The risk of ZIKV emergence following an imported case will depend on the likelihood of mosquito-borne transmission. For emerging diseases like ZIKV, the public health and research communities initially face considerable uncertainty in the drivers and rates of transmission, given the lack of field and experimental studies and epidemiological data, and often derive insights through analogy to similar diseases. For our case study, we estimated county-level ZIKV transmission potential by
Ae. aegypti using a recently published model [
20], that derives some of its key parameters from DENV data. The utility of our framework depends on the validity of such estimates and will increase as our knowledge of ZIKV improves. However, we expect our results to be robust to most sources of uncertainty regarding ZIKV and DENV epidemiology, as they may influence the absolute but not relative county-level risks.
We estimated the ZIKV reproduction number (
R
0
), the average number of secondary infections caused by a single infectious individual in a fully susceptible population, for each Texas county following the method described in Perkins et al. [
20]. The method calculates
R
0
using a temperature-dependent formulation of the Ross-Macdonald model, where mosquito mortality rate (μ) and extrinsic incubation period of ZIKV (n) are temperature dependent functions; the human-mosquito transmission probability (b = 0.4), number of days of human infectiousness (c/
r = 3.5), and the mosquito biting rate (a = 0.67) are held constant at previously calculated values [
20‐
25]; and the economic-modulated mosquito-human contact scaling factor (m) is a function of county mosquito abundance and GDP data fit to historic ZIKV seroprevalence data [
20]. To account for uncertainty in the temperature-dependent functions (the extrinsic incubation period (EIP) and mosquito mortality rate) and in the relationship between economic index and the mosquito-to-human contact rate, Perkins et al. generated functional distributions via 1000 Monte Carlo samples from the underlying parameter distributions. We assume DENV estimates for these temperature-dependent functions, since we lack such data for ZIKV and these Flaviviruses are likely to exhibit similar relationships between temperature and EIP in
Ae. Aegypti [
25]. We used the resulting distributions to estimate
R
0
for each county, based on county estimates for the average August temperature, mosquito abundance from Kraemer et al. [
24], and GDP [
25]. Our
R
0
estimates were similar to those reported by Perkins et al. [
20] with 95% confidence intervals spanning from 0 to 3.1 (Additional file
1: Figure S3). Given this uncertainty, and that our primary aim is to demonstrate the risk assessment framework rather than provide accurate estimates of
R
0
for Texas, we use these estimates to estimate relative county-level transmission risks (by scaling the county
R
0
estimates from 0 to 1). In each simulation, we assume that a county’s
R
0
is the product of its relative risk and a chosen maximum
R
0
. For our case study, we assume a maximum county-level
R
0
of 1.5 This is consistent with historical arbovirus activity in Texas (which has never sustained a large arbovirus epidemic) and demonstrates the particular utility of the approach in distinguishing outbreaks from epidemics around the epidemic threshold of
R
0
= 1.