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1.
PLoS One ; 16(11): e0258868, 2021.
Article in English | MEDLINE | ID: covidwho-1505861

ABSTRACT

Human mobility is crucial to understand the transmission pattern of COVID-19 on spatially embedded geographic networks. This pattern seems unpredictable, and the propagation appears unstoppable, resulting in over 350,000 death tolls in the U.S. by the end of 2020. Here, we create the spatiotemporal inter-county mobility network using 10 TB (Terabytes) trajectory data of 30 million smart devices in the U.S. in the first six months of 2020. We investigate the bond percolation process by removing the weakly connected edges. As we increase the threshold, the mobility network nodes become less interconnected and thus experience surprisingly abrupt phase transitions. Despite the complex behaviors of the mobility network, we devised a novel approach to identify a small, manageable set of recurrent critical bridges, connecting the giant component and the second-largest component. These adaptive links, located across the United States, played a key role as valves connecting components in divisions and regions during the pandemic. Beyond, our numerical results unveil that network characteristics determine the critical thresholds and the bridge locations. The findings provide new insights into managing and controlling the connectivity of mobility networks during unprecedented disruptions. The work can also potentially offer practical future infectious diseases both globally and locally.


Subject(s)
COVID-19/mortality , COVID-19/transmission , Communicable Diseases/mortality , Communicable Diseases/transmission , Computer Simulation , Humans , Phase Transition , SARS-CoV-2/pathogenicity
2.
Sci Rep ; 11(1): 13822, 2021 07 05.
Article in English | MEDLINE | ID: covidwho-1297313

ABSTRACT

The need for improved models that can accurately predict COVID-19 dynamics is vital to managing the pandemic and its consequences. We use machine learning techniques to design an adaptive learner that, based on epidemiological data available at any given time, produces a model that accurately forecasts the number of reported COVID-19 deaths and cases in the United States, up to 10 weeks into the future with a mean absolute percentage error of 9%. In addition to being the most accurate long-range COVID predictor so far developed, it captures the observed periodicity in daily reported numbers. Its effectiveness is based on three design features: (1) producing different model parameters to predict the number of COVID deaths (and cases) from each time and for a given number of weeks into the future, (2) systematically searching over the available covariates and their historical values to find an effective combination, and (3) training the model using "last-fold partitioning", where each proposed model is validated on only the last instance of the training dataset, rather than being cross-validated. Assessments against many other published COVID predictors show that this predictor is 19-48% more accurate.


Subject(s)
COVID-19/mortality , Communicable Diseases/mortality , Forecasting , SARS-CoV-2/pathogenicity , Humans , Machine Learning , Models, Statistical , United States
3.
J Aging Soc Policy ; 33(4-5): 493-499, 2021.
Article in English | MEDLINE | ID: covidwho-883015

ABSTRACT

Advance care planning (ACP) for medical decision-making at the end of life has developed around the expectation of death from long-term, progressive chronic illnesses. We reexamine advance care planning in light of the increased probability of death from COVID-19, an exemplar of death that occurs relatively quickly after disease onset. We draw several conclusions about ACP in the context of infectious diseases: interpersonal and sociostructural barriers to ACP are high; ACP is not well-oriented toward decision-making for treatment of an acute illness; and the U.S. health care system is not well positioned to fulfill patients' end of life preferences in a pandemic. Passing the peak of the crisis will reduce, but not eliminate, these problems.


Subject(s)
Advance Care Planning , COVID-19/mortality , Communicable Diseases/mortality , Decision Making , Chronic Disease , Delivery of Health Care , Humans , Social Isolation
4.
Sci Data ; 7(1): 329, 2020 10 15.
Article in English | MEDLINE | ID: covidwho-872718

ABSTRACT

The COVID-19 pandemic has ignited interest in age-specific manifestations of infection but surprisingly little is known about relative severity of infectious disease between the extremes of age. In a systematic analysis we identified 142 datasets with information on severity of disease by age for 32 different infectious diseases, 19 viral and 13 bacterial. For almost all infections, school-age children have the least severe disease, and severity starts to rise long before old age. Indeed, for many infections even young adults have more severe disease than children, and dengue was the only infection that was most severe in school-age children. Together with data on vaccine response in children and young adults, the findings suggest peak immune function is reached around 5-14 years of age. Relative immune senescence may begin much earlier than assumed, before accelerating in older age groups. This has major implications for understanding resilience to infection, optimal vaccine scheduling, and appropriate health protection policies across the life course.


Subject(s)
Age Factors , Communicable Diseases/immunology , Adolescent , Adult , Betacoronavirus , COVID-19 , Child , Child, Preschool , Communicable Diseases/mortality , Coronavirus Infections , Datasets as Topic , Humans , Immunosenescence , Pandemics , Pneumonia, Viral , SARS-CoV-2 , Severity of Illness Index , Young Adult
6.
BMJ Open ; 10(8): e039856, 2020 08 05.
Article in English | MEDLINE | ID: covidwho-695386

ABSTRACT

OBJECTIVES: Our objective was to review the literature on the inferred duration of the infectious period of COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, and provide an overview of the variation depending on the methodological approach. DESIGN: Rapid scoping review. Literature review with fixed search terms, up to 1 April 2020. Central tendency and variation of the parameter estimates for infectious period in (A) asymptomatic and (B) symptomatic cases from (1) virological studies (repeated testing), (2) tracing studies and (3) modelling studies were gathered. Narrative review of viral dynamics. INFORMATION SOURCES: Search strategies developed and the following searched: PubMed, Google Scholar, MedRxiv and BioRxiv. Additionally, the Health Information Quality Authority (Ireland) viral load synthesis was used, which screened literature from PubMed, Embase, ScienceDirect, NHS evidence, Cochrane, medRxiv and bioRxiv, and HRB open databases. RESULTS: There was substantial variation in the estimates, and how infectious period was inferred. One study provided approximate median infectious period for asymptomatic cases of 6.5-9.5 days. Median presymptomatic infectious period across studies varied over <1-4 days. Estimated mean time from symptom onset to two negative RT-PCR tests was 13.4 days (95% CI 10.9 to 15.8) but was shorter when studies included children or less severe cases. Estimated mean duration from symptom onset to hospital discharge or death (potential maximal infectious period) was 18.1 days (95% CI 15.1 to 21.0); time to discharge was on average 4 days shorter than time to death. Viral dynamic data and model infectious parameters were often shorter than repeated diagnostic data. CONCLUSIONS: There are limitations of inferring infectiousness from repeated diagnosis, viral loads and viral replication data alone and also potential patient recall bias relevant to estimating exposure and symptom onset times. Despite this, available data provide a preliminary evidence base to inform models of central tendency for key parameters and variation for exploring parameter space and sensitivity analysis.


Subject(s)
Betacoronavirus , Communicable Diseases/transmission , Coronavirus Infections/transmission , Pneumonia, Viral/transmission , Adult , COVID-19 , Child , Communicable Diseases/complications , Communicable Diseases/mortality , Communicable Diseases/virology , Coronavirus Infections/complications , Coronavirus Infections/mortality , Coronavirus Infections/virology , Global Health , Hospitalization , Humans , Pandemics , Pneumonia, Viral/complications , Pneumonia, Viral/mortality , Pneumonia, Viral/virology , Polymerase Chain Reaction , SARS-CoV-2 , Viral Load
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