# Network Intervention Planning without Actual Network Data for Infectious Disease Control

> **NIH NIH K01** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2024 · $134,811

## Abstract

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).

## Key facts

- **NIH application ID:** 10763890
- **Project number:** 5K01AI166347-03
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** Akihiro Nishi
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $134,811
- **Award type:** 5
- **Project period:** 2022-02-25 → 2026-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10763890

## Citation

> US National Institutes of Health, RePORTER application 10763890, Network Intervention Planning without Actual Network Data for Infectious Disease Control (5K01AI166347-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10763890. Licensed CC0.

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