How individuals' opinions influence society's resistance to epidemics: an agent-based model approach.

BACKGROUND: Protecting public health from infectious diseases often relies on the cooperation of citizens, especially when self-care interventions are the only viable tools for disease mitigation. Accordingly, social aspects related to public opinion have been studied in the context of the recent COVID-19 pandemic. However, a comprehensive understanding of the effects of opinion-related factors on disease spread still requires further exploration. METHODS: We propose an agent-based simulation framework incorporating opinion dynamics within an epidemic model based on the assumption that mass media channels play a leading role in opinion dynamics. The model simulates how opinions about preventive interventions change over time and how these changes affect the cumulative number of cases. We calibrated our simulation model using YouGov survey data and WHO COVID-19 new cases data from 15 different countries. Based on the calibrated models, we examine how different opinion-related factors change the consequences of the epidemic. We track the number of total new infections for analysis. RESULTS: Our results reveal that the initial level of public opinion on preventive interventions has the greatest impact on the cumulative number of cases. Its normalized permutation importance varies between 69.67% and 96.65% in 15 models. The patterns shown in the partial dependence plots indicate that other factors, such as the usage of the pro-intervention channel and the response time of media channels, can also bring about substantial changes in disease dynamics, but only within specific ranges of the dominant factor. CONCLUSIONS: Our results reveal the importance of public opinion on intervention during the early stage of the pandemic in protecting public health. The findings suggest that persuading the public to take actions they may be hesitant about in the early stages of epidemics is very costly because taking early action is critical for mitigating infectious diseases. Other opinion-related factors can also lead to significant changes in epidemics, depending on the average level of public opinion in the initial stage. These findings underscore the importance of media channels and authorities in delivering accurate information and persuading community members to cooperate with public health policies.

Multi-season transmission model of Eastern Equine Encephalitis.

Eastern Equine Encephalitis (EEE) is an arbovirus that, while it has been known to exist since the 1930's, recently had a spike in cases. This increased prevalence is particularly concerning due to the severity of the disease with 1 in 3 symptomatic patients dying. The cause of this peak is currently unknown but could be due to changes in climate, the virus itself, or host behavior. In this paper we propose a novel multi-season deterministic model of EEE spread and its stochastic counterpart. Models were parameterized using a dataset from the Florida Department of Health with sixteen years of sentinel chicken seroconversion rates. The different roles of the enzootic and bridge mosquito vectors were explored. As expected, enzootic mosquitoes like Culiseta melanura were more important for EEE persistence, while bridge vectors were implicated in the disease burden in humans. These models were used to explore hypothetical viral mutations and host behavior changes, including increased infectivity, vertical transmission, and host feeding preferences. Results showed that changes in the enzootic vector transmission increased cases among birds more drastically than equivalent changes in the bridge vector. Additionally, a 5% difference in the bridge vector's bird feeding preference can increase cumulative dead-end host infections more than 20-fold. Taken together, this suggests changes in many parts of the transmission cycle can augment cases in birds, but the bridge vectors feeding preference acts as a valve limiting the enzootic circulation from its impact on dead-end hosts, such as humans. Our what-if scenario analysis reveals and measures possible threats regarding EEE and relevant environmental changes and hypothetically suggests how to prevent potential damage to public health and the equine economy.