The ongoing COVID-19 pandemic has cemented the understanding among public health 43 researchers, practitioners, and policy makers that the spread of infectious disease is more than a 44 purely epidemiological process. While COVID-19 has strained hospital capacity (1), the global 45 supply of personal protective equipment (2, 3), food supply chains (4, 5) and unemployment 46 insurance processing capacity (6), the greatest challenges in understanding, predicting, and 47 planning mitigation for the ongoing spread of the disease lies in how to understand, anticipate, 48 and influence human behavioral responses (7). People's behavioural responses to the pandemic will vary considerably, often between 57 locations and over time, driven by variations in local government policy as well as individual 58 behaviors over time (e.g. as "stay at home" orders were enacted and relaxed; (15)), and across 59 social and demographic groups (e.g. conservative vs. liberal; (12, 16)). These are not static 60 parameters; these shifting patterns of behavior are not independent of the spread of infection but 61 are instead inextricably coupled with the spatial and temporal patterns of disease incidence. is high in their physical environment), which will influence rates of adherence to disease-101 defensive public health policies. For the same reasons, multiplex structure should influence when 102 . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprint this version posted July 11, 2020. communities, with the modularity of these subdivisions also able to differ between the contact 119 and communication layers of the network. We assess the severity of disease progression by a 120 joint measure considering both the time and magnitude of the epidemic peak in each community 121 (see details in Methods) to allow for discussion of public health strategies that focus both on 122 sufficient time to act and have the capacity for action to handle peak surge demands. 123 Our models assume that social influence can be effective at elevating individual concern 124 about disease, but only direct observation of an absence of infection in a community will provide 125 reassurance (i.e. cause a decrease of concern). We use this model to explore the impact of 126 modularity and homophily according to predisposition regarding the severity of disease 127 progression through communities, the relative impact of social vs. observational contributions 128 towards estimation of disease risk on the disease outcomes in communities, and the impact of 129 reassurance on the likely dynamics in risk behaviors over time, again affecting disease outcomes.
While this model is parameterized to reflect current COVID-19 features and challenges, it seems 131 clear that socio-behavioral dynamics that shape the nature of risk perception, and therefore 132 . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprint this version posted July 11, 2020. . https://doi.org/10.1101/2020.07.09.20146985 doi: medRxiv preprint disease-defensive behaviors should be important for any pandemic preparedness planning for the 133 future. We therefore here explore and present a broad set of scenarios.
We simulated the spread of infection and concern through nine multiplex networks that 137 differed in community structure and homophily between two predispositions (see Fig. 1 for an 138 example). An individual's concern about infection determined their probability of social 139 distancing. Individuals that socially-distanced cut 50% of their social contacts in the infection 140 layer, reducing their chances of becoming infected and helping to "flatten the curve" (Fig. S1 ). In the absence of any form of learning, communities hit later in the epidemic typically 153 have more severe outbreaks than those hit earlier (Figs. 2 & S2, Tables S1-5), with a reduced 154 time from the start of an outbreak to its peak, and a higher peak (Fig. S1 ). This pattern remains Table S1 ) or low (20% adherent; Table S2 ). These patterns were qualitatively similar 157 across all of the nine multiplex networks simulated. Outbreaks were typically more severe, but 158 with severity increasing less over time, when the infection layer was less modular. Outbreaks 159 were also more severe on average when modularity in the infection layer and communication (Tables S1-S5).
. CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprint this version posted July 11, 2020. . https://doi.org/10.1101/2020.07.09.20146985 doi: medRxiv preprint 164 Different forms of learning flatten the curve in different ways 165 Increased adherence to social distancing through Awareness of ill individuals and 166 through Social Construction of concern both reduced the severity of the outbreak but also 167 resulted in strikingly different epidemic outcomes (Fig. 2) . When individuals increased social 168 distancing due to Awareness, the height of the epidemic peak was reduced but the time to reach 169 the peak was not (Fig. 2) . In contrast when individuals became more likely to be adherent to Social Construction of concern had a much greater impact on mitigating epidemic 175 severity than awareness of actual incidence of illness among those with whom they 176 communicate. Only moderate influence of the social norms of communication neighbours had a 177 sizeable mitigating effect. It is important to note, however, that low levels of Social Construction 178 were ineffective (Fig. 2) . In contrast, increases in the impact of Awareness had a more linear 179 effect on the outcome of outbreaks (Fig. 2 ).
Results were qualitatively similar regardless of the modularity or homophily of the 181 network. However, the importance of Social Construction of concern in mitigating outbreak 182 severity was highest when the modularity of the communication network was lower (Qrel=0.4) 183 and when there was homophily according to predisposition (Tables S1-5).
Social Construction of concern driving the probability of adherence to social distancing is 186 particularly important for later hit communities 187 Increased adherence through Social Construction, but not as a result of awareness, helped 188 later-hit communities disproportionately whereby, rather than being more severe, outbreaks in 189 these communities were no worse or even less severe than those in communities hit early (Tables   190   S1-2) . Similarly, to the previous result, this outcome was not achieved with only weak Social 191 Construction (Tables S1-2). The importance of Social Construction in mitigating this effect was 192 highest when the infection layer of the multiplex network was more modular (Qrel=0.6) and in its 193 . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted July 11, 2020. . https://doi.org/10.1101/2020.07.09.20146985 doi: medRxiv preprint absence there was the steepest increase in outbreak severity with later starting date (Tables S1-194 5).
The Reassurance effect when neighbors in the communication layer are all healthy 197 amplifies these differences 198 In the absence of Social Construction, the reassurance effect of people becoming more 199 relaxed in their probability to adhere to social distancing over time is critical for epidemic 200 dynamics. When Social Construction of concern is absent, or only increases concern slowly, then 201 a strong Reassurance effect causes outbreaks within communities to be more severe on average 202 and disproportionately impacts later-hit communities so that outbreaks tend to be much more 
When people pay more attention to the concern of their network neighbours more, the proportion 212 of non-adherent people remains low even in later-hit communities (Figs. 4 & S5). As above, the 213 effectiveness of Social Construction of concern in preventing the adverse effects of relaxation CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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Our results clearly show the importance of all of the considered mechanisms (Awareness 225 of infection, Social Construction and Reassurance) in driving concern and shaping the ongoing 226 dynamics of disease progression (in both timing and severity) through distinct social 227 communities. Understanding how each mechanism acts, both independently and synergistically 228 with the other mechanisms, will be of critical importance in accurately anticipating infection 229 risks in large, heterogeneous populations. Differences in concern about risk between 230 communities will clearly amplify patchiness in spatiotemporal spread of infection (Fig. 2) . One 231 clear, unfortunate impact of effective protective behaviors that serve to buffer or insulate a 232 community from early disease spread is that they can foster social reassurance, degrading 233 concern in the need to maintain them (Fig. 3a) . It may therefore be that communities hit later in For the spread of COVID-19, these results indicate the potential for a "perfect storm".
The delay between behavioral response and local increase in disease prevalence hinders concern-239 based protective behaviors, whether based on observational awareness or on Social Construction. Policy may have therefore inadvertently exacerbated frustration by enforcing protective 249 behaviors in populations that had not internalized social norms of increased concern, nor had yet 250 experienced sufficiently high levels of infection as to be observable by the average individual. In 251 this way, protective policies likely served to insulate these communities from the initiation of 252 widespread transmission of disease, slowing the timeline for observable infection, and thereby 253 allowing the Reassurance effect to decrease in concern, and therefore in the belief in the need for 254 . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted July 11, 2020. . https://doi.org/10.1101/2020.07.09.20146985 doi: medRxiv preprint action. In a very practical sense, the jarringly imposed adoption of "shelter at home" protocols in 255 regions without widespread outbreaks likely allowed for the normalisation of perceived risk of 256 infection before the actual escalation of local disease incidence could be felt, leading to rejection 257 of the normalisation of the protective behaviors. If correct, we are just now primed by these 258 seemingly benevolent actions to experience worse outbreaks in thus-far-minimally-affected areas 259 that should have been able to use the delay to plan and prepare (both practically and 260 psychologically) to more effectively mount mitigation efforts when disease inevitably arrives.
Importantly, these models allow us to move beyond trying to explain currently observed 262 patterns or predict near-term coupled behavioral-epidemiological dynamics across communities.
They also allow us to consider shaping policies that rely on the normalization of risk mitigation 264 behaviors when the population may be most accepting of, and therefore adherent to them due to 265 having already normalized perceived risk of infection. By timing and scaling the magnitude of 266 proposed interventions to match both individually observable disease incidence and the socially-267 constructed concern for disease risks in each community, we may actually be able to prevent 268 spread more effectively than enforcing draconian measures that would be more 269 epidemiologically effective with broad public adherence, but are also more and more likely to be place" orders, in some cases suggesting that these pulses could continue even until a vaccine 273 enables us to achieve herd-immunity levels of protection (14). Instead, we propose that such 274 dynamic models should pulse in accordance with community-level concern. Critically, we do not 275 mean to suggest that mitigation efforts should respond/react to concern-driven demand, but 276 instead that policies can anticipate when they might be well enough received to enable concern-277 driven acceptance and adherence. Ultimately, even an ideal public health protocol will be 278 compromised if it does not achieve support and adherence from the public it strives to protect.
Vaccination is an excellent example -MMR inoculation can eradicate regional measles, mumps, 280 and rubella risks, but outbreaks in highly developed and resource-rich nations still occur due to is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted July 11, 2020. . https://doi.org/10.1101/2020.07.09.20146985 doi: medRxiv preprint public health policy for vaccination and incorporate explicit focus (including modeling efforts) to 286 anticipate public concern, acceptance of, and adherence to, any recommendations.
Beyond the results presented here, there are obvious, immediate, additional questions that 288 need to be considered. These range from characterizing regional differences in the overlap These effects may go far beyond the simple community dynamics we have investigated here.
That said, even from just these first models, there are already clear policy implications.
Strong, reinforced social norms are critical in maintaining a community's adherence to 302 social distancing over time. It is likely that there is a highly sensitive window during which the 303 perception of risk is high enough to foster adoption of protective behaviors that may be 304 normalized before lack of global prevalence provides false reassurance that could undermine 305 their adoption. Once normalized, behaviors may be passively (rather than actively maintained), 306 meaning that reassurance is less likely to decrease their observance. We call this window the 307 behaviourally receptive phase. Reassurance effects easily compromise effective early 308 interventions, even in the absence of social amplification (social construction could only increase 309 concern in our models), meaning that communities that succeed in delaying incipient outbreaks 310 may experience worse overall outcomes than those hit with higher incidence earlier on. Beyond 311 understanding how and why these dynamics might be observed, these models also suggest 312 methods for purposeful intervention based explicitly on combatting reassurance effects. Asking 313 community leaders to be vocal about their understanding of risk in the community can support 314 ongoing social construction of concern that will strengthen social norms around protective 315 behaviors. Municipal efforts to support families with loved ones who have been diagnosed may 316 . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted July 11, 2020. . https://doi.org/10.1101/2020.07.09.20146985 doi: medRxiv preprint meaningfully amplify awareness in ways that combat reassurance while focusing on positive 317 aspects of community rather than individual fear and isolation. 318 Our work has demonstrated the critical and inextricably intertwined roles that social 319 construction of risk perception, community awareness of disease incidence, and reassurance 320 effects from local absence of active cases can have on the success of outbreak mitigative 321 policies. We see footprints of these effects in the observed dynamics of the COVID-19 pandemic 322 as we write, and we advocate for additional discussion building on these explicit insights to 323 become a greater focus for both research and policy. We used stochastic simulations to test how the spread of concern influenced epidemic dynamic categories: children, young (low-risk) adults and old (high-risk) adults and split between two 331 distinct "predispositions". We then simulated realistic social networks for our populations, using 332 coupled (multiplex) networks for the spread of infection and the spread of concern about the 333 disease (Fig. 5) . We recorded epidemic outcomes for a range of network structures and proposed 334 models for the change in concern of individuals over time. All modelling was conducted in CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted July 11, 2020. . https://doi.org/10.1101/2020.07.09.20146985 doi: medRxiv preprint Social network generation 347 We generated 9 multiplex social networks that connected all individuals within a 348 "communication" layer that influenced the spread of concern about the disease and an Concern model 365 We modelled the spread of concern about the disease through the communication layer as 366 a complex contagion (58). Whether an individual was adherent or not (binary trait) depended on 367 a Bernoulli draw based on an underlying probability which we term concern. Consequently, it 368 was possible for individuals to move from being non-adherent to adherent but also for them to 369 return to being non-adherent. Individuals with intermediate levels of concern were likely to 370 fluctuate between adherent and non-adherent states. Initial levels of concern resulted in a 50%, 371 20% or5% chance of adherence. The underlying concern of all adults could then be influenced 372 by Social Construction, Awareness or Reassurance (Fig. 5) . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted July 11, 2020. . https://doi.org/10.1101/2020.07.09.20146985 doi: medRxiv preprint meaning that the probability of transmission across them was negligible. If an individual became 378 non-adherent then these edge weights returned to their initial values.
Infectious disease model 381 We modelled the spread of SARS-CoV-2 using an age-structured stochastic SEIRD To test the combined effects of social construction of concern, increases in concern due 400 to awareness of incidence of disease, and relaxation of adherence to social distancing due to 401 reassurance effects from not knowing infected people, we first calculated a measure of outbreak 402 severity that took into account the height and timing of the epidemic peak in each community 403 (see Supplementary Methods). We then used linear mixed effects models to ascribe variation in CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted July 11, 2020. . https://doi.org/10.1101/2020.07.09.20146985 doi: medRxiv preprint Acknowledgments 409 We thank Mario Small for useful conversations in formulating these questions and for comments 410 on the draft. We thank Dave Hodgson for allowing MJS time to pursue this work. We also 411 gratefully acknowledge funding support for this work from NSF DEB 2028710. . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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The copyright holder for this preprint this version posted July 11, 2020. .
Nadini CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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