scholarly journals Modeling the onset of symptoms of COVID-19: Effects of SARS-CoV-2 variant

2021 ◽  
Vol 17 (12) ◽  
pp. e1009629
Author(s):  
Joseph R. Larsen ◽  
Margaret R. Martin ◽  
John D. Martin ◽  
James B. Hicks ◽  
Peter Kuhn

Identifying order of symptom onset of infectious diseases might aid in differentiating symptomatic infections earlier in a population thereby enabling non-pharmaceutical interventions and reducing disease spread. Previously, we developed a mathematical model predicting the order of symptoms based on data from the initial outbreak of SARS-CoV-2 in China using symptom occurrence at diagnosis and found that the order of COVID-19 symptoms differed from that of other infectious diseases including influenza. Whether this order of COVID-19 symptoms holds in the USA under changing conditions is unclear. Here, we use modeling to predict the order of symptoms using data from both the initial outbreaks in China and in the USA. Whereas patients in China were more likely to have fever before cough and then nausea/vomiting before diarrhea, patients in the USA were more likely to have cough before fever and then diarrhea before nausea/vomiting. Given that the D614G SARS-CoV-2 variant that rapidly spread from Europe to predominate in the USA during the first wave of the outbreak was not present in the initial China outbreak, we hypothesized that this mutation might affect symptom order. Supporting this notion, we found that as SARS-CoV-2 in Japan shifted from the original Wuhan reference strain to the D614G variant, symptom order shifted to the USA pattern. Google Trends analyses supported these findings, while weather, age, and comorbidities did not affect our model’s predictions of symptom order. These findings indicate that symptom order can change with mutation in viral disease and raise the possibility that D614G variant is more transmissible because infected people are more likely to cough in public before being incapacitated with fever.

2021 ◽  
Vol 8 ◽  
Author(s):  
Heather Z. Brooks ◽  
Unchitta Kanjanasaratool ◽  
Yacoub H. Kureh ◽  
Mason A. Porter

The COVID-19 pandemic has led to significant changes in how people are currently living their lives. To determine how to best reduce the effects of the pandemic and start reopening communities, governments have used mathematical models of the spread of infectious diseases. In this article, we introduce a popular type of mathematical model of disease spread. We discuss how the results of analyzing mathematical models can influence government policies and human behavior, such as encouraging mask wearing and physical distancing to help slow the spread of a disease.


2020 ◽  
Vol 15 ◽  
pp. 34 ◽  
Author(s):  
Jayrold P. Arcede ◽  
Randy L. Caga-anan ◽  
Cheryl Q. Mentuda ◽  
Youcef Mammeri

A mathematical model was developed describing the dynamic of the COVID-19 virus over a population considering that the infected can either be symptomatic or not. The model was calibrated using data on the confirmed cases and death from several countries like France, Philippines, Italy, Spain, United Kingdom, China, and the USA. First, we derived the basic reproduction number, R0, and estimated the effective reproduction Reff for each country. Second, we were interested in the merits of interventions, either by distancing or by treatment. Results revealed that total and partial containment is effective in reducing the transmission. However, its duration may be long to eradicate the disease (104 days for France). By setting the end of containment as the day when hospital capacity is reached, numerical simulations showed that the duration can be reduced (up to only 39 days for France if the capacity is 1000 patients). Further, results pointed out that the effective reproduction number remains large after containment. Therefore, testing and isolation are necessary to stop the disease.


2021 ◽  
Vol 8 (1) ◽  
pp. 75-86
Author(s):  
Swati Tyagi ◽  
Shaifu Gupta ◽  
Syed Abbas ◽  
Krishna Pada Das ◽  
Baazaoui Riadh

Abstract In literature, various mathematical models have been developed to have a better insight into the transmission dynamics and control the spread of infectious diseases. Aiming to explore more about various aspects of infectious diseases, in this work, we propose conceptual mathematical model through a SEIQR (Susceptible-Exposed-Infected-Quarantined-Recovered) mathematical model and its control measurement. We establish the positivity and boundedness of the solutions. We also compute the basic reproduction number and investigate the stability of equilibria for its epidemiological relevance. To validate the model and estimate the parameters to predict the disease spread, we consider the special case for COVID-19 to study the real cases of infected cases from [2] for Russia and India. For better insight, in addition to mathematical model, a history based LSTM model is trained to learn temporal patterns in COVID-19 time series and predict future trends. In the end, the future predictions from mathematical model and the LSTM based model are compared to generate reliable results.


Author(s):  
В. Н. Мелькумов ◽  
Г. А. Кузнецова ◽  
А. В. Панин ◽  
М. Я. Панов

Постановка задачи. Процессы вентиляции оказывают значительное влияние на распространение инфекций, передающихся воздушно-капельным путем. Необходимо использовать воздухообмен для снижения вероятности распространения подобных инфекций. Результаты. С использованием схемы воздушной передачи инфекционных заболеваний Уэллса-Райли разработана математическая модель распространения коронавирусной инфекции в лечебном учреждении, состоящем из группы сообщающихся помещений, в которых постоянно находятся и перемещаются как здоровые, так и инфицированные люди. Математическая модель позволяет учитывать перемещение людей по помещениям и оседание квантов генерации инфекции больным человеком при циркуляции воздуха. Получено общее решение математической модели, позволяющее рассчитать концентрацию квантов генерации инфекции в помещениях при функционировании лечебного учреждения. Выводы. Разработанная математическая модель лечебного учреждения позволяет глубже понять возможности распространения коронавирусной инфекции и учесть эти риски при проектировании лечебных учреждений. Statement of the problem. Ventilation processes have a significant impact on the spread of airborne infections. It is necessary to use air exchange to reduce the likelihood of spreading such infections. Results. Using the Wells - Riley model of airborne transmission of infectious diseases, a mathematical model has been developed for the spread of the coronavirus infection in a medical institution consisting of a group of communicating rooms in which both healthy and infected people are constantly located and moved. The mathematical model makes it possible to take into account the movement of people around the premises and the settling of quanta of the generation of infection by a sick person when air moves. The general solution of the mathematical model is obtained, which allows one to calculate the concentration of quanta of generation of infection in the premises during the operation of a medical institution. Conclusions. The developed mathematical model of a medical institution provides a deeper understanding of the possibilities of the spread of the coronavirus infection and taking these risks into account when designing medical institutions.


Author(s):  
Keisuke Ejima ◽  
Kwang Su Kim ◽  
Christina Ludema ◽  
Ana I. Bento ◽  
Shoya Iwanami ◽  
...  

AbstractThe incubation period, or the time from infection to symptom onset of COVID-19 has been usually estimated using data collected through interviews with cases and their contacts. However, this estimation is influenced by uncertainty in recalling effort of exposure time. We propose a novel method that uses viral load data collected over time since hospitalization, hindcasting the timing of infection with a mathematical model for viral dynamics. As an example, we used the reported viral load data from multiple countries (Singapore, China, Germany, France, and Korea) and estimated the incubation period. The median, 2.5, and 97.5 percentiles of the incubation period were 5.23 days (95% CI: 5.17, 5.25), 3.29 days (3.25, 3.37), and 8.22 days (8.02, 8.46), respectively, which are comparable to the values estimated in previous studies. Using viral load to estimate the incubation period might be a useful approach especially when impractical to directly observe the infection event.


Author(s):  
V. N. Melkumov ◽  
G. A. Kuznetsova ◽  
A. V. Panin ◽  
M. Ya. Panov

Statement of the problem. Ventilation processes have a significant impact on the spread of airborne infections. It is necessary to use air exchange to reduce the likelihood of spreading such infections. Mathematical model. Using the Wells - Riley model of airborne transmission of infectious diseases, a mathematical model has been developed for the spread of coronavirus infection in a medical institution, consisting of a group of communicating rooms in which both healthy and infected people are constantly located and moved. The mathematical model makes it possible to take into account the movement of people around the premises and the settling of quanta of the generation of infection by a sick person when air moves. Results. The general solution of the mathematical model is obtained, which allows calculating the concentration of quanta of generation of infection in the premises during the operation of a medical institution.Conclusions. The developed mathematical model of a medical institution allows a deeper understanding of the possibilities of the spread of coronavirus infection and taking these risks into account when designing medical institutions.


2021 ◽  
Vol 10 (13) ◽  
pp. 2761
Author(s):  
Tatiana Filonets ◽  
Maxim Solovchuk ◽  
Wayne Gao ◽  
Tony Wen-Hann Sheu

Case isolation and contact tracing are two essential parts of control measures to prevent the spread of COVID-19, however, additional interventions, such as mask wearing, are required. Taiwan successfully contained local COVID-19 transmission after the initial imported cases in the country in early 2020 after applying the above-mentioned interventions. In order to explain the containment of the disease spread in Taiwan and understand the efficiency of different non-pharmaceutical interventions, a mathematical model has been developed. A stochastic model was implemented in order to estimate the effectiveness of mask wearing together with case isolation and contact tracing. We investigated different approaches towards mask usage, estimated the effect of the interventions on the basic reproduction number (R0), and simulated the possibility of controlling the outbreak. With the assumption that non-medical and medical masks have 20% and 50% efficiency, respectively, case isolation works on 100%, 70% of all people wear medical masks, and R0 = 2.5, there is almost 80% probability of outbreak control with 60% contact tracing, whereas for non-medical masks the highest probability is only about 20%. With a large proportion of infectiousness before the onset of symptoms (40%) and the presence of asymptomatic cases, the investigated interventions (isolation of cases, contact tracing, and mask wearing by all people), implemented on a high level, can help to control the disease spread. Superspreading events have also been included in our model in order to estimate their impact on the outbreak and to understand how restrictions on gathering and social distancing can help to control the outbreak. The obtained quantitative results are in agreement with the empirical COVID-19 data in Taiwan.


2021 ◽  
pp. bmjinnov-2020-000557
Author(s):  
Sharon Rikin ◽  
Eric J Epstein ◽  
Inessa Gendlina

IntroductionAt the early epicentre of the COVID-19 crisis in the USA, our institution saw a surge in the demand for inpatient consultations for areas impacted by COVID-19 (eg, infectious diseases, nephrology, palliative care) and shortages in personal protective equipment (PPE). We aimed to provide timely specialist input for consult requests during the COVID-19 pandemic by implementing an Inpatient eConsult Programme.MethodsWe used the reach, effectiveness, adoption, implementation and maintenance implementation science framework and run chart analysis to evaluate the reach, adoption and maintenance of the Inpatient eConsult Programme compared with traditional in-person consults. We solicited qualitative feedback from frontline physicians and specialists for programme improvements.ResultsDuring the study period, there were 46 available in-person consult orders and 21 new eConsult orders. At the peak of utilisation, 42% of all consult requests were eConsults, and by the end of the study period, utilisation fell to 20%. Qualitative feedback revealed subspecialties best suited for eConsults (infectious diseases, nephrology, haematology, endocrinology) and influenced improvements to the ordering workflow, documentation, billing and education regarding use.DiscussionWhen offered inpatient eConsult requests as an alternative to in-person consults in the context of a surge in patients with COVID-19, frontline physicians used eConsult requests and decreased use of in-person consults. As the demand for consults decreased and PPE shortages were no longer a major concern, eConsult utilisation decreased, revealing a preference for in-person consultations when possible.ConclusionsLessons learnt can be used to develop and implement inpatient eConsults to meet context-specific challenges at other institutions.


Life ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 601
Author(s):  
Caterina Aurilio ◽  
Pasquale Sansone ◽  
Antonella Paladini ◽  
Manlio Barbarisi ◽  
Francesco Coppolino ◽  
...  

Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, is often complicated by severe acute respiratory syndrome. The new coronavirus outbreak started in China in December 2019 and rapidly spread around the world. The high diffusibility of the virus was the reason for the outbreak of the pandemic viral disease, reaching more than 100 million infected people globally by the first three months of 2021. In the various treatments used up to now, the use of antimicrobial drugs for the management, especially of bacterial co-infections, is very frequent in patients admitted to intensive care. In addition, critically ill patients with SARS-CoV-2 infection are subjected to prolonged mechanical ventilation and other therapeutic procedures often responsible for developing hospital co-infections due to multidrug-resistant bacteria. Co-infections contribute to the increase in the morbidity–mortality of viral respiratory infections. We performed this study to review the recent articles published on the antibiotic bacterial resistance and viruses to predict risk factors of coronavirus disease 2019 and to assess the multidrug resistance in patients hospitalized in the COVID-19 area.


2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
N. H. Sweilam ◽  
S. M. Al-Mekhlafi ◽  
A. O. Albalawi ◽  
D. Baleanu

Abstract In this paper, a novel coronavirus (2019-nCov) mathematical model with modified parameters is presented. This model consists of six nonlinear fractional order differential equations. Optimal control of the suggested model is the main objective of this work. Two control variables are presented in this model to minimize the population number of infected and asymptotically infected people. Necessary optimality conditions are derived. The Grünwald–Letnikov nonstandard weighted average finite difference method is constructed for simulating the proposed optimal control system. The stability of the proposed method is proved. In order to validate the theoretical results, numerical simulations and comparative studies are given.


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