Time-series forecasting and analysis of COVID-19 outbreak in highly populated countries- A data-driven approach

A novel corona virus, COVID-19 is spreading across different countries in an alarming proportion and it has become a major threat to the existence of human community. With more than eight lakh death count within a very short span of seven months, this deadly virus has affected more than 24 million people across 213 countries and territories around the world. Time-series analysis, modeling and forecasting is an important research area that explores the hidden insights from larger set of time-bound data for arriving better decisions. In this work, data analysis on COVID-19 dataset is performed by comparing the top six populated countries in the world. The data used for the evaluation is taken for a time period from 22nd January 2020 to 23rd August 2020.A novel time-series forecasting approach based on Auto-regressive integrated moving average (ARIMA) model is also proposed. The results will help the researchers from medical and scientific community to gauge the trend of the disease spread and improvise containment strategies accordingly.

Time-Series Forecasting and Analysis of COVID-19 Outbreak in Highly Populated Countries

A novel corona virus, COVID-19 is spreading across different countries in an alarming proportion and it has become a major threat to the existence of human community. With more than eight lakh death count within a very short span of seven months, this deadly virus has affected more than 24 million people across 213 countries and territories around the world. Time-series analysis, modeling and forecasting is an important research area that explores the hidden insights from larger set of time-bound data for arriving better decisions. In this work, data analysis on COVID-19 dataset is performed by comparing the top six populated countries in the world. The data used for the evaluation is taken for a time period from 22nd January 2020 to 23rd August 2020.A novel time-series forecasting approach based on Auto-regressive integrated moving average (ARIMA) model is also proposed. The results will help the researchers from medical and scientific community to gauge the trend of the disease spread and improvise containment strategies accordingly.

Design of a University Pandemic Response Decision Support System

The global effort to combat the COVID-19 pandemic has changed how people conduct their daily lives. Institutions of higher education have been greatly impacted by these changes and must find ways to adapt to this new environment. Universities are a unique case because they must control disease spread, while maintaining the same or similar quality of education. The University Pandemic Response Decision Support System (UPRDSS) is a system designed to help universities pick the most suitable method for instruction delivery when faced with any pandemic. Using George Mason University as a case study, the goal was to design a system that allows university administrations to make an educated operations decision. The UPRDSS achieves this by simulating the spread of disease, analyzing learning outcome data, and using a multi-attribute utility function to determine the most appropriate method of instruction that enables positive learning and health outcomes.

Viral dynamics and duration of PCR positivity of the SARS-CoV-2 Omicron variant

Background. The Omicron SARS-CoV-2 variant is responsible for a major wave of COVID-19, with record case counts reflecting high transmissibility and escape from prior immunity. Defining the time course of Omicron viral proliferation and clearance is crucial to inform isolation protocols aiming to minimize disease spread. Methods. We obtained longitudinal, quantitative RT-qPCR test results using combined anterior nares and oropharyngeal samples (n = 10,324) collected between July 5th, 2021 and January 10th, 2022 from the National Basketball Association's (NBA) occupational health program. We quantified the fraction of tests with PCR cycle threshold (Ct) values <30, chosen as a proxy for potential infectivity and antigen test positivity, on each day after first detection of suspected and confirmed Omicron infections, stratified by individuals detected under frequent testing protocols and those detected due to symptom onset or concern for contact with an infected individual. We quantified the duration of viral proliferation, clearance rate, and peak viral concentration for individuals with acute Omicron and Delta variant SARS-CoV-2 infections. Results. A total of 97 infections were confirmed or suspected to be from the Omicron variant and 107 from the Delta variant. Of 27 Omicron-infected individuals testing positive ≤1 day after a previous negative or inconclusive test, 52.0% (13/25) were PCR positive with Ct values <30 at day 5, 25.0% (6/24) at day 6, and 13.0% (3/23) on day 7 post detection. Of 70 Omicron-infected individuals detected ≥2 days after a previous negative or inconclusive test, 39.1% (25/64) were PCR positive with Ct values <30 at day 5, 33.3% (21/63) at day 6, and 22.2% (14/63) on day 7 post detection. Overall, Omicron infections featured a mean duration of 9.87 days (95% CI 8.83-10.9) relative to 10.9 days (95% CI 9.41-12.4) for Delta infections. The peak viral RNA based on Ct values was lower for Omicron infections than for Delta infections (Ct 23.3, 95% CI 22.4-24.3 for Omicron; Ct 20.5, 95% CI 19.2-21.8 for Delta) and the clearance phase was shorter for Omicron infections (5.35 days, 95% CI 4.78-6.00 for Omicron; 6.23 days, 95% CI 5.43-7.17 for Delta), though the rate of clearance was similar (3.13 Ct/day, 95% CI 2.75-3.54 for Omicron; 3.15 Ct/day, 95% CI 2.69-3.64 for Delta). Conclusions. While Omicron infections feature lower peak viral RNA and a shorter clearance phase than Delta infections on average, it is unclear to what extent these differences are attributable to more immunity in this largely vaccinated population or intrinsic characteristics of the Omicron variant. Further, these results suggest that Omicron's infectiousness may not be explained by higher viral load measured in the nose and mouth by RT-PCR. The substantial fraction of individuals with Ct values <30 at days 5 of infection, particularly in those detected due to symptom onset or concern for contact with an infected individual, underscores the heterogeneity of the infectious period, with implications for isolation policies.

Citrus Stubborn Disease: Current Insights on an Enigmatic Problem Prevailing in Citrus Orchards

Citrus stubborn was initially observed in California in 1915 and was later proven as a graft-transmissible disease in 1942. In the field, diseased citrus trees have compressed and stunted appearances, and yield poor-quality fruits with little market value. The disease is caused by Spiroplasma citri, a phloem-restricted pathogenic mollicute, which belongs to the Spiroplasmataceae family (Mollicutes). S. citri has the largest genome of any Mollicutes investigated, with a genome size of roughly 1780 Kbp. It is a helical, motile mollicute that lacks a cell wall and peptidoglycan. Several quick and sensitive molecular-based and immuno-enzymatic pathogen detection technologies are available. Infected weeds are the primary source of transmission to citrus, with only a minor percentage of transmission from infected citrus to citrus. Several phloem-feeding leafhopper species (Cicadellidae, Hemiptera) support the natural spread of S. citri in a persistent, propagative manner. S. citri-free buds are used in new orchard plantings and bud certification, and indexing initiatives have been launched. Further, a quarantine system for newly introduced types has been implemented to limit citrus stubborn disease (CSD). The present state of knowledge about CSD around the world is summarized in this overview, where recent advances in S. citri detection, characterization, control and eradication were highlighted to prevent or limit disease spread through the adoption of best practices.

Vaccination, politics and COVID-19 impacts

The development of safe and effective COVID-19 vaccines provides a clear path to bring the pandemic to an end. Vaccination rates, however, have been insufficient to prevent disease spread. A critical factor in so many people choosing not to be vaccinated is their political views. In this study, a path model is developed and tested to explore the impacts of political views on vaccination rates and COVID-19 cases and deaths per 100,000 residents in U.S. counties. The data strongly supported the model. In counties with a high percentage of Republican voters, vaccination rates were significantly lower and COVID-19 cases and deaths per 100,000 residents were much higher. Moving forward, it is critical to find ways to overcome political division and rebuild trust in science and health professionals.

American Medical Sociology and Health Problems in the Global South

COVID-19 has focused global attention on disease spread across borders. But how has research on infectious and noncommunicable disease figured into the sociological imagination historically, and to what degree has American medical sociology examined health problems beyond U.S. borders? Our 35-year content analysis of 2,588 presentations in the American Sociological Association’s (ASA) Section on Medical Sociology and 922 articles within the section’s official journal finds less than 15 percent of total research examined contexts outside the United States. Research on three infectious diseases in the top eight causes of death in low-income countries (diarrheal disease, malaria, and tuberculosis [TB]) and emerging diseases—Ebola, Middle East Respiratory Syndrome (MERS), and Severe Acute Respiratory Syndrome (SARS)—was nearly absent, as was research on major noncommunicable diseases. Human Immunodeficiency Virus/Acquired Immunodeficiency Syndrome (HIV/AIDS) received much more focus, although world regions hit hardest received scant attention. Interviews suggest a number of factors shape geographic foci of research, but this epistemic parochialism may ultimately impoverish sociological understanding of illness and disease.

Spatiotemporal Associations between Local Safety Level Index and COVID-19 Infection Risks across Capital Regions in South Korea

This study aims provide understanding of the local-level spatiotemporal evolution of COVID-19 spread across capital regions of South Korea during the second and third waves of the pandemic (August 2020~June 2021). To explain transmission, we rely upon the local safety level indices along with latent influences from the spatial alignment of municipalities and their serial (temporal) correlation. Utilizing a flexible hierarchical Bayesian model as an analytic operational framework, we exploit the modified BYM (BYM2) model with the Penalized Complexity (PC) priors to account for latent effects (unobserved heterogeneity). The outcome reveals that a municipality with higher population density is likely to have an elevated infection risk, whereas one with good preparedness for infectious disease tends to have a reduction in risk. Furthermore, we identify that including spatial and temporal correlations into the modeling framework significantly improves the performance and explanatory power, justifying our adoption of latent effects. Based on these findings, we present the dynamic evolution of COVID-19 across the Seoul Capital Area (SCA), which helps us verify unique patterns of disease spread as well as regions of elevated risk for further policy intervention and for supporting informed decision making for responding to infectious diseases.

Optimal experimental designs for estimating genetic and non-genetic effects underlying infectious disease transmission

BACKGROUND: Infectious disease spread in populations is controlled by individuals' susceptibility (propensity to acquire infection), infectivity (propensity to pass on infection to others) and recoverability (propensity to recover/die). Estimating the effects of genetic risk factors on these host epidemiological traits can help reduce disease spread through genetic control strategies. However, the effects of previously identified "disease resistance SNPs" on these epidemiological traits are usually unknown. Recent advances in computational statistics make it now possible to estimate the effects of single nucleotide polymorphisms (SNPs) on these traits from longitudinal epidemic data (e.g. infection and/or recovery times of individuals or diagnostic test results). However, little is known how to optimally design disease transmission experiments or field studies to maximise the precision at which pleiotropic SNP effects estimates for susceptibility, infectivity and recoverability can be estimated. RESULTS: We develop and validate analytical expressions for the precision of SNP effects estimates on the three host traits assuming a disease transmission experiment with one or more non-interacting contact groups. Maximising these leads to three distinct "experimental" designs, each specifying a different set of ideal SNP genotype compositions across groups: a) appropriate for a single contact-group, b) a multi-group design termed "pure", and c) a multi-group design termed "mixed", where "pure" and "mixed" refer to contact groups consisting of individuals with the same or different SNP genotypes, respectively. Precision estimates for susceptibility and recoverability were found to be less sensitive to the experimental design than infectivity. Data from multiple groups were found more informative about infectivity effects than from a single group containing the same number of individuals. Whilst the analytical expressions suggest that the multi-group pure and mixed designs estimate SNP effects with similar precision, the mixed design is preferable because it uses information from naturally occurring infections rather than those artificially induced. The same optimal design principles apply to estimating other categorical fixed effects, such as vaccinations status, helping to more effectively quantify their epidemiological impact. An online software tool SIRE-PC has been developed which calculates the precision of estimated substitution and dominance effects of a single SNP (or vaccine status) associated with all three traits depending on experimental design parameters. CONCLUSIONS: The developed methodology and software tool can be used to aid the design of disease transmission experiments for estimating the effect of individual SNPs and other categorical variables underlying host susceptibility, infectivity and recoverability.