The duration of travel impacts the spatial dynamics of infectious diseases

Abstract

Humans can impact the spatial transmission dynamics of infectious diseases by introducing pathogens into susceptible environments. The rate at which this occurs depends in part on human-mobility patterns. Increasingly, mobile-phone usage data are used to quantify human mobility and investigate the impact on disease dynamics. Although the number of trips between locations and the duration of those trips could both affect infectious-disease dynamics, there has been limited work to quantify and model the duration of travel in the context of disease transmission. Using mobility data inferred from mobile-phone calling records in Namibia, we calculated both the number of trips between districts and the duration of these trips from 2010 to 2014. We fit hierarchical Bayesian models to these data to describe both the mean trip number and duration. Results indicate that trip duration is positively related to trip distance, but negatively related to the destination population density. The highest volume of trips and shortest trip durations were among high-density districts, whereas trips among low-density districts had lower volume with longer duration. We also analyzed the impact of including trip duration in spatial-transmission models for a range of pathogens and introduction locations. We found that inclusion of trip duration generally delays the rate of introduction, regardless of pathogen, and that the variance and uncertainty around spatial spread increases proportionally with pathogen-generation time. These results enhance our understanding of disease-dispersal dynamics driven by human mobility, which has potential to elucidate optimal spatial and temporal scales for epidemic interventions.

Spatial distribution characteristics of the COVID-19 pandemic in Beijing and its relationship with environmental factors

“…Population density and distance from living quarters can reflect the intensity of human activity in a region. This reveals patterns of infectious disease outbreaks ( Giles et al, 2020 ). During the development of COVID-19 in Beijing, there was always a relation between COVID-19 outbreak, population density, and distance to living places.…”

“…Atmospheric pollutants increase the vulnerability to infectious viruses because they reduce human immunity ( Ogen, 2020 ). However, while environmental factors can reveal patterns in the development of infectious diseases on a large scale, close contact due to population movements may significantly impact the spread of infectious diseases ( Giles et al, 2020 ; Wesolowski et al, 2015 ). Population density and distance from living quarters can reflect the intensity of human activity in a region.…”

Predictive limitations of spatial interaction models: a non-Gaussian analysis

“…but other examples include mean squared errors 29 , and Pearson correlation coefficients 30,31 . However, real data sets give integer valued data, feature no negative flows, and usually have a high proportion of very small flows.…”

Trip duration modifies spatial spread of infectious diseases

“…The challenge is to identify the kinds of data that can expand our theories for how pathogens spread between populations using such models. Giles et al (9) achieve this with cell-phone data from Namibia. They analyzed over 259 million trips to determine how far people travel, how long they stay there, and how these factors are determined by the sizes of originating and destination locations.…”

“…The nonrandomness of trip duration is a key piece of data not typically available and incorporated into models of infectious disease transmission. Giles et al (9) used their data on trip duration in Namibia, in combination with data on where people typically move from and to, to evaluate how different infectious diseases are predicted to spread through the country and to quantify how models that ignore trip duration are likely to make errors in predicting spatial spread. They considered a range of infectious diseases, including Ebola, influenza, and measles, that vary in pathogen traits-particularly how likely transmission is when an infectious person and a susceptible person come into contact and how long an infected person remains infectious.…”