Epidemiologic Methods最新文献

{"title":"A study of the impact of policy interventions on daily COVID scenario in India using interrupted time series analysis","authors":"Subhankar Chattopadhyay, D. Ghosh, Raju Maiti, Samarjit Das, A. Biswas, Bibhas Chakraborty","doi":"10.1515/em-2022-0113","DOIUrl":"https://doi.org/10.1515/em-2022-0113","url":null,"abstract":"Abstract Objectives The rapid increase both in daily cases and daily deaths made the second wave of COVID-19 pandemic in India more lethal than the first wave. Record number of infections and casualties were reported all over India during this period. Delhi and Maharashtra are the two most affected places in India during the second wave. So in response to this, the Indian government implemented strict intervention policies (“lockdowns”, “social distancing” and “vaccination drive”) in every state during this period to prohibit the spread of this virus. The objective of this article is to conduct an interrupted time series (ITS) analysis to study the impact of the interventions on the daily cases and deaths. Methods We collect daily data for Delhi and Maharashtra before and after the intervention points with a 14-day (incubation period of COVID-19) observation window. A segmented linear regression analysis is done to study the post-intervention slopes as well as whether there were any immediate changes after the interventions or not. We also add the counterfactuals and delayed time effects in the analysis to investigate the significance of our ITS design. Results Here, we observe the post-intervention trends to be statistically significant and negative for both the daily cases and the daily deaths. We also find that there is no immediate change in trend after the start of intervention, and hence we study some delayed time effects which display how changes in the trends happened over time. And from the Counterfactuals in our study, we can have an idea what would have happened to the COVID scenario had the interventions not been implemented. Conclusions We statistically try to figure out different circumstances of COVID scenario for both Delhi and Maharashtra by exploring all possible ingredients of ITS design in our analysis in order to present a feasible design to show the importance of implementation of proper intervention policies for tackling this type of pandemic which can have various highly contagious variants.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88001062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Outliers in nutrient intake data for U.S. adults: national health and nutrition examination survey 2017–2018","authors":"Sara Burcham, Yuki Liu, Ashley L. Merianos, Angelico Mendy","doi":"10.1515/em-2023-0018","DOIUrl":"https://doi.org/10.1515/em-2023-0018","url":null,"abstract":"Abstract Objectives An important step in preparing data for statistical analysis is outlier detection and removal, yet no gold standard exists in current literature. The objective of this study is to identify the ideal decision test using the National Health and Nutrition Examination Survey (NHANES) 2017–2018 dietary data. Methods We conducted a secondary analysis of NHANES 24-h dietary recalls, considering the survey's multi-stage cluster design. Six outlier detection and removal strategies were assessed by evaluating the decision tests' impact on the Pearson's correlation coefficient among macronutrients. Furthermore, we assessed changes in the effect size estimates based on pre-defined sample sizes. The data were collected as part of the 2017–2018 24-h dietary recall among adult participants (N=4,893). Results Effect estimate changes for macronutrients varied from 6.5 % for protein to 39.3 % for alcohol across all decision tests. The largest proportion of outliers removed was 4.0 % in the large sample size, for the decision test, >2 standard deviations from the mean. The smallest sample size, particularly for alcohol analysis, was most affected by the six decision tests when compared to no decision test. Conclusions This study, the first to use 2017–2018 NHANES dietary data for outlier evaluation, emphasizes the importance of selecting an appropriate decision test considering factors such as statistical power, sample size, normality assumptions, the proportion of data removed, effect estimate changes, and the consistency of estimates across sample sizes. We recommend the use of non-parametric tests for non-normally distributed variables of interest.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135610575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A compartmental model of the COVID-19 pandemic course in Germany","authors":"Yıldırım Adalıoğlu, Çağan Kaplan","doi":"10.1515/em-2022-0126","DOIUrl":"https://doi.org/10.1515/em-2022-0126","url":null,"abstract":"Abstract Objectives In late 2019, the novel coronavirus, known as COVID-19, emerged in Wuhan, China, and rapidly spread worldwide, including in Germany. To mitigate the pandemic’s impact, various strategies, including vaccination and non-pharmaceutical interventions, have been implemented. However, the emergence of new, highly infectious SARS-CoV-2 strains has become the primary driving force behind the disease’s spread. Mathematical models, such as deterministic compartmental models, are essential for estimating contagion rates in different scenarios and predicting the pandemic’s behavior. Methods In this study, we present a novel model that incorporates vaccination dynamics, the three most prevalent virus strains (wild-type, alpha, and delta), infected individuals’ detection status, and pre-symptomatic transmission to represent the pandemic’s course in Germany from March 2, 2020, to August 17, 2021. Results By analyzing the behavior of the German population over 534 days and 25 time intervals, we estimated various parameters, including transmission, recovery, mortality, and detection. Furthermore, we conducted an alternative analysis of vaccination scenarios under the same interval conditions, emphasizing the importance of vaccination administration and awareness. Conclusions Our 534-day analysis provides policymakers with a range of circumstances and parameters that can be used to simulate future scenarios. The proposed model can also be used to make predictions and inform policy decisions related to pandemic control in Germany and beyond.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"56 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84699830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Orthostatic intolerance and neurocognitive impairment in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS).","authors":"Caroline L Gaglio,&nbsp;Mohammed F Islam,&nbsp;Joseph Cotler,&nbsp;Leonard A Jason","doi":"10.1515/em-2021-0033","DOIUrl":"https://doi.org/10.1515/em-2021-0033","url":null,"abstract":"<p><strong>Objectives: </strong>The Institute of Medicine (IOM 2015. Beyond Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Redefining an Illness. Washington: The National Academies Press) suggested new criteria for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS), which requires an endorsement of either neurocognitive impairment or orthostatic intolerance (OI) in addition to other core symptoms. While some research supports the inclusion of OI as a core symptom, others argue that overlap with neurocognitive impairment does not justify the either/or option. The current study assessed methods of operationalizing OI using items from the DePaul Symptom Questionnaire (DSQ-1 and -2) as a part of the IOM criteria. Evaluating the relationship between OI and neurocognitive symptoms may lead to a better understanding of diagnostic criteria for ME/CFS.</p><p><strong>Methods: </strong>Two-hundred and forty-two participants completed the DSQ. We examined how many participants met the IOM criteria while endorsing different frequencies and severities of various OI symptoms.</p><p><strong>Results: </strong>Neurocognitive impairment was reported by 93.4% of respondents. OI without concurrent neurocognitive symptoms only allowed for an additional 1.7-4.5% of participants to meet IOM criteria.</p><p><strong>Conclusions: </strong>Neurocognitive symptoms and OI overlap in ME/CFS, and our results do not support the IOM's inclusion of neurocognitive impairment and OI as interchangeable symptoms. Furthermore, our findings highlight the need for a uniform method of defining and measuring OI via self-report in order to accurately study OI as a symptom of ME/CFS.</p>","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"11 1","pages":"20210033"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9550273/pdf/em-11-1-em-2021-0033.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40655332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Measuring COVID-19 spreading speed through the mean time between infections indicator","authors":"G. Pena, Ver'onica Moreno, N. R. Barraza","doi":"10.1515/em-2022-0106","DOIUrl":"https://doi.org/10.1515/em-2022-0106","url":null,"abstract":"Abstract Objectives To introduce a novel way of measuring the spreading speed of an epidemic. Methods We propose to use the mean time between infections (MTBI) metric obtained from a recently introduced nonhomogeneous Markov stochastic model. Different types of parameter calibration are performed. We estimate the MTBI using data from different time windows and from the whole stage history and compare the results. In order to detect waves and stages in the input data, a preprocessing filtering technique is applied. Results The results of applying this indicator to the COVID-19 reported data of infections from Argentina, Germany and the United States are shown. We find that the MTBI behaves similarly with respect to the different data inputs, whereas the model parameters completely change their behaviour. Evolution over time of the parameters and the MTBI indicator is also shown. Conclusions We show evidence to support the claim that the MTBI is a rather good indicator in order to measure the spreading speed of an epidemic, having similar values whatever the input data size.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83728773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accounting for the role of asymptomatic patients in understanding the dynamics of the COVID-19 pandemic: a case study from Singapore","authors":"Fu Teck Liew, P. Ghosh, Bibhas Chakraborty","doi":"10.1515/em-2021-0031","DOIUrl":"https://doi.org/10.1515/em-2021-0031","url":null,"abstract":"Abstract Objectives To forecast the true growth of COVID-19 cases in Singapore after accounting for asymptomatic infections, we study and make modifications to the SEIR (Susceptible-Exposed-Infected-Recovered) epidemiological model by incorporating hospitalization dynamics and the presence of asymptomatic cases. We then compare the simulation results of our three epidemiological models of interest against the daily reported COVID-19 case counts during the time period from 23rd January to 6th April 2020. Finally, we compare and evaluate on the performance and accuracy of the aforementioned models’ simulations. Methods Three epidemiological models are used to forecast the true growth of COVID-19 case counts by accounting for asymptomatic infections in Singapore. They are the exponential model, SEIR model with hospitalization dynamics (SEIHRD), and the SEIHRD model with inclusion of asymptomatic cases (SEAIHRD). Results Simulation results of all three models reflect underestimation of COVID-19 cases in Singapore during the early stages of the pandemic. At a 40% asymptomatic proportion, we report basic reproduction number R 0 = 3.28 and 3.74 under the SEIHRD and SEAIHRD models respectively. At a 60% asymptomatic proportion, we report R 0 = 3.48 and 3.96 under the SEIHRD and SEAIHRD models respectively. Conclusions Based on the results of different simulation scenarios, we are highly confident that the number of COVID-19 cases in Singapore was underestimated during the early stages of the pandemic. This is supported by the exponential increase of COVID-19 cases in Singapore as the pandemic evolved.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74053156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"COVID-19 vaccine hesitancy among undergraduate students in Thailand during the peak of the third wave of the coronavirus pandemic in 2021","authors":"Sulan Lin, C. Rattanapan, A. Mongkolchati, M. N. Aung, W. Ounsaneha, N. Sritoomma, O. Laosee","doi":"10.1515/em-2022-0109","DOIUrl":"https://doi.org/10.1515/em-2022-0109","url":null,"abstract":"Abstract Objectives To determine the point prevalence of undergraduate students who are hesitant to accept COVID-19 vaccination and to identify the predictors of COVID-19 vaccine hesitancy in university students. Methods A cross-sectional study was conducted during June–July 2021. A total of 542 undergraduate students from universities in three central provinces of Thailand participated in an online survey via Google Form. We used a transculturally translated, Thai version of the Oxford Coronavirus Explanations, Attitudes, and Narratives Survey (OCEANS II). Results There were 217 undergraduate students (40%) who were hesitant to receive the COVID-19 vaccine and the significant predictors for this hesitancy were: being students in Year 2 and higher (AOR: 2.73; 95% CI: 1.55–4.84); having negative beliefs toward the COVID-19 vaccine (AOR: 10.99; 95% CI: 6.82–17.73); and having a perceived positive general vaccine conspiracy belief (AOR: 1.90; 95% CI: 1.02–3.52). Conclusions It is important to minimize vaccine hesitancy among Thai undergraduate students with a negative perception of vaccines by clarifying false information.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73388770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Incidence moments: a simple method to study the memory and short term forecast of the COVID-19 incidence time-series","authors":"Mauricio Canals L, Andrea Canals C, Cristóbal Cuadrado N","doi":"10.1515/em-2021-0029","DOIUrl":"https://doi.org/10.1515/em-2021-0029","url":null,"abstract":"Abstract Objectives The ability to predict COVID-19 dynamic has been very low, reflected in unexpected changes in the number of cases in different settings. Here the objective was to study the temporal memory of the reported daily incidence time series and propose a simple model for short-term forecast of the incidence. Methods We propose a new concept called incidence moments that allows exploring the memory of the reported incidence time series, based on successive products of the incidence and the reproductive number that allow a short term forecast of the future incidence. We studied the correlation between the predictions of and the reported incidence determining the best predictor. We compared the predictions and observed COVID-19 incidences with the mean arctangent absolute percentage error (MAAPE) analyses for the world, 43 countries and for Chile and its regions. Results The best predictor was the third moment of incidence, determining a short temporal prediction window of 15 days. After 15 days the absolute percentage error of the prediction increases significantly. The method perform better for larger populations and presents distortions in contexts of abrupt changes in incidence. Conclusions The epidemic dynamics of COVID 19 had a very short prediction window, probably associated with an intrinsic chaotic behavior of its dynamics. The incident moment modeling approach could be useful as a tool whose simplicity is appealing, since it allows rapid implementation in different settings, even with limited epidemiological technical capabilities and without requiring a large amount of computational data.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75699923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Numerical modelling of coronavirus pandemic in Peru","authors":"C. Jiménez, M. Merma","doi":"10.1515/em-2020-0026","DOIUrl":"https://doi.org/10.1515/em-2020-0026","url":null,"abstract":"Abstract Objectives The main objective of this research is to demonstrate the effectiveness of non-pharmaceutical interventions (social isolation and quarantine) and of vaccination. Methods The SIR epidemiological numerical model has been revised to obtain a new model (SAIRDQ), which involves additional variables: the population that died due to the disease (D), the isolated (A), quarantined population (Q) and the effect of vaccination. We have obtained the epidemiological parameters from the data, which are not constant during the evolution of the pandemic, using an iterative approximation method. Results Analysis of the data of infected and deceased suggest that the evolution of the coronavirus epidemic in Peru has arrived at the end of the second wave (around October 2021). We have simulated the effect of quarantine and vaccination, which are effective measures to reduce the impact of the pandemic. For a variable infection and isolation rate, due to the end of the quarantine, the death toll would be around 200 thousand; if the isolation and quarantine were relaxed since March 01, 2021, there could be more than 280 thousand deaths. Conclusions Without non-pharmaceutical interventions and vaccination, the number of deaths would be much higher than 280 thousand.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80880935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impact of test positivity on surveillance with asymptomatic carriers","authors":"M. Gaspari","doi":"10.1101/2022.06.10.22276234","DOIUrl":"https://doi.org/10.1101/2022.06.10.22276234","url":null,"abstract":"Abstract Objectives Recent studies show that Test Positivity Rate (TPR) gains a better correlation than incidence with the number of hospitalized patients in COVID-19 pandemic. Nevertheless, epidemiologists remain sceptical concerning the widespread use of this metric for surveillance, and indicators based on known cases like incidence rate are still preferred despite the large number of asymptomatic carriers, which remain unknown. Our aim is to compare TPR and incidence rate, to determine which of the two has the best characteristics to predict the trend of hospitalized patients in the COVID-19 pandemic. Methods We perform a retrospective study considering 60 outbreak cases, using global and local data from Italy in different waves of the pandemic, in order to detect peaks in TPR time series, and peaks in incidence rate, finding which of the two indicators has the best ability to anticipate peaks in patients admitted in hospitals. Results On average, the best TPR-based approach anticipates the incidence rate of about 4.6 days (95 % CI 2.8, 6.4), more precisely the average distance between TPR peaks and hospitalized peaks is 17.6 days (95 % CI 15.0, 20.4) with respect to 13.0 days (95 % CI 10.4, 15.8) obtained for incidence. Moreover, the average difference between TPR and incidence rate increased to more than 6 days in the Delta outbreak during summer 2021, where presumably the percentage of asymptomatic carriers was larger. Conclusions We conclude that TPR should be used as the primary indicator to enable early intervention, and for predicting hospital admissions in infectious diseases with asymptomatic carriers.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89581540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}