Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 20 de 213
Filter
1.
J R Soc Med ; 115(2): 45, 2022 Feb.
Article in English | MEDLINE | ID: covidwho-2194831

Subject(s)
Probability , Humans
2.
Prev Med ; 164: 107127, 2022 Nov.
Article in English | MEDLINE | ID: covidwho-2184533

ABSTRACT

It is well known that the statistical analyses in health-science and medical journals are frequently misleading or even wrong. Despite many decades of reform efforts by hundreds of scientists and statisticians, attempts to fix the problem by avoiding obvious error and encouraging good practice have not altered this basic situation. Statistical teaching and reporting remain mired in damaging yet editorially enforced jargon of "significance", "confidence", and imbalanced focus on null (no-effect or "nil") hypotheses, leading to flawed attempts to simplify descriptions of results in ordinary terms. A positive development amidst all this has been the introduction of interval estimates alongside or in place of significance tests and P-values, but intervals have been beset by similar misinterpretations. Attempts to remedy this situation by calling for replacement of traditional statistics with competitors (such as pure-likelihood or Bayesian methods) have had little impact. Thus, rather than ban or replace P-values or confidence intervals, we propose to replace traditional jargon with more accurate and modest ordinary-language labels that describe these statistics as measures of compatibility between data and hypotheses or models, which have long been in use in the statistical modeling literature. Such descriptions emphasize the full range of possibilities compatible with observations. Additionally, a simple transform of the P-value called the surprisal or S-value provides a sense of how much or how little information the data supply against those possibilities. We illustrate these reforms using some examples from a highly charged topic: trials of ivermectin treatment for Covid-19.


Subject(s)
COVID-19 , Humans , Data Interpretation, Statistical , Bayes Theorem , COVID-19/prevention & control , Probability , Models, Statistical , Confidence Intervals
3.
Am J Clin Pathol ; 157(5): 731-741, 2022 05 04.
Article in English | MEDLINE | ID: covidwho-2114225

ABSTRACT

BACKGROUND: Detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern associated with immune escape is important to safeguard vaccination efficacy. We describe the potential of delayed N gene amplification in the Allplex SARS-CoV-2 Assay (Seegene) for screening of the B.1.351 (20H/501.V2, variant of concern 2 [VOC.V2], South African SARS-CoV-2 variant) lineage. METHODS: In a study cohort of 397 consecutive polymerase chain reaction-positive samples genotyped by whole-genome sequencing, amplification curves of E/N/S-RdRP targets indicated delayedN vs E gene amplification characteristic of B.1.351. Logistic regression was used to calculate a VOC.V2 probability score that was evaluated as a separate screening test in an independent validation cohort vs sequencing. RESULTS: B.1.351 showed a proportionally delayed amplification of the  N vs E gene. In logistic regression, only N and E gene cycle thresholds independently contributed to B.1.351 prediction, allowing calculation of a VOC.V2 probability score with an area under the curve of 0.94. At an optimal dichotomous cutoff point of 0.12, the VOC.V2 probability score achieved 98.7% sensitivity at 79.9% specificity, resulting in a negative predictive value (NPV) of 99.6% and a positive predictive value of 54.6%. The probability of B.1.351 increased with an increasing VOC.V2 probability score, achieving a likelihood ratio of 12.01 above 0.5. A near-maximal NPV was confirmed in 153 consecutive validation samples. CONCLUSIONS: Delayed N vs E gene amplification in the Allplex SARS-CoV-2 Assay can be used for fast and highly sensitive screening of B.1.351.


Subject(s)
COVID-19 , SARS-CoV-2 , COVID-19/diagnosis , Humans , Probability , SARS-CoV-2/genetics , Whole Genome Sequencing
4.
PLoS Comput Biol ; 17(2): e1008618, 2021 02.
Article in English | MEDLINE | ID: covidwho-2109274

ABSTRACT

For practical reasons, many forecasts of case, hospitalization, and death counts in the context of the current Coronavirus Disease 2019 (COVID-19) pandemic are issued in the form of central predictive intervals at various levels. This is also the case for the forecasts collected in the COVID-19 Forecast Hub (https://covid19forecasthub.org/). Forecast evaluation metrics like the logarithmic score, which has been applied in several infectious disease forecasting challenges, are then not available as they require full predictive distributions. This article provides an overview of how established methods for the evaluation of quantile and interval forecasts can be applied to epidemic forecasts in this format. Specifically, we discuss the computation and interpretation of the weighted interval score, which is a proper score that approximates the continuous ranked probability score. It can be interpreted as a generalization of the absolute error to probabilistic forecasts and allows for a decomposition into a measure of sharpness and penalties for over- and underprediction.


Subject(s)
COVID-19/epidemiology , Communicable Diseases/epidemiology , Pandemics , COVID-19/virology , Forecasting , Humans , Probability , SARS-CoV-2/isolation & purification
5.
PLoS One ; 17(10): e0274133, 2022.
Article in English | MEDLINE | ID: covidwho-2089401

ABSTRACT

Among other diseases, Covid 19 creates a critical situation around the world. Five layers have been recorded so far, resulting in the loss of millions of lives in different countries. The virus was thought to be contagious, so the government initially severely forced citizens to keep a distance from each other. Since then, several vaccines have been developed that play an important role in controlling mortality. In the case of Covid-19 mortality, the government should be forced to take significant steps in the form of lockdown, keeping you away or forcing citizens to vaccinate. In this paper, modeling of Covid-19 death rates is discussed via probability distributions. To delineate the performance of the best fitted model, the mortality rate of Pakistan and Afghanistan is considered. Numerical results conclude that the NFW model can be used to predict the mortality rate for Covid-19 patients more accurately than other probability models.


Subject(s)
COVID-19 , Humans , COVID-19/epidemiology , SARS-CoV-2 , Communicable Disease Control , Probability , Government
6.
Sci Rep ; 12(1): 16076, 2022 09 27.
Article in English | MEDLINE | ID: covidwho-2050511

ABSTRACT

How to mitigate the spread of infectious diseases like COVID-19 in indoor environments remains an important research question. In this study, we propose an agent-based modeling framework to evaluate facility usage policies that aim to lower the probability of outbreaks. The proposed framework is individual-based, spatially-resolved with time resolution of up to 1 s, and takes into detailed account specific floor layouts, occupant schedules and movement. It enables decision makers to compute realistic contact networks and generate risk profiles of their facilities without relying on wearable devices, smartphone tagging or surveillance cameras. Our demonstrative modeling results indicate that not all facility occupants present the same risk of starting an outbreak, where the driver of outbreaks varies with facility layouts as well as individual occupant schedules. Therefore, generic mitigation strategies applied across all facilities should be considered inferior to tailored policies that take into account individual characteristics of the facilities of interest. The proposed modeling framework, implemented in Python and now available to the public in an open-source platform, enables such strategy evaluation.


Subject(s)
COVID-19 , Communicable Diseases , COVID-19/epidemiology , COVID-19/prevention & control , Disease Outbreaks/prevention & control , Humans , Probability , Systems Analysis
7.
J Math Biol ; 85(4): 43, 2022 09 28.
Article in English | MEDLINE | ID: covidwho-2048224

ABSTRACT

We present a unifying, tractable approach for studying the spread of viruses causing complex diseases requiring to be modeled using a large number of types (e.g., infective stage, clinical state, risk factor class). We show that recording each infected individual's infection age, i.e., the time elapsed since infection, has three benefits. First, regardless of the number of types, the age distribution of the population can be described by means of a first-order, one-dimensional partial differential equation (PDE) known as the McKendrick-von Foerster equation. The frequency of type i is simply obtained by integrating the probability of being in state i at a given age against the age distribution. This representation induces a simple methodology based on the additional assumption of Poisson sampling to infer and forecast the epidemic. We illustrate this technique using French data from the COVID-19 epidemic. Second, our approach generalizes and simplifies standard compartmental models using high-dimensional systems of ordinary differential equations (ODEs) to account for disease complexity. We show that such models can always be rewritten in our framework, thus, providing a low-dimensional yet equivalent representation of these complex models. Third, beyond the simplicity of the approach, we show that our population model naturally appears as a universal scaling limit of a large class of fully stochastic individual-based epidemic models, where the initial condition of the PDE emerges as the limiting age structure of an exponentially growing population starting from a single individual.


Subject(s)
COVID-19 , Epidemics , COVID-19/epidemiology , Forecasting , Humans , Models, Biological , Probability
8.
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
9.
Front Public Health ; 10: 920103, 2022.
Article in English | MEDLINE | ID: covidwho-2022948

ABSTRACT

Rumors regarding COVID-19 have been prevalent on the Internet and affect the control of the COVID-19 pandemic. Using 1,296 COVID-19 rumors collected from an online platform (piyao.org.cn) in China, we found measurable differences in the content characteristics between true and false rumors. We revealed that the length of a rumor's headline is negatively related to the probability of a rumor being true [odds ratio (OR) = 0.37, 95% CI (0.30, 0.44)]. In contrast, the length of a rumor's statement is positively related to this probability [OR = 1.11, 95% CI (1.09, 1.13)]. In addition, we found that a rumor is more likely to be true if it contains concrete places [OR = 20.83, 95% CI (9.60, 48.98)] and it specifies the date or time of events [OR = 22.31, 95% CI (9.63, 57.92)]. The rumor is also likely to be true when it does not evoke positive or negative emotions [OR = 0.15, 95% CI (0.08, 0.29)] and does not include a call for action [OR = 0.06, 95% CI (0.02, 0.12)]. By contrast, the presence of source cues [OR = 0.64, 95% CI (0.31, 1.28)] and visuals [OR = 1.41, 95% CI (0.53, 3.73)] is related to this probability with limited significance. Our findings provide some clues for identifying COVID-19 rumors using their content characteristics.


Subject(s)
COVID-19 , China , Humans , Internet , Pandemics , Probability
10.
Clin Neurol Neurosurg ; 220: 107356, 2022 09.
Article in English | MEDLINE | ID: covidwho-2015021

ABSTRACT

INTRODUCTION: There are multiple treatments for a chronic subdural hematoma, a significant cause of neurosurgical morbidity that cost the healthcare system $5B in 2007, but few generalizable prospective studies. The purpose of this study was to examine outcomes of bedside Subdural Evacuation Port System (SEPS) placement as compared to operating room burr hole evacuation (BHE) to acquire data to support a randomized trial. METHODS: All procedures were performed in a single institution between 2011 and 2019. Patients were included if > 18 years of age, had chronic subdural hematoma, and were treated by SEPS or BHE. Patients with prior neurosurgical history, mass lesions or bilateral hematomas were excluded. Patients who met inclusion for SEPS (n = 55) or BHE (n = 105). Samples were propensity matched to account for variability. Non-inferiority tests compared outcomes. Cost data was obtained through billable charges. RESULTS: Patients with multiple comorbidities were more likely to undergo SEPS drainage. Noninferiority tests reported no statistically significant evidence to suggest SEPS drains were worse in reoperation-rate (18% vs 9%), post-operative seizure, or functional outcome. SEPS drain placement trended towards a faster time to procedure (3 h faster; p = 0.07) but the overall hospital stay was longer (4.23 vs 5.81, p = 0.01). SEPS drain placement costs are less than BHE, but these patients had 25% higher overall hospital costs (p = 0.01) due to comorbidities and increased hospital stay.


Subject(s)
Hematoma, Subdural, Chronic , Case-Control Studies , Craniotomy/methods , Drainage/methods , Hematoma, Subdural, Chronic/etiology , Hematoma, Subdural, Chronic/surgery , Humans , Probability , Prospective Studies , Treatment Outcome
11.
Dtsch Arztebl Int ; 118(5): 66, 2021 02 05.
Article in English | MEDLINE | ID: covidwho-1383845
12.
Stat Methods Med Res ; 31(11): 2164-2188, 2022 Nov.
Article in English | MEDLINE | ID: covidwho-1968494

ABSTRACT

Cure models are a class of time-to-event models where a proportion of individuals will never experience the event of interest. The lifetimes of these so-called cured individuals are always censored. It is usually assumed that one never knows which censored observation is cured and which is uncured, so the cure status is unknown for censored times. In this paper, we develop a method to estimate the probability of cure in the mixture cure model when some censored individuals are known to be cured. A cure probability estimator that incorporates the cure status information is introduced. This estimator is shown to be strongly consistent and asymptotically normally distributed. Two alternative estimators are also presented. The first one considers a competing risks approach with two types of competing events, the event of interest and the cure. The second alternative estimator is based on the fact that the probability of cure can be written as the conditional mean of the cure status. Hence, nonparametric regression methods can be applied to estimate this conditional mean. However, the cure status remains unknown for some censored individuals. Consequently, the application of regression methods in this context requires handling missing data in the response variable (cure status). Simulations are performed to evaluate the finite sample performance of the estimators, and we apply them to the analysis of two datasets related to survival of breast cancer patients and length of hospital stay of COVID-19 patients requiring intensive care.


Subject(s)
COVID-19 , Models, Statistical , Humans , Survival Analysis , Probability , Regression Analysis , Computer Simulation
13.
PLoS One ; 17(6): e0269097, 2022.
Article in English | MEDLINE | ID: covidwho-1963000

ABSTRACT

BACKGROUND: One common way to share health data for secondary analysis while meeting increasingly strict privacy regulations is to de-identify it. To demonstrate that the risk of re-identification is acceptably low, re-identification risk metrics are used. There is a dearth of good risk estimators modeling the attack scenario where an adversary selects a record from the microdata sample and attempts to match it with individuals in the population. OBJECTIVES: Develop an accurate risk estimator for the sample-to-population attack. METHODS: A type of estimator based on creating a synthetic variant of a population dataset was developed to estimate the re-identification risk for an adversary performing a sample-to-population attack. The accuracy of the estimator was evaluated through a simulation on four different datasets in terms of estimation error. Two estimators were considered, a Gaussian copula and a d-vine copula. They were compared against three other estimators proposed in the literature. RESULTS: Taking the average of the two copula estimates consistently had a median error below 0.05 across all sampling fractions and true risk values. This was significantly more accurate than existing methods. A sensitivity analysis of the estimator accuracy based on variation in input parameter accuracy provides further application guidance. The estimator was then used to assess re-identification risk and de-identify a large Ontario COVID-19 behavioral survey dataset. CONCLUSIONS: The average of two copula estimators consistently provides the most accurate re-identification risk estimate and can serve as a good basis for managing privacy risks when data are de-identified and shared.


Subject(s)
COVID-19 , COVID-19/epidemiology , Humans , Information Dissemination , Privacy , Probability , Risk
14.
PLoS One ; 17(6): e0266901, 2022.
Article in English | MEDLINE | ID: covidwho-1962992

ABSTRACT

OBJECTIVES: While corticosteroids have been hypothesized to exert protective benefits in patients infected with SARS-CoV-2, data remain mixed. This study sought to investigate the outcomes of methylprednisone administration in an Italian cohort of hospitalized patients with confirmed SARS-CoV-2 infection. METHODS: Patients with confirmatory testing for SARS-CoV-2 were retrospectively enrolled from a tertiary university hospital in Milan, Italy from March 1st to April 30th, 2020 and divided into two groups by administration of corticosteroids. Methylprednisolone was administered to patients not responding to pharmacological therapy and ventilatory support at 0.5-1mg/kg/day for 4 to 7 days. Inverse probability of treatment weighting (IPTW) was used to adjust for baseline differences between the steroid and non-steroid cohorts via inverse probability of treatment weight. Primary outcomes included acute respiratory failure (ARF), shock, and 30-day mortality among surviving patients. RESULTS: Among 311 patients enrolled, 71 patients received steroids and 240 did not receive steroids. The mean age was 63.1 years, 35.4% were female, and hypertension, diabetes, heart disease, and chronic pulmonary disease were present in 3.5%, 1.3%, 14.8% and 12.2% respectively. Crude analysis revealed no statistically significant reduction in the incidence of 30-day mortality (36,6% vs 21,7%; OR, 2.09; 95% CI, 1.18-3.70; p = 0.011), shock (2.8% vs 4.6%; OR, 0.60; 95% CI = 0.13-2.79; p = 0.514) or ARF (12.7% vs 15%; OR, 0.82; 95% CI = 0.38-1.80; p = 0.625) between the steroid and non-steroid groups. After IPTW analysis, the steroid-group had lower incidence of shock (0.9% vs 4.1%; OR, 0.21; 95% CI,0.06-0.77; p = 0.010), ARF (6.6% vs 16.0%; OR, 0.37; 95% CI, 0.22-0.64; p<0.001) and 30-day mortality (20.3% vs 22.8%; OR 0.86; 95% CI, 0.59-1.26 p = 0.436); even though, for the latter no statistical significance was reached. Steroid use was also associated with increased length of hospital stay both in crude and IPTW analyses. Subgroup analysis revealed that patients with cardiovascular comorbidities or chronic lung diseases were more likely to be steroid responsive. No significant survival benefit was seen after steroid treatment. CONCLUSIONS: Physicians should avoid routine methylprednisolone use in SARS-CoV-2 patients, since it does not reduce 30-day mortality. However, they must consider its use for severe patients with cardiovascular or respiratory comorbidities in order to reduce the incidence of either shock or acute respiratory failure.


Subject(s)
COVID-19 , Respiratory Distress Syndrome , Respiratory Insufficiency , Adrenal Cortex Hormones , COVID-19/drug therapy , Female , Humans , Male , Methylprednisolone/therapeutic use , Middle Aged , Probability , Respiratory Insufficiency/chemically induced , Retrospective Studies , SARS-CoV-2
15.
Comput Math Methods Med ; 2022: 4148801, 2022.
Article in English | MEDLINE | ID: covidwho-1956950

ABSTRACT

The COVID-19 pandemic has shocked nations due to its exponential death rates in various countries. According to the United Nations (UN), in Russia, there were 895, in Mexico 303, in Indonesia 77, in Ukraine 317, and in Romania 252, and in Pakistan, 54 new deaths were recorded on the 5th of October 2021 in the period of months. Hence, it is essential to study the future waves of this virus so that some preventive measures can be adopted. In statistics, under uncertainty, there is a possibility to use probability models that leads to defining future pattern of deaths caused by COVID-19. Based on probability models, many research studies have been conducted to model the future trend of a particular disease and explore the effect of possible treatments (as in the case of coronavirus, the effect of Pfizer, Sinopharm, CanSino, Sinovac, and Sputnik) towards a specific disease. In this paper, varieties of probability models have been applied to model the COVID-19 death rate more effectively than the other models. Among others, exponentiated flexible exponential Weibull (EFEW) distribution is pointed out as the best fitted model. Various statistical properties have been presented in addition to real-life applications by using the total deaths of the COVID-19 outbreak (in millions) in Pakistan and Afghanistan. It has been verified that EFEW leads to a better decision rather than other existing lifetime models, including FEW, W, EW, E, AIFW, and GAPW distributions.


Subject(s)
COVID-19 , Afghanistan/epidemiology , Humans , Pakistan/epidemiology , Pandemics , Probability
16.
Elife ; 112022 06 06.
Article in English | MEDLINE | ID: covidwho-1954753

ABSTRACT

Background: The variation in the pathogen type as well as the spatial heterogeneity of predictors make the generality of any associations with pathogen discovery debatable. Our previous work confirmed that the association of a group of predictors differed across different types of RNA viruses, yet there have been no previous comparisons of the specific predictors for RNA virus discovery in different regions. The aim of the current study was to close the gap by investigating whether predictors of discovery rates within three regions-the United States, China, and Africa-differ from one another and from those at the global level. Methods: Based on a comprehensive list of human-infective RNA viruses, we collated published data on first discovery of each species in each region. We used a Poisson boosted regression tree (BRT) model to examine the relationship between virus discovery and 33 predictors representing climate, socio-economics, land use, and biodiversity across each region separately. The discovery probability in three regions in 2010-2019 was mapped using the fitted models and historical predictors. Results: The numbers of human-infective virus species discovered in the United States, China, and Africa up to 2019 were 95, 80, and 107 respectively, with China lagging behind the other two regions. In each region, discoveries were clustered in hotspots. BRT modelling suggested that in all three regions RNA virus discovery was better predicted by land use and socio-economic variables than climatic variables and biodiversity, although the relative importance of these predictors varied by region. Map of virus discovery probability in 2010-2019 indicated several new hotspots outside historical high-risk areas. Most new virus species since 2010 in each region (6/6 in the United States, 19/19 in China, 12/19 in Africa) were discovered in high-risk areas as predicted by our model. Conclusions: The drivers of spatiotemporal variation in virus discovery rates vary in different regions of the world. Within regions virus discovery is driven mainly by land-use and socio-economic variables; climate and biodiversity variables are consistently less important predictors than at a global scale. Potential new discovery hotspots in 2010-2019 are identified. Results from the study could guide active surveillance for new human-infective viruses in local high-risk areas. Funding: FFZ is funded by the Darwin Trust of Edinburgh (https://darwintrust.bio.ed.ac.uk/). MEJW has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 874735 (VEO) (https://www.veo-europe.eu/).


Subject(s)
RNA Viruses , Viruses , Africa , Biodiversity , Humans , Probability , RNA , United States
18.
Support Care Cancer ; 30(9): 7635-7643, 2022 Sep.
Article in English | MEDLINE | ID: covidwho-1941715

ABSTRACT

Control of transmissible diseases as COVID-19 needs a testing and an isolation strategy. The PARIS score developed by Torjdman et al. was aimed at improving patient selection for testing and quarantining but was derived from a general population. We performed a retrospective analysis of the validity of the PARIS score in a cancer patient population. We included 164 patients counting for 181 visits at the emergency department of the Jules Bordet Institute between March 10th and May 18th which had a SARS-CoV-2 RT-PCR test at admission. Twenty-six cases (14.3%) were tested positive with a higher proportion of positive tests among hematological patients compared to those with solid tumors (26% vs 11% p = 0.02). No clinical symptoms were associated with a positive SARS-CoV-2 PCR. No association between anticancer treatment and SARS-CoV-2 infection was found. The PARIS score failed to differentiate SARS-CoV-2-positive and SARS-CoV-2-negative groups (AUC 0.61 95% CI 0.48-0.73). The negative predictive value of a low probability PARIS score was 0.89 but this concerned only 11% of the patients. A high probability PARIS score concerned 49% patients but the positive predictive value was 0.18. CT scan had a sensitivity of 0.77, specificity 0.51, a positive predictive value of 0.24, and a negative predictive value of 0.92. The performance of the PARIS score is thus very poor in this cancer population. A low-risk score can be of some utility but this concerns a minority of patients.


Subject(s)
COVID-19 , Neoplasms , COVID-19/diagnosis , COVID-19 Testing , Humans , Probability , Retrospective Studies , SARS-CoV-2 , Sensitivity and Specificity
19.
Stat Methods Med Res ; 31(9): 1656-1674, 2022 09.
Article in English | MEDLINE | ID: covidwho-1932991

ABSTRACT

We compare two multi-state modelling frameworks that can be used to represent dates of events following hospital admission for people infected during an epidemic. The methods are applied to data from people admitted to hospital with COVID-19, to estimate the probability of admission to intensive care unit, the probability of death in hospital for patients before and after intensive care unit admission, the lengths of stay in hospital, and how all these vary with age and gender. One modelling framework is based on defining transition-specific hazard functions for competing risks. A less commonly used framework defines partially-latent subpopulations who will experience each subsequent event, and uses a mixture model to estimate the probability that an individual will experience each event, and the distribution of the time to the event given that it occurs. We compare the advantages and disadvantages of these two frameworks, in the context of the COVID-19 example. The issues include the interpretation of the model parameters, the computational efficiency of estimating the quantities of interest, implementation in software and assessing goodness of fit. In the example, we find that some groups appear to be at very low risk of some events, in particular intensive care unit admission, and these are best represented by using 'cure-rate' models to define transition-specific hazards. We provide general-purpose software to implement all the models we describe in the flexsurv R package, which allows arbitrarily flexible distributions to be used to represent the cause-specific hazards or times to events.


Subject(s)
COVID-19 , Hospitalization , Hospitals , Humans , Intensive Care Units , Probability
20.
BMC Public Health ; 22(1): 1258, 2022 06 27.
Article in English | MEDLINE | ID: covidwho-1910294

ABSTRACT

BACKGROUND: Mass immunization is a potentially effective approach to finally control the local outbreak and global spread of the COVID-19 pandemic. However, it can also lead to undesirable outcomes if mass vaccination results in increased transmission of effective contacts and relaxation of other public health interventions due to the perceived immunity from the vaccine. METHODS: We designed a mathematical model of COVID-19 transmission dynamics that takes into consideration the epidemiological status, public health intervention status (quarantined/isolated), immunity status of the population, and strain variations. Comparing the control reproduction numbers and the final epidemic sizes (attack rate) in the cases with and without vaccination, we quantified some key factors determining when vaccination in the population is beneficial for preventing and controlling future outbreaks. RESULTS: Our analyses predicted that there is a critical (minimal) vaccine efficacy rate (or a critical quarantine rate) below which the control reproduction number with vaccination is higher than that without vaccination, and the final attack rate in the population is also higher with the vaccination. We also predicted the worst case scenario occurs when a high vaccine coverage rate is achieved for a vaccine with a lower efficacy rate and when the vaccines increase the transmission efficient contacts. CONCLUSIONS: The analyses show that an immunization program with a vaccine efficacy rate below the predicted critical values will not be as effective as simply investing in the contact tracing/quarantine/isolation implementation. We reached similar conclusions by considering the final epidemic size (or attack rates). This research then highlights the importance of monitoring the impact on transmissibility and vaccine efficacy of emerging strains.


Subject(s)
COVID-19 , Pandemics , COVID-19/epidemiology , COVID-19/prevention & control , COVID-19 Vaccines , Humans , Pandemics/prevention & control , Probability , Vaccination , Vaccination Coverage
SELECTION OF CITATIONS
SEARCH DETAIL