Leveraging environmental drivers to predict vector-borne disease transmission

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