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This research presents implications of the global pandemic for local government resiliency in the United States. The authors explore insights from local government officials and managers on the front lines of response and recovery efforts to the biological natural disaster. Findings from the latest nationwide survey of U.S. local governments regarding their preparedness for weather-related natural disasters also inform responses to the current crisis. Results indicate that local governments are innovating and taking strategic actions to fight the virus, even as COVID-19 has exposed social inequities that are exacerbated as the virus spreads. Survey findings of disaster readiness of local governments to weather-related disasters shows that small, resource-poor governments will not be able to respond well and social inequities will grow. Policy strategies at all levels of government must recognize and account for these inequities as threat of this virus subsides, to support stronger, more effective readiness for the next biological catastrophe.

Artificial intelligence (AI) chatbots like ChatGPT and Google Bard are computer programs that use AI and natural language processing to understand customer questions and generate natural, fluid, dialogue-like responses to their inputs. ChatGPT, an AI chatbot created by OpenAI, has rapidly become a widely used tool on the internet. AI chatbots have the potential to improve patient care and public health. However, they are trained on massive amounts of people’s data, which may include sensitive patient data and business information. The increased use of chatbots introduces data security issues, which should be handled yet remain understudied. This paper aims to identify the most important security problems of AI chatbots and propose guidelines for protecting sensitive health information. It explores the impact of using ChatGPT in health care. It also identifies the principal security risks of ChatGPT and suggests key considerations for security risk mitigation. It concludes by discussing the policy implications of using AI chatbots in health care.

The COVID-19 pandemic has caused a complementary infodemic, whereby various outlets and digital media portals shared false information and unsourced recommendations on health. In addition, journals and authors published a mass of academic articles at a speed that suggests a non-existent or a non-rigorous peer review process. Such lapses can promote false information and adoption of health policies based on misleading data. Reliable information is vital for designing and implementing preventive measures and promoting health awareness in the fight against COVID-19. In the age of social media, information travels wide and fast, emphasizing a need for accurate data to be corroborated swiftly and for preventing misleading information from wide dissemination. Here, we discuss the implications of the COVID-19 infodemic and explore practical ways to leverage health communication strategies to overcome it. We propose the “Infodemic Response Checklist” as a comprehensive tool to overcome the challenges posed by the current and any future infodemics.

This chapter contains sections titled: Introduction Background The Circle Algorithm: An MQAP The F AMS‐ACE 2 (Circle + Consensus Method) Algorithm Conclusions References