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1.
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
2.
Comput Math Methods Med ; 2021: 2689000, 2021.
Article in English | MEDLINE | ID: covidwho-1566408

ABSTRACT

We have studied one of the most common distributions, namely, Lindley distribution, which is an important continuous mixed distribution with great ability to represent different systems. We studied this distribution with three parameters because of its high flexibility in modelling life data. The parameters were estimated by five different methods, namely, maximum likelihood estimation, ordinary least squares, weighted least squares, maximum product of spacing, and Cramér-von Mises. Simulation experiments were performed with different sample sizes and different parameter values. The different methods were compared on the generated data by mean square error and mean absolute error. In addition, we compared the methods for real data, which represent COVID-19 data in Iraq/Anbar Province.


Subject(s)
COVID-19/epidemiology , Public Health Informatics/methods , Algorithms , Computer Simulation , Humans , Iraq , Least-Squares Analysis , Likelihood Functions , Models, Statistical , Public Health Informatics/standards , SARS-CoV-2 , Statistics as Topic
3.
BMC Infect Dis ; 21(1): 1185, 2021 Nov 25.
Article in English | MEDLINE | ID: covidwho-1538061

ABSTRACT

BACKGROUND: The first confirmed cases of COVID-19 in Iran were reported in Qom city. Subsequently, the neighboring provinces and gradually all 31 provinces of Iran were involved. This study aimed to investigate the case fatility rate, basic reproductive number in different period of epidemic, projection of daily and cumulative incidence cases and also spatiotemporal mapping of SARS-CoV-2 in Alborz province, Iran. METHODS: A confirmed case of COVID-19 infection was defined as a case with a positive result of viral nucleic acid testing in respiratory specimens. Serial interval (SI) was fitted by gamma distribution and considered the likelihood-based R0 using a branching process with Poisson likelihood. Seven days average of cases, deaths, doubling times and CFRs used to draw smooth charts. kernel density tool in Arc GIS (Esri) software has been employed to compute hot spot area of the study site. RESULTS: The maximum-likelihood value of R0 was 2.88 (95%, CI: 2.57-3.23) in the early 14 days of epidemic. The case fatility rate for Alborz province (Iran) on March 10, was 8.33% (95%, CI:6.3-11), and by April 20, it had an increasing trend and reached 12.9% (95%,CI:11.5-14.4). The doubling time has been increasing from about two days and then reached about 97 days on April 20, 2020, which shows the slowdown in the spread rate of the disease. Also, from March 26 to April 2, 2020 the whole Geographical area of Karj city was almost affected by SARS-CoV-2. CONCLUSIONS: The R0 of COVID-19 in Alborz province was substantially high at the beginning of the epidemic, but with preventive measures and public education and GIS based monitoring of the cases,it has been reduced to 1.19 within two months. This reduction highpoints the attainment of preventive measures in place, however we must be ready for any second epidemic waves during the next months.


Subject(s)
COVID-19 , Epidemics , Geographic Information Systems , Humans , Iran/epidemiology , Likelihood Functions , SARS-CoV-2
4.
Comput Intell Neurosci ; 2021: 5918511, 2021.
Article in English | MEDLINE | ID: covidwho-1463058

ABSTRACT

A new five-parameter transmuted generalization of the Lomax distribution (TGL) is introduced in this study which is more flexible than current distributions and has become the latest distribution theory trend. Transmuted generalization of Lomax distribution is the name given to the new model. This model includes some previously unknown distributions. The proposed distribution's structural features, closed forms for an rth moment and incomplete moments, quantile, and Rényi entropy, among other things, are deduced. Maximum likelihood estimate based on complete and Type-II censored data is used to derive the new distribution's parameter estimators. The percentile bootstrap and bootstrap-t confidence intervals for unknown parameters are introduced. Monte Carlo simulation research is discussed in order to estimate the characteristics of the proposed distribution using point and interval estimation. Other competitive models are compared to a novel TGL. The utility of the new model is demonstrated using two COVID-19 real-world data sets from France and the United Kingdom.


Subject(s)
COVID-19 , Models, Statistical , Humans , Likelihood Functions , Monte Carlo Method , SARS-CoV-2
5.
Dis Markers ; 2021: 2571912, 2021.
Article in English | MEDLINE | ID: covidwho-1463050

ABSTRACT

Background: Coronavirus disease 2019 (COVID-19) is highly contagious and continues to spread rapidly. However, there are no simple and timely laboratory techniques to determine the severity of COVID-19. In this meta-analysis, we assessed the potential of the neutrophil-lymphocyte ratio (NLR) as an indicator of severe versus nonsevere COVID-19 cases. Methods: A search for studies on the NLR in severe and nonsevere COVID-19 cases published from January 1, 2020, to July 1, 2021, was conducted on the PubMed, EMBASE, and Cochrane Library databases. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio (DOR), and area under the curve (AUC) analyses were done on Stata 14.0 and Meta-disc 1.4 to assess the performance of the NLR. Results: Thirty studies, including 5570 patients, were analyzed. Of these, 1603 and 3967 patients had severe and nonsevere COVID-19, respectively. The overall sensitivity and specificity were 0.82 (95% confidence interval (CI), 0.77-0.87) and 0.77 (95% CI, 0.70-0.83), respectively; positive and negative correlation ratios were 3.6 (95% CI, 2.7-4.7) and 0.23 (95% CI, 0.17-0.30), respectively; DOR was 16 (95% CI, 10-24), and the AUC was 0.87 (95% CI, 0.84-0.90). Conclusion: The NLR could accurately determine the severity of COVID-19 and can be used to identify patients with severe disease to guide clinical decision-making.


Subject(s)
COVID-19/immunology , Lymphocytes/immunology , Neutrophils/immunology , SARS-CoV-2 , Area Under Curve , Biomarkers/blood , COVID-19/blood , Confidence Intervals , Humans , Leukocyte Count , Likelihood Functions , Odds Ratio , Sensitivity and Specificity , Severity of Illness Index
6.
BMC Infect Dis ; 21(1): 1039, 2021 Oct 07.
Article in English | MEDLINE | ID: covidwho-1455943

ABSTRACT

BACKGROUND: The COVID-19 pandemic poses serious threats to global health, and the emerging mutation in SARS-CoV-2 genomes, e.g., the D614G substitution, is one of the major challenges of disease control. Characterizing the role of the mutation activities is of importance to understand how the evolution of pathogen shapes the epidemiological outcomes at population scale. METHODS: We developed a statistical framework to reconstruct variant-specific reproduction numbers and estimate transmission advantage associated with the mutation activities marked by single substitution empirically. Using likelihood-based approach, the model is exemplified with the COVID-19 surveillance data from January 1 to June 30, 2020 in California, USA. We explore the potential of this framework to generate early warning signals for detecting transmission advantage on a real-time basis. RESULTS: The modelling framework in this study links together the mutation activity at molecular scale and COVID-19 transmissibility at population scale. We find a significant transmission advantage of COVID-19 associated with the D614G substitution, which increases the infectivity by 54% (95%CI: 36, 72). For the early alarming potentials, the analytical framework is demonstrated to detect this transmission advantage, before the mutation reaches dominance, on a real-time basis. CONCLUSIONS: We reported an evidence of transmission advantage associated with D614G substitution, and highlighted the real-time estimating potentials of modelling framework.


Subject(s)
COVID-19 , Genome, Viral , SARS-CoV-2 , COVID-19/virology , Humans , Likelihood Functions , Mutation , Pandemics , SARS-CoV-2/genetics
7.
Contemp Clin Trials ; 110: 106575, 2021 11.
Article in English | MEDLINE | ID: covidwho-1439914

ABSTRACT

In longitudinal clinical trials, missing data are inevitable due to intercurrent events (ICEs) such as treatment interruption or premature discontinuation for different reasons. The COVID-19 pandemic has had substantial impact on clinical trials since early 2020 as it may result in missing data due to missed visits and premature discontinuations. The missing data due to COVID-19 can reasonably be assumed as missing at random (MAR). We propose a combined hypothetical strategy for sensitivity analyses to handle missing data due to both COVID-19 and non-COVID reasons. We modify the commonly used missing not at random (MNAR) methods, reference based imputation (RBI) and tipping point analysis, under this strategy. We propose the standard multiple imputation approach and derive an analytic likelihood based approach to implement the proposed methods to improve efficiency in applications. The proposed strategy and methods are applicable to a more general scenario when there are missing data due to both MAR and MNAR reasons.


Subject(s)
COVID-19 , Humans , Likelihood Functions , Pandemics , SARS-CoV-2
8.
J Theor Biol ; 529: 110861, 2021 11 21.
Article in English | MEDLINE | ID: covidwho-1437518

ABSTRACT

One of the key epidemiological characteristics that shape the transmission of coronavirus disease 2019 (COVID-19) is the serial interval (SI). Although SI is commonly considered following a probability distribution at a population scale, recent studies reported a slight shrinkage (or contraction) of the mean of effective SI across transmission generations or over time. Here, we develop a likelihood-based statistical inference framework with truncation to explore the change in SI across transmission generations after adjusting the impacts of case isolation. The COVID-19 contact tracing surveillance data in Hong Kong are used for exemplification. We find that for COVID-19, the mean of individual SI is likely to shrink with a factor at 0.72 per generation (95%CI: 0.54, 0.96) as the transmission generation increases, where a threshold may exist as the lower boundary of this shrinking process. We speculate that one of the probable explanations for the shrinkage in SI might be an outcome due to the competition among multiple candidate infectors within the same case cluster. Thus, the nonpharmaceutical interventive strategies are crucially important to block the transmission chains, and mitigate the COVID-19 epidemic.


Subject(s)
COVID-19 , Contact Tracing , Hong Kong , Humans , Likelihood Functions , SARS-CoV-2
9.
Int J Environ Res Public Health ; 18(19)2021 09 24.
Article in English | MEDLINE | ID: covidwho-1438605

ABSTRACT

Widespread misinformation about COVID-19 poses a significant threat to citizens long-term health and the combating of the disease. To fight the spread of misinformation, Chinese governments have used official social media accounts to participate in fact-checking activities. This study aims to investigate why citizens share fact-checks about COVID-19 and how to promote this activity. Based on the elaboration likelihood model, we explore the effects of peripheral cues (social media capital, social media strategy, media richness, and source credibility) and central cues (content theme and content importance) on the number of shares of fact-checks posted by official Chinese Government social media accounts. In total, 820 COVID-19 fact-checks from 413 Chinese Government Sina Weibo accounts were obtained and evaluated. Results show that both peripheral and central cues play important roles in the sharing of fact-checks. For peripheral cues, social media capital and media richness significantly promote the number of shares. Compared with the push strategy, both the pull strategy and networking strategy facilitate greater fact-check sharing. Fact-checks posted by Central Government social media accounts receive more shares than local government accounts. For central cues, content importance positively predicts the number of shares. In comparison to fact-checks about the latest COVID-19 news, government actions received fewer shares, while social conditions received more shares.


Subject(s)
COVID-19 , Social Media , China , Communication , Humans , Likelihood Functions , Local Government , SARS-CoV-2
10.
Stat Med ; 40(28): 6277-6294, 2021 12 10.
Article in English | MEDLINE | ID: covidwho-1396959

ABSTRACT

The demand for rapid surveillance and early detection of local outbreaks has been growing recently. The rapid surveillance can select timely and appropriate interventions toward controlling the spread of emerging infectious diseases, such as the coronavirus disease 2019 (COVID-19). The Farrington algorithm was originally proposed by Farrington et al (1996), extended by Noufaily et al (2012), and is commonly used to estimate excess death. However, one of the major challenges in implementing this algorithm is the lack of historical information required to train it, especially for emerging diseases. Without sufficient training data the estimation/prediction accuracy of this algorithm can suffer leading to poor outbreak detection. We propose a new statistical algorithm-the geographically weighted generalized Farrington (GWGF) algorithm-by incorporating both geographically varying and geographically invariant covariates, as well as geographical information to analyze time series count data sampled from a spatially correlated process for estimating excess death. The algorithm is a type of local quasi-likelihood-based regression with geographical weights and is designed to achieve a stable detection of outbreaks even when the number of time points is small. We validate the outbreak detection performance by using extensive numerical experiments and real-data analysis in Japan during COVID-19 pandemic. We show that the GWGF algorithm succeeds in improving recall without reducing the level of precision compared with the conventional Farrington algorithm.


Subject(s)
COVID-19 , Pandemics , Algorithms , Disease Outbreaks/prevention & control , Humans , Likelihood Functions , SARS-CoV-2
11.
J Vet Sci ; 22(1): e12, 2021 Jan.
Article in English | MEDLINE | ID: covidwho-1389650

ABSTRACT

BACKGROUND: Bats have been considered natural reservoirs for several pathogenic human coronaviruses (CoVs) in the last two decades. Recently, a bat CoV was detected in the Republic of Korea; its entire genome was sequenced and reported to be genetically similar to that of the severe acute respiratory syndrome CoV (SARS-CoV). OBJECTIVES: The objective of this study was to compare the genetic sequences of SARS-CoV, SARS-CoV-2, and the two Korean bat CoV strains 16BO133 and B15-21, to estimate the likelihood of an interaction between the Korean bat CoVs and the human angiotensin-converting enzyme 2 (ACE2) receptor. METHODS: The phylogenetic analysis was conducted with the maximum-likelihood (ML) method using MEGA 7 software. The Korean bat CoVs receptor binding domain (RBD) of the spike protein was analyzed by comparative homology modeling using the SWISS-MODEL server. The binding energies of the complexes were calculated using PRODIGY and MM/GBGA. RESULTS: Phylogenetic analyses of the entire RNA-dependent RNA polymerase, spike regions, and the complete genome revealed that the Korean CoVs, along with SARS-CoV and SARS-CoV-2, belong to the subgenus Sarbecovirus, within BetaCoVs. However, the two Korean CoVs were distinct from SARS-CoV-2. Specifically, the spike gene of the Korean CoVs, which is involved in host infection, differed from that of SARS-CoV-2, showing only 66.8%-67.0% nucleotide homology and presented deletions within the RBD, particularly within regions critical for cross-species transmission and that mediate interaction with ACE2. Binding free energy calculation revealed that the binding affinity of Korean bat CoV RBD to hACE2 was drastically lower than that of SARS-CoV and SARS-CoV-2. CONCLUSIONS: These results suggest that Korean bat CoVs are unlikely to bind to the human ACE2 receptor.


Subject(s)
Chiroptera/virology , Coronavirus/genetics , SARS Virus/genetics , SARS-CoV-2/genetics , Animals , Genes, Viral/genetics , Genome, Viral/genetics , Genomics , Humans , Likelihood Functions , Phylogeny , Receptor, Angiotensin, Type 2/genetics , Receptor, Angiotensin, Type 2/metabolism , Republic of Korea , Sequence Analysis, DNA , Sequence Homology , Spike Glycoprotein, Coronavirus/genetics , Virus Attachment
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.
PLoS One ; 16(8): e0241942, 2021.
Article in English | MEDLINE | ID: covidwho-1379825

ABSTRACT

The SARS-CoV-2 disease, first detected in Wuhan, China, in December 2019 has become a global pandemic and is causing an unprecedented burden on health care systems and the economy globally. While the travel history of index cases may suggest the origin of infection, phylogenetic analysis of isolated strains from these cases and contacts will increase the understanding and link between local transmission and other global populations. The objective of this analysis was to provide genomic data on the first six cases of SARS-CoV-2 in The Gambia and to determine the source of infection. This ultimately provide baseline data for subsequent local transmission and contribute genomic diversity information towards local and global data. Our analysis has shown that the SARS-CoV-2 virus identified in The Gambia are of European and Asian origin and sequenced data matched patients' travel history. In addition, we were able to show that two COVID-19 positive cases travelling in the same flight had different strains of SARS-CoV-2. Although whole genome sequencing (WGS) data is still limited in sub-Saharan Africa, this approach has proven to be a highly sensitive, specific and confirmatory tool for SARS-CoV-2 detection.


Subject(s)
COVID-19/pathology , Genome, Viral , SARS-CoV-2/genetics , COVID-19/virology , Gambia , Genetic Variation , Humans , Likelihood Functions , Phylogeny , SARS-CoV-2/classification , SARS-CoV-2/isolation & purification , Whole Genome Sequencing
14.
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
15.
Int J Epidemiol ; 50(6): 1788-1794, 2022 01 06.
Article in English | MEDLINE | ID: covidwho-1276175

ABSTRACT

BACKGROUND: The COVID-19 epidemic has spread rapidly within aged-care facilities (ACFs), where the infection-fatality ratio is high. It is therefore urgent to evaluate the efficiency of infection prevention and control (IPC) measures in reducing SARS-CoV-2 transmission. METHODS: We analysed the COVID-19 outbreaks that took place between March and May 2020 in 12 ACFs using reverse transcription-polymerase chain reaction (RT-PCR) and serological tests for SARS-CoV-2 infection. Using maximum-likelihood approaches and generalized linear mixed models, we analysed the proportion of infected residents in ACFs and identified covariates associated with the proportion of infected residents. RESULTS: The secondary-attack risk was estimated at 4.1%, suggesting a high efficiency of the IPC measures implemented in the region. Mask wearing and the establishment of COVID-19 zones for infected residents were the two main covariates associated with lower secondary-attack risks. CONCLUSIONS: Wearing masks and isolating potentially infected residents appear to be associated with a more limited spread of SARS-CoV-2 in ACFs.


Subject(s)
COVID-19 , Epidemics , Aged , Humans , Likelihood Functions , Masks , SARS-CoV-2
16.
Int J Environ Res Public Health ; 18(12)2021 06 15.
Article in English | MEDLINE | ID: covidwho-1270058

ABSTRACT

(1) Background: The vaccine supply is likely to be limited in 2021 due to constraints in manufacturing. To maximize the benefit from the rollout phase, an optimal strategy of vaccine allocation is necessary based on each country's epidemic status. (2) Methods: We first developed a heterogeneous population model considering the transmission matrix using maximum likelihood estimation based on the epidemiological records of individual COVID-19 cases in the Republic of Korea. Using this model, the vaccine priorities for minimizing mortality or incidence were investigated. (3) Results: The simulation results showed that the optimal vaccine allocation strategy to minimize the mortality (or incidence) was to prioritize elderly and healthcare workers (or adults) as long as the reproductive number was below 1.2 (or over 0.9). (4) Conclusion: Our simulation results support the current Korean government vaccination priority strategy, which prioritizes healthcare workers and senior groups to minimize mortality, under the condition that the reproductive number remains below 1.2. This study revealed that, in order to maintain the current vaccine priority policy, it is important to ensure that the reproductive number does not exceed the threshold by concurrently implementing nonpharmaceutical interventions.


Subject(s)
COVID-19 , Vaccines , Adult , Aged , COVID-19 Vaccines , Humans , Likelihood Functions , Republic of Korea , SARS-CoV-2 , Vaccination
17.
Am J Epidemiol ; 190(6): 1075-1080, 2021 06 01.
Article in English | MEDLINE | ID: covidwho-1249276

ABSTRACT

Increasing hospitalizations for COVID-19 in the United States and elsewhere have ignited debate over whether to reinstate shelter-in-place policies adopted early in the pandemic to slow the spread of infection. The debate includes claims that sheltering in place influences deaths unrelated to infection or other natural causes. Testing this claim should improve the benefit/cost accounting that informs choice on reimposing sheltering in place. We used time-series methods to compare weekly nonnatural deaths in California with those in Florida. California was the first state to begin, and among the last to end, sheltering in place, while sheltering began later and ended earlier in Florida. During weeks when California had shelter-in-place orders in effect, but Florida did not, the odds that a nonnatural death occurred in California rather than Florida were 14.4% below expected levels. Sheltering-in-place policies likely reduce mortality from mechanisms unrelated to infection or other natural causes of death.


Subject(s)
COVID-19/prevention & control , Cause of Death/trends , Quarantine/statistics & numerical data , COVID-19/mortality , California/epidemiology , Florida/epidemiology , Humans , Likelihood Functions , SARS-CoV-2 , United States
18.
Front Public Health ; 9: 604455, 2021.
Article in English | MEDLINE | ID: covidwho-1236779

ABSTRACT

Background: The asymptomatic proportion is a critical epidemiological characteristic that modulates the pandemic potential of emerging respiratory virus, which may vary depending on the nature of the disease source, population characteristics, source-host interaction, and environmental factors. Methods: We developed a simple likelihood-based framework to estimate the instantaneous asymptomatic proportion of infectious diseases. Taking the COVID-19 epidemics in Hong Kong as a case study, we applied the estimation framework to estimate the reported asymptomatic proportion (rAP) using the publicly available surveillance data. We divided the time series of daily cases into four stages of epidemics in Hong Kong by examining the persistency of the epidemic and compared the rAPs of imported cases and local cases at different stages. Results: As of July 31, 2020, there were two intermittent epidemics in Hong Kong. The first one was dominated by imported cases, accounting for 63.2% of the total cases, and the second one was dominated by local cases, accounting for 86.5% of the total cases. The rAP was estimated at 23.1% (95% CI: 10.8-39.7%) from January 23 to July 31, and the rAPs were estimated at 22.6% (95% CI: 11.1-38.9%) among local cases and 38.7% (95% CI: 9.0-72.0%) among imported cases. Our results showed that the rAPs of local cases were not significantly different between the two epidemics, but increased gradually during the first epidemic period. In contrast, the rAPs of imported cases in the latter epidemic period were significantly higher than that in the previous epidemic period. Conclusion: Hong Kong has a high rAP of imported COVID-19 cases and should continue to strengthen the detection and isolation of imported individuals to prevent the resurgence of the disease.


Subject(s)
COVID-19 , Hong Kong/epidemiology , Humans , Likelihood Functions , Pandemics , SARS-CoV-2
19.
Stat Med ; 40(19): 4252-4268, 2021 08 30.
Article in English | MEDLINE | ID: covidwho-1222698

ABSTRACT

Since the outbreak of the new coronavirus disease (COVID-19), a large number of scientific studies and data analysis reports have been published in the International Journal of Medicine and Statistics. Taking the estimation of the incubation period as an example, we propose a low-cost method to integrate external research results and available internal data together. By using empirical likelihood method, we can effectively incorporate summarized information even if it may be derived from a misspecified model. Taking the possible uncertainty in summarized information into account, we augment a logarithm of the normal density in the log empirical likelihood. We show that the augmented log-empirical likelihood can produce enhanced estimates for the underlying parameters compared with the method without utilizing auxiliary information. Moreover, the Wilks' theorem is proved to be true. We illustrate our methodology by analyzing a COVID-19 incubation period data set retrieved from Zhejiang Province and summarized information from a similar study in Shenzhen, China.


Subject(s)
COVID-19 , Humans , Likelihood Functions , Research Design , SARS-CoV-2 , Uncertainty
20.
Homeopathy ; 110(3): 160-167, 2021 08.
Article in English | MEDLINE | ID: covidwho-1209205

ABSTRACT

BACKGROUND/OBJECTIVE: Coronavirus disease 2019 (COVID-19) is a new disease; its clinical profile and natural history are evolving. Each well-recorded case in homeopathic practice is important for deciding the future course of action. This study aims at identifying clinically useful homeopathic remedies and their prescribing symptoms using the prognostic factor research model. METHODS: This was an open-label, multi-centric, observational study performed from April 2020 to July 2020 at various public health care clinics. The data were collected prospectively from clinical practice at integrated COVID-19 care facilities in India. Good-quality cases were selected using a specific set of criteria. These cases were analyzed for elucidating prognostic factors by calculating the likelihood ratio (LR) of each frequently occurring symptom. The symptoms with high LR values (>1) were considered as prescribing indications of the specific remedy. RESULTS: Out of 327 COVID-19 cases reported, 211 met the selection criteria for analysis. The most common complaints were fatigue, sore throat, dry cough, myalgia, fever, dry mouth and throat, increased thirst, headache, decreased appetite, anxiety, and altered taste. Twenty-seven remedies were prescribed and four of them-Arsenicum album, Bryonia alba, Gelsemium sempervirens, and Pulsatilla nigricans-were the most frequently used. A high LR was obtained for certain symptoms, which enabled differentiation between the remedies for a given patient. CONCLUSION: Homeopathic medicines were associated with improvement in symptoms of COVID-19 cases. Characteristic symptoms of four frequently indicated remedies have been identified using prognostic factor research, findings that can contribute to accurate homeopathic prescribing during future controlled research in COVID-19.


Subject(s)
COVID-19/therapy , Homeopathy , Adolescent , Adult , Female , Humans , India/epidemiology , Likelihood Functions , Male , Middle Aged , Prognosis , Prospective Studies , SARS-CoV-2 , Young Adult
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