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Populations, Volume 1, Issue 1 (March 2025) – 5 articles

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21 pages, 10791 KiB  
Review
Diffusion and Percolation: How COVID-19 Spread Through Populations
by Jeffrey E. Harris
Populations 2025, 1(1), 5; https://doi.org/10.3390/populations1010005 - 20 Feb 2025
Viewed by 305
Abstract
I rely on the key concepts of diffusion and percolation to characterize the sequential but overlapping phases of the spread of infection through entire populations during the first year of the COVID-19 pandemic. Data from Los Angeles County demonstrate an extended initial diffusion [...] Read more.
I rely on the key concepts of diffusion and percolation to characterize the sequential but overlapping phases of the spread of infection through entire populations during the first year of the COVID-19 pandemic. Data from Los Angeles County demonstrate an extended initial diffusion phase propelled by radial geographic spread, followed by percolation within hotspots fueled by the presence of multigenerational households. Data from New York City, by contrast, reveal rapid initial diffusion along a unique, extensive subway network. Subsequent percolation within multiple hotspots, similarly powered by a high density of multigenerational households, exerted a positive feedback effect that further enhanced diffusion. Data from Florida counties support the generality of the phenomenon of viral transmission from more mobile, younger individuals to less mobile, older individuals. Data from the South Brooklyn hotspot reveal the limitations of some forms of government regulation in controlling mobility patterns that were critical to the continued percolation of the viral infection. Data from a COVID-19 outbreak at the University of Wisconsin—Madison demonstrate the critical role of a cluster of off-campus bars as an attractor for the continued percolation of infection. The evidence also demonstrates the efficacy of quarantine as a control strategy when the hotspot is contained and well identified. Full article
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Figure 1

Figure 1
<p>Weekly cumulative incidence of confirmed COVID-19 cases among County Statistical Areas (CSAs) within Los Angeles County from 22 March through 26 April 2020. The lighter-shaded CSAs correspond to a cumulative incidence between 120 and 360 per 100,000, while the darker-shaded CSAs correspond to a cumulative incidence of at least 360 per 100,000. For CSA definitions and boundaries, see [<a href="#B22-populations-01-00005" class="html-bibr">22</a>]. For data on COVID-19 case incidence, see [<a href="#B23-populations-01-00005" class="html-bibr">23</a>]. For further details of the data analysis, see [<a href="#B20-populations-01-00005" class="html-bibr">20</a>].</p>
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<p>(<b>a</b>) CSA-specific incidence of confirmed COVID-19 cases diagnosed in Los Angeles County during 26 October 2020–10 January 2021. (<b>b</b>) CSA-specific prevalence of at-risk multigenerational households. An at-risk multigenerational household was defined as having at least four persons, of whom at least one person was 18–34 years of age and at least one other person was at least 45 years of age. For data on COVID-19 incidence in panel (<b>a</b>), see [<a href="#B23-populations-01-00005" class="html-bibr">23</a>]. Outcome variables in both panels are classified in septiles. For data from the American Community Survey 2015-2019 in panel (<b>b</b>), see [<a href="#B24-populations-01-00005" class="html-bibr">24</a>].</p>
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<p>Daily COVID-19 incidence (smaller datapoints) and weekly incidence (larger datapoints) during 1 March–28 June 2020 in four Florida counties, for age groups 20–59 and 60 or more years. The plateau starting in mid-March followed the governor’s executive order (EO) 20-68 (17 March), imposing restrictions on bars, nightclubs, and restaurants [<a href="#B25-populations-01-00005" class="html-bibr">25</a>], and EO 20-91 (3 April), limiting movement outside the home to essential activities and confining business activities to essential services [<a href="#B26-populations-01-00005" class="html-bibr">26</a>]. The renewed upswing starting in May followed EO 20-112 (4 May) [<a href="#B27-populations-01-00005" class="html-bibr">27</a>], reopening most activities to 25 percent capacity, and EO 20-123 (18 May) [<a href="#B28-populations-01-00005" class="html-bibr">28</a>], to 50 percent activity. Data reported from a study of the 16 most populous counties [<a href="#B29-populations-01-00005" class="html-bibr">29</a>].</p>
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<p>(<b>a</b>) COVID-19 hospital admissions by borough of residence, New York City, 29 February–8 March 2020. (<b>b</b>) Timing and location of 78 viral isolates with dominant clade A2a collected from patients in the Mount Sinai Health System, New York City, 11–19 March 2020. Pink bubbles in panel (<b>b</b>) denote a cluster of 17 samples sharing a common point mutation, A1844V, in open reading frame (ORF) 1b. Sources: (<b>a</b>) [<a href="#B30-populations-01-00005" class="html-bibr">30</a>], (<b>b</b>) [<a href="#B31-populations-01-00005" class="html-bibr">31</a>,<a href="#B32-populations-01-00005" class="html-bibr">32</a>].</p>
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<p>Cumulative reported COVID-19 cases per 10,000 population (<b>a</b>) by 31 March 2020 and (<b>b</b>) by 8 April 2020 in New York City’s 177 Zip Code Tabulation Areas (ZCTAs). Source: [<a href="#B30-populations-01-00005" class="html-bibr">30</a>].</p>
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<p>(<b>a</b>) ZCTA-specific incidence of COVID-19 cases diagnosed in New York City during 8 April–5 May 2020. (<b>b</b>) ZCTA-specific prevalence of at-risk multigenerational households. An at-risk multigenerational household was defined as having at least four persons, of whom at least one person was 18–34 years of age and at least one other person was at least 45 years of age. ZCTA-specific estimates were aggregated over census block groups, shown in the right-hand map. Outcome measures in both panels are classified in quartiles. For data on COVID-19 incidence, see [<a href="#B30-populations-01-00005" class="html-bibr">30</a>]. For data from the American Community Survey 2015–2019, see [<a href="#B24-populations-01-00005" class="html-bibr">24</a>].</p>
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<p>Subway lines in New York City. At the start of the pandemic, 80.8% of residents lived in a Zip Code Tabulation Area (ZCTA) containing a subway station, while another 13.9% lived in a geographically (or <span class="html-italic">g</span>-) contiguous ZCTA. Source: [<a href="#B39-populations-01-00005" class="html-bibr">39</a>].</p>
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<p>The 7 (Flushing) subway line connecting Manhattan to Queens. (<b>a</b>) Classification of stations into three categories: Manhattan (4 stations, colored gray); hotspot Queens (6 stations, yellow); and other Queens (12 stations, pink). The hotspot stations were situated in the cluster of ZCTAs in the Elmhurst area of Queens, which experienced a cumulative COVID-19 incidence exceeding 75 per 10,000 by 31 March 2020. (<b>b</b>) Principal origins of smartphone movements to the census block groups of the hotspot Queens stations. Source: [<a href="#B39-populations-01-00005" class="html-bibr">39</a>].</p>
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<p>Schematic illustrations of <span class="html-italic">g</span>-continuity, <span class="html-italic">s</span>-contiguity, and compound contiguity. Numbered squares are geographic areas. Connected circles are stations on subway lines. (<b>a</b>) Areas 1 and 2 are <span class="html-italic">g</span>-contiguous. Areas 2 and 3 are <span class="html-italic">s</span>-contiguous but they are not <span class="html-italic">g</span>-contiguous, as 2 and 3 are separated by a body of water. Areas 1 and 3 are <span class="html-italic">gs</span>-contiguous. (<b>b</b>) Areas 1 and 3 are <span class="html-italic">g</span><sup>2</sup>-contiguous. Areas 2 and 3 are s-contiguous, while areas 3 and 4 are also <span class="html-italic">s</span>-contiguous, but areas 2 and 4 are not <span class="html-italic">s</span><sup>2</sup>-contiguous. Note that an area cannot be <span class="html-italic">g</span>- or <span class="html-italic">s</span>-contiguous with itself.</p>
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<p>Geographic versus subway-contiguous areas surrounding ZCTA 11219 in Brooklyn and ZCTA 11415 in Queens. The left column (panels (<b>a</b>,<b>c</b>)) highlights neighboring ZCTAs that are (<span class="html-italic">g</span> + <span class="html-italic">g</span><sup>2</sup>)-contiguous with the focus ZCTA—that is, within a two-ZCTA geographic radius. The right column (panels (<b>b</b>,<b>d</b>)) highlights ZCTAs that are (<span class="html-italic">g</span> + <span class="html-italic">gs</span> + <span class="html-italic">gs</span><sup>2</sup> + <span class="html-italic">gs</span><sup>3</sup> + <span class="html-italic">gs</span><sup>4</sup> + <span class="html-italic">gs</span><sup>5</sup>)-contiguous with the focus ZCTA—that is, within a radius of 0–5 connected subway stops from an adjacent ZCTA.</p>
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<p>Dependence of estimated spatial effect on allowable radius of influence, New York City. (<b>a</b>) Radius in geographic space. (<b>b</b>) Radius in subway space. In geographic space, the apparent upward trend was not statistically significant (in variance-weighted linear regression, <span class="html-italic">p</span> = 0.269). In subway space, by contrast, a statically significant trend was noted (in variance-weighted linear regression, <span class="html-italic">p</span> = 0.002).</p>
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<p>(<b>a</b>) Confirmed COVID-19 cases per 100,000 population per week: regulated area in South Brooklyn, week ending 5 September through week ending 28 November 2020. (<b>b</b>) Smartphone movements between ZCTAs in the regulated area of South Brooklyn during 25 October–28 November 2020. The then-governor’s regulations, which took effect on 9 October 2020, were stricter in the central red zone. In panel (<b>b</b>), the central red zone is superimposed on a map of all affected ZCTAs. The width of each arrow is proportional to the number of device movements. The longer, dashed arrows represent movements between non-contiguous ZCTAs. Transits between ZCTAs with less than 5000 movements are not shown. The largest numbers of device movements (totaling 21,139) were recorded originating in ZCTA 11229 (Homecrest) to 11235 (Sheepshead Bay). Source: [<a href="#B44-populations-01-00005" class="html-bibr">44</a>].</p>
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<p>Section of University of Wisconsin—Madison campus map, with census tract and census block group (CBG) boundaries. Indicated are the locations of 20 nearby off-campus bars and 68 comparison restaurants, as well as the high-COVID-19-incidence Sellery–Witte (SW) residence halls located in census block group (CBG) 16.06-4 and low-COVID-19-incidence Ogg–Smith (OG) residence halls located in CBG 16.06-3. Source: [<a href="#B53-populations-01-00005" class="html-bibr">53</a>].</p>
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<p>Daily positive COVID-19 cases per 1000 population in two key census tracts in Madison, Wisconsin, 23 August–4 October 2020. During the first wave within census tract 16.04, an outbreak had been detected on September 2 in nine off-campus fraternities. Source: [<a href="#B53-populations-01-00005" class="html-bibr">53</a>].</p>
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18 pages, 1141 KiB  
Article
Does Genetic Predisposition Explain the “Immigrant Health Paradox”? Evidence for Non-Hispanic White Older Adults in the U.S.
by Zoya Gubernskaya and Dalton Conley
Populations 2025, 1(1), 4; https://doi.org/10.3390/populations1010004 - 29 Jan 2025
Viewed by 983
Abstract
This study uses data from the 2006–2012 Health and Retirement Study (HRS) genetic sample (N = 11,667) to explore the “immigrant health paradox” from a novel perspective by examining the nativity differences in genetic predisposition to health-related outcomes. Polygenic indices (PGIs) were used [...] Read more.
This study uses data from the 2006–2012 Health and Retirement Study (HRS) genetic sample (N = 11,667) to explore the “immigrant health paradox” from a novel perspective by examining the nativity differences in genetic predisposition to health-related outcomes. Polygenic indices (PGIs) were used to evaluate whether older non-Hispanic white foreign-born individuals have genotypes that predispose them to better health profiles compared to their U.S.-born counterparts. The results show an immigrant advantage with respect to genetic predisposition to cognitive function, BMI, and smoking frequency. There are no significant differences in genetic predisposition to height, smoking initiation, and depression. Including respective PGIs in multinomial regression models partially explains an immigrant advantage with respect to cognitive function and obesity. The findings are consistent with the “healthy immigrant effect” or selective migration of individuals with a favorable genetic predisposition to health as one of the explanations of the immigrant health paradox. Full article
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Figure 1
<p>Unstandardized coefficients from the OLS regression models predicting polygenic indices (PGIs) for selected traits.</p>
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<p>Relative Risk Ratio coefficients for being foreign-born (vs. U.S.-born) from the multinominal logistic regression models predicting selected traits. Model 1 includes age, age squared, sex, region, and year of survey; Model 2: Model 1 + PGI and 10 principal component variables; Model 3: Model 2 + education (in years).</p>
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21 pages, 2631 KiB  
Article
Bayesian Evidence Synthesis to Infer Unobserved Population Dynamics: An Application to International Migration Into the United States, 2000–2019
by Nicolas A. Menzies
Populations 2025, 1(1), 3; https://doi.org/10.3390/populations1010003 - 29 Jan 2025
Viewed by 673
Abstract
For the United States, detailed estimates of the number of resident migrants and the rates of migrant arrival are valuable for understanding population dynamics and for determining the impact of economic and political changes that influence migration. The goal of this analysis was [...] Read more.
For the United States, detailed estimates of the number of resident migrants and the rates of migrant arrival are valuable for understanding population dynamics and for determining the impact of economic and political changes that influence migration. The goal of this analysis was to derive estimates of the U.S. foreign-born population and how this population has changed in recent years, as well as estimates of recent and historical immigration volumes. Using data from large population surveys (the 2000 U.S. decennial census and 2001–2019 American Community Survey (ACS)), a Bayesian evidence synthesis was conducted to pool survey data across years while accounting for various biases and logical constraints that apply to these data. This analysis produced highly disaggregated estimates of the foreign-born population residing in the United States over the period 2000–2019, as well as estimates of immigration volume for 1950–2019. These population estimates demonstrated high in- and out-of-sample predictive performance, with substantially greater precision than that for raw survey estimates. Estimated immigration flows tracked other available time series, although with higher precision and with the potential to include undocumented immigration not represented in other immigration data. This study documents immigration from 100 countries of origin into the United States and demonstrates how the results of repeated cross-sectional population surveys can be used to infer migration dynamics that are difficult to measure directly. Full article
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Figure 1
<p>Schematic of the estimation model for the 1950 immigration cohort. ‘Age’ = current age; ‘YOE’ = year of entry; ‘Year’ = current year; ‘E+M’ = emigration and mortality. Rectangles represent a population in a given migration cohort (by age, year of entry, and calendar year). Solid arrows represent population flows due to immigration, emigration, aging, and death.</p>
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<p>Total population estimates for 2019, comparing modeled true population estimates to raw survey estimates, for the 100 countries of origin by population size. Estimates ordered by modeled population size. Population estimates shown on a log-scale. Country indicated by ISO 3166-1 alpha-3 (ISO3) code.</p>
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<p>Distribution of the 2019 U.S. resident population by single year of age and single year since entry for immigrants from Somalia, Mexico, and Vietnam comparing raw (<b>A</b>) and modeled (<b>B</b>) survey estimates. The color gradient demonstrates differences between high population numbers (warmer colors) and lower population numbers (cooler colors). Grey cells indicate illogical values (years since entry greater than current age). Empty (white) cells in Panel (<b>A</b>) indicate that no one with those characteristics were included in the 2019 ACS sample.</p>
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<p>Predicted distribution of survey estimates for Somalia, Mexico, and Vietnam in 2015, estimated with 2015 data held out. Prediction intervals represent expected 95% intervals for the data values. Uncertainty intervals represent expected 95% intervals for the mean estimate. (<b>A</b>) represents population estimates by year of entry into the United States. (<b>B</b>) represents population estimates by age at entry into the United States. (<b>C</b>) represents population estimates by current age.</p>
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<p>Estimates of annual immigration rates for seven example countries, as well as all countries of origin combined, compared to other measures describing immigration volume. LPR admissions = reported number of individuals granted U.S. entry as a legal permanent resident. ASC = American Community Survey. ACS estimates for new immigrants (dotted blue line) doubled since estimates for individuals entering the current year will under-estimate total immigrants for that year by approximately 50% given the rolling survey design. Uncertainty intervals (light blue) represent expected 95% intervals for the mean estimate.</p>
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<p>Estimates of immigration rates from 1980 to 2019 by world region of origin. Solid lines represent point estimates, and shaded regions represent 95% uncertainty intervals. Migration estimates shown on a log-scale. Lines represent World Bank regional classifications.</p>
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3 pages, 147 KiB  
Editorial
Welcome to Populations: A New Platform for Demographic and Population Science Research
by David Lopez-Carr
Populations 2025, 1(1), 2; https://doi.org/10.3390/populations1010002 - 31 Oct 2024
Viewed by 1746
Abstract
With great excitement and pride, I introduce the inaugural issue of Populations, a new research journal dedicated to advancing our understanding of population dynamics and its interactions with socio-economic, political, and environmental processes [...] Full article
1 pages, 133 KiB  
Editorial
Publisher’s Note: Announcing the Launch of Populations—A New Open Access Journal
by Colin Wee
Populations 2025, 1(1), 1; https://doi.org/10.3390/populations1010001 - 29 Aug 2024
Viewed by 992
Abstract
Population research is an evergreen topic of interest for governments across the world and accurate measurement of the demographic, environmental, and economic variables associated with a region’s population is invaluable in terms of informing governmental decision-making, planning, and policy [...] Full article
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