions in weight.

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The evolution of cooperation in network-structured populations has been a major focus of theoretical work in recent years. When players are embedded in fixed networks, cooperators are more likely to interact with, and benefit from, other cooperators. In theory, this clustering can foster cooperation on fixed networks under certain circumstances. Laboratory experiments with humans, however, have thus far found no evidence that fixed network structure actually promotes cooperation. Here, we provide such evidence and help to explain why others failed to find it. First, we show that static networks can lead to a stable high level of cooperation, outperforming well-mixed populations. We then systematically vary the benefit that cooperating provides to one's neighbors relative to the cost required to cooperate (b/c), as well as the average number of neighbors in the network (k). When b/c > k, we observe high and stable levels of cooperation. Conversely, when b/c ≤ k or players are randomly shuffled, cooperation decays. Our results are consistent with a quantitative evolutionary game theoretic prediction for when cooperation should succeed on networks and, for the first time to our knowledge, provide an experimental demonstration of the power of static network structure for stabilizing human cooperation.Prisoner's Dilemma | evolutionary game theory | economic games | structured populations | assortment H uman societies, in both ancient and modernized circumstances, are characterized by complex networks of cooperative relationships (1-10). These cooperative interactions, where individuals incur costs to benefit others, increase the greater good but are undercut by self-interest. How, then, did the selfish process of natural selection give rise to cooperation, and how might social arrangements or institutions foster cooperative behavior? Evolutionary game theory has offered various explanations, in the form of mechanisms for the evolution of cooperation (10). For example, theory predicts (and experiments confirm) that repeated interactions between individuals and within groups can promote cooperation (11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22), as can competition between groups (23-26).However, one important class of theoretical explanations remains without direct experimental support: the prediction that static (i.e., fixed) network structure should have an important effect on cooperation (27-40). When interactions are structured, such that people only interact with their network "neighbors" rather than the whole population, the emergence of clustering (or "assortment") is facilitated. Clustering means that cooperators are more likely to interact with other cooperators, and therefore to preferentially receive the benefits of others' cooperation. Thus, clustering increases the payoffs of cooperators relative to defectors and helps to stabilize cooperation. Across a wide array of model details and assumptions, theoretical work has shown that static networks can promote cooperation, making spatial structure one of the...

Intermediate-scale (or "meso-scale") structures in networks have received considerable attention, as the algorithmic detection of such structures makes it possible to discover network features that are not apparent either at the local scale of nodes and edges or at the global scale of summary statistics. Numerous types of meso-scale structures can occur in networks, but investigations of such features have focused predominantly on the identification and study of community structure. In this paper, we develop a new method to investigate the meso-scale feature known as core-periphery structure, which entails identifying densely connected core nodes and sparsely connected peripheral nodes. In contrast to communities, the nodes in a core are also reasonably well-connected to those in a network's periphery. Our new method of computing core-periphery structure can identify multiple cores in a network and takes into account different possible core structures. We illustrate the differences between our method and several existing methods for identifying which nodes belong to a core, and we use our technique to examine core-periphery structure in examples of friendship, collaboration, transportation, and voting networks. For this new SIGEST version of our paper, we also discuss our work's relevance in the context of recent developments in the study of core-periphery structure.

We conduct the largest ever investigation into the relationship between meteorological conditions and the sentiment of human expressions. To do this, we employ over three and a half billion social media posts from tens of millions of individuals from both Facebook and Twitter between 2009 and 2016. We find that cold temperatures, hot temperatures, precipitation, narrower daily temperature ranges, humidity, and cloud cover are all associated with worsened expressions of sentiment, even when excluding weather-related posts. We compare the magnitude of our estimates with the effect sizes associated with notable historical events occurring within our data.