Hawkes process modeling of COVID-19 with mobility leading indicators and spatial covariates.
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ABSTRACT:
SUBMITTER: Chiang WH
PROVIDER: S-EPMC8275517 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The COVID-19 pandemic is a global threat presenting health, economic, and social challenges that continue to escalate. Metapopulation epidemic modeling studies in the susceptible-exposed-infectious-removed (SEIR) style have played important roles in informing public health policy making to mitigate the spread of COVID-19. These models typically rely on a key assumption on the homogeneity of the population. This assumption certainly cannot be expected to hold true in real situations; various geographic, socioeconomic, and cultural environments affect the behaviors that drive the spread of COVID-19 in different communities. What's more, variation of intracounty environments creates spatial heterogeneity of transmission in different regions. To address this issue, we develop a human mobility flow-augmented stochastic SEIR-style epidemic modeling framework with the ability to distinguish different regions and their corresponding behaviors. This modeling framework is then combined with data assimilation and machine learning techniques to reconstruct the historical growth trajectories of COVID-19 confirmed cases in two counties in Wisconsin. The associations between the spread of COVID-19 and business foot traffic, race and ethnicity, and age structure are then investigated. The results reveal that, in a college town (Dane County), the most important heterogeneity is age structure, while, in a large city area (Milwaukee County), racial and ethnic heterogeneity becomes more apparent. Scenario studies further indicate a strong response of the spread rate to various reopening policies, which suggests that policy makers may need to take these heterogeneities into account very carefully when designing policies for mitigating the ongoing spread of COVID-19 and reopening.
| S-EPMC8214685 | biostudies-literature
Project description:MotivationSomatic mutations result from processes related to DNA replication or environmental/lifestyle exposures. Knowing the activity of mutational processes in a tumor can inform personalized therapies, early detection, and understanding of tumorigenesis. Computational methods have revealed 30 validated signatures of mutational processes active in human cancers, where each signature is a pattern of single base substitutions. However, half of these signatures have no known etiology, and some similar signatures have distinct etiologies, making patterns of mutation signature activity hard to interpret. Existing mutation signature detection methods do not consider tumor-level clinical/demographic (e.g. smoking history) or molecular features (e.g. inactivations to DNA damage repair genes).ResultsTo begin to address these challenges, we present the Tumor Covariate Signature Model (TCSM), the first method to directly model the effect of observed tumor-level covariates on mutation signatures. To this end, our model uses methods from Bayesian topic modeling to change the prior distribution on signature exposure conditioned on a tumor's observed covariates. We also introduce methods for imputing covariates in held-out data and for evaluating the statistical significance of signature-covariate associations. On simulated and real data, we find that TCSM outperforms both non-negative matrix factorization and topic modeling-based approaches, particularly in recovering the ground truth exposure to similar signatures. We then use TCSM to discover five mutation signatures in breast cancer and predict homologous recombination repair deficiency in held-out tumors. We also discover four signatures in a combined melanoma and lung cancer cohort-using cancer type as a covariate-and provide statistical evidence to support earlier claims that three lung cancers from The Cancer Genome Atlas are misdiagnosed metastatic melanomas.Availability and implementationTCSM is implemented in Python 3 and available at https://github.com/lrgr/tcsm, along with a data workflow for reproducing the experiments in the paper.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC6612886 | biostudies-literature
Project description:BackgroundIn response to COVID-19, the Swedish government imposed few travel and mobility restrictions. This contrasted with its Scandinavian neighbours which implemented stringent restrictions. The influence these different approaches had on mobility, and thus on COVID-19 mortality was investigated.MethodsDatasets indicating restriction severity and community mobility were examined; Google's 'Community Movement Reports' (CMR) show activity at key location categories; the Oxford COVID-19 Government Response Tracker collates legislative restrictions into a 'Stringency Index' (SI).ResultsCMR mobility categories were negatively correlated with COVID-19 mortality. The strongest correlations were obtained by negatively time lagging mortality data, suggesting restrictions had a delayed influence. During the 'first wave' a model using SI (AIC 632.87) proved favorable to one using contemporaneous CMR data and SI (AIC 1193.84), or lagged CMR data and SI (AIC 642.35). Validation using 'second wave' data confirmed this; the model using SI solely again being optimal (RMSE: 0.2486 vs. 0.522 and 104.62). Cross-country differences were apparent in all models; Swedish data, independent of SI and CMR, proved significant throughout. There was a significant association for Sweden and the death number across models.ConclusionSI may provide a broader, more accurate, representation of changes in movement in response to COVID-19 restrictions.
| S-EPMC7999697 | biostudies-literature
Project description:In this manuscript we propose a novel approach for the analysis of longitudinal data that have informative dropout. We jointly model the slopes of covariates of interest and the censoring process for which we assume a survival model with logistic non-constant dropout hazard in a likelihood function that is integrated over the random effects. Maximization of the marginal likelihood function results in acquiring maximum likelihood estimates for the population slopes and empirical Bayes estimates for the individual slopes that are predicted using Gaussian quadrature. Our simulation study results indicated that the performance of this model is superior in terms of accuracy and validity of the estimates compared to other models such as logistic non-constant hazard censoring model that does not include covariates, logistic constant censoring model with covariates, bootstrapping approach as well as mixed models. Sensitivity analyses for the dropout hazard and non-Gaussian errors were also undertaken to assess robustness of the proposed approach to such violations. Our model was illustrated using a cohort of renal transplant patients with estimated glomerular filtration rate as the outcome of interest.
| S-EPMC5010875 | biostudies-literature
Project description:The ongoing outbreak of COVID-19 challenges the health systems and epidemiological responses of all countries worldwide. Although preventive measures have been globally considered, the spatial heterogeneity of its effectiveness is evident, underscoring global health inequalities. Using Bayesian-based Markov chain Monte Carlo simulations, we identify the spatial association of socioeconomic factors and the risk for dying from COVID-19 in Colombia. We confirm that from March 16 to October 04, 2020, the COVID-19 case-fatality rate and the multidimensional poverty index have a heterogeneous spatial distribution. Spatial analysis reveals that the risk of dying from COVID-19 increases in regions with a higher proportion of poor people with dwelling (RR 1.74 95%CI = 1.54-9.75), educational (RR 1.69 95%CI = 1.36-5.94), childhood/youth (RR 1.35 95%CI = 1.08-4.03), and health (RR 1.16 95%CI = 1.06-2.04) deprivations. These findings evidence the vulnerability of most disadvantaged members of society to dying in a pandemic and assist the spatial planning of preventive strategies focused on vulnerable communities.
| S-EPMC8956344 | biostudies-literature
Project description:Covid-19 interventions are greatly affecting patterns of human mobility. Changes in mobility during Covid-19 have differed across socio-economic gradients during the first wave. We use fine-scale network mobility data in Ontario, Canada to study the association between three different mobility measures and four socio-economic indicators throughout the first and second wave of Covid-19 (January to December 2020). We find strong associations between mobility and the socio-economic indicators and that relationships between mobility and other socio-economic indicators vary over time. We further demonstrate that understanding how mobility has changed in response to Covid-19 varies considerably depending on how mobility is measured. Our findings have important implications for understanding how mobility data should be used to study interventions across space and time. Our results support that Covid-19 non-pharmaceutical interventions have resulted in geographically disparate responses to mobility and quantifying mobility changes at fine geographical scales is crucial to understanding the impacts of Covid-19.
| S-EPMC8514267 | biostudies-literature
Project description:IntroductionThis study aimed to examine the heterogeneity of the associations between social determinants and COVID-19 fully vaccinated rate.MethodsThis study proposes 3 multiscale dimensions of spatial process, including level of influence (the percentage of population affected by a certain determinant across the entire area), scalability (the spatial process of a determinant into global, regional, and local process), and specificity (the determinant that has the strongest association with the fully vaccinated rate). The multiscale geographically weighted regression was applied to the COVID-19 fully vaccinated rates in U.S. counties (N=3,106) as of October 26, 2021, and the analyses were conducted in May 2022.ResultsThe results suggest the following: (1) Percentage of Republican votes in the 2020 presidential election is a primary influencer because 84% of the U.S. population lived in counties where this determinant is found the most dominant; (2) Demographic compositions (e.g., percentages of racial/ethnic minorities) play a larger role than socioeconomic conditions (e.g., unemployment) in shaping fully vaccinated rates; (3) The spatial process underlying fully vaccinated rates is largely local.ConclusionsThe findings challenge the 1-size-fits-all approach to designing interventions promoting COVID-19 vaccination and highlight the importance of a place-based perspective in ecological health research.
| S-EPMC9259504 | biostudies-literature
Project description:This research studies the evolution of COVID-19 crude incident rates, effective reproduction number R(t) and their relationship with incidence spatial autocorrelation patterns in the 19 months following the disease outbreak in Catalonia (Spain). A cross-sectional ecological panel design based on n = 371 health-care geographical units is used. Five general outbreaks are described, systematically preceded by generalized values of R(t) > 1 in the two previous weeks. No clear regularities concerning possible initial focus appear when comparing waves. As for autocorrelation, we identify a wave's baseline pattern in which global Moran's I increases rapidly in the first weeks of the outbreak to descend later. However, some waves significantly depart from the baseline. In the simulations, both baseline pattern and departures can be reproduced when measures aimed at reducing mobility and virus transmissibility are introduced. Spatial autocorrelation is inherently contingent on the outbreak phase and is also substantially modified by external interventions affecting human behavior.
| S-EPMC10272129 | biostudies-literature
Project description:The Coronavirus Disease 2019 (COVID-19) has proved a globally prevalent outbreak since December 2019. As a focused country to alleviate the epidemic impact, China implemented a range of public health interventions to prevent the disease from further transmission, including the pandemic lockdown in Wuhan and other cities. This paper establishes China's mobility network by a flight dataset and proposes a model without epidemiological parameters to indicate the spread risks through the network, which is termed as epidemic strength. By simply adjusting an intervention parameter, traffic volumes under different travel-restriction levels can be simulated to analyze how the containment strategy can mitigate the virus dissemination through traffic. This approach is successfully applied to a network of Chinese provinces and the epidemic strength is smoothly interpreted by flow maps. Through this node-to-node interpretation of transmission risks, both overall and detailed epidemic hazards are properly analyzed, which can provide valuable intervention advice during public health emergencies.
| S-EPMC7685462 | biostudies-literature
Project description:BackgroundActive mobility and public transport increase physical activity (PA) levels. With varying intensity and effectiveness, European cities implement Sustainable Urban Mobility Plans (SUMPs) to spur transport-related PA. Therefore, we aim to examine drivers and barriers to SUMP implementation and assess its influence on PA across European cities.MethodsWe screened policy reports to gain insights into SUMP implementation in one Danish, two German and two Polish cities. Further, we conducted semi-structured interviews with SUMP stakeholders in these cities to explore their experiences with SUMP implementation. Thematic analysis of interview transcripts was applied to identify similarities and differences across cities. To assess the effect of SUMP implementation on PA, we searched for data on indicators of transport-related PA.ResultsAll investigated cities are committed to sustainable mobility. Nonetheless, complex institutional structures, the dominant role of motorized traffic as well as complex regional and local policy integration hamper SUMP implementation. Danish, German and Polish cities face different contexts in terms of financing, national guidelines and the prominence of sustainability as a policy objective. Each city adopts unique indicators for monitoring the effects of SUMPs on transport-related PA. The variety of indicators and limited data availability impede a comparative evaluation across cities. Constrained by this restriction, we identified motorization rate, modal split and public transport ridership as suitable indicators.ConclusionsLocal idiosyncrasies need to be accounted for when assessing the implementation of SUMPs. Nonetheless, consistent indicators and data transparency are essential for comparing the effectiveness of SUMPs and their impact on PA.
| S-EPMC9706112 | biostudies-literature