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1.
Stat Med ; 41(16): 3131-3148, 2022 Jul 20.
Article in English | MEDLINE | ID: covidwho-1850242

ABSTRACT

To strengthen inferences meta-analyses are commonly used to summarize information from a set of independent studies. In some cases, though, the data may not satisfy the assumptions underlying the meta-analysis. Using three Bayesian methods that have a more general structure than the common meta-analytic ones, we can show the extent and nature of the pooling that is justified statistically. In this article, we reanalyze data from several reviews whose objective is to make inference about the COVID-19 asymptomatic infection rate. When it is unlikely that all of the true effect sizes come from a single source researchers should be cautious about pooling the data from all of the studies. Our findings and methodology are applicable to other COVID-19 outcome variables, and more generally.


Subject(s)
COVID-19 , Bayes Theorem , Humans , Markov Chains , Monte Carlo Method
2.
Sci Rep ; 12(1): 3860, 2022 03 09.
Article in English | MEDLINE | ID: covidwho-1799576

ABSTRACT

Non-structural protein 15 (Nsp15) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) forms a homo hexamer and functions as an endoribonuclease. Here, we propose that Nsp15 activity may be inhibited by preventing its hexamerization through drug binding. We first explored the stable conformation of the Nsp15 monomer as the global free energy minimum conformation in the free energy landscape using a combination of parallel cascade selection molecular dynamics (PaCS-MD) and the Markov state model (MSM), and found that the Nsp15 monomer forms a more open conformation with larger druggable pockets on the surface. Targeting the pockets with high druggability scores, we conducted ligand docking and identified compounds that tightly bind to the Nsp15 monomer. The top poses with Nsp15 were subjected to binding free energy calculations by dissociation PaCS-MD and MSM (dPaCS-MD/MSM), indicating the stability of the complexes. One of the identified pockets, which is distinctively bound by inosine analogues, may be an alternative binding site to stabilize viral RNA binding and/or an alternative catalytic site. We constructed a stable RNA structure model bound to both UTP and alternative binding sites, providing a reasonable proposed model of the Nsp15/RNA complex.


Subject(s)
Endoribonucleases/metabolism , RNA, Viral/chemistry , SARS-CoV-2/metabolism , Viral Nonstructural Proteins/metabolism , Antiviral Agents/chemistry , Antiviral Agents/metabolism , Binding Sites , COVID-19/pathology , COVID-19/virology , Endoribonucleases/antagonists & inhibitors , Humans , Markov Chains , Molecular Docking Simulation , Molecular Dynamics Simulation , Nucleic Acid Conformation , Protein Multimerization , SARS-CoV-2/genetics , SARS-CoV-2/isolation & purification , Static Electricity , Viral Nonstructural Proteins/antagonists & inhibitors
3.
Sci Rep ; 12(1): 5459, 2022 03 31.
Article in English | MEDLINE | ID: covidwho-1768857

ABSTRACT

The recent increase in the global incidence of dengue fever resulted in over 2.7 million cases in Latin America and many cases in Southeast Asia and has warranted the development and application of early warning systems (EWS) for futuristic outbreak prediction. EWS pertaining to dengue outbreaks is imperative; given the fact that dengue is linked to environmental factors owing to its dominance in the tropics. Prediction is an integral part of EWS, which is dependent on several factors, in particular, climate, geography, and environmental factors. In this study, we explore the role of increased susceptibility to a DENV serotype and climate variability in developing novel predictive models by analyzing RT-PCR and DENV-IgM confirmed cases in Singapore and Honduras, which reported high dengue incidence in 2019 and 2020, respectively. A random-sampling-based susceptible-infected-removed (SIR) model was used to obtain estimates of the susceptible fraction for modeling the dengue epidemic, in addition to the Bayesian Markov Chain Monte Carlo (MCMC) technique that was used to fit the model to Singapore and Honduras case report data from 2012 to 2020. Regression techniques were used to implement climate variability in two methods: a climate-based model, based on individual climate variables, and a seasonal model, based on trigonometrically varying transmission rates. The seasonal model accounted for 98.5% and 92.8% of the variance in case count in the 2020 Singapore and 2019 Honduras outbreaks, respectively. The climate model accounted for 75.3% and 68.3% of the variance in Singapore and Honduras outbreaks respectively, besides accounting for 75.4% of the variance in the major 2013 Singapore outbreak, 71.5% of the variance in the 2019 Singapore outbreak, and over 70% of the variance in 2015 and 2016 Honduras outbreaks. The seasonal model accounted for 14.2% and 83.1% of the variance in the 2013 and 2019 Singapore outbreaks, respectively, in addition to 91% and 59.5% of the variance in the 2015 and 2016 Honduras outbreaks, respectively. Autocorrelation lag tests showed that the climate model exhibited better prediction dynamics for Singapore outbreaks during the dry season from May to August and in the rainy season from June to October in Honduras. After incorporation of susceptible fractions, the seasonal model exhibited higher accuracy in predicting outbreaks of higher case magnitude, including those of the 2019-2020 dengue epidemic, in comparison to the climate model, which was more accurate in outbreaks of smaller magnitude. Such modeling studies could be further performed in various outbreaks, such as the ongoing COVID-19 pandemic to understand the outbreak dynamics and predict the occurrence of future outbreaks.


Subject(s)
COVID-19 , Dengue , Bayes Theorem , Dengue/epidemiology , Disease Outbreaks , Humans , Markov Chains , Pandemics
4.
Viruses ; 14(2)2022 02 15.
Article in English | MEDLINE | ID: covidwho-1687060

ABSTRACT

Mathematical modelling of infection processes in cells is of fundamental interest. It helps to understand the SARS-CoV-2 dynamics in detail and can be useful to define the vulnerability steps targeted by antiviral treatments. We previously developed a deterministic mathematical model of the SARS-CoV-2 life cycle in a single cell. Despite answering many questions, it certainly cannot accurately account for the stochastic nature of an infection process caused by natural fluctuation in reaction kinetics and the small abundance of participating components in a single cell. In the present work, this deterministic model is transformed into a stochastic one based on a Markov Chain Monte Carlo (MCMC) method. This model is employed to compute statistical characteristics of the SARS-CoV-2 life cycle including the probability for a non-degenerate infection process. Varying parameters of the model enables us to unveil the inhibitory effects of IFN and the effects of the ACE2 binding affinity. The simulation results show that the type I IFN response has a very strong effect on inhibition of the total viral progeny whereas the effect of a 10-fold variation of the binding rate to ACE2 turns out to be negligible for the probability of infection and viral production.


Subject(s)
Angiotensin-Converting Enzyme 2/metabolism , Interferon Type I/immunology , Models, Theoretical , SARS-CoV-2/immunology , SARS-CoV-2/physiology , Angiotensin-Converting Enzyme 2/immunology , Computer Simulation , Humans , Kinetics , Life Cycle Stages , Markov Chains , Protein Binding , SARS-CoV-2/growth & development , Stochastic Processes
5.
PLoS One ; 17(2): e0263047, 2022.
Article in English | MEDLINE | ID: covidwho-1677582

ABSTRACT

Fitting Susceptible-Infected-Recovered (SIR) models to incidence data is problematic when not all infected individuals are reported. Assuming an underlying SIR model with general but known distribution for the time to recovery, this paper derives the implied differential-integral equations for observed incidence data when a fixed fraction of newly infected individuals are not observed. The parameters of the resulting system of differential equations are identifiable. Using these differential equations, we develop a stochastic model for the conditional distribution of current disease incidence given the entire past history of reported cases. We estimate the model parameters using Bayesian Markov Chain Monte-Carlo sampling of the posterior distribution. We use our model to estimate the transmission rate and fraction of asymptomatic individuals for the current Coronavirus 2019 outbreak in eight American Countries: the United States of America, Brazil, Mexico, Argentina, Chile, Colombia, Peru, and Panama, from January 2020 to May 2021. Our analysis reveals that the fraction of reported cases varies across all countries. For example, the reported incidence fraction for the United States of America varies from 0.3 to 0.6, while for Brazil it varies from 0.2 to 0.4.


Subject(s)
COVID-19/epidemiology , Argentina/epidemiology , Bayes Theorem , Brazil/epidemiology , Chile/epidemiology , Colombia/epidemiology , Humans , Incidence , Markov Chains , Mexico/epidemiology , Panama/epidemiology , Peru/epidemiology , Stochastic Processes , United States/epidemiology
6.
PLoS One ; 16(12): e0259579, 2021.
Article in English | MEDLINE | ID: covidwho-1637068

ABSTRACT

Happiness levels often fluctuate from one day to the next, and an exogenous shock such as a pandemic can likely disrupt pre-existing happiness dynamics. This paper fits a Marko Switching Dynamic Regression Model (MSDR) to better understand the dynamic patterns of happiness levels before and during a pandemic. The estimated parameters from the MSDR model include each state's mean and duration, volatility and transition probabilities. Once these parameters have been estimated, we use the one-step method to predict the unobserved states' evolution over time. This gives us unique insights into the evolution of happiness. Furthermore, as maximising happiness is a policy priority, we determine the factors that can contribute to the probability of increasing happiness levels. We empirically test these models using New Zealand's daily happiness data for May 2019 -November 2020. The results show that New Zealand seems to have two regimes, an unhappy and happy regime. In 2019 the happy regime dominated; thus, the probability of being unhappy in the next time period (day) occurred less frequently, whereas the opposite is true for 2020. The higher frequency of time periods with a probability of being unhappy in 2020 mostly correspond to pandemic events. Lastly, we find the factors positively and significantly related to the probability of being happy after lockdown to be jobseeker support payments and international travel. On the other hand, lack of mobility is significantly and negatively related to the probability of being happy.


Subject(s)
COVID-19/psychology , Happiness , Markov Chains , COVID-19/epidemiology , Humans , New Zealand/epidemiology , Nonlinear Dynamics , Pandemics , Regression Analysis , Statistics as Topic
7.
PLoS One ; 17(1): e0260836, 2022.
Article in English | MEDLINE | ID: covidwho-1613339

ABSTRACT

In the era of open data, Poisson and other count regression models are increasingly important. Still, conventional Poisson regression has remaining issues in terms of identifiability and computational efficiency. Especially, due to an identification problem, Poisson regression can be unstable for small samples with many zeros. Provided this, we develop a closed-form inference for an over-dispersed Poisson regression including Poisson additive mixed models. The approach is derived via mode-based log-Gaussian approximation. The resulting method is fast, practical, and free from the identification problem. Monte Carlo experiments demonstrate that the estimation error of the proposed method is a considerably smaller estimation error than the closed-form alternatives and as small as the usual Poisson regressions. For counts with many zeros, our approximation has better estimation accuracy than conventional Poisson regression. We obtained similar results in the case of Poisson additive mixed modeling considering spatial or group effects. The developed method was applied for analyzing COVID-19 data in Japan. This result suggests that influences of pedestrian density, age, and other factors on the number of cases change over periods.


Subject(s)
COVID-19/epidemiology , Humans , Japan/epidemiology , Markov Chains , Models, Statistical , Monte Carlo Method , Normal Distribution , Poisson Distribution , Regression Analysis , SARS-CoV-2/pathogenicity , Spatial Analysis , Spatio-Temporal Analysis
8.
Sci Rep ; 11(1): 23622, 2021 12 08.
Article in English | MEDLINE | ID: covidwho-1559938

ABSTRACT

Spike glycoprotein (Sgp) is liable for binding of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to the host receptors. Since Sgp is the main target for vaccine and drug designing, elucidating its mutation pattern could help in this regard. This study is aimed at investigating the correspondence of specific residues to the SgpSARS-CoV-2 functionality by explorative interpretation of sequence alignments. Centrality analysis of the Sgp dissects the importance of these residues in the interaction network of the RBD-ACE2 (receptor-binding domain) complex and furin cleavage site. Correspondence of RBD to threonine500 and asparagine501 and furin cleavage site to glutamine675, glutamine677, threonine678, and alanine684 was observed; all residues are exactly located at the interaction interfaces. The harmonious location of residues dictates the RBD binding property and the flexibility, hydrophobicity, and accessibility of the furin cleavage site. These species-specific residues can be assumed as real targets of evolution, while other substitutions tend to support them. Moreover, all these residues are parts of experimentally identified epitopes. Therefore, their substitution may affect vaccine efficacy. Higher rate of RBD maintenance than furin cleavage site was predicted. The accumulation of substitutions reinforces the probability of the multi-host circulation of the virus and emphasizes the enduring evolutionary events.


Subject(s)
SARS-CoV-2/metabolism , Spike Glycoprotein, Coronavirus/genetics , Amino Acid Sequence , Angiotensin-Converting Enzyme 2/chemistry , Angiotensin-Converting Enzyme 2/metabolism , Binding Sites , COVID-19/pathology , COVID-19/virology , Cluster Analysis , Humans , Markov Chains , Mutation , Protein Binding , Protein Domains/genetics , SARS-CoV-2/isolation & purification , Sequence Alignment , Spike Glycoprotein, Coronavirus/chemistry , Spike Glycoprotein, Coronavirus/metabolism
9.
Brief Bioinform ; 23(1)2022 01 17.
Article in English | MEDLINE | ID: covidwho-1545905

ABSTRACT

Spatial transcriptomics has been emerging as a powerful technique for resolving gene expression profiles while retaining tissue spatial information. These spatially resolved transcriptomics make it feasible to examine the complex multicellular systems of different microenvironments. To answer scientific questions with spatial transcriptomics and expand our understanding of how cell types and states are regulated by microenvironment, the first step is to identify cell clusters by integrating the available spatial information. Here, we introduce SC-MEB, an empirical Bayes approach for spatial clustering analysis using a hidden Markov random field. We have also derived an efficient expectation-maximization algorithm based on an iterative conditional mode for SC-MEB. In contrast to BayesSpace, a recently developed method, SC-MEB is not only computationally efficient and scalable to large sample sizes but is also capable of choosing the smoothness parameter and the number of clusters. We performed comprehensive simulation studies to demonstrate the superiority of SC-MEB over some existing methods. We applied SC-MEB to analyze the spatial transcriptome of human dorsolateral prefrontal cortex tissues and mouse hypothalamic preoptic region. Our analysis results showed that SC-MEB can achieve a similar or better clustering performance to BayesSpace, which uses the true number of clusters and a fixed smoothness parameter. Moreover, SC-MEB is scalable to large 'sample sizes'. We then employed SC-MEB to analyze a colon dataset from a patient with colorectal cancer (CRC) and COVID-19, and further performed differential expression analysis to identify signature genes related to the clustering results. The heatmap of identified signature genes showed that the clusters identified using SC-MEB were more separable than those obtained with BayesSpace. Using pathway analysis, we identified three immune-related clusters, and in a further comparison, found the mean expression of COVID-19 signature genes was greater in immune than non-immune regions of colon tissue. SC-MEB provides a valuable computational tool for investigating the structural organizations of tissues from spatial transcriptomic data.


Subject(s)
Algorithms , COVID-19/metabolism , Computer Simulation , Gene Expression Profiling , SARS-CoV-2/metabolism , Animals , Colon/metabolism , Colorectal Neoplasms/metabolism , Humans , Hypothalamus/metabolism , Markov Chains , Mice
10.
PLoS Comput Biol ; 17(9): e1008949, 2021 09.
Article in English | MEDLINE | ID: covidwho-1470647

ABSTRACT

A current strategy for obtaining haplotype information from several individuals involves short-read sequencing of pooled amplicons, where fragments from each individual is identified by a unique DNA barcode. In this paper, we report a new method to recover the phylogeny of haplotypes from short-read sequences obtained using pooled amplicons from a mixture of individuals, without barcoding. The method, AFPhyloMix, accepts an alignment of the mixture of reads against a reference sequence, obtains the single-nucleotide-polymorphisms (SNP) patterns along the alignment, and constructs the phylogenetic tree according to the SNP patterns. AFPhyloMix adopts a Bayesian inference model to estimate the phylogeny of the haplotypes and their relative abundances, given that the number of haplotypes is known. In our simulations, AFPhyloMix achieved at least 80% accuracy at recovering the phylogenies and relative abundances of the constituent haplotypes, for mixtures with up to 15 haplotypes. AFPhyloMix also worked well on a real data set of kangaroo mitochondrial DNA sequences.


Subject(s)
DNA Barcoding, Taxonomic , Phylogeny , Algorithms , Bayes Theorem , DNA, Mitochondrial/genetics , Humans , Markov Chains , Monte Carlo Method , Polymorphism, Single Nucleotide
11.
PLoS Comput Biol ; 17(9): e1009347, 2021 09.
Article in English | MEDLINE | ID: covidwho-1403289

ABSTRACT

We construct a recursive Bayesian smoother, termed EpiFilter, for estimating the effective reproduction number, R, from the incidence of an infectious disease in real time and retrospectively. Our approach borrows from Kalman filtering theory, is quick and easy to compute, generalisable, deterministic and unlike many current methods, requires no change-point or window size assumptions. We model R as a flexible, hidden Markov state process and exactly solve forward-backward algorithms, to derive R estimates that incorporate all available incidence information. This unifies and extends two popular methods, EpiEstim, which considers past incidence, and the Wallinga-Teunis method, which looks forward in time. We find that this combination of maximising information and minimising assumptions significantly reduces the bias and variance of R estimates. Moreover, these properties make EpiFilter more statistically robust in periods of low incidence, where several existing methods can become destabilised. As a result, EpiFilter offers improved inference of time-varying transmission patterns that are advantageous for assessing the risk of upcoming waves of infection or the influence of interventions, in real time and at various spatial scales.


Subject(s)
Basic Reproduction Number/statistics & numerical data , Communicable Diseases/epidemiology , Communicable Diseases/transmission , Epidemics/statistics & numerical data , Algorithms , Basic Reproduction Number/prevention & control , Bayes Theorem , Bias , COVID-19/epidemiology , Communicable Disease Control/statistics & numerical data , Computational Biology , Computer Simulation , Computer Systems , Epidemics/prevention & control , Epidemiological Monitoring , Humans , Incidence , Influenza A Virus, H1N1 Subtype , Influenza, Human/epidemiology , Linear Models , Markov Chains , Models, Statistical , New Zealand/epidemiology , Retrospective Studies , SARS-CoV-2 , Time Factors , United States/epidemiology
12.
Mol Biol Evol ; 38(4): 1537-1543, 2021 04 13.
Article in English | MEDLINE | ID: covidwho-1387956

ABSTRACT

The rooting of the SARS-CoV-2 phylogeny is important for understanding the origin and early spread of the virus. Previously published phylogenies have used different rootings that do not always provide consistent results. We investigate several different strategies for rooting the SARS-CoV-2 tree and provide measures of statistical uncertainty for all methods. We show that methods based on the molecular clock tend to place the root in the B clade, whereas methods based on outgroup rooting tend to place the root in the A clade. The results from the two approaches are statistically incompatible, possibly as a consequence of deviations from a molecular clock or excess back-mutations. We also show that none of the methods provide strong statistical support for the placement of the root in any particular edge of the tree. These results suggest that phylogenetic evidence alone is unlikely to identify the origin of the SARS-CoV-2 virus and we caution against strong inferences regarding the early spread of the virus based solely on such evidence.


Subject(s)
COVID-19/virology , Genome, Viral , Mutation , Phylogeny , SARS-CoV-2/genetics , Algorithms , Animals , Bayes Theorem , Evolution, Molecular , Humans , Likelihood Functions , Markov Chains , Models, Genetic , Models, Statistical , Monte Carlo Method , Mutation, Missense , RNA, Viral/genetics , Uncertainty
13.
Sci Rep ; 11(1): 17421, 2021 08 31.
Article in English | MEDLINE | ID: covidwho-1380913

ABSTRACT

Corona Virus Disease 2019 (COVID-19) has spread rapidly to countries all around the world from the end of 2019, which caused a great impact on global health and has had a huge impact on many countries. Since there is still no effective treatment, it is essential to making effective predictions for relevant departments to make responses and arrangements in advance. Under the limited data, the prediction error of LSTM model will increase over time, and its prone to big bias for medium- and long-term prediction. To overcome this problem, our study proposed a LSTM-Markov model, which uses Markov model to reduce the prediction error of LSTM model. Based on confirmed case data in the US, Britain, Brazil and Russia, we calculated the training errors of LSTM and constructed the probability transfer matrix of the Markov model by the errors. And finally, the prediction results were obtained by combining the output data of LSTM model with the prediction errors of Markov Model. The results show that: compared with the prediction results of the classical LSTM model, the average prediction error of LSTM-Markov is reduced by more than 75%, and the RMSE is reduced by more than 60%, the mean [Formula: see text] of LSTM-Markov is over 0.96. All those indicators demonstrate that the prediction accuracy of proposed LSTM-Markov model is higher than that of the LSTM model to reach more accurate prediction of COVID-19.


Subject(s)
COVID-19/epidemiology , Brazil/epidemiology , Deep Learning , Humans , Markov Chains , Neural Networks, Computer , Research Design , Russia/epidemiology , United Kingdom/epidemiology , United States
14.
Sci Rep ; 11(1): 17328, 2021 08 30.
Article in English | MEDLINE | ID: covidwho-1379336

ABSTRACT

Public health officials discouraged travel and non-household gatherings for Thanksgiving, but data suggests that travel increased over the holidays. The objective of this analysis was to assess associations between holiday gatherings and SARS-CoV-2 positivity in the weeks following Thanksgiving. Using an online survey, we sampled 7770 individuals across 10 US states from December 4-18, 2020, about 8-22 days post-Thanksgiving. Participants were asked about Thanksgiving, COVID-19 symptoms, and SARS-CoV-2 testing and positivity in the prior 2 weeks. Logistic regression was used to identify factors associated with SARS-CoV-2 positivity and COVID-19 symptoms in the weeks following Thanksgiving. An activity score measured the total number of non-essential activities an individual participated in the prior 2 weeks. The probability of community transmission was estimated using Markov Chain Monte Carlo (MCMC) methods. While 47.2% had Thanksgiving at home with household members, 26.9% had guests and 25.9% traveled. There was a statistically significant interaction between how people spent Thanksgiving, the frequency of activities, and SARS-CoV-2 test positivity in the prior 2 weeks (p < 0.05). Those who had guests for Thanksgiving or traveled were only more likely to test positive for SARS-CoV-2 if they also had high activity (e.g., participated in > one non-essential activity/day in the prior 2 weeks). Had individuals limited the number and frequency of activities post-Thanksgiving, cases in surveyed individuals would be reduced by > 50%. As travel continues to increase and the more contagious Delta variant starts to dominate transmission, it is critical to promote how to gather in a "low-risk" manner (e.g., minimize other non-essential activities) to mitigate the need for nationwide shelter-at-home orders.


Subject(s)
COVID-19/epidemiology , COVID-19/transmission , Travel/statistics & numerical data , Adult , COVID-19 Testing , Female , Holidays , Humans , Male , Markov Chains , Middle Aged , Monte Carlo Method , Public Health , United States/epidemiology
15.
PLoS One ; 16(8): e0251378, 2021.
Article in English | MEDLINE | ID: covidwho-1354756

ABSTRACT

BACKGROUND: The benefit of tocilizumab on mortality and time to recovery in people with severe COVID pneumonia may depend on appropriate timing. The objective was to estimate the impact of tocilizumab administration on switching respiratory support states, mortality and time to recovery. METHODS: In an observational study, a continuous-time Markov multi-state model was used to describe the sequence of respiratory support states including: no respiratory support (NRS), oxygen therapy (OT), non-invasive ventilation (NIV) or invasive mechanical ventilation (IMV), OT in recovery, NRS in recovery. RESULTS: Two hundred seventy-one consecutive adult patients were included in the analyses contributing to 695 transitions across states. The prevalence of patients in each respiratory support state was estimated with stack probability plots, comparing people treated with and without tocilizumab since the beginning of the OT state. A positive effect of tocilizumab on the probability of moving from the invasive and non-invasive mechanical NIV/IMV state to the OT in recovery state (HR = 2.6, 95% CI = 1.2-5.2) was observed. Furthermore, a reduced risk of death was observed in patients in NIV/IMV (HR = 0.3, 95% CI = 0.1-0.7) or in OT (HR = 0.1, 95% CI = 0.0-0.8) treated with tocilizumab. CONCLUSION: To conclude, we were able to show the positive impact of tocilizumab used in different disease stages depicted by respiratory support states. The use of the multi-state Markov model allowed to harmonize the heterogeneous mortality and recovery endpoints and summarize results with stack probability plots. This approach could inform randomized clinical trials regarding tocilizumab, support disease management and hospital decision making.


Subject(s)
Antibodies, Monoclonal, Humanized/therapeutic use , COVID-19/drug therapy , Respiratory Therapy/methods , Aged , Female , Humans , Male , Markov Chains , Middle Aged , Noninvasive Ventilation , Oxygen Inhalation Therapy , Respiration, Artificial , Time Factors , Treatment Outcome
16.
J Res Health Sci ; 21(2): e00517, 2021 Jun 28.
Article in English | MEDLINE | ID: covidwho-1326176

ABSTRACT

BACKGROUND: The basic reproduction number (R0) is an important concept in infectious disease epidemiology and the most important parameter to determine the transmissibility of a pathogen. This study aimed to estimate the nine-month trend of time-varying R of COVID-19 epidemic using the serial interval (SI) and Markov Chain Monte Carlo in Lorestan, west of Iran. STUDY DESIGN: Descriptive study. METHODS: This study was conducted based on a cross-sectional method. The SI distribution was extracted from data and log-normal, Weibull, and Gamma models were fitted. The estimation of time-varying R0, a likelihood-based model was applied, which uses pairs of cases to estimate relative likelihood. RESULTS: In this study, Rt was estimated for SI 7-day and 14-day time-lapses from 27 February-14 November 2020. To check the robustness of the R0 estimations, sensitivity analysis was performed using different SI distributions to estimate the reproduction number in 7-day and 14-day time-lapses. The R0 ranged from 0.56 to 4.97 and 0.76 to 2.47 for 7-day and 14-day time-lapses. The doubling time was estimated to be 75.51 days (95% CI: 70.41, 81.41). CONCLUSION: Low R0 of COVID-19 in some periods in Lorestan, west of Iran, could be an indication of preventive interventions, namely quarantine and isolation. To control the spread of the disease, the reproduction number should be reduced by decreasing the transmission and contact rates and shortening the infectious period.


Subject(s)
Basic Reproduction Number , COVID-19/epidemiology , Epidemics , COVID-19/prevention & control , COVID-19/transmission , COVID-19/virology , Cross-Sectional Studies , Humans , Iran/epidemiology , Likelihood Functions , Markov Chains , Monte Carlo Method , Pandemics , SARS-CoV-2
17.
PLoS Comput Biol ; 17(7): e1009211, 2021 07.
Article in English | MEDLINE | ID: covidwho-1325367

ABSTRACT

The effective reproduction number Reff is a critical epidemiological parameter that characterizes the transmissibility of a pathogen. However, this parameter is difficult to estimate in the presence of silent transmission and/or significant temporal variation in case reporting. This variation can occur due to the lack of timely or appropriate testing, public health interventions and/or changes in human behavior during an epidemic. This is exactly the situation we are confronted with during this COVID-19 pandemic. In this work, we propose to estimate Reff for the SARS-CoV-2 (the etiological agent of the COVID-19), based on a model of its propagation considering a time-varying transmission rate. This rate is modeled by a Brownian diffusion process embedded in a stochastic model. The model is then fitted by Bayesian inference (particle Markov Chain Monte Carlo method) using multiple well-documented hospital datasets from several regions in France and in Ireland. This mechanistic modeling framework enables us to reconstruct the temporal evolution of the transmission rate of the COVID-19 based only on the available data. Except for the specific model structure, it is non-specifically assumed that the transmission rate follows a basic stochastic process constrained by the observations. This approach allows us to follow both the course of the COVID-19 epidemic and the temporal evolution of its Reff(t). Besides, it allows to assess and to interpret the evolution of transmission with respect to the mitigation strategies implemented to control the epidemic waves in France and in Ireland. We can thus estimate a reduction of more than 80% for the first wave in all the studied regions but a smaller reduction for the second wave when the epidemic was less active, around 45% in France but just 20% in Ireland. For the third wave in Ireland the reduction was again significant (>70%).


Subject(s)
Basic Reproduction Number , COVID-19/epidemiology , COVID-19/transmission , Pandemics , SARS-CoV-2 , Algorithms , Basic Reproduction Number/statistics & numerical data , Bayes Theorem , Computational Biology , Epidemics/statistics & numerical data , France/epidemiology , Humans , Ireland/epidemiology , Markov Chains , Models, Statistical , Monte Carlo Method , Pandemics/statistics & numerical data , Seroepidemiologic Studies , Stochastic Processes , Time Factors
18.
Sci Rep ; 11(1): 11606, 2021 06 02.
Article in English | MEDLINE | ID: covidwho-1253981

ABSTRACT

The devastating trail of Covid-19 is characterized by one of the highest mortality-to-infected ratio for a pandemic. Restricted therapeutic and early-stage vaccination still renders social exclusion through lockdown as the key containment mode.To understand the dynamics, we propose PHIRVD, a mechanistic infection propagation model that Machine Learns (Bayesian Markov Chain Monte Carlo) the evolution of six infection stages, namely healthy susceptible (H), predisposed comorbid susceptible (P), infected (I), recovered (R), herd immunized (V) and mortality (D), providing a highly reliable mortality prediction profile for 18 countries at varying stages of lockdown. Training data between 10 February to 29 June 2020, PHIRVD can accurately predict mortality profile up to November 2020, including the second wave kinetics. The model also suggests mortality-to-infection ratio as a more dynamic pandemic descriptor, substituting reproduction number. PHIRVD establishes the importance of early and prolonged but strategic lockdown to contain future relapse, complementing futuristic vaccine impact.


Subject(s)
COVID-19/epidemiology , COVID-19/prevention & control , Basic Reproduction Number , Bayes Theorem , COVID-19/etiology , Communicable Disease Control/methods , Comorbidity , Disease Susceptibility , Humans , Immunity, Herd , India/epidemiology , Kinetics , Machine Learning , Markov Chains , Models, Theoretical , Monte Carlo Method , Mortality , United Kingdom/epidemiology
19.
BMC Infect Dis ; 21(1): 476, 2021 May 25.
Article in English | MEDLINE | ID: covidwho-1243804

ABSTRACT

BACKGROUND: The COVID-19 outbreak in Wuhan started in December 2019 and was under control by the end of March 2020 with a total of 50,006 confirmed cases by the implementation of a series of nonpharmaceutical interventions (NPIs) including unprecedented lockdown of the city. This study analyzes the complete outbreak data from Wuhan, assesses the impact of these public health interventions, and estimates the asymptomatic, undetected and total cases for the COVID-19 outbreak in Wuhan. METHODS: By taking different stages of the outbreak into account, we developed a time-dependent compartmental model to describe the dynamics of disease transmission and case detection and reporting. Model coefficients were parameterized by using the reported cases and following key events and escalated control strategies. Then the model was used to calibrate the complete outbreak data by using the Monte Carlo Markov Chain (MCMC) method. Finally we used the model to estimate asymptomatic and undetected cases and approximate the overall antibody prevalence level. RESULTS: We found that the transmission rate between Jan 24 and Feb 1, 2020, was twice as large as that before the lockdown on Jan 23 and 67.6% (95% CI [0.584,0.759]) of detectable infections occurred during this period. Based on the reported estimates that around 20% of infections were asymptomatic and their transmission ability was about 70% of symptomatic ones, we estimated that there were about 14,448 asymptomatic and undetected cases (95% CI [12,364,23,254]), which yields an estimate of a total of 64,454 infected cases (95% CI [62,370,73,260]), and the overall antibody prevalence level in the population of Wuhan was 0.745% (95% CI [0.693%,0.814%]) by March 31, 2020. CONCLUSIONS: We conclude that the control of the COVID-19 outbreak in Wuhan was achieved via the enforcement of a combination of multiple NPIs: the lockdown on Jan 23, the stay-at-home order on Feb 2, the massive isolation of all symptomatic individuals via newly constructed special shelter hospitals on Feb 6, and the large scale screening process on Feb 18. Our results indicate that the population in Wuhan is far away from establishing herd immunity and provide insights for other affected countries and regions in designing control strategies and planing vaccination programs.


Subject(s)
COVID-19/epidemiology , Communicable Disease Control/methods , Disease Outbreaks/statistics & numerical data , Models, Theoretical , SARS-CoV-2 , COVID-19/transmission , China/epidemiology , Communicable Disease Control/organization & administration , Humans , Markov Chains , Monte Carlo Method
20.
JCO Glob Oncol ; 7: 716-725, 2021 05.
Article in English | MEDLINE | ID: covidwho-1231249

ABSTRACT

PURPOSE: The COVID-19 pandemic has placed unprecedented demands on the health system. This led to delays in the initiation and completion of cancer treatment. We assessed the long-term health consequences because of the delay in diagnosis and treatment for cervical cancer due to COVID-19 in India. METHODS: We used a Markov-model-based analysis assessing the lifetime health outcomes of the cohort of women population at risk from cervical cancer in India. The decrease in survival for those with the treatment interruption was calculated based on the number of days the treatment was extended beyond the standard duration. Furthermore, to model the impact of late diagnosis and delayed treatment initiation, the patients were assumed to have upstaged during the delay period, as per natural progression of disease. RESULTS: We estimate 2.52% (n = 795) to 3.80% (n = 2,160) lifetime increase in the deaths caused by cervical cancer with treatment restrictions ranging from 9 weeks to 6 months, respectively, as compared to no delay. On the contrary, 88-238 deaths because of COVID-19 disease are estimated to be saved during this restriction period among the patients with cervical cancer. Overall, the excess mortality because of cervical cancer led to 18,159-53,626 life-years being lost and an increase of 16,808-50,035 disability-adjusted life-years. CONCLUSION: Delays in diagnosis and treatment are likely to lead to more cervical cancer deaths as compared to COVID-19 mortality averted among the patients with cervical cancer. Health systems must reorganize in terms of priority setting for provision of care, starting with prioritizing the treatment of patients with early-stage cervical cancer, increasing use of teleconsultation, and strengthening the role of primary care physicians in provision of cancer care.


Subject(s)
COVID-19 , Delayed Diagnosis , Disease Progression , Time-to-Treatment , Uterine Cervical Neoplasms , Female , Humans , India/epidemiology , Markov Chains , Pandemics , Uterine Cervical Neoplasms/mortality , Uterine Cervical Neoplasms/therapy
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