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1.
JMIR Public Health Surveill ; 8(11): e40751, 2022 Nov 18.
Article in English | MEDLINE | ID: covidwho-2109572

ABSTRACT

BACKGROUND: As of August 25, 2021, Jiangsu province experienced the largest COVID-19 outbreak in eastern China that was seeded by SARS-CoV-2 Delta variants. As one of the key epidemiological parameters characterizing the transmission dynamics of COVID-19, the incubation period plays an essential role in informing public health measures for epidemic control. The incubation period of COVID-19 could vary by different age, sex, disease severity, and study settings. However, the impacts of these factors on the incubation period of Delta variants remains uninvestigated. OBJECTIVE: The objective of this study is to characterize the incubation period of the Delta variant using detailed contact tracing data. The effects of age, sex, and disease severity on the incubation period were investigated by multivariate regression analysis and subgroup analysis. METHODS: We extracted contact tracing data of 353 laboratory-confirmed cases of SARS-CoV-2 Delta variants' infection in Jiangsu province, China, from July to August 2021. The distribution of incubation period of Delta variants was estimated by using likelihood-based approach with adjustment for interval-censored observations. The effects of age, sex, and disease severity on the incubation period were expiated by using multivariate logistic regression model with interval censoring. RESULTS: The mean incubation period of the Delta variant was estimated at 6.64 days (95% credible interval: 6.27-7.00). We found that female cases and cases with severe symptoms had relatively longer mean incubation periods than male cases and those with nonsevere symptoms, respectively. One-day increase in the incubation period of Delta variants was associated with a weak decrease in the probability of having severe illness with an adjusted odds ratio of 0.88 (95% credible interval: 0.71-1.07). CONCLUSIONS: In this study, the incubation period was found to vary across different levels of sex, age, and disease severity of COVID-19. These findings provide additional information on the incubation period of Delta variants and highlight the importance of continuing surveillance and monitoring of the epidemiological characteristics of emerging SARS-CoV-2 variants as they evolve.


Subject(s)
COVID-19 , SARS-CoV-2 , Female , Humans , Male , COVID-19/epidemiology , Infectious Disease Incubation Period , Likelihood Functions , SARS-CoV-2/genetics , Retrospective Studies
2.
Front Public Health ; 10: 986933, 2022.
Article in English | MEDLINE | ID: covidwho-2080294

ABSTRACT

Background: With the rapid development of "Internet + medicine" and the impact of the COVID-19 epidemic, online health communities have become an important way for patients to seek medical treatment. However, the mistrust between physicians and patients in online health communities has long existed and continues to impact the decision-making behavior of patients. The purpose of this article is to explore the influencing factors of patient decision-making in online health communities by identifying the relationship between physicians' online information and patients' selection behavior. Methods: In this study, we selected China's Good Doctor (www.haodf.com) as the source of data, scrapped 10,446 physician data from December 2020 to June 2021 to construct a logit model of online patients' selection behavior, and used regression analysis to test the hypotheses. Results: The number of types of services, number of scientific articles, and avatar in physicians' personal information all has a positive effect on patients' selection behavior, while the title and personal introduction hurt patients' selection behavior. Online word-of-mouth positively affected patients' selection behavior and disease risk had a moderating effect. Conclusion: Focusing on physician-presented information, this article organically combines the Elaboration likelihood model with trust source theory and online word-of-mouth from the perspective of the trusted party-physician, providing new ideas for the study of factors influencing patients' selection behavior in online health communities. The findings provide useful insights for patients, physicians, and community managers about the relationship between physician information and patients' selection behavior.


Subject(s)
COVID-19 , Physicians , Humans , Likelihood Functions , COVID-19/epidemiology , Trust
3.
Bull Math Biol ; 84(11): 127, 2022 09 22.
Article in English | MEDLINE | ID: covidwho-2035259

ABSTRACT

Mathematical modeling is a tool used for understanding diseases dynamics. The discrete-time model is an especial case in modeling that satisfactorily describes the epidemiological dynamics because of the discrete nature of the real data. However, discrete models reduce their descriptive and fitting potential because of assuming a homogeneous population. Thus, in this paper, we proposed contagion probability functions according to two infection paradigms that consider factors associated with transmission dynamics. For example, we introduced probabilities of establishing an infectious interaction, the number of contacts with infectious and the level of connectivity or social distance within populations. Through the probabilities design, we overcame the homogeneity assumption. Also, we evaluated the proposed probabilities through their introduction into discrete-time models for two diseases and different study zones with real data, COVID-19 for Germany and South Korea, and dengue for Colombia. Also, we described the oscillatory dynamics for the last one using the contagion probabilities alongside parameters with a biological sense. Finally, we highlight the implementation of the proposed probabilities would improve the simulation of the public policy effect of control strategies over an infectious disease outbreak.


Subject(s)
COVID-19 , Models, Biological , COVID-19/epidemiology , Computer Simulation , Humans , Likelihood Functions , Mathematical Concepts , Probability
4.
BMJ Open ; 12(8): e057746, 2022 08 29.
Article in English | MEDLINE | ID: covidwho-2020034

ABSTRACT

INTRODUCTION: Increasing numbers of patients with non-haematological diseases are infected with invasive pulmonary aspergillosis (IPA), with a high mortality reported which is mainly due to delayed diagnosis. The diagnostic capability of mycological tests for IPA including galactomannan test, (1,3)-ß-D-glucan test, lateral flow assay, lateral flow device and PCR for the non-haematological patients remains unknown. This protocol aims to conduct a systematic review and meta-analysis of the diagnostic performance of mycological tests to facilitate the early diagnosis and treatments of IPA in non-haematological diseases. METHODS AND ANALYSIS: Database including PubMed, CENTRAL and EMBASE will be searched from 2002 until the publication of results. Cohort or cross-sectional studies that assessing the diagnostic capability of mycological tests for IPA in patients with non-haematological diseases will be included. The true-positive, false-positive, true-negative and false-negative of each test will be extracted and pooled in bivariate random-effects model, by which the sensitivity and specificity will be calculated with 95% CI. The second outcomes will include positive (negative) likelihood ratio, area under the receiver operating characteristic curve and diagnostic OR will also be computed in the bivariate model. When applicable, subgroup analysis will be performed with several prespecified covariates to explore potential sources of heterogeneity. Factors that may impact the diagnostic effects of mycological tests will be examined by sensitivity analysis. The risk of bias will be appraised by the Quality Assessment tool for Diagnostic Accuracy Studies (QUADAS-2). ETHICS AND DISSEMINATION: This protocol is not involved with ethics approval, and the results will be peer-reviewed and disseminated on a recognised journal. PROSPERO REGISTRATION NUMBER: CRD42021241820.


Subject(s)
Diagnostic Tests, Routine , Invasive Pulmonary Aspergillosis , Meta-Analysis as Topic , Systematic Reviews as Topic , Cross-Sectional Studies , Diagnostic Tests, Routine/standards , Hematology , Humans , Invasive Pulmonary Aspergillosis/diagnosis , Invasive Pulmonary Aspergillosis/microbiology , Likelihood Functions , Odds Ratio , ROC Curve , Sensitivity and Specificity , Systematic Reviews as Topic/methods
5.
Emerg Infect Dis ; 28(9): 1873-1876, 2022 09.
Article in English | MEDLINE | ID: covidwho-1974601

ABSTRACT

To model estimated deaths averted by COVID-19 vaccines, we used state-of-the-art mathematical modeling, likelihood-based inference, and reported COVID-19 death and vaccination data. We estimated that >1.5 million deaths were averted in 12 countries. Our model can help assess effectiveness of the vaccination program, which is crucial for curbing the COVID-19 pandemic.


Subject(s)
COVID-19 , COVID-19/epidemiology , COVID-19/prevention & control , COVID-19 Vaccines , Humans , Immunization Programs , Likelihood Functions , Pandemics/prevention & control , SARS-CoV-2 , Vaccination
6.
Int J Environ Res Public Health ; 19(15)2022 07 27.
Article in English | MEDLINE | ID: covidwho-1969211

ABSTRACT

COVID-19 country spikes have been reported at varying temporal scales as a result of differences in the disease-driving factors. Factors affecting case load and mortality rates have varied between countries and regions. We investigated the association between socio-economic, weather, demographic and health variables with the reported cases of COVID-19 in Eswatini using the maximum likelihood estimation method for count data. A generalized Poisson regression (GPR) model was fitted with the data comprising 15 covariates to predict COVID-19 risk in the whole of Eswatini. The results show that the variables that were key determinants in the spread of the disease were those that included the proportion of elderly above 55 years at 98% (95% CI: 97-99%) and the proportion of youth below the age of 35 years at 8% (95% CI: 1.7-38%) with a pseudo R-square of 0.72. However, in the early phase of the virus when cases were fewer, results from the Poisson regression showed that household size, household density and poverty index were associated with reported COVID-19 cases in the country. We then produced a disease-risk map of predicted COVID-19 in Eswatini using variables that were selected by the regression model at a 5% significance level. The map could be used by the country to plan and prioritize health interventions against COVID-19. The identified areas of high risk may be further investigated to find out the risk amplifiers and assess what could be done to prevent them.


Subject(s)
COVID-19 , Adolescent , Adult , Aged , COVID-19/epidemiology , Eswatini , Humans , Likelihood Functions , Poverty
7.
Contemp Clin Trials ; 120: 106859, 2022 09.
Article in English | MEDLINE | ID: covidwho-1959360

ABSTRACT

Missing data are inevitable in longitudinal clinical trials due to intercurrent events (ICEs) such as treatment interruption or premature discontinuation for different reasons. Missing at random (MAR) assumption is usually unverifiable and sensitivity analyses are often requested under missing not at random (MNAR) assumption. Return to baseline (RTB) imputation is a commonly used MNAR method. In practice, not all dropout missingness can be assumed MNAR. For example, missingness or dropouts due to COVID-19 can be reasonably assumed MAR. Therefore, traditional RTB is not applicable when there is both MAR and MNAR dropout missingness. Here we propose a hybrid strategy for RTB imputation which can handle missing data due to MAR and MNAR dropouts at the same time. Standard multiple imputation approach is proposed and an analytic likelihood based approach is derived to improve efficiency.


Subject(s)
COVID-19 , Data Interpretation, Statistical , Humans , Likelihood Functions , Models, Statistical , Research Design
8.
PLoS One ; 17(5): e0268130, 2022.
Article in English | MEDLINE | ID: covidwho-1923682

ABSTRACT

Bayesian empirical likelihood (BEL) models are becoming increasingly popular as an attractive alternative to fully parametric models. However, they have only recently been applied to spatial data analysis for small area estimation. This study considers the development of spatial BEL models using two popular conditional autoregressive (CAR) priors, namely BYM and Leroux priors. The performance of the proposed models is compared with their parametric counterparts and with existing spatial BEL models using independent Gaussian priors and generalised Moran basis priors. The models are applied to two benchmark spatial datasets, simulation study and COVID-19 data. The results indicate promising opportunities for these models to capture new insights into spatial data. Specifically, the spatial BEL models outperform the parametric spatial models when the underlying distributional assumptions of data appear to be violated.


Subject(s)
COVID-19 , Bayes Theorem , COVID-19/epidemiology , Humans , Likelihood Functions , Normal Distribution , Spatial Analysis
9.
PLoS Comput Biol ; 18(6): e1010281, 2022 06.
Article in English | MEDLINE | ID: covidwho-1910467

ABSTRACT

In the context of infectious disease transmission, high heterogeneity in individual infectiousness indicates that a few index cases can generate large numbers of secondary cases, a phenomenon commonly known as superspreading. The potential of disease superspreading can be characterized by describing the distribution of secondary cases (of each seed case) as a negative binomial (NB) distribution with the dispersion parameter, k. Based on the feature of NB distribution, there must be a proportion of individuals with individual reproduction number of almost 0, which appears restricted and unrealistic. To overcome this limitation, we generalized the compound structure of a Poisson rate and included an additional parameter, and divided the reproduction number into independent and additive fixed and variable components. Then, the secondary cases followed a Delaporte distribution. We demonstrated that the Delaporte distribution was important for understanding the characteristics of disease transmission, which generated new insights distinct from the NB model. By using real-world dataset, the Delaporte distribution provides improvements in describing the distributions of COVID-19 and SARS cases compared to the NB distribution. The model selection yielded increasing statistical power with larger sample sizes as well as conservative type I error in detecting the improvement in fitting with the likelihood ratio (LR) test. Numerical simulation revealed that the control strategy-making process may benefit from monitoring the transmission characteristics under the Delaporte framework. Our findings highlighted that for the COVID-19 pandemic, population-wide interventions may control disease transmission on a general scale before recommending the high-risk-specific control strategies.


Subject(s)
COVID-19 , Communicable Diseases , COVID-19/epidemiology , Communicable Diseases/epidemiology , Humans , Likelihood Functions , Models, Statistical , Pandemics/prevention & control
10.
PLoS One ; 17(6): e0269450, 2022.
Article in English | MEDLINE | ID: covidwho-1879323

ABSTRACT

This study suggested a new four-parameter Exponentiated Odd Lomax Exponential (EOLE) distribution by compounding an exponentiated odd function with Lomax distribution as a generator. The proposed model is unimodal and positively skewed whereas the hazard rate function is monotonically increasing and inverted bathtubs. Some important properties of the new distribution are derived such as quintile function and median; asymptotic properties and mode; moments; mean residual life, mean path time; mean deviation; order statistics; and Bonferroni & Lorenz curve. The value of the parameters is obtained from the maximum likelihood estimation, least-square estimation, and Cramér-Von-Mises methods. Here, a simulation study and two real data sets, "the number of deaths per day due to COVID-19 of the first wave in Nepal" and ''failure stresses (In Gpa) of single carbon fibers of lengths 50 mm", have been applied to validate the different theoretical findings. The finding of an order of COVID-19 deaths in 153 days in Nepal obey the proposed distribution, it has a significantly positive relationship between the predictive test positive rate and the predictive number of deaths per day. Therefore, the intended model is an alternative model for survival data and lifetime data analysis.


Subject(s)
COVID-19 , COVID-19/epidemiology , Humans , Least-Squares Analysis , Likelihood Functions , Nepal/epidemiology , Statistical Distributions
11.
J R Soc Interface ; 19(191): 20220124, 2022 06.
Article in English | MEDLINE | ID: covidwho-1874074

ABSTRACT

We present a new method for analysing stochastic epidemic models under minimal assumptions. The method, dubbed dynamic survival analysis (DSA), is based on a simple yet powerful observation, namely that population-level mean-field trajectories described by a system of partial differential equations may also approximate individual-level times of infection and recovery. This idea gives rise to a certain non-Markovian agent-based model and provides an agent-level likelihood function for a random sample of infection and/or recovery times. Extensive numerical analyses on both synthetic and real epidemic data from foot-and-mouth disease in the UK (2001) and COVID-19 in India (2020) show good accuracy and confirm the method's versatility in likelihood-based parameter estimation. The accompanying software package gives prospective users a practical tool for modelling, analysing and interpreting epidemic data with the help of the DSA approach.


Subject(s)
COVID-19 , Epidemics , Animals , COVID-19/epidemiology , Likelihood Functions , Prospective Studies , Survival Analysis
12.
J Biomed Inform ; 131: 104097, 2022 07.
Article in English | MEDLINE | ID: covidwho-1867315

ABSTRACT

BACKGROUND: Observational studies incorporating real-world data from multiple institutions facilitate study of rare outcomes or exposures and improve generalizability of results. Due to privacy concerns surrounding patient-level data sharing across institutions, methods for performing regression analyses distributively are desirable. Meta-analysis of institution-specific estimates is commonly used, but has been shown to produce biased estimates in certain settings. While distributed regression methods are increasingly available, methods for analyzing count outcomes are currently limited. Count data in practice are commonly subject to overdispersion, exhibiting greater variability than expected under a given statistical model. OBJECTIVE: We propose a novel computational method, a one-shot distributed algorithm for quasi-Poisson regression (ODAP), to distributively model count outcomes while accounting for overdispersion. METHODS: ODAP incorporates a surrogate likelihood approach to perform distributed quasi-Poisson regression without requiring patient-level data sharing, only requiring sharing of aggregate data from each participating institution. ODAP requires at most three rounds of non-iterative communication among institutions to generate coefficient estimates and corresponding standard errors. In simulations, we evaluate ODAP under several data scenarios possible in multi-site analyses, comparing ODAP and meta-analysis estimates in terms of error relative to pooled regression estimates, considered the gold standard. In a proof-of-concept real-world data analysis, we similarly compare ODAP and meta-analysis in terms of relative error to pooled estimatation using data from the OneFlorida Clinical Research Consortium, modeling length of stay in COVID-19 patients as a function of various patient characteristics. In a second proof-of-concept analysis, using the same outcome and covariates, we incorporate data from the UnitedHealth Group Clinical Discovery Database together with the OneFlorida data in a distributed analysis to compare estimates produced by ODAP and meta-analysis. RESULTS: In simulations, ODAP exhibited negligible error relative to pooled regression estimates across all settings explored. Meta-analysis estimates, while largely unbiased, were increasingly variable as heterogeneity in the outcome increased across institutions. When baseline expected count was 0.2, relative error for meta-analysis was above 5% in 25% of iterations (250/1000), while the largest relative error for ODAP in any iteration was 3.59%. In our proof-of-concept analysis using only OneFlorida data, ODAP estimates were closer to pooled regression estimates than those produced by meta-analysis for all 15 covariates. In our distributed analysis incorporating data from both OneFlorida and the UnitedHealth Group Clinical Discovery Database, ODAP and meta-analysis estimates were largely similar, while some differences in estimates (as large as 13.8%) could be indicative of bias in meta-analytic estimates. CONCLUSIONS: ODAP performs privacy-preserving, communication-efficient distributed quasi-Poisson regression to analyze count outcomes using data stored within multiple institutions. Our method produces estimates nearly matching pooled regression estimates and sometimes more accurate than meta-analysis estimates, most notably in settings with relatively low counts and high outcome heterogeneity across institutions.


Subject(s)
COVID-19 , Algorithms , COVID-19/epidemiology , Humans , Likelihood Functions , Models, Statistical , Regression Analysis
13.
Am J Cardiol ; 172: 115-120, 2022 06 01.
Article in English | MEDLINE | ID: covidwho-1838526

ABSTRACT

Previous studies have shown that bovine arch incidence is higher in patients with thoracic aortic aneurysms than in patients without an aneurysm. Although thoracic aortic aneurysm disease is known to be familial in some cases, it remains unknown if bovine arch results from a genetic mutation, thus allowing it to be inherited. Our objective was to determine the heritability of bovine arch from phenotypic pedigrees. We identified 24 probands from an institutional database of 202 living patients with bovine arch who had previously been diagnosed with thoracic aortic aneurysm and who had family members with previous chest computed tomography or magnetic resonance imaging scans. Aortic arch configuration of all first-degree and second-degree relatives was determined from available scans. Heritability of bovine arch was estimated using maximum-likelihood-based variance decomposition methodology implemented by way of the SOLAR package (University of Maryland, Catonsville, Maryland). 43 relatives of 24 probands with bovine arch had preexisting imaging available for review. The prevalence of bovine arch in relatives with chest imaging was 53% (n = 23) and did not differ significantly by gender (male: 64.3%, female: 55.6%, p = 1). The bovine arch was shown to be highly heritable with a heritability estimate (h2) of 0.71 (p = 0.048). In conclusion, the high heritability of bovine arch in our sample population suggests a genetic basis.


Subject(s)
Aneurysm , Aortic Aneurysm, Thoracic , Aneurysm/complications , Aorta, Thoracic/diagnostic imaging , Aortic Aneurysm, Thoracic/epidemiology , Aortic Aneurysm, Thoracic/genetics , Female , Humans , Incidence , Likelihood Functions , Male , Retrospective Studies
14.
Comput Intell Neurosci ; 2022: 5134507, 2022.
Article in English | MEDLINE | ID: covidwho-1799192

ABSTRACT

This article investigates the estimation of the parameters for power hazard function distribution and some lifetime indices such as reliability function, hazard rate function, and coefficient of variation based on adaptive Type-II progressive censoring. From the perspective of frequentism, we derive the point estimations through the method of maximum likelihood estimation. Besides, delta method is implemented to construct the variances of the reliability characteristics. Markov chain Monte Carlo techniques are proposed to construct the Bayes estimates. To this end, the results of the Bayes estimates are obtained under squared error and linear exponential loss functions. Also, the corresponding credible intervals are constructed. A simulation study is utilized to assay the performance of the proposed methods. Finally, a real data set of COVID-19 mortality rate is analyzed to validate the introduced inference methods.


Subject(s)
COVID-19 , Bayes Theorem , Computer Simulation , Humans , Likelihood Functions , Monte Carlo Method , Reproducibility of Results
15.
BMC Med Res Methodol ; 22(1): 116, 2022 04 20.
Article in English | MEDLINE | ID: covidwho-1799118

ABSTRACT

BACKGROUND: The COVID-19 pandemic has led to a high interest in mathematical models describing and predicting the diverse aspects and implications of the virus outbreak. Model results represent an important part of the information base for the decision process on different administrative levels. The Robert-Koch-Institute (RKI) initiated a project whose main goal is to predict COVID-19-specific occupation of beds in intensive care units: Steuerungs-Prognose von Intensivmedizinischen COVID-19 Kapazitäten (SPoCK). The incidence of COVID-19 cases is a crucial predictor for this occupation. METHODS: We developed a model based on ordinary differential equations for the COVID-19 spread with a time-dependent infection rate described by a spline. Furthermore, the model explicitly accounts for weekday-specific reporting and adjusts for reporting delay. The model is calibrated in a purely data-driven manner by a maximum likelihood approach. Uncertainties are evaluated using the profile likelihood method. The uncertainty about the appropriate modeling assumptions can be accounted for by including and merging results of different modelling approaches. The analysis uses data from Germany describing the COVID-19 spread from early 2020 until March 31st, 2021. RESULTS: The model is calibrated based on incident cases on a daily basis and provides daily predictions of incident COVID-19 cases for the upcoming three weeks including uncertainty estimates for Germany and its subregions. Derived quantities such as cumulative counts and 7-day incidences with corresponding uncertainties can be computed. The estimation of the time-dependent infection rate leads to an estimated reproduction factor that is oscillating around one. Data-driven estimation of the dark figure purely from incident cases is not feasible. CONCLUSIONS: We successfully implemented a procedure to forecast near future COVID-19 incidences for diverse subregions in Germany which are made available to various decision makers via an interactive web application. Results of the incidence modeling are also used as a predictor for forecasting the need of intensive care units.


Subject(s)
COVID-19 , COVID-19/epidemiology , Decision Making , Forecasting , Germany/epidemiology , Humans , Likelihood Functions , Pandemics , SARS-CoV-2
16.
Int J Environ Res Public Health ; 19(4)2022 02 18.
Article in English | MEDLINE | ID: covidwho-1753480

ABSTRACT

The global COVID-19 mass vaccination program has created a polemic amongst pro- and anti-vaccination groups on social media. However, the working mechanism on how the shared information might influence an individual decision to be vaccinated is still limited. This study embarks on adopting the elaboration likelihood model (ELM) framework. We examined the function of central route factors (information completeness and information accuracy) as well as peripheral route factors (experience sharing and social pressure) in influencing attitudes towards vaccination and the intention to obtain the vaccine. We use a factorial design to create eight different scenarios in the form of Twitter posts to test the interaction and emulate the situation on social media. In total, 528 respondents were involved in this study. Findings from this study indicated that both the central route and peripheral route significantly influence individually perceived informativeness and perceived persuasiveness. Consequently, these two factors significantly influence attitude towards vaccination and intention to obtain the vaccine. According to the findings, it is suggested that, apart from evidence-based communication, the government or any interested parties can utilize both experience sharing and social pressure elements to increase engagement related to COVID-19 vaccines on social media, such as Twitter.


Subject(s)
COVID-19 , Social Media , Attitude , COVID-19/prevention & control , COVID-19 Vaccines , Humans , Intention , Likelihood Functions , SARS-CoV-2 , Vaccination
17.
Appl Psychol Health Well Being ; 14(3): 842-861, 2022 08.
Article in English | MEDLINE | ID: covidwho-1699825

ABSTRACT

We examined the effects of anticipatory emotions induced by episodic future thinking on the basic decision-process of delay discounting and preventive behaviors during the most stringent COVID-19 "lockdown" period in China. We define anticipatory emotions as any discrete emotions induced from anticipating decision outcomes and felt during decision-making. In an online study conducted with healthy volunteers, anticipatory emotions were induced and appraised by asking participants to rate various emotions they feel when thinking they may be infected by COVID-19 (N = 246). The participants in the control group reported their present emotions during the COVID-19 pandemic (N = 245). Compared with the control group, the participants in the anticipatory emotion group had a higher future-oriented preference for monetary rewards, with a significantly lower delay discounting rate. These participants also had a higher intention to engage in proactive, preventive behaviors. The likelihood estimate of being infected by COVID-19 mediated these effects. Moreover, anticipatory disgust increased the preference for larger-and-later rewards. Anticipatory emotions induced by future thinking guide fast and rational decision-making in a health crisis.


Subject(s)
COVID-19 , Delay Discounting , COVID-19/prevention & control , Emotions , Humans , Likelihood Functions , Pandemics , Thinking
18.
J Glob Health ; 11: 05028, 2021.
Article in English | MEDLINE | ID: covidwho-1687375

ABSTRACT

BACKGROUND: The COVID-19 pandemic poses serious threats to public health globally, and the emerging mutations in SARS-CoV-2 genomes has become one of the major challenges of disease control. In the second epidemic wave in Nigeria, the roles of co-circulating SARS-CoV-2 Alpha (ie, B.1.1.7) and Eta (ie, B.1.525) variants in contributing to the epidemiological outcomes were of public health concerns for investigation. METHODS: We developed a mathematical model to capture the transmission dynamics of different types of strains in Nigeria. By fitting to the national-wide COVID-19 surveillance data, the transmission advantages of SARS-CoV-2 variants were estimated by likelihood-based inference framework. RESULTS: The reproduction numbers were estimated to decrease steadily from 1.5 to 0.8 in the second epidemic wave. In December 2020, when both Alpha and Eta variants were at low prevalent levels, their transmission advantages (against the wild type) were estimated at 1.51 (95% credible intervals (CrI) = 1.48, 1.54), and 1.56 (95% CrI = 1.54, 1.59), respectively. In January 2021, when the original variants almost vanished, we estimated a weak but significant transmission advantage of Eta against Alpha variants with 1.14 (95% CrI = 1.11, 1.16). CONCLUSIONS: Our findings suggested evidence of the transmission advantages for both Alpha and Eta variants, of which Eta appeared slightly more infectious than Alpha. We highlighted the critical importance of COVID-19 control measures in mitigating the outbreak size and relaxing the burdens to health care systems in Nigeria.


Subject(s)
COVID-19 , SARS-CoV-2 , COVID-19/transmission , COVID-19/virology , Humans , Likelihood Functions , Nigeria/epidemiology , Pandemics , Retrospective Studies
19.
Stat Methods Med Res ; 31(2): 253-266, 2022 02.
Article in English | MEDLINE | ID: covidwho-1582663

ABSTRACT

Poisson regression can be challenging with sparse data, in particular with certain data constellations where maximum likelihood estimates of regression coefficients do not exist. This paper provides a comprehensive evaluation of methods that give finite regression coefficients when maximum likelihood estimates do not exist, including Firth's general approach to bias reduction, exact conditional Poisson regression, and a Bayesian estimator using weakly informative priors that can be obtained via data augmentation. Furthermore, we include in our evaluation a new proposal for a modification of Firth's approach, improving its performance for predictions without compromising its attractive bias-correcting properties for regression coefficients. We illustrate the issue of the nonexistence of maximum likelihood estimates with a dataset arising from the recent outbreak of COVID-19 and an example from implant dentistry. All methods are evaluated in a comprehensive simulation study under a variety of realistic scenarios, evaluating their performance for prediction and estimation. To conclude, while exact conditional Poisson regression may be confined to small data sets only, both the modification of Firth's approach and the Bayesian estimator are universally applicable solutions with attractive properties for prediction and estimation. While the Bayesian method needs specification of prior variances for the regression coefficients, the modified Firth approach does not require any user input.


Subject(s)
COVID-19 , Bayes Theorem , Bias , Humans , Likelihood Functions , SARS-CoV-2
20.
PLoS One ; 16(11): e0259097, 2021.
Article in English | MEDLINE | ID: covidwho-1575776

ABSTRACT

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a high risk of transmission in close-contact indoor settings, which may include households. Prior studies have found a wide range of household secondary attack rates and may contain biases due to simplifying assumptions about transmission variability and test accuracy. METHODS: We compiled serological SARS-CoV-2 antibody test data and prior SARS-CoV-2 test reporting from members of 9,224 Utah households. We paired these data with a probabilistic model of household importation and transmission. We calculated a maximum likelihood estimate of the importation probability, mean and variability of household transmission probability, and sensitivity and specificity of test data. Given our household transmission estimates, we estimated the threshold of non-household transmission required for epidemic growth in the population. RESULTS: We estimated that individuals in our study households had a 0.41% (95% CI 0.32%- 0.51%) chance of acquiring SARS-CoV-2 infection outside their household. Our household secondary attack rate estimate was 36% (27%- 48%), substantially higher than the crude estimate of 16% unadjusted for imperfect serological test specificity and other factors. We found evidence for high variability in individual transmissibility, with higher probability of no transmissions or many transmissions compared to standard models. With household transmission at our estimates, the average number of non-household transmissions per case must be kept below 0.41 (0.33-0.52) to avoid continued growth of the pandemic in Utah. CONCLUSIONS: Our findings suggest that crude estimates of household secondary attack rate based on serology data without accounting for false positive tests may underestimate the true average transmissibility, even when test specificity is high. Our finding of potential high variability (overdispersion) in transmissibility of infected individuals is consistent with characterizing SARS-CoV-2 transmission being largely driven by superspreading from a minority of infected individuals. Mitigation efforts targeting large households and other locations where many people congregate indoors might curb continued spread of the virus.


Subject(s)
COVID-19/epidemiology , COVID-19/transmission , Family Characteristics , Humans , Incidence , Likelihood Functions , Pandemics/statistics & numerical data , SARS-CoV-2/pathogenicity , Sensitivity and Specificity , Serologic Tests/methods , Utah/epidemiology
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