Finding disease outbreak locations from human mobility data

Abstract

Finding the origin location of an infectious disease outbreak quickly is crucial in mitigating its further dissemination. Current methods to identify outbreak locations early on rely on interviewing affected individuals and correlating their movements, which is a manual, time-consuming, and error-prone process. Other methods such as contact tracing, genomic sequencing or theoretical models of epidemic spread offer help, but they are not applicable at the onset of an outbreak as they require highly processed information or established transmission chains. Digital data sources such as mobile phones offer new ways to find outbreak sources in an automated way. Here, we propose a novel method to determine outbreak origins from geolocated movement data of individuals affected by the outbreak. Our algorithm scans movement trajectories for shared locations and identifies the outbreak origin as the most dominant among them. We test the method using various empirical and synthetic datasets, and demonstrate that it is able to single out the true outbreak location with high accuracy, requiring only data of $N=4$ N = 4 individuals. The method can be applied to scenarios with multiple outbreak locations, and is even able to estimate the number of outbreak sources if unknown, while being robust to noise. Our method is the first to offer a reliable, accurate out-of-the-box approach to identify outbreak locations in the initial phase of an outbreak. It can be easily and quickly applied in a crisis situation, improving on previous manual approaches. The method is not only applicable in the context of disease outbreaks, but can be used to find shared locations in movement data in other contexts as well.

Influence of public health and infection control interventions during the severe acute respiratory syndrome coronavirus 2 pandemic on the in-hospital epidemiology of pathogens: in hospital versus community circulating pathogens

“…Furthermore, the extent to which the changes in the quarterly detection rates during the pandemic correlated with changes in mobility in Germany and the county of Marburg-Biedenkopf was analyzed. Here, the mobility data analyzed by the COVID-19 mobility project of the Humboldt University Berlin in cooperation with the Robert-Koch-Institute were included [9]. The correlation coefficient of the bivariate correlation was calculated according to Spearman-Rho with a significance level of P = 0.05 in SPSS.…”

Influence of public health and infection control interventions during the severe acute respiratory syndrome coronavirus 2 pandemic on the in-hospital epidemiology of pathogens: In hospital versus community circulating pathogens

“…Here, the mobility data analyzed by the COVID-19 mobility project of the Humboldt University Berlin in cooperation with the Robert-Koch-Institute were included [9]. The occurrences during the COVID-19 pandemic and the accompanying prevention measures, were displayed on a timeline.…”

Impact of Network Centrality and Income on Slowing Infection Spread after Outbreaks

“…These studies also overlook how differences in social isolation levels across regions are shaped by socioeconomic inequalities. Given recent literature on the outsized influence of the outbreak region on the trajectory of a communicable disease [24], this study simulates outbreaks beginning in every region of the MRSP, to allow for generalizable findings that do not assume that the next outbreak will begin in the same region as the last.…”