PROJECT SUMMARY (ABSTRACT) Contact network epidemiology is a compelling epidemiologic framework that aims to model dynamic interactions of people over their social networks in order to track infection cascades, especially for communicable diseases. Network-based simulations in contact network epidemiology can incorporate variations in people’s attributes and behaviors (e.g. age, race/ethnicity, wearing a facial mask), their interaction patterns (e.g. homophily or assortativity), and social structures (e.g. social norms and policies including non-pharmaceutical interventions [NPIs]). Although obtaining precise network data is challenging, it can guide us to identify potential working network intervention strategies, which may prove beneficial in addressing the COVID-19 pandemic. Using the framework of network interventions, a pilot simulation study proposed alternative NPI strategies to the stay-at-home order, in which transmission is mitigated while people’s socioeconomic activities are sustained (Nishi et al, 2020, PNAS). In the most effective dividing + balancing groups strategy, a social group (e.g. employees of the same workplace and students of the same school) is divided randomly into two subgroups with an equal number to reduce the number of physical contacts. If it is operated in a spatial manner, additional space for the subgroups is prepared; if it is operated in a temporal manner, the two subgroups will engage in their activities during different business hours. Therefore, the strategy would allow people to engage in the same magnitude of economic activities. The strength of the proposed strategy is that it does not require actual network data, which is difficult to obtain in most cases. Following the pilot study, this research seeks to create other novel NPI strategies for infectious disease control (the targets are both COVID-19 and other emerging diseases) (Aim 1). This research also seeks to create novel network intervention strategies for vaccine allocation (Aim 2). The proposed strategies for mitigating an epidemic and optimizing vaccine allocation will not, in principle, require actual network data. Therefore, their potential effect needs to be examined using network-based simulations with realistic assumptions or using other approaches, including mathematical modeling. The utilized social network will be based on a sample city of 10,000 individuals (Nishi et al, 2020, PNAS) and various network structures that are publicly available (the use of secondary data). Moreover, this research will analyze the role of early warning signals (EWS), which has been developed in non-linear dynamical systems in the infectious disease control context. I plan to use the 76 California County COVID-19 data (Aim 3).

PUBLIC HEALTH RELEVANCE STATEMENT (NARRATIVE) The COVID-19 pandemic is the most threatening public health challenge in the 21st century – the number of cumulative cases has reached 184.8 million as of July 7, 2021 with 3.996 million deaths (case-fatality rate of 2.2%), making COVID-19 the third leading cause of death in the US in 2020. Before effective measures such as mRNA vaccines are developed, non-pharmaceutical interventions (NPIs) are key for effective control; following development, optimal vaccine allocation becomes vital. This research explores novel NPI and vaccine allocation strategies using simulations for controlling COVID-19 and other infectious diseases (pandemic preparedness).