Human mobility and the spatial transmission of influenza in the United States.
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ABSTRACT:
SUBMITTER: Charu V
PROVIDER: S-EPMC5349690 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The Coronavirus Disease 19 (COVID-19) has quickly spread across the United States (U.S.) since community transmission was first identified in January 2020. While a number of studies have examined individual-level risk factors for COVID-19, few studies have examined geographic hotspots and community drivers associated with spatial patterns in local transmission. The objective of the study is to understand the spatial determinants of the pandemic in counties across the U.S. by comparing socioeconomic variables to case and death data from January 22nd to June 30th 2020. A cluster analysis was performed to examine areas of high-risk, followed by a three-stage regression to examine contextual factors associated with elevated risk patterns for morbidity and mortality. The factors associated with community-level vulnerability included age, disability, language, race, occupation, and urban status. We recommend that cluster detection and spatial analysis be included in population-based surveillance strategies to better inform early case detection and prioritize healthcare resources.
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Project description:As of June 11, 2009, a total of 17,855 probable or confirmed cases of 2009 pandemic influenza A (H1N1) had been reported in the United States. Risk factors for transmission remain largely uncharacterized. We characterize the risk factors and describe the transmission of the virus within households.Probable and confirmed cases of infection with the 2009 H1N1 virus in the United States were reported to the Centers for Disease Control and Prevention with the use of a standardized case form. We investigated transmission of infection in 216 households--including 216 index patients and their 600 household contacts--in which the index patient was the first case patient and complete information on symptoms and age was available for all household members.An acute respiratory illness developed in 78 of 600 household contacts (13%). In 156 households (72% of the 216 households), an acute respiratory illness developed in none of the household contacts; in 46 households (21%), illness developed in one contact; and in 14 households (6%), illness developed in more than one contact. The proportion of household contacts in whom acute respiratory illness developed decreased with the size of the household, from 28% in two-member households to 9% in six-member households. Household contacts 18 years of age or younger were twice as susceptible as those 19 to 50 years of age (relative susceptibility, 1.96; Bayesian 95% credible interval, 1.05 to 3.78; P=0.005), and household contacts older than 50 years of age were less susceptible than those who were 19 to 50 years of age (relative susceptibility, 0.17; 95% credible interval, 0.02 to 0.92; P=0.03). Infectivity did not vary with age. The mean time between the onset of symptoms in a case patient and the onset of symptoms in the household contacts infected by that patient was 2.6 days (95% credible interval, 2.2 to 3.5).The transmissibility of the 2009 H1N1 influenza virus in households is lower than that seen in past pandemics. Most transmissions occur soon before or after the onset of symptoms in a case patient.
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Project description:With the onset of COVID-19 and the resulting shelter in place guidelines combined with remote working practices, human mobility in 2020 has been dramatically impacted. Existing studies typically examine whether mobility in specific localities increases or decreases at specific points in time and relate these changes to certain pandemic and policy events. However, a more comprehensive analysis of mobility change over time is needed. In this paper, we study mobility change in the US through a five-step process using mobility footprint data. (Step 1) Propose the Delta Time Spent in Public Places (ΔTSPP) as a measure to quantify daily changes in mobility for each US county from 2019-2020. (Step 2) Conduct Principal Component Analysis (PCA) to reduce the ΔTSPP time series of each county to lower-dimensional latent components of change in mobility. (Step 3) Conduct clustering analysis to find counties that exhibit similar latent components. (Step 4) Investigate local and global spatial autocorrelation for each component. (Step 5) Conduct correlation analysis to investigate how various population characteristics and behavior correlate with mobility patterns. Results show that by describing each county as a linear combination of the three latent components, we can explain 59% of the variation in mobility trends across all US counties. Specifically, change in mobility in 2020 for US counties can be explained as a combination of three latent components: 1) long-term reduction in mobility, 2) no change in mobility, and 3) short-term reduction in mobility. Furthermore, we find that US counties that are geographically close are more likely to exhibit a similar change in mobility. Finally, we observe significant correlations between the three latent components of mobility change and various population characteristics, including political leaning, population, COVID-19 cases and deaths, and unemployment. We find that our analysis provides a comprehensive understanding of mobility change in response to the COVID-19 pandemic.
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Project description:The impact of the next influenza pandemic may be mitigated by inducing immunity in individuals prior to the start of national epidemics using a pre-pandemic vaccine targeted against current avian influenza strains. The US Department of Health and Human Services (HHS) intends that pre-pandemic vaccines will be allocated to states in proportion to the size of their population in predefined priority groups, i.e. approximately pro-rata. We show that such an equitable policy is likely to be the least efficient in terms of the number of infections averted. We demonstrate that the potential benefits could be substantial if a fully discretionary policy is allowed, i.e. if some regions are allocated sufficient vaccines to achieve herd immunity while other regions are allocated no vaccine. Since such an inequitable policy may be impractical, we consider the sensitivity of an intermediate policy (in which 50% of the stockpile is allocated on a pro-rata basis) to key transmission uncertainties. The benefits of the 50% discretionary policy are sensitive to parameter values which cannot be known in advance. Therefore, despite substantial potential benefits of non-pro-rata policies, our results suggest that the current HHS policy of pro-rata allocation by state is a good compromise in terms of simplicity, robustness, equity and efficiency.
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Project description:Recurrent epidemics of influenza are observed seasonally around the world with considerable health and economic consequences. A key quantity for the control of infectious diseases is the reproduction number, which measures the transmissibility of a pathogen and determines the magnitude of public health interventions necessary to control epidemics. Here we applied a simple epidemic model to weekly indicators of influenza mortality to estimate the reproduction numbers of seasonal influenza epidemics spanning three decades in the United States, France, and Australia. We found similar distributions of reproduction number estimates in the three countries, with mean value 1.3 and important year-to-year variability (range 0.9-2.1). Estimates derived from two different mortality indicators (pneumonia and influenza excess deaths and influenza-specific deaths) were in close agreement for the United States (correlation=0.61, P60%) in healthy individuals who respond well to vaccine, in addition to periodic re-vaccination due to evolving viral antigens and waning population immunity.
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Project description:Despite intensive study, much remains unknown about the dynamics of seasonal influenza virus epidemic establishment and spread in the United States (US) each season. By reconstructing transmission lineages from seasonal influenza virus genomes collected in the US from 2014 to 2023, we show that most epidemics consisted of multiple distinct transmission lineages. Spread of these lineages exhibited strong spatiotemporal hierarchies and lineage size was correlated with timing of lineage establishment in the US. Mechanistic epidemic simulations suggest that mobility-driven competition between lineages determined the extent of individual lineages' geographical spread. Based on phylogeographic analyses and epidemic simulations, lineage-specific movement patterns were dominated by human commuting behavior. These results suggest that given the locations of early-season epidemic sparks, the topology of inter-state human mobility yields repeatable patterns of which influenza viruses will circulate where, but the importance of short-term processes limits predictability of regional and national epidemics.
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Project description:BackgroundThe seasonality of influenza is thought to vary according to environmental factors and human behavior. During winter holidays, potential disease-causing contact and travel deviate from typical patterns. We aim to understand these changes on age-specific and spatial influenza transmission.MethodsWe characterized the changes to transmission and epidemic trajectories among children and adults in a spatial context before, during, and after the winter holidays among aggregated physician medical claims in the United States from 2001 to 2009 and among synthetic data simulated from a deterministic, age-specific spatial metapopulation model.ResultsWinter holidays reduced influenza transmission and delayed the trajectory of influenza season epidemics. The holiday period was marked by a shift in the relative risk of disease from children toward adults. Model results indicated that holidays delayed epidemic peaks and synchronized incidence across locations, and that contact reductions from school closures, rather than age-specific mixing and travel, produced these observed holiday influenza dynamics.ConclusionsWinter holidays delay seasonal influenza epidemic peaks and shift disease risk toward adults because of changes in contact patterns. These findings may inform targeted influenza information and vaccination campaigns during holiday periods.
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Project description:Influenza pandemics are associated with severe morbidity, mortality, and social and economic disruption. Every summer in the United States, youths attending agricultural fairs are exposed to genetically diverse influenza A viruses (IAVs) circulating in exhibition swine, resulting in over 450 lab-confirmed zoonotic infections since 2010. Exhibition swine represent a small, defined population (∼1.5% of the U.S. herd), presenting a realistic opportunity to mitigate a pandemic threat by reducing IAV transmission in the animals themselves. Through intensive surveillance and genetic sequencing of IAVs in exhibition swine in six U.S. states in 2018 (n = 212), we characterized how a heterogeneous circuit of swine shows, comprising fairs with different sizes and geographic coverage, facilitates IAV transmission among exhibition swine and into humans. Specifically, we identified the role of an early-season national show in the propagation and spatial dissemination of a specific virus (H1δ-2) that becomes dominant among exhibition swine and is associated with the majority of zoonotic infections in 2018. These findings suggest that a highly targeted mitigation strategy, such as postponing swine shows for 1 to 2 weeks following the early-season national show, could potentially reduce IAV transmission in exhibition swine and spillover into humans, and this merits further study.IMPORTANCE The varying influenza A virus (IAV) exposure and infection status of individual swine facilitates introduction, transmission, and dissemination of diverse IAVs. Since agricultural fairs bring people into intimate contact with swine, they provide a unique interface for zoonotic transmission of IAV. Understanding the dynamics of IAV transmission through exhibition swine is critical to mitigating the high incidence of variant IAV cases reported in association with agricultural fairs. We used genomic sequences from our exhibition swine surveillance to characterize the hemagglutinin and full genotypic diversity of IAV at early-season shows and the subsequent dissemination through later-season agricultural fairs. We were able to identify a critical time point with important implications for downstream IAV and zoonotic transmission. With improved understanding of evolutionary origins of zoonotic IAV, we can inform public health mitigation strategies to ultimately reduce zoonotic IAV transmission and risk of pandemic IAV emergence.
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