Influence of Lived Experiences on Public Responses to Future Diseases via (De)Sensitization of Concern.

OBJECTIVES: Public responses to a future novel disease might be influenced by a subset of individuals who are either sensitized or desensitized to concern-generating processes through their lived experiences during the coronavirus disease 2019 (COVID-19) pandemic. Such influences may be critical for shaping public health messaging during the next emerging threat. METHODS: This study explored the potential outcomes of the influence of lived experiences by using a dynamic multiplex network model to simulate a COVID-19 outbreak in a population of 2000 individuals, connected by means of disease and communication layers. Then a new disease was introduced, and a subset of individuals (50% or 100% of hospitalized during the COVID-19 outbreak) was assumed to be either sensitized or desensitized to concern-generating processes relative to the general population, which alters their adoption of non-pharmaceutical interventions (social distancing). RESULTS: Altered perceptions and behaviors from lived experiences with COVID-19 did not necessarily result in a strong mitigating effect for the novel outbreak. When public disease response is already strong or sensitization is assumed to be a robust effect, then a sensitized subset may enhance public mitigation of an outbreak through social distancing. CONCLUSIONS: In preparing for future outbreaks, assuming an experienced and disease-aware public may compromise effective design of effective public health messaging and mitigative action.

Balancing timeliness of reporting with increasing testing probability for epidemic data.

Reporting of epidemiological data requires coordinated action by numerous agencies, across a multitude of logistical steps. Using collated and reported information to inform direct interventions can be challenging due to associated delays. Mitigation can, however, occur indirectly through the public generation of concern, which facilitates adherence to protective behaviors. We utilized a coupled-dynamic multiplex network model with a communication- and disease-layer to examine how variation in reporting delay and testing probability are likely to impact adherence to protective behaviors, such as reducing physical contact. Individual concern mediated adherence and was informed by new- or active-case reporting, at the population- or community-level. Individuals received information from the communication layer: direct connections that were sick or adherent to protective behaviors increased their concern, but absence of illness eroded concern. Models revealed that the relative benefit of timely reporting and a high probability of testing was contingent on how much information was already obtained. With low rates of testing, increasing testing probability was of greater mitigating value. With high rates of testing, maximizing timeliness was of greater value. Population-level reporting provided advanced warning of disease risk from nearby communities; but we explore the relative costs and benefits of delays due to scale against the assumption that people may prioritize community-level information. Our findings emphasize the interaction of testing accuracy and reporting timeliness for the indirect mitigation of disease in a complex social system.