COVID-19 Pandemic Data Modeling in Pakistan Using Time-Series SIR.
Ontology highlight
ABSTRACT:
SUBMITTER: Taimoor M
PROVIDER: S-EPMC9256309 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:The Covid-19 pandemic has spread across the world since the beginning of 2020. Many regions have experienced its effects. The state of South Carolina in the USA has seen cases since early March 2020 and a primary peak in early April 2020. A lockdown was imposed on April 6th but lifting of restrictions started on April 24th. The daily case and death data as reported by NCHS (deaths) via the New York Times GitHUB repository have been analyzed and approaches to modeling of the data are presented. Prediction is also considered and the role of asymptomatic transmission is assessed as a latent unobserved effect. Two different time periods are examined and one step prediction is provided. The results suggest that both socio-economic disadvantage, asymptomatic transmission and spatial confounding are important ingredients in any model pertaining to county level case dynamics.
| S-EPMC7968659 | biostudies-literature
Project description:BackgroundDuring the second wave of COVID-19 in August 2020, the Tokyo Metropolitan Government implemented public health and social measures to reduce on-site dining. Assessing the associations between human behavior, infection, and social measures is essential to understand achievable reductions in cases and identify the factors driving changes in social dynamics.ObjectiveThe aim of this study was to investigate the association between nighttime population volumes, the COVID-19 epidemic, and the implementation of public health and social measures in Tokyo.MethodsWe used mobile phone location data to estimate populations between 10 PM and midnight in seven Tokyo metropolitan areas. Mobile phone trajectories were used to distinguish and extract on-site dining from stay-at-work and stay-at-home behaviors. Numbers of new cases and symptom onsets were obtained. Weekly mobility and infection data from March 1 to November 14, 2020, were analyzed using a vector autoregression model.ResultsAn increase in the number of symptom onsets was observed 1 week after the nighttime population volume increased (coefficient=0.60, 95% CI 0.28 to 0.92). The effective reproduction number significantly increased 3 weeks after the nighttime population volume increased (coefficient=1.30, 95% CI 0.72 to 1.89). The nighttime population volume increased significantly following reports of decreasing numbers of confirmed cases (coefficient=-0.44, 95% CI -0.73 to -0.15). Implementation of social measures to restaurants and bars was not significantly associated with nighttime population volume (coefficient=0.004, 95% CI -0.07 to 0.08).ConclusionsThe nighttime population started to increase after decreasing incidence of COVID-19 was announced. Considering time lags between infection and behavior changes, social measures should be planned in advance of the surge of an epidemic, sufficiently informed by mobility data.
| S-EPMC8115398 | biostudies-literature
Project description:In the paper, we propose a semiparametric framework for modeling the COVID-19 pandemic. The stochastic part of the framework is based on Bayesian inference. The model is informed by the actual COVID-19 data and the current epidemiological findings about the disease. The framework combines many available data sources (number of positive cases, number of patients in hospitals and in intensive care, etc.) to make outputs as accurate as possible and incorporates the times of non-pharmaceutical governmental interventions which were adopted worldwide to slow-down the pandemic. The model estimates the reproduction number of SARS-CoV-2, the number of infected individuals and the number of patients in different disease progression states in time. It can be used for estimating current infection fatality rate, proportion of individuals not detected and short term forecasting of important indicators for monitoring the state of the healthcare system. With the prediction of the number of patients in hospitals and intensive care units, policy makers could make data driven decisions to potentially avoid overloading the capacities of the healthcare system. The model is applied to Slovene COVID-19 data showing the effectiveness of the adopted interventions for controlling the epidemic by reducing the reproduction number of SARS-CoV-2. It is estimated that the proportion of infected people in Slovenia was among the lowest in Europe (0.350%, 90% CI [0.245-0.573]%), that infection fatality rate in Slovenia until the end of first wave was 1.56% (90% CI [0.94-2.21]%) and the proportion of unidentified cases was 88% (90% CI [83-93]%). The proposed framework can be extended to more countries/regions, thus allowing for comparison between them. One such modification is exhibited on data for Slovene hospitals.
| S-EPMC7482592 | biostudies-literature
Project description:BackgroundSuicide amidst the coronavirus disease (COVID-19) pandemic is an important issue. In Japan, the number of suicides in April 2020 decreased by nearly 20% from that in 2019. To assess the impact of an infectious disease pandemic, excess mortality is often discussed. Our main purpose was evaluating excess mortality from suicide in Japan during the early pandemic period.MethodsWe used data on suicides collected by the National Police Agency of Japan until June 2020. We estimated excess mortality during the early pandemic period (March-June 2020) using a time-series model of the number of suicides before the pandemic. A quasi-Poisson model was employed for the estimation. We evaluated excess mortalities by the categories of age and sex, and prefectures.ResultsNo significant excess mortality was observed throughout the early pandemic; instead, a downward trend in the number of suicides for both sexes was noted. For males, negative values of excess mortalities below the lower bound of the 95% prediction interval were observed in April and May. All numbers of females during the period were included in the interval, and the excess mortalities in June were positive and higher than those in April and May. In Tokyo, the number of suicides was below the lower bound throughout the period.ConclusionOur results suggest that various changes such as communication, and social conditions amid the early COVID-19 pandemic induced a decrease in suicides in Japan. However, continuous monitoring is needed to evaluate the long-term effects of the pandemic on suicides.
| S-EPMC7813773 | biostudies-literature
Project description:COVID-19 declared as a global pandemic by WHO, has emerged as the most aggressive disease, impacting more than 90% countries of the world. The virus started from a single human being in China, is now increasing globally at a rate of 3% to 5% daily and has become a never ending process. Some studies even predict that the virus will stay with us forever. India being the second most populous country of the world, is also not saved, and the virus is spreading as a community level transmitter. Therefore, it become really important to analyse the possible impact of COVID-19 in India and forecast how it will behave in the days to come. In present work, prediction models based on genetic programming (GP) have been developed for confirmed cases (CC) and death cases (DC) across three most affected states namely Maharashtra, Gujarat and Delhi as well as whole India. The proposed prediction models are presented using explicit formula, and impotence of prediction variables are studied. Here, statistical parameters and metrics have been used for evaluated and validate the evolved models. From the results, it has been found that the proposed GEP-based models use simple linkage functions and are highly reliable for time series prediction of COVID-19 cases in India.
| S-EPMC7260529 | biostudies-literature
Project description:BackgroundThe COVID-19 pandemic has impacted healthcare behaviors, leading to fewer pediatric visits in Japan and potentially fewer visits by adult patients. However, existing Japanese studies on treatment interruptions have generally relied on questionnaire-based methods. In this study, we assessed the impact of the pandemic on antihypertensive treatment interruption using real-world prescription data.MethodsWe conducted an interrupted time series analysis using the National Health Insurance Database in Okayama Prefecture, Japan. Participants included individuals aged 40-69 years with at least one antihypertensive prescription between 2018 and 2020. Treatment interruption was defined as a 3-month or longer gap in prescriptions after medication depletion. We used segmented Poisson regression with models unadjusted and adjusted for seasonality and over-dispersion to assess monthly treatment interruptions before and after Japan's April 2020 emergency.ResultsDuring the study period, 23.0% of 55,431 participants experienced treatment interruptions. Cyclical fluctuations in interruptions were observed. The crude analysis indicated a 1.2-fold increase in treatment interruptions following the pandemic; however, the adjusted models showed no significant changes. Even among higher-risk groups, such as women, younger adults, and those with shorter prescriptions, no significant alterations were observed.ConclusionWe found no significant impact of the COVID-19 pandemic on antihypertensive treatment interruption in Okayama Prefecture. The less severe outbreak in the area or increased use of telemedicine and extended prescriptions may have contributed to treatment continuity. Further research is needed using a more stable and comprehensive database, broader regional data, and detailed prescription records to validate and extend our findings.
| S-EPMC10927915 | biostudies-literature
Project description:BackgroundClassical infectious disease models during epidemics have widespread usage, from predicting the probability of new infections to developing vaccination plans for informing policy decisions and public health responses. However, it is important to correctly classify reported data and understand how this impacts estimation of model parameters. The COVID-19 pandemic has provided an abundant amount of data that allow for thorough testing of disease modelling assumptions, as well as how we think about classical infectious disease modelling paradigms.ObjectiveWe aim to assess the appropriateness of model parameter estimates and prediction results in classical infectious disease compartmental modelling frameworks given available data types (infected, active, quarantined, and recovered cases) for situations where just one data type is available to fit the model. Our main focus is on how model prediction results are dependent on data being assigned to the right model compartment.MethodsWe first use simulated data to explore parameter reliability and prediction capability with three formulations of the classical Susceptible-Infected-Removed (SIR) modelling framework. We then explore two applications with reported data to assess which data and models are sufficient for reliable model parameter estimation and prediction accuracy: a classical influenza outbreak in a boarding school in England and COVID-19 data from the fall of 2020 in Missoula County, Montana, USA.ResultsWe demonstrated the magnitude of parameter estimation errors and subsequent prediction errors resulting from data misclassification to model compartments with simulated data. We showed that prediction accuracy in each formulation of the classical disease modelling framework was largely determined by correct data classification versus misclassification. Using a classical example of influenza epidemics in an England boarding school, we argue that the Susceptible-Infected-Quarantined-Recovered (SIQR) model is more appropriate than the commonly employed SIR model given the data collected (number of active cases). Similarly, we show in the COVID-19 disease model example that reported active cases could be used inappropriately in the SIR modelling framework if treated as infected.ConclusionsWe demonstrate the role of misclassification of disease data and thus the importance of correctly classifying reported data to the proper compartment using both simulated and real data. For both a classical influenza data set and a COVID-19 case data set, we demonstrate the implications of using the "right" data in the "wrong" model. The importance of correctly classifying reported data will have downstream impacts on predictions of number of infections, as well as minimal vaccination requirements.
| S-EPMC9755423 | biostudies-literature
Project description:BackgroundDeaths by suicide can increase during infectious disease outbreaks. This study analysed suspected suicide rates in 2020 relative to 2015-19 to assess any early effects of the COVID-19 pandemic in Queensland, Australia.MethodsWe analysed data from the interim Queensland Suicide Register (iQSR), a state-wide real-time suicide surveillance system, using an interrupted time-series design. The data source for the iQSR is the Form 1 police report of a death to a coroner. Two QSR staff independently classed the probability of a death by suicide as possible, probable, or beyond reasonable doubt. The analysis included the probable or beyond reasonable doubt categories as suspected suicides. The primary outcome was the monthly suspected suicide rate. We applied Poisson and negative binomial regressions to assess whether Queensland's Public Health Emergency Declaration on Jan 29, 2020, affected suspected suicides from Feb 1 to Aug 31, 2020. Secondary outcomes included absolute or relative changes in police-reported motives of recent unemployment, financial problems, domestic violence, and relationship breakdown.Findings3793 suspected suicides were recorded with an unadjusted monthly rate of 14·85 deaths per 100 000 people (from Jan 1, 2015, to Jan 31, 2020) before the declaration, and 443 suspected suicides were recorded with an unadjusted monthly rate of 14·07 deaths per 100 000 people (Feb 1, 2020, onwards) after the declaration. An interrupted time-series Poisson regression model unadjusted (rate ratio [RR] 0·94, 95% CI 0·82-1·06) and adjusted for overdispersion, seasonality, and pre-exposure trends (RR 1·02, 95% CI 0·83-1·25) indicated no evidence of a change in suspected suicide rates. We found no absolute or relative increases in the motives for suspected suicides, including recent unemployment, financial problems, relationship breakdown, or domestic violence from February to August, 2020, compared with the pre-exposure period.InterpretationThere does not yet appear to be an overall change in the suspected suicide rate in the 7 months since Queensland declared a public health emergency. Despite this, COVID-19 has contributed to some suspected suicides in Queensland. Ongoing community spread and increasing death rates of COVID-19, and its impact on national economies and mental health, reinforces the need for governments to maintain the monitoring and reporting of suicide mortality in real time.FundingNone.
| S-EPMC7836943 | biostudies-literature
Project description:As sequencing technologies have advanced, the amount of information regarding the composition of bacterial communities from various environments (for example, skin or soil) has grown exponentially. To date, most work has focused on cataloging taxa present in samples and determining whether the distribution of taxa shifts with exogenous covariates. However, important questions regarding how taxa interact with each other and their environment remain open thus preventing in-depth ecological understanding of microbiomes. Time-series data from 16S rDNA amplicon sequencing are becoming more common within microbial ecology, but methods to infer ecological interactions from these longitudinal data are limited. We address this gap by presenting a method of analysis using Poisson regression fit with an elastic-net penalty that (1) takes advantage of the fact that the data are time series; (2) constrains estimates to allow for the possibility of many more interactions than data; and (3) is scalable enough to handle data consisting of thousands of taxa. We test the method on gut microbiome data from white-throated woodrats (Neotoma albigula) that were fed varying amounts of the plant secondary compound oxalate over a period of 22 days to estimate interactions between OTUs and their environment.
| S-EPMC5649163 | biostudies-literature
Project description:Understanding the local dynamics of COVID-19 transmission calls for an approach that characterizes the incidence curve in a small geographical unit. Given that incidence curves exhibit considerable day-to-day variation, the fractal structure of the time series dynamics is investigated for the Flanders and Brussels Regions of Belgium. For each statistical sector, the smallest administrative geographical entity in Belgium, fractal dimensions of COVID-19 incidence rates, based on rolling time spans of 7, 14, and 21 days were estimated using four different estimators: box-count, Hall-Wood, variogram, and madogram. We found varying patterns of fractal dimensions across time and location. The fractal dimension is further summarized by its mean, variance, and autocorrelation over time. These summary statistics are then used to cluster regions with different incidence rate patterns using k-means clustering. Fractal dimension analysis of COVID-19 incidence thus offers important insight into the past, current, and arguably future evolution of an infectious disease outbreak.
| S-EPMC10016183 | biostudies-literature