Impact of human travel on infectious disease dynamics

Infectious diseases impact the social, economic, and wellbeing of societies. Their transmission depends on the dynamics of human movement; however, there is no systematic model for human movement dynamics. Humans can enable the spread of infectious diseases on local and global scales. Policy makers and researchers urgently need an understanding of the epidemiological impact of human travel, both to refine the basic understanding of the nonlinear dynamics of epidemics and to provide guidance on planning public health responses. Human mobility has been nearly impossible to measure on the time scales and for sample sizes relevant to disease transmission. The recent advent of novel methods and data sets have changed the potential to characterize human travel, population susceptibility, and disease dynamics, especially in low income countries where the burden of disease is high.

Human mobility: There is no general model for the dynamics of human travel that has been validated in low-income settings. Using the location of calls and text messages sent by mobile phone subscribers, the researcher has pioneered methods to extract mobility patterns and incorporate these patterns into disease models. They have established a network to access mobile phone data sets from across Africa (Kenya, Rwanda, Namibia, Cote d’Ivoire, Senegal, Sierra Leone, and Madagascar) and Asia (Pakistan, Nepal, Malaysia, Cambodia, India, Thailand, and Bangladesh). Through this unique position, they will develop a generalizable model describing travel behavior for both populations and individuals across continents.

Directly transmitted diseases: Seasonal variations in transmission are an important driver of epidemics and an excellent probe for the relative epidemiological impact of seasonal human movement and climatic influences. A time series analysis of seasonal travel will be combined with reported cases of seasonal directly transmitted diseases 20 years in Thailand. These comparisons will provide data-driven estimates of the temporal relationships between drivers of disease prevalence, incidence, and mobility across infections, hence greatly improving forecasting models.

Importation of vector-borne diseases: Human travel underlies the emergence of dengue and malaria in previously non-endemic areas. To control or eliminate these diseases, researchers and policy makers need to identify spatial risk regions with a high rate of pathogen importation (sinks) and primary parasite reservoirs (sources). Using statistical techniques learned during graduate school, the researcher will identify dynamic source and sink regions for targeted control and surveillance.

Mobility and drug-resistance spread for malaria: Highly mobile populations in Asia are more likely to become infected with malaria parasites resistant to the first line course of drug treatment. The researcher will use survey and mobile phone data to identify high-risk routes of travel across Asia. This will identify the possible impact of mobility on drug-resistant parasite importation.

In this fellowship, the researcher will perform a thorough analysis of the role of human travel on infectious disease dynamics by developing a general model of travel and in disease specific contexts of seasonality and disease importation. Beyond insights into fundamental science, this work has a broad range of policy applications to understand the impact of targeted public health interventions and forecasting methods to predict disease outbreaks.

Drug Resistance
Epidemiology
Indigenous Cases & Importation
Modeling

Jul 2016 — Jun 2021

$70,000

United States