Urban development has major impacts on connectivity among wildlife populations and is thus likely an important factor shaping pathogen transmission in wildlife. However, most investigations of wildlife diseases in urban areas focus on prevalence and infection risk rather than potential effects of urbanization on transmission itself. Feline immunodeficiency virus (FIV) is a directly transmitted retrovirus that infects many felid species and can be used as a model for studying pathogen transmission at landscape scales. We investigated phylogenetic relationships among FIV isolates sampled from five bobcat (
The impact of sampling bias on viral phylogeographic reconstruction
Genomic epidemiology plays an ever-increasing role in our understanding of and response to the spread of infectious pathogens. Phylogeography, the reconstruction of the historical location and movement of pathogens from the evolutionary relationships among sampled pathogen sequences, can inform policy decisions related to viral movement among jurisdictions. However, phylogeographic reconstruction is impacted by the fact that the sampling and virus sequencing policies differ among jurisdictions, and these differences can cause bias in phylogeographic reconstructions. Here we assess the potential impacts of geographic-based sampling bias on estimated viral locations in the past, and on whether key viral movements can be detected. We quantify the effect of bias using simulated phylogenies with known geographic histories, and determine the impact of the biased sampling and of the underlying migration rate on the accuracy of estimated past viral locations. We find that overall, the accuracy of phylogeographic reconstruction is high, particularly when the migration rate is low. However, results depend on sampling, and sampling bias can have a large impact on the numbers and nature of estimated migration events. We apply these insights to the geographic spread of Ebolavirus in the 2014-2016 West Africa epidemic. This work highlights how sampling policy can both impact geographic inference and be optimized to best ensure the accuracy of specific features of geographic spread.
more »
« less
- Award ID(s):
- 2054347
- PAR ID:
- 10433225
- Editor(s):
- Falcão de Oliveira, Everton
- Date Published:
- Journal Name:
- PLOS Global Public Health
- Volume:
- 2
- Issue:
- 9
- ISSN:
- 2767-3375
- Page Range / eLocation ID:
- e0000577
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
Abstract Lynx rufus ) populations in coastal southern California that appear isolated due to major highways and dense urban development. Divergence dates among FIV phylogenetic lineages in several cases reflected historical urban growth and construction of major highways. We found strong FIV phylogeographic structure among three host populations north‐west of Los Angeles, largely coincident with host genetic structure. In contrast, relatively little FIV phylogeographic structure existed among two genetically distinct host populations south‐east of Los Angeles. Rates of FIV transfer among host populations did not vary significantly, with the lack of phylogenetic structure south‐east of Los Angeles unlikely to reflect frequent contemporary transmission among populations. Our results indicate that major barriers to host gene flow can also act as barriers to pathogen spread, suggesting potentially reduced susceptibility of fragmented populations to novel directly transmitted pathogens. Infrequent exchange of FIV among host populations suggests that populations would best be managed as distinct units in the event of a severe disease outbreak. Phylogeographic inference of pathogen transmission is useful for estimating the ability of geographic barriers to constrain disease spread and can provide insights into contemporary and historical drivers of host population connectivity. -
This paper presents an integrated computational framework combining a Molecular Dynamics (MD) based social force pedestrian movement model and a stochastic infection dynamics model to evaluate the spread of viral infectious diseases during air-transportation. We apply the multiscale model for three infectious (1) Ebola (2) Influenza (H1N1 strain) and (3) SARS pathogens with different transmission mechanisms and compare the pattern of propagation during an Airbus A320 carrier boarding and deplaning at an airport gate. The objective of this analysis is to assess the influence of pedestrian movement on infection spread during air travel.more » « less
-
This paper presents an integrated computational framework combining a molecular Dynamics (MD) based social force pedestrian movement model and a stochastic infection dynamics model to evaluate the spread of viral infectious diseases during air-transportation. We apply the multiscale model for three infectious (1) Ebola (2) Influenza (H1N1 strain) and (3) SARS pathogens with different transmission mechanisms and compare the pattern of propagation during an Airbus A320 carrier boarding and deplaning at an airport gate. The objective of this analysis is to assess the influence of pedestrian movement on infection spread during air travelmore » « less
-
The estimation of malaria parasite migration can play a vital role in informing elimination strategies by pinpointing regions with higher parasite migration that act as transmission sources, and that could be the focus of elimination interventions. Gene flow simulation methods such as Estimated Effective Migration Surfaces (EEMS) and Migration and Population-Size Surfaces (MAPS) use a Markov Chain Monte Carlo simulation-based approach to visualize a species' migration and diversity. These methods utilize georeferenced genomic data and present output in the form of migration contour maps. Despite their potential, there is uncertainty in EEMS and MAPS outputs when sampling locations are sparse - an aspect that remains under-explored in current research. We present a framework designed to systematically assess the impact of sample locations and sample size on migration contours in gene flow simulations that goes beyond the posterior probability map available in EEMS. We test our framework using publicly available genomic data collected from Cambodia and border regions of Thailand, Vietnam, and Laos during 2008-2013. The methodology leverages kernel density estimation and topological skeletons in conjunction with other spatial analysis methods to quantify the impact of sparse sample locations on gene flow simulations. Multiple sample resolutions were tested against a baseline resolution, and the findings highlight how migration contours vary with sampling resolution and how our approach can be applied to guide the production and mapping of reliable migration contours. Our research provides valuable insights about both the reliability and precision of model outputs when employing gene flow simulation techniques e.g., EEMS and MAPS, to estimate malaria parasite migration. The findings revealed that by employing our approach, we were able to maintain approximately 67% consistency between the contours and the reference dataset, even when utilizing only half of the sample locations. This knowledge will improve both the reliability and precision of these model outputs in future studies.more » « less
-
more » « less
Abstract Susceptibility to infectious diseases such as COVID-19 depends on how those diseases spread. Many studies have examined the decrease in COVID-19 spread due to reduction in travel. However, less is known about how much functional geographic regions, which capture natural movements and social interactions, limit the spread of COVID-19. To determine boundaries between functional regions, we apply community-detection algorithms to large networks of mobility and social-media connections to construct geographic regions that reflect natural human movement and relationships at the county level in the coterminous United States. We measure COVID-19 case counts, case rates, and case-rate variations across adjacent counties and examine how often COVID-19 crosses the boundaries of these functional regions. We find that regions that we construct using GPS-trace networks and especially commute networks have the lowest COVID-19 case rates along the boundaries, so these regions may reflect natural partitions in COVID-19 transmission. Conversely, regions that we construct from geolocated Facebook friendships and Twitter connections yield less effective partitions. Our analysis reveals that regions that are derived from movement flows are more appropriate geographic units than states for making policy decisions about opening areas for activity, assessing vulnerability of populations, and allocating resources. Our insights are also relevant for policy decisions and public messaging in future emergency situations.
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1371/journal.pgph.0000577
