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Whitfield, J.

Publications and source records attributed to Whitfield, J..

2 recordsLinked to original sources

Population Structure and Migration in the Eastern Highlands of Papua New Guinea; a Region Impacted by the kuru Epidemic

Populations of the Eastern Highlands of Papua New Guinea (EHPNG, area 11,157 km2) lived in relative isolation from the rest of the world until the mid-20th century, and the region contains a wealth of linguistic and cultural diversity. Notably, several populations of EHPNG were devastated by an epidemic prion disease, kuru, which at its peak in the mid-twentieth century led to some villages being almost depleted of adult women. Until now, population genetic analyses to learn about genetic diversity, migration, admixture and the impact of the kuru epidemic have been restricted to a small number of variants or samples. Here, we present a population genetic analysis of the region based on genome-wide genotype data of 943 individuals from 21 linguistic groups and 68 villages in EHPNG, including 34 villages in the South Fore linguistic group, which the group most affected by kuru. We find a striking degree of genetic population structure in the relatively small region (average FST between linguistic groups 0.024; area similar to Jamaica). The genetic population structure correlates well with linguistic grouping, with some noticeable exceptions. Also, we find evidence of a series of discrete admixture events that appear to coincide with previously expected introduction dates and movement of the sweet potato in the region. Finally, we find signatures of more recent migration within the EHPNG region and observe a substantial excess of female migration into the heavily kuru-affected South Fore linguistic group (p=0.0017, Chi Squared Test), likely reflecting the sex-bias in incidence of kuru. These data provide an in-depth look at the population genetics of a region devastated by a prion disease epidemic and which was until recently relatively isolated from the rest of the world.

genetics↗

Modeling Zika Virus Spread in Colombia Using Google Search Queries and Logistic Power Models

Public health agencies generally have a small window to respond to burgeoning disease outbreaks in order to mitigate the potential impact. There has been significant interest in developing forecasting models that can predict how and where a disease will spread. However, since clinical surveillance systems typically publish data with a lag of two or more weeks, there is a need for complimentary data streams that can close this gap. We examined the usefulness of Google Trends search data for analyzing the 2016 Zika epidemic in Colombia and evaluating their ability to predict its spread. We calculated the correlation and the time delay between the reported case data and the Google Trends data using variations of the logistic growth model, and showed that the data sets were systematically offset from each other, implying a lead time in the Google Trends data. Our study showed how Internet data can potentially complement clinical surveillance data and may be used as an effective early detection tool for disease outbreaks.

epidemiology↗