New Computer Model Assesses Risk of a Zika Epidemic in Real-Time

ZIKV importation and transmission risk estimates across Texas for August 2016.

A new model for assessing real-time risk of a Zika virus epidemic in the United States is described in research published in BMCInfectious Diseases. The computer simulation, based on data from Texas including population dynamics, historical infection rates, socioeconomics, and mosquito density, is designed to help policymakers gauge the underlying epidemic threat as cases first appear in U.S. cities.

BioMed Central notes that in 2016, the Centers for Disease Control and Prevention (CDC) recommended that public health officials trigger epidemic intervention when two non-familial locally-acquired cases of Zika are reported in an area. However, Zika importation and transmission rates vary widely, meaning that two such cases may pose very different threats in different locations. In this study, the authors describe a computer model that can be used to calculate the probability that the presence of two Zika cases in a given area will lead to an epidemic, based on real-time simulations of all the counties in the state of Texas.

Across the 254 counties in the state of Texas, the model predicted that Harris County, which includes the city of Houston, and Travis County, which includes the city of Austin, have the highest rates of Zika introductions by infected travelers. The counties located in the southeastern area of Texas were found to have the highest risk of Zika transmission from one person to another. The one Zika outbreak that occurred in Texas in Cameron County in November of 2016 falls within this region.

By combining all the data, the researchers found that the risk of a Zika epidemic varies widely across Texas counties. Even if two cases are reported locally, most Texas counties will have nearly no risk of an epidemic, while a few will have greater than 50 percent epidemic risk.

Spencer Fox, co-lead author and Ph.D. student at the University of Texas at Austin, said: “Our model was designed to quantify the risk of local Zika outbreaks as cases accumulate across Texas, taking into account international travel patterns, mosquito habitat, and the low detection rate of Zika infections. Its flexible framework can be readily applied to other US states and adapted for risk assessments of other emerging arboviruses, including Chikungunya, Dengue, and Yellow fever.”

Lauren Castro, co-lead author and Ph.D. student at The University of Texas at Austin, said: “The CDC’s recommendation to intervene following two reported Zika cases should ensure early action everywhere, even though Zika epidemic risk can vary enormously, even within a single state. Our model quantifies that variation in risk and can help officials prioritize high risk areas for monitoring and intervention resources.”

Dr. Lauren Ancel Meyers, senior author and Professor at The University of Texas at Austin, added: “Zika outbreaks require the importation of the disease by infected travelers followed by local mosquito-borne transmission. Our model combines these processes to estimate local emergence risk. It enables policymakers to think carefully about risk tolerance — the certainty required before intervening and the potential consequences of premature or delayed interventions.”

BioMed notes that this is the first study to assess both the risk of Zika arrival to an area and the risk of local spread by mosquitoes. The flexibility of the model design means that as new information becomes available on Zika dynamics, epidemiology and biology it can be updated to help public health officials assess situational awareness, according to the researchers

— Read more in Lauren A. Casgtro et al., “Assessing real-time Zika risk in the United States,” BMC Infectious Diseases (4 May 2017) (DOI: 10.1186/s12879-017-2394-9)

This article is published courtesy of Homeland Security News Wire

No Comments Yet

Leave a Reply

Your email address will not be published.

©2023. Global Health News Wire. Use Our Intel. All Rights Reserved. Washington, D.C.