# Leveraging environmental drivers to predict vector-borne disease transmission

> **NIH NIH R35** · STANFORD UNIVERSITY · 2020 · $52,568

## Abstract

PROJECT SUMMARY
Designing long-term control strategies to slow COVID-19 epidemics requires understanding the
impact of non-pharmaceutical interventions, their interaction with seasonal climate, and their
economic costs. We developed an epidemiological model, based on an SEIR framework, that
captures non-symptomatic and symptomatic transmission and time lags between infection,
hospitalization, and death, to explore the impact of non-pharmaceutical interventions. We
parameterized the model using publicly available data on biological rates and times and daily
COVID-19 death data from two California Bay Area counties. We estimated that the basic
reproduction number, R0, ranges from 2.6-4.3, and that current shelter-in-place orders have
reduced the effective reproduction number, Re, below one. In this project, we will use the model
to: 1) estimate transmission parameters and the impact of social distancing on long-term control
strategies for every county in California; 2) understand the interaction between climate
seasonality and control interventions; and 3) study the socio-economic factors underlying
epidemiological disparities across California counties, and the economic costs and benefits of
intervention strategies. This research will help to guide public health responses to the COVID-19
crisis and develop safe and effective exit strategies from stay-at-home orders.

## Key facts

- **NIH application ID:** 10154455
- **Project number:** 3R35GM133439-01S1
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Erin Mordecai
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $52,568
- **Award type:** 3
- **Project period:** 2019-09-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10154455

## Citation

> US National Institutes of Health, RePORTER application 10154455, Leveraging environmental drivers to predict vector-borne disease transmission (3R35GM133439-01S1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10154455. Licensed CC0.

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