Dataset Information

Predicting the Trajectory of Any COVID19 Epidemic From the Best Straight Line.

ABSTRACT: A pipeline involving data acquisition, curation, carefully chosen graphs and mathematical models, allows analysis of COVID-19 outbreaks at 3,546 locations world-wide (all countries plus smaller administrative divisions with data available). Comparison of locations with over 50 deaths shows all outbreaks have a common feature: H(t) defined as log_e(X(t)/X(t-1)) decreases linearly on a log scale, where X(t) is the total number of Cases or Deaths on day, t (we use ln for log_e). The downward slopes vary by about a factor of three with time constants (1/slope) of between 1 and 3 weeks; this suggests it may be possible to predict when an outbreak will end. Is it possible to go beyond this and perform early prediction of the outcome in terms of the eventual plateau number of total confirmed cases or deaths? We test this hypothesis by showing that the trajectory of cases or deaths in any outbreak can be converted into a straight line. Specifically Y(t) ≡ -ln(ln(N / X(t)), is a straight line for the correct plateau value N, which is determined by a new method, Best-Line Fitting (BLF). BLF involves a straight-line facilitation extrapolation needed for prediction; it is blindingly fast and amenable to optimization. We find that in some locations that entire trajectory can be predicted early, whereas others take longer to follow this simple functional form. Fortunately, BLF distinguishes predictions that are likely to be correct in that they show a stable plateau of total cases or death (N value). We apply BLF to locations that seem close to a stable predicted N value and then forecast the outcome at some locations that are still growing wildly. Our accompanying web-site will be updated frequently and provide all graphs and data described here.

SUBMITTER: Levitt M

PROVIDER: S-EPMC7325180 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:The COVID19 pandemic has forced the world to be closed in a shell. It has affected large population worldwide, but studies regarding its effect on children very limited. The majority of the children, who may not be able to grasp the entire emergency, are at a bigger risk with other problems lurking behind the attack of SARS-CoV-2 virus. The risk of infection in children was 1.3%, 1.5%, and 1.7% of total confirmed COVID-19 cases in China, Italy and United States respectively which is less compared to 2003 epidemic of severe acute respiratory syndrome (SARS), when 5-7% of the positive cases were children, with no deaths reported while another recent multinational multicentric study from Europe which included 582 PCR (polymerase chain reaction) confirmed children of 0-18 year of age, provide deeper and generalize incite about clinical effects of COVID19 infection in children. According to this study 25% children have some pre-existing illness and 8% required ICU (intensive care unit) admission with 0.69% case fatality among all infected children. Common risk factor for serious illness as per this study are younger age, male sex and pre-existing underlying chronic medical condition. However, we need to be more concerned about possible implications of indirect and parallel psychosocial and mental health damage due to closure of schools, being in confinement and lack of peer interaction due to COVID19 related lockdown and other containment measures. The effects can range from mood swings, depression, anxiety symptoms to Post Traumatic Stress Disorder, while no meaningful impact on COVID19 related mortality reduction is evident with school closure measures. The objective of this paper is to look at both the positive & negative effects in children due to COVID19 related indirect effects following lockdown and other containment measures. There is a need to gear up in advance with psychological strategies to deal with it post the pandemic by involving all stakeholders (parents, teachers, paediatricians, psychologists, psychiatrists, psychiatric social workers, counsellors), proposing an integrated approach to help the children to overcome the pandemic aftermath.

Project description:Dedicated breast CT prototypes used in clinical investigations utilize single circular source trajectory and cone-beam geometry with flat-panel detectors that do not satisfy data-sufficiency conditions and could lead to cone beam artifacts. Hence, this work investigated the glandular dose characteristics of a circle-plus-line trajectory that fulfills data-sufficiency conditions for image reconstruction in dedicated breast CT.Monte Carlo-based computer simulations were performed using the GEANT4 toolkit and was validated with previously reported normalized glandular dose coefficients for one prototype breast CT system. Upon validation, Monte Carlo simulations were performed to determine the normalized glandular dose coefficients as a function of x-ray source position along the line scan. The source-to-axis of rotation distance and the source-to-detector distance were maintained constant at 65 and 100 cm, respectively, in all simulations. The ratio of the normalized glandular dose coefficient at each source position along the line scan to that for the circular scan, defined as relative normalized glandular dose coefficient (RD(g)N), was studied by varying the diameter of the breast at the chest wall, chest-wall to nipple distance, skin thickness, x-ray beam energy, and glandular fraction of the breast.The RD(g)N metric when stated as a function of source position along the line scan, relative to the maximum length of line scan needed for data sufficiency, was found to be minimally dependent on breast diameter, chest-wall to nipple distance, skin thickness, glandular fraction, and x-ray photon energy. This observation facilitates easy estimation of the average glandular dose of the line scan. Polynomial fit equations for computing the RD(g)N and hence the average glandular dose are provided.For a breast CT system that acquires 300-500 projections over 2π for the circular scan, the addition of a line trajectory with equal source spacing and constant x-ray beam quality (kVp and HVL) and mAs matched to the circular scan, will result in less than 0.18% increase in average glandular dose to the breast per projection along the line scan.

Dataset Information

Predicting the Trajectory of Any COVID19 Epidemic From the Best Straight Line.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets