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1.
Biophys J ; 120(14): 2872-2879, 2021 07 20.
Article in English | MEDLINE | ID: covidwho-1605779

ABSTRACT

We study the transition of an epidemic from growth phase to decay of the active infections in a population when lockdown health measures are introduced to reduce the probability of disease transmission. Although in the case of uniform lockdown, a simple compartmental model would indicate instantaneous transition to decay of the epidemic, this is not the case when partially isolated active clusters remain with the potential to create a series of small outbreaks. We model this using the Gillespie stochastic simulation algorithm based on a connected set of stochastic susceptible-infected-removed/recovered networks representing the locked-down majority population (in which the reproduction number is less than 1) weakly coupled to a large set of small clusters in which the infection may propagate. We find that the presence of such active clusters can lead to slower than expected decay of the epidemic and significantly delayed onset of the decay phase. We study the relative contributions of these changes, caused by the active clusters within the population, to the additional total infected population. We also demonstrate that limiting the size of the inevitable active clusters can be efficient in reducing their impact on the overall size of the epidemic outbreak. The deceleration of the decay phase becomes apparent when the active clusters form at least 5% of the population.


Subject(s)
Disease Outbreaks , Epidemics , Algorithms , Computer Simulation , Humans , Probability
2.
Sci Rep ; 11(1): 24498, 2021 12 30.
Article in English | MEDLINE | ID: covidwho-1597845

ABSTRACT

When a virus spreads, it may mutate into, e.g., vaccine resistant or fast spreading lineages, as was the case for the Danish Cluster-5 mink variant (belonging to the B.1.1.298 lineage), the British B.1.1.7 lineage, and the South African B.1.351 lineage of the SARS-CoV-2 virus. A way to handle such spreads is through a containment strategy, where the population in the affected area is isolated until the spread has been stopped. Under such circumstances, it is important to monitor whether the mutated virus is extinct via massive testing for the virus sub-type. If successful, the strategy will lead to lower and lower numbers of the sub-type, and it will eventually die out. An important question is, for how long time one should wait to be sure the sub-type is extinct? We use a hidden Markov model for infection spread and an approximation of a two stage sampling scheme to infer the probability of extinction. The potential of the method is illustrated via a simulation study. Finally, the model is used to assess the Danish containment strategy when SARS-CoV-2 spread from mink to man during the summer of 2020, including the Cluster-5 sub-type. In order to avoid further spread and mink being a large animal virus reservoir, this situation led to the isolation of seven municipalities in the Northern part of the country, the culling of the entire Danish 17 million large mink population, and a bill to interim ban Danish mink production until the end of 2021.


Subject(s)
COVID-19 , Models, Theoretical , Pandemics , SARS-CoV-2/genetics , Animals , COVID-19/epidemiology , COVID-19/virology , Humans , Probability
3.
J R Soc Interface ; 18(184): 20210575, 2021 11.
Article in English | MEDLINE | ID: covidwho-1522457

ABSTRACT

Emerging epidemics and local infection clusters are initially prone to stochastic effects that can substantially impact the early epidemic trajectory. While numerous studies are devoted to the deterministic regime of an established epidemic, mathematical descriptions of the initial phase of epidemic growth are comparatively rarer. Here, we review existing mathematical results on the size of the epidemic over time, and derive new results to elucidate the early dynamics of an infection cluster started by a single infected individual. We show that the initial growth of epidemics that eventually take off is accelerated by stochasticity. As an application, we compute the distribution of the first detection time of an infected individual in an infection cluster depending on testing effort, and estimate that the SARS-CoV-2 variant of concern Alpha detected in September 2020 first appeared in the UK early August 2020. We also compute a minimal testing frequency to detect clusters before they exceed a given threshold size. These results improve our theoretical understanding of early epidemics and will be useful for the study and control of local infectious disease clusters.


Subject(s)
COVID-19 , Epidemics , Humans , Probability , SARS-CoV-2 , Stochastic Processes
4.
Sci Rep ; 11(1): 12110, 2021 06 08.
Article in English | MEDLINE | ID: covidwho-1517640

ABSTRACT

Wearing surgical masks or other similar face coverings can reduce the emission of expiratory particles produced via breathing, talking, coughing, or sneezing. Although it is well established that some fraction of the expiratory airflow leaks around the edges of the mask, it is unclear how these leakage airflows affect the overall efficiency with which masks block emission of expiratory aerosol particles. Here, we show experimentally that the aerosol particle concentrations in the leakage airflows around a surgical mask are reduced compared to no mask wearing, with the magnitude of reduction dependent on the direction of escape (out the top, the sides, or the bottom). Because the actual leakage flowrate in each direction is difficult to measure, we use a Monte Carlo approach to estimate flow-corrected particle emission rates for particles having diameters in the range 0.5-20 µm. in all orientations. From these, we derive a flow-weighted overall number-based particle removal efficiency for the mask. The overall mask efficiency, accounting both for air that passes through the mask and for leakage flows, is reduced compared to the through-mask filtration efficiency, from 93 to 70% for talking, but from only 94-90% for coughing. These results demonstrate that leakage flows due to imperfect sealing do decrease mask efficiencies for reducing emission of expiratory particles, but even with such leakage surgical masks provide substantial control.


Subject(s)
Aerosols , Communicable Disease Control/methods , Cough , Exhalation , Filtration , Masks , Virus Diseases/prevention & control , Adolescent , Adult , COVID-19/prevention & control , Equipment Failure , Female , Humans , Male , Middle Aged , Monte Carlo Method , Particle Size , Probability , Respiration , Sneezing , Young Adult
5.
PLoS One ; 16(11): e0259257, 2021.
Article in English | MEDLINE | ID: covidwho-1504723

ABSTRACT

Protective behaviors such as mask wearing and physical distancing are critical to slow the spread of COVID-19, even in the context of vaccine scale-up. Understanding the variation in self-reported COVID-19 protective behaviors is critical to developing public health messaging. The purpose of the study is to provide nationally representative estimates of five self-reported COVID-19 protective behaviors and correlates of such behaviors. In this cross-sectional survey study of US adults, surveys were administered via internet and telephone. Adults were surveyed from April 30-May 4, 2020, a time of peaking COVID-19 incidence within the US. Participants were recruited from the probability-based AmeriSpeak® national panel. Brief surveys were completed by 994 adults, with 73.0% of respondents reported mask wearing, 82.7% reported physical distancing, 75.1% reported crowd avoidance, 89.8% reported increased hand-washing, and 7.7% reported having prior COVID-19 testing. Multivariate analysis (p critical value .05) indicates that women were more likely to report protective behaviors than men, as were those over age 60. Respondents who self-identified as having low incomes, histories of criminal justice involvement, and Republican Party affiliation, were less likely to report four protective behaviors, though Republicans and individuals with criminal justice histories were more likely to report having received COVID-19 testing. The majority of Americans engaged in COVID-19 protective behaviors, with low-income Americans, those with histories of criminal justice involvement, and self-identified Republicans less likely to engage in these preventive behaviors. Culturally competent public health messaging and interventions might focus on these latter groups to prevent future infections. These findings will remain highly relevant even with vaccines widely available, given the complementarities between vaccines and protective behaviors, as well as the many challenges in delivering vaccines.


Subject(s)
COVID-19 Testing , COVID-19 Vaccines , COVID-19/epidemiology , COVID-19/prevention & control , Hand Disinfection , Masks , Adolescent , Adult , Aged , Communicable Disease Control , Cross-Sectional Studies , Female , Geography , Health Behavior , Humans , Infectious Disease Medicine/methods , Internet , Male , Middle Aged , Multivariate Analysis , Poverty , Probability , SARS-CoV-2 , Surveys and Questionnaires , United States/epidemiology , Young Adult
6.
PLoS One ; 16(11): e0258760, 2021.
Article in English | MEDLINE | ID: covidwho-1502068

ABSTRACT

Ali-M3, an artificial intelligence program, analyzes chest computed tomography (CT) and detects the likelihood of coronavirus disease (COVID-19) based on scores ranging from 0 to 1. However, Ali-M3 has not been externally validated. Our aim was to evaluate the accuracy of Ali-M3 for detecting COVID-19 and discuss its clinical value. We evaluated the external validity of Ali-M3 using sequential Japanese sampling data. In this retrospective cohort study, COVID-19 infection probabilities for 617 symptomatic patients were determined using Ali-M3. In 11 Japanese tertiary care facilities, these patients underwent reverse transcription-polymerase chain reaction (RT-PCR) testing. They also underwent chest CT to confirm a diagnosis of COVID-19. Of the 617 patients, 289 (46.8%) were RT-PCR-positive. The area under the curve (AUC) of Ali-M3 for predicting a COVID-19 diagnosis was 0.797 (95% confidence interval: 0.762‒0.833) and the goodness-of-fit was P = 0.156. With a cut-off probability of a diagnosis of COVID-19 by Ali-M3 set at 0.5, the sensitivity and specificity were 80.6% and 68.3%, respectively. A cut-off of 0.2 yielded a sensitivity and specificity of 89.2% and 43.2%, respectively. Among the 223 patients who required oxygen, the AUC was 0.825. Sensitivity at a cut-off of 0.5% and 0.2% was 88.7% and 97.9%, respectively. Although the sensitivity was lower when the days from symptom onset were fewer, the sensitivity increased for both cut-off values after 5 days. We evaluated Ali-M3 using external validation with symptomatic patient data from Japanese tertiary care facilities. As Ali-M3 showed sufficient sensitivity performance, despite a lower specificity performance, Ali-M3 could be useful in excluding a diagnosis of COVID-19.


Subject(s)
COVID-19 Testing/methods , COVID-19/diagnosis , Deep Learning , Diagnosis, Computer-Assisted/methods , Tomography, X-Ray Computed/methods , Adult , Aged , Algorithms , Area Under Curve , Cohort Studies , Female , Humans , Image Processing, Computer-Assisted , Japan/epidemiology , Male , Middle Aged , Probability , ROC Curve , Reproducibility of Results , Retrospective Studies , Reverse Transcriptase Polymerase Chain Reaction , SARS-CoV-2 , Sensitivity and Specificity
7.
Sci Rep ; 11(1): 21084, 2021 10 26.
Article in English | MEDLINE | ID: covidwho-1493213

ABSTRACT

In contrast to the conventional approach of directly comparing genomic sequences using sequence alignment tools, we propose a computational approach that performs comparisons between sequence generators. These sequence generators are learned via a data-driven approach that empirically computes the state machine generating the genomic sequence of interest. As the state machine based generator of the sequence is independent of the sequence length, it provides us with an efficient method to compute the statistical distance between large sets of genomic sequences. Moreover, our technique provides a fast and efficient method to cluster large datasets of genomic sequences, characterize their temporal and spatial evolution in a continuous manner, get insights into the locality sensitive information about the sequences without any need for alignment. Furthermore, we show that the technique can be used to detect local regions with mutation activity, which can then be applied to aid alignment techniques for the fast discovery of mutations. To demonstrate the efficacy of our technique on real genomic data, we cluster different strains of SARS-CoV-2 viral sequences, characterize their evolution and identify regions of the viral sequence with mutations.


Subject(s)
COVID-19/virology , Computational Biology/methods , Genomics , Mutation , SARS-CoV-2/genetics , Algorithms , Cluster Analysis , DNA Mutational Analysis , Genome, Viral , Humans , Machine Learning , Models, Theoretical , Probability , Stochastic Processes
8.
Sci Rep ; 11(1): 20964, 2021 10 25.
Article in English | MEDLINE | ID: covidwho-1483147

ABSTRACT

Multicentre, retrospective cohort study with multivariable Cox proportional-hazards modelling and survival-time inverse-probability-weighting, evaluating the impact of different treatments on survival of proven COVID-19 patients admitted to two Hospitals in the province of Piacenza, Italy. Use of tocilizumab and of high doses of low molecular weight heparin, but not of antivirals (either alone or in combination), azithromycin, and any corticosteroid, was independently associated with lower mortality. Our results support further clinical evaluation of high doses of low molecular weight heparin and tocilizumab as COVID-19 therapeutics.


Subject(s)
Antibodies, Monoclonal, Humanized/administration & dosage , Antiviral Agents/administration & dosage , COVID-19/drug therapy , COVID-19/epidemiology , Heparin/administration & dosage , Adrenal Cortex Hormones/administration & dosage , Aged , Azithromycin/administration & dosage , Female , Hospital Mortality , Hospitalization , Humans , Italy/epidemiology , Male , Middle Aged , Patient Admission , Probability , Proportional Hazards Models , Retrospective Studies , SARS-CoV-2 , Treatment Outcome
9.
Sci Rep ; 11(1): 20715, 2021 10 21.
Article in English | MEDLINE | ID: covidwho-1479810

ABSTRACT

The current COVID-19 pandemic has created unmeasurable damages to society at a global level, from the irreplaceable loss of life, to the massive economic losses. In addition, the disease threatens further biodiversity loss. Due to their shared physiology with humans, primates, and particularly great apes, are susceptible to the disease. However, it is still uncertain how their populations would respond in case of infection. Here, we combine stochastic population and epidemiological models to simulate the range of potential effects of COVID-19 on the probability of extinction of mountain gorillas. We find that extinction is sharply driven by increases in the basic reproductive number and that the probability of extinction is greatly exacerbated if the immunity lasts less than 6 months. These results stress the need to limit exposure of the mountain gorilla population, the park personnel and visitors, as well as the potential of vaccination campaigns to extend the immunity duration.


Subject(s)
Ape Diseases/epidemiology , Ape Diseases/physiopathology , COVID-19/epidemiology , COVID-19/physiopathology , Animals , Animals, Newborn , COVID-19/veterinary , Computer Simulation , Endangered Species , Female , Gorilla gorilla , Immune System , Male , Models, Statistical , Pandemics , Probability , SARS-CoV-2 , Stochastic Processes
10.
Comput Math Methods Med ; 2021: 6636396, 2021.
Article in English | MEDLINE | ID: covidwho-1476878

ABSTRACT

Group testing (or pool testing), for example, Dorfman's method or grid method, has been validated for COVID-19 RT-PCR tests and implemented widely by most laboratories in many countries. These methods take advantages since they reduce resources, time, and overall costs required for a large number of samples. However, these methods could have more false negative cases and lower sensitivity. In order to maintain both accuracy and efficiency for different prevalence, we provide a novel pooling strategy based on the grid method with an extra pool set and an optimized rule inspired by the idea of error-correcting codes. The mathematical analysis shows that (i) the proposed method has the best sensitivity among all the methods we compared, if the false negative rate (FNR) of an individual test is in the range [1%, 20%] and the FNR of a pool test is closed to that of an individual test, and (ii) the proposed method is efficient when the prevalence is below 10%. Numerical simulations are also performed to confirm the theoretical derivations. In summary, the proposed method is shown to be felicitous under the above conditions in the epidemic.


Subject(s)
COVID-19 Testing/methods , COVID-19 Testing/standards , COVID-19/diagnosis , Algorithms , Computer Simulation , False Negative Reactions , Humans , Laboratories/standards , Models, Theoretical , Prevalence , Probability , Reproducibility of Results
11.
Sci Rep ; 11(1): 20072, 2021 10 08.
Article in English | MEDLINE | ID: covidwho-1462036

ABSTRACT

The World Health Organization (WHO) has declared the Corona pandemic as a public health emergency. This pandemic affects the main pillars of food security. This study aimed to investigate the relationship between food insecurity and the probability of hospitalization and the length of the recovery period after getting COVID-19. The cross-sectional study was performed through the census on COVID-19 patients diagnosed in Fasa, Iran. Informed consent, demographic, and food security questionnaire were completed over the phone. Then, all patients were followed up until recovery. Data were analyzed using SPSS26 and Chi-square test, t-test, and logistic regression (P < 0.05). In this study, 219 COVID-19 patients [100 (54.7%) male and 119 (54.3%) female] with a mean age of 40.05 ± 15.54 years old were examined. Possibility of hospitalization and the length of the recovery period of more than one month was significantly longer in the food-insecure group (P = 0.001) and (P = 0.37), respectively, but the mean length of hospital stay in the two groups was not significantly different (P = 0.76). After adjusting for all confounding variables, people with food insecurity were 3.9 times more likely to be hospitalized than those with food security. Overall, we observed that food-insecure people were significantly more likely to be hospitalized than the secure group.


Subject(s)
COVID-19/epidemiology , Food Insecurity , Adult , Cross-Sectional Studies , Female , Hospitalization , Humans , Iran/epidemiology , Length of Stay , Male , Middle Aged , Poverty , Probability , SARS-CoV-2/isolation & purification , Socioeconomic Factors
12.
Nat Commun ; 12(1): 5918, 2021 10 11.
Article in English | MEDLINE | ID: covidwho-1462004

ABSTRACT

Fuelled by epidemiological studies of SARS-CoV-2, contact tracing by mobile phones has been put to use in many countries. Over a year into the pandemic, we lack conclusive evidence on its effectiveness. To address this gap, we used a unique real world contact data set, collected during the rollout of the first Norwegian contact tracing app in the Spring of 2020. Our dataset involves millions of contacts between 12.5% of the adult population, which enabled us to measure the real-world app performance. The technological tracing efficacy was measured at 80%, and we estimated that at least 11.0% of the discovered close contacts could not have been identified by manual contact tracing. Our results also indicated that digital contact tracing can flag individuals with excessive contacts, which can help contain superspreading related outbreaks. The overall effectiveness of digital tracing depends strongly on app uptake, but significant impact can be achieved for moderate uptake numbers. Used as a supplement to manual tracing and other measures, digital tracing can be instrumental in controlling the pandemic. Our findings can thus help informing public health policies in the coming months.


Subject(s)
COVID-19/epidemiology , COVID-19/prevention & control , Contact Tracing , Pandemics/prevention & control , Humans , Mobile Applications , Norway/epidemiology , Probability , SARS-CoV-2/physiology
13.
J Exp Anal Behav ; 114(1): 72-86, 2020 07.
Article in English | MEDLINE | ID: covidwho-1451867

ABSTRACT

Choosing a larger-later reward over a smaller-sooner reward may be thought of as altruism toward one's future self. A question that arises in this connection is: What is the relation between delay and social discounting? To begin to answer this question, social and delay discount functions need to be comparable. Delay is ordinarily measured on a ratio scale (time), which allows for meaningful division and addition. Social distance is ordinarily measured on an ordinal scale (rank order of social closeness). To convert social distance to a ratio scale we use a psychophysical distance function obtained via magnitude estimation (Stevens, 1956). The distance functions obtained are well described by a power function (median exponent = 1.9); we show how they may be used to rescale ordinal to ratio social discount functions.


Subject(s)
Delay Discounting , Social Isolation/psychology , Social Perception/psychology , Adult , Altruism , Female , Humans , Male , Models, Psychological , Probability
14.
Pan Afr Med J ; 39: 227, 2021.
Article in English | MEDLINE | ID: covidwho-1449269

ABSTRACT

Introduction: as the COVID-19 pandemic rages on, sub-Saharan Africa remains at high risk given the poor adherence to pandemic control protocols. Misconceptions about the contagion may have given rise to adverse risk behaviours across population groups. This study evaluates risk perception among 2,244 residents of seven countries in sub-Saharan Africa (Botswana, Kenya, Malawi, Nigeria, Tanzania, Zambia and Zimbabwe) in relation to socio-demographic determinants. Methods: an online survey was conducted via social media platforms to a random sample of participants. Risk perception was evaluated across six domains: loss of income, food scarcity, having a relative infected, civil disorder, criminal attacks, or losing a friend or relative to COVID-19. A multivariable ordinal logistic regression was conducted to assess socio-demographic factors associated with the perceived risk of being affected by COVID-19. Results: 595 (27%) respondents did not consider themselves to be at risk, while 33% perceived themselves to be at high risk of being affected by the pandemic with respect to the six domains evaluated. Hospital-based workers had the highest proportional odds (3.5; 95%CI: 2.3-5.6) high perceived risk. Teenage respondents had the highest predictive probability (54.6%; 95% CI: 36.6-72.7%) of perceiving themselves not to be at risk of being affected by COVID-19, while Zambia residents had the highest predictive probability (40.7%; 95% CI: 34.3-47.0%) for high-risk perception. Conclusion: this study reveals the need to increase awareness of risks among socio-demographic groups such as younger people and the unemployed. Targeted risk communication strategies will create better risk consciousness, as well as adherence to safety measures.


Subject(s)
COVID-19/epidemiology , Guideline Adherence , Risk-Taking , Adult , Africa South of the Sahara , Age Factors , COVID-19/psychology , Communication , Cross-Sectional Studies , Female , Humans , Male , Middle Aged , Perception , Personnel, Hospital/statistics & numerical data , Probability , Risk Factors , Surveys and Questionnaires , Unemployment , Young Adult
15.
PLoS One ; 16(9): e0257613, 2021.
Article in English | MEDLINE | ID: covidwho-1430545

ABSTRACT

This paper analyses COVID-19 patients' dynamics during the first wave in the region of Castilla y León (Spain) with around 2.4 million inhabitants using multi-state competing risk survival models. From the date registered as the start of the clinical process, it is assumed that a patient can progress through three intermediate states until reaching an absorbing state of recovery or death. Demographic characteristics, epidemiological factors such as the time of infection and previous vaccinations, clinical history, complications during the course of the disease and drug therapy for hospitalised patients are considered as candidate predictors. Regarding risk factors associated with mortality and severity, consistent results with many other studies have been found, such as older age, being male, and chronic diseases. Specifically, the hospitalisation (death) rate for those over 69 is 27.2% (19.8%) versus 5.3% (0.7%) for those under 70, and for males is 14.5%(7%) versus 8.3%(4.6%)for females. Among patients with chronic diseases the highest rates of hospitalisation are 26.1% for diabetes and 26.3% for kidney disease, while the highest death rate is 21.9% for cerebrovascular disease. Moreover, specific predictors for different transitions are given, and estimates of the probability of recovery and death for each patient are provided by the model. Some interesting results obtained are that for patients infected at the end of the period the hazard of transition from hospitalisation to ICU is significatively lower (p < 0.001) and the hazard of transition from hospitalisation to recovery is higher (p < 0.001). For patients previously vaccinated against pneumococcus the hazard of transition to recovery is higher (p < 0.001). Finally, internal validation and calibration of the model are also performed.


Subject(s)
COVID-19/diagnosis , COVID-19/mortality , Disease Progression , Hospital Records , Hospitals , Primary Health Care , Adolescent , Adult , Aged , Aged, 80 and over , COVID-19/complications , COVID-19/drug therapy , Calibration , Child , Child, Preschool , Comorbidity , Confidence Intervals , Female , Humans , Infant , Infant, Newborn , Male , Middle Aged , Probability , Proportional Hazards Models , Reproducibility of Results , Spain/epidemiology , Young Adult
16.
PLoS Comput Biol ; 17(9): e1009355, 2021 09.
Article in English | MEDLINE | ID: covidwho-1430515

ABSTRACT

Many countries are currently dealing with the COVID-19 epidemic and are searching for an exit strategy such that life in society can return to normal. To support this search, computational models are used to predict the spread of the virus and to assess the efficacy of policy measures before actual implementation. The model output has to be interpreted carefully though, as computational models are subject to uncertainties. These can stem from, e.g., limited knowledge about input parameters values or from the intrinsic stochastic nature of some computational models. They lead to uncertainties in the model predictions, raising the question what distribution of values the model produces for key indicators of the severity of the epidemic. Here we show how to tackle this question using techniques for uncertainty quantification and sensitivity analysis. We assess the uncertainties and sensitivities of four exit strategies implemented in an agent-based transmission model with geographical stratification. The exit strategies are termed Flattening the Curve, Contact Tracing, Intermittent Lockdown and Phased Opening. We consider two key indicators of the ability of exit strategies to avoid catastrophic health care overload: the maximum number of prevalent cases in intensive care (IC), and the total number of IC patient-days in excess of IC bed capacity. Our results show that uncertainties not directly related to the exit strategies are secondary, although they should still be considered in comprehensive analysis intended to inform policy makers. The sensitivity analysis discloses the crucial role of the intervention uptake by the population and of the capability to trace infected individuals. Finally, we explore the existence of a safe operating space. For Intermittent Lockdown we find only a small region in the model parameter space where the key indicators of the model stay within safe bounds, whereas this region is larger for the other exit strategies.


Subject(s)
COVID-19/prevention & control , Communicable Disease Control/methods , Computer Simulation , Uncertainty , COVID-19/epidemiology , COVID-19/virology , Contact Tracing , Humans , Probability , SARS-CoV-2/isolation & purification
17.
Aging Clin Exp Res ; 33(9): 2633, 2021 09.
Article in English | MEDLINE | ID: covidwho-1406187
18.
J Korean Med Sci ; 36(35): e248, 2021 Sep 06.
Article in English | MEDLINE | ID: covidwho-1399125

ABSTRACT

BACKGROUND: Prediction of mortality in patients with coronavirus disease 2019 (COVID-19) is a key to improving the clinical outcomes, considering that the COVID-19 pandemic has led to the collapse of healthcare systems in many regions worldwide. This study aimed to identify the factors associated with COVID-19 mortality and to develop a nomogram for predicting mortality using clinical parameters and underlying diseases. METHODS: This study was performed in 5,626 patients with confirmed COVID-19 between February 1 and April 30, 2020 in South Korea. A Cox proportional hazards model and logistic regression model were used to construct a nomogram for predicting 30-day and 60-day survival probabilities and overall mortality, respectively in the train set. Calibration and discrimination were performed to validate the nomograms in the test set. RESULTS: Age ≥ 70 years, male, presence of fever and dyspnea at the time of COVID-19 diagnosis, and diabetes mellitus, cancer, or dementia as underling diseases were significantly related to 30-day and 60-day survival and mortality in COVID-19 patients. The nomogram showed good calibration for survival probabilities and mortality. In the train set, the areas under the curve (AUCs) for 30-day and 60-day survival was 0.914 and 0.954, respectively; the AUC for mortality of 0.959. In the test set, AUCs for 30-day and 60-day survival was 0.876 and 0.660, respectively, and that for mortality was 0.926. The online calculators can be found at https://koreastat.shinyapps.io/RiskofCOVID19/. CONCLUSION: The prediction model could accurately predict COVID-19-related mortality; thus, it would be helpful for identifying the risk of mortality and establishing medical policies during the pandemic to improve the clinical outcomes.


Subject(s)
COVID-19/mortality , Nomograms , SARS-CoV-2 , Adolescent , Adult , Aged , Aged, 80 and over , Child , Child, Preschool , Female , Humans , Infant , Infant, Newborn , Logistic Models , Male , Middle Aged , Probability , Proportional Hazards Models , Severity of Illness Index , Young Adult
19.
Int J Environ Res Public Health ; 18(17)2021 09 03.
Article in English | MEDLINE | ID: covidwho-1390635

ABSTRACT

Multiplicity arises when data analysis involves multiple simultaneous inferences, increasing the chance of spurious findings. It is a widespread problem frequently ignored by researchers. In this paper, we perform an exploratory analysis of the Web of Science database for COVID-19 observational studies. We examined 100 top-cited COVID-19 peer-reviewed articles based on p-values, including up to 7100 simultaneous tests, with 50% including >34 tests, and 20% > 100 tests. We found that the larger the number of tests performed, the larger the number of significant results (r = 0.87, p < 10-6). The number of p-values in the abstracts was not related to the number of p-values in the papers. However, the highly significant results (p < 0.001) in the abstracts were strongly correlated (r = 0.61, p < 10-6) with the number of p < 0.001 significances in the papers. Furthermore, the abstracts included a higher proportion of significant results (0.91 vs. 0.50), and 80% reported only significant results. Only one reviewed paper addressed multiplicity-induced type I error inflation, pointing to potentially spurious results bypassing the peer-review process. We conclude the need to pay special attention to the increased chance of false discoveries in observational studies, including non-replicated striking discoveries with a potentially large social impact. We propose some easy-to-implement measures to assess and limit the effects of multiplicity.


Subject(s)
COVID-19 , Humans , Peer Review , Probability , SARS-CoV-2
20.
Elife ; 102021 02 23.
Article in English | MEDLINE | ID: covidwho-1389775

ABSTRACT

SARS-CoV-2 is difficult to contain because many transmissions occur during pre-symptomatic infection. Unlike influenza, most SARS-CoV-2-infected people do not transmit while a small percentage infect large numbers of people. We designed mathematical models which link observed viral loads with epidemiologic features of each virus, including distribution of transmissions attributed to each infected person and duration between symptom onset in the transmitter and secondarily infected person. We identify that people infected with SARS-CoV-2 or influenza can be highly contagious for less than 1 day, congruent with peak viral load. SARS-CoV-2 super-spreader events occur when an infected person is shedding at a very high viral load and has a high number of exposed contacts. The higher predisposition of SARS-CoV-2 toward super-spreading events cannot be attributed to additional weeks of shedding relative to influenza. Rather, a person infected with SARS-CoV-2 exposes more people within equivalent physical contact networks, likely due to aerosolization.


Subject(s)
COVID-19/transmission , Carrier State , Viral Load , Virus Shedding , Aerosols , Basic Reproduction Number , COVID-19/epidemiology , China/epidemiology , Computer Simulation , Contact Tracing , Humans , Influenza, Human/epidemiology , Influenza, Human/transmission , Models, Theoretical , Pandemics , Probability , SARS-CoV-2 , Time Factors
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