Rory Wilson’s scientific contributions

Policy epidemiology utilizes human subject-matter experts (SMEs) to systematically surface, analyze, and categorize legally-enforceable policies. The Analysis and Mapping of Policies for Emerging Infectious Diseases project systematically collects and assesses health-related policies from all United Nations Member States. The recent proliferation of generative artificial intelligence (GAI) tools powered by large language models have led to suggestions that such technologies be incorporated into our project and similar research efforts to decrease the human resources required. To test the accuracy and precision of GAI in identifying and interpreting health policies, we designed a study to systematically assess the responses produced by a GAI tool versus those produced by a SME. We used two validated policy datasets, on emergency and childhood vaccination policy and quarantine and isolation policy in each United Nations Member State. We found that the SME and GAI tool were concordant 78.09% and 67.01% of the time respectively. It also significantly hastened the data collection processes. However, our analysis of non-concordant results revealed systematic inaccuracies and imprecision across different World Health Organization regions. Regarding vaccination, over 50% of countries in the African, Southeast Asian, and Eastern Mediterranean regions were inaccurately represented in GAI responses. This trend was similar for quarantine and isolation, with the African and Eastern Mediterranean regions least concordant. Furthermore, GAI responses only provided laws or information missed by the SME 2.14% and 2.48% of the time for the vaccination dataset and for the quarantine and isolation dataset, respectively. Notably, the GAI was least concordant with the SME when tasked with policy interpretation. These results suggest that GAI tools require further development to accurately identify policies across diverse global regions and interpret context-specific information. However, we found that GAI is a useful tool for quality assurance and quality control processes in health policy identification.

Introduction Countries across the world implemented diverse quarantine and isolation policies throughout the COVID-19 pandemic with varying levels of effectiveness. Their widespread use invites new considerations regarding the effectiveness of domestic quarantine and isolation policies, the ways they are enforced, and the jurisdictions responsible for ordering these measures. Methods We systematically analyzed legally-enforceable policies in current standing in each United Nations (UN) member state, assessing the authorities to quarantine and isolate individuals within national borders. We captured the text of each policy and categorized the responsible jurisdictional authority and enforcement mechanisms. Results Of UN member states, 91.67% (176/192) had legally-enforceable policies that addressed both quarantine and isolation. Two countries only had quarantine policies, seven only had isolation policies, and seven countries had neither. Jurisdictional quarantine authority was primarily vested in the national level (74.16%; 132/178), with the remainder mixed (22.47%; 40/178) and subnational only (3.37%; 6/178). Isolation authority was also primarily at the national level (69.40%; 127/183) but with a greater proportion mixed (27.87%; 51/183) and subnational only (2.73%; 5/183). Quarantine enforcement mechanisms were codified in a majority of countries (80.91%; 144/178) with nearly all (94.44%; 136/144) enforcing quarantine through monetary fines or incarceration penalties for non-compliant individuals. Isolation enforcement mechanisms were codified in an even greater number of countries (86.89%; 159/183), with 95.51% (149/156) having penalties for non-compliant individuals. Conclusion We created a novel repository for quarantine and isolation policies to assist in future outbreak responses. We identify specific country-level policy gaps, which can be addressed through epidemic and pandemic preparedness efforts. Finally, the repository provides the necessary evidence base for future research analyzing the impact of quarantine and isolation policies upon disease outbreak response outcomes.

Throughout the COVID-19 pandemic, militaries around the world mobilized at an unprecedented scale to support domestic response efforts. This was consistent with the growing trend of asset mobilization for military operations other than war during public health emergencies. However, the global scale and vast breadth of civil-military cooperation during the pandemic invites new considerations regarding the authority and scope of domestic operations of militaries during public health emergencies. We have systematically analyzed domestic military deployment policies in each UN member state, focusing on the authority, execution and scope of military involvement pertaining to domestic public health emergencies. We analyzed legally enforceable policies, including Constitutions, defense ministry authorizations, and legal frameworks. We categorized how each country codified the deployment of military assets, who holds authority for deployment and the procedural mechanisms for deployments. Our findings revealed that of countries with active military forces, nearly all (170/171) have codified rules on domestic military deployment and 90.59% (154/170) allow military mobilization through executive orders. Furthermore, 58.48% (100/171) of countries with an active military have codified separation of powers to ensure that civilian decision makers are exclusively empowered to mobilize military forces. Finally, we found that 74.85% (128/171) of countries included language that authorized military involvement in domestic military operations other than war. Our findings provide critical data for analyzing the relationship between military operations and public health outcomes, including how specific domestic military deployment policies impact the speed and effectiveness of military involvement in public health emergencies.

Policy epidemiology utilizes human subject-matter experts (SMEs) to systematically surface, analyze, and categorize legally-enforceable policies. The Analysis and Mapping of Policies for Emerging Infectious Diseases project systematically collects and assesses health-related policies from all United Nations Member States. The recent proliferation of generative artificial intelligence (GAI) tools powered by large language models have led to suggestions that such technologies be incorporated into our project and similar research efforts to decrease the human resources required. To test the accuracy and precision of GAI in identifying and interpreting health policies, we designed a study to systematically assess the responses produced by a GAI tool versus those produced by a SME. We used two validated policy datasets, on emergency and childhood vaccination policy and quarantine and isolation policy in each United Nations Member State. We found that the SME and GAI tool were concordant 78.09% and 67.01% of the time respectively. It also significantly hastened the data collection processes. However, our analysis of non-concordant results revealed systematic inaccuracies and imprecision across different World Health Organization regions. Regarding vaccination, over 50% of countries in the African, Southeast Asian, and Eastern Mediterranean regions were inaccurately represented in GAI responses. This trend was similar for quarantine and isolation, with the African and Eastern Mediterranean regions least concordant. Furthermore, GAI responses only provided laws or information missed by the SME 2.14% and 2.48% of the time for the vaccination dataset and for the quarantine and isolation dataset, respectively. Notably, the GAI was least concordant with the SME when tasked with policy interpretation. These results suggest that GAI tools require further development to accurately identify policies across diverse global regions and interpret context-specific information. However, we found that GAI is a useful tool for quality assurance and quality control processes in health policy identification.