scholarly journals Intra-county modeling of COVID-19 infection with human mobility: assessing spatial heterogeneity with business traffic, age and race

2020 ◽  
Author(s):  
Xiao Hou ◽  
Song Gao ◽  
Qin Li ◽  
Yuhao Kang ◽  
Nan Chen ◽  
...  
Keyword(s):  
Spatial Heterogeneity ◽  
Human Mobility ◽  
Large City ◽  
Machine Learning Techniques ◽  
Epidemic Modeling ◽  
Strong Response ◽  
Modeling Framework ◽  
Social Challenges ◽  
Novel Coronavirus ◽  
Global Threat

ABSTRACTThe novel coronavirus disease (COVID-19) pandemic is a global threat presenting health, economic and social challenges that continue to escalate. Meta-population epidemic modeling studies in the susceptible-exposed-infectious-removed (SEIR) style have played important roles in informing public health and shaping policy making to mitigate the spread of COVID-19. These models typically rely on a key assumption on the homogeneity of the population. This assumption certainly cannot be expected to hold true in real situations; various geographic, socioeconomic and cultural environments affect the behaviors that drive the spread of COVID-19 in different communities. What’s more, variation of intra-county environments creates spatial heterogeneity of transmission in different sub-regions. To address this issue, we develop a new human mobility flow-augmented stochastic SEIR-style epidemic modeling framework with the ability to distinguish different regions and their corresponding behavior. This new modeling framework is then combined with data assimilation and machine learning techniques to reconstruct the historical growth trajectories of COVID-19 confirmed cases in two counties in Wisconsin. The associations between the spread of COVID-19 and human mobility, business foot-traffic, race & ethnicity, and age-group are then investigated. The results reveal that in a college town (Dane County) the most important heterogeneity is spatial, while in a large city area (Milwaukee County) ethnic heterogeneity becomes more apparent. Scenario studies further indicate a strong response of the spread rate on various reopening policies, which suggests that policymakers may need to take these heterogeneities into account very carefully when designing policies for mitigating the spread of COVID-19 and reopening.

scholarly journals Intracounty modeling of COVID-19 infection with human mobility: Assessing spatial heterogeneity with business traffic, age, and race

2021 ◽  
Vol 118 (24) ◽  
pp. e2020524118
Author(s):  
Xiao Hou ◽  
Song Gao ◽  
Qin Li ◽  
Yuhao Kang ◽  
Nan Chen ◽  
...  
Keyword(s):  
Spatial Heterogeneity ◽  
Age Structure ◽  
Race And Ethnicity ◽  
Human Mobility ◽  
Large City ◽  
Machine Learning Techniques ◽  
Epidemic Modeling ◽  
Strong Response ◽  
Modeling Framework ◽  
Global Threat

The COVID-19 pandemic is a global threat presenting health, economic, and social challenges that continue to escalate. Metapopulation epidemic modeling studies in the susceptible–exposed–infectious–removed (SEIR) style have played important roles in informing public health policy making to mitigate the spread of COVID-19. These models typically rely on a key assumption on the homogeneity of the population. This assumption certainly cannot be expected to hold true in real situations; various geographic, socioeconomic, and cultural environments affect the behaviors that drive the spread of COVID-19 in different communities. What’s more, variation of intracounty environments creates spatial heterogeneity of transmission in different regions. To address this issue, we develop a human mobility flow-augmented stochastic SEIR-style epidemic modeling framework with the ability to distinguish different regions and their corresponding behaviors. This modeling framework is then combined with data assimilation and machine learning techniques to reconstruct the historical growth trajectories of COVID-19 confirmed cases in two counties in Wisconsin. The associations between the spread of COVID-19 and business foot traffic, race and ethnicity, and age structure are then investigated. The results reveal that, in a college town (Dane County), the most important heterogeneity is age structure, while, in a large city area (Milwaukee County), racial and ethnic heterogeneity becomes more apparent. Scenario studies further indicate a strong response of the spread rate to various reopening policies, which suggests that policy makers may need to take these heterogeneities into account very carefully when designing policies for mitigating the ongoing spread of COVID-19 and reopening.

scholarly journals Characterization of the SARS-CoV-2 coronavirus X4-like accessory protein

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Olanrewaju Ayodeji Durojaye ◽  
Nkwachukwu Oziamara Okoro ◽  
Arome Solomon Odiba
Keyword(s):  
Rapid Development ◽  
Protein A ◽  
The Novel ◽  
Quality Model ◽  
A Value ◽  
Model Protein ◽  
Novel Coronavirus ◽  
Global Threat

Abstract Background The novel coronavirus SARS-CoV-2 is currently a global threat to health and economies. Therapeutics and vaccines are in rapid development; however, none of these therapeutics are considered as absolute cure, and the potential to mutate makes it necessary to find therapeutics that target a highly conserved regions of the viral structure. Results In this study, we characterized an essential but poorly understood coronavirus accessory X4 protein, a core and stable component of the SARS-CoV family. Sequence analysis shows a conserved ~ 90% identity between the SARS-CoV-2 and previously characterized X4 protein in the database. QMEAN Z score of the model protein shows a value of around 0.5, within the acceptable range 0–1. A MolProbity score of 2.96 was obtained for the model protein and indicates a good quality model. The model has Ramachandran values of φ = − 57o and ψ = − 47o for α-helices and values of φ = − 130o and ψ = + 140o for twisted sheets. Conclusions The protein data obtained from this study provides robust information for further in vitro and in vivo experiment, targeted at devising therapeutics against the virus. Phylogenetic analysis further supports previous evidence that the SARS-CoV-2 is positioned with the SL-CoVZC45, BtRs-BetaCoV/YN2018B and the RS4231 Bat SARS-like corona viruses.

scholarly journals COVID-19 Pandemic Response Simulation in a Large City: Impact of Nonpharmaceutical Interventions on Reopening Society

2021 ◽  
pp. 0272989X2110030
Author(s):  
Serin Lee ◽  
Zelda B. Zabinsky ◽  
Judith N. Wasserheit ◽  
Stephen M. Kofsky ◽  
Shan Liu
Keyword(s):  
Large City ◽  
Face Mask ◽  
School Closure ◽  
Contact Tracing ◽  
The Novel ◽  
Agent Based ◽  
Social Distancing ◽  
Variable Compliance ◽  
Novel Coronavirus ◽  
Nonpharmaceutical Interventions

As the novel coronavirus (COVID-19) pandemic continues to expand, policymakers are striving to balance the combinations of nonpharmaceutical interventions (NPIs) to keep people safe and minimize social disruptions. We developed and calibrated an agent-based simulation to model COVID-19 outbreaks in the greater Seattle area. The model simulated NPIs, including social distancing, face mask use, school closure, testing, and contact tracing with variable compliance and effectiveness to identify optimal NPI combinations that can control the spread of the virus in a large urban area. Results highlight the importance of at least 75% face mask use to relax social distancing and school closure measures while keeping infections low. It is important to relax NPIs cautiously during vaccine rollout in 2021.

scholarly journals Twitter reveals human mobility dynamics during the COVID-19 pandemic

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0241957 ◽  
Author(s):  
Xiao Huang ◽  
Zhenlong Li ◽  
Yuqin Jiang ◽  
Xiaoming Li ◽  
Dwayne Porter
Keyword(s):  
Time Window ◽  
Human Mobility ◽  
Global Scale ◽  
Mitigation Measures ◽  
Mobility Patterns ◽  
Protection Measures ◽  
Social Challenges ◽  
Rapid Spread ◽  
Collaborative Efforts ◽  
State Scale

The current COVID-19 pandemic raises concerns worldwide, leading to serious health, economic, and social challenges. The rapid spread of the virus at a global scale highlights the need for a more harmonized, less privacy-concerning, easily accessible approach to monitoring the human mobility that has proven to be associated with viral transmission. In this study, we analyzed over 580 million tweets worldwide to see how global collaborative efforts in reducing human mobility are reflected from the user-generated information at the global, country, and U.S. state scale. Considering the multifaceted nature of mobility, we propose two types of distance: the single-day distance and the cross-day distance. To quantify the responsiveness in certain geographic regions, we further propose a mobility-based responsive index (MRI) that captures the overall degree of mobility changes within a time window. The results suggest that mobility patterns obtained from Twitter data are amenable to quantitatively reflect the mobility dynamics. Globally, the proposed two distances had greatly deviated from their baselines after March 11, 2020, when WHO declared COVID-19 as a pandemic. The considerably less periodicity after the declaration suggests that the protection measures have obviously affected people’s travel routines. The country scale comparisons reveal the discrepancies in responsiveness, evidenced by the contrasting mobility patterns in different epidemic phases. We find that the triggers of mobility changes correspond well with the national announcements of mitigation measures, proving that Twitter-based mobility implies the effectiveness of those measures. In the U.S., the influence of the COVID-19 pandemic on mobility is distinct. However, the impacts vary substantially among states.

scholarly journals Emerging Prevention and Treatment Strategies to Control COVID-19

Pathogens ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 501 ◽  
Author(s):  
Vipul K. Singh ◽  
Abhishek Mishra ◽  
Shubhra Singh ◽  
Premranjan Kumar ◽  
Manisha Singh ◽  
...  
Keyword(s):  
Early Stage ◽  
Treatment Options ◽  
Treatment Strategies ◽  
Clinical Development ◽  
Definitive Treatment ◽  
Prevention Therapy ◽  
Potential Alternative ◽  
Novel Coronavirus ◽  
Global Threat ◽  
New Vaccines

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), has now become a serious global threat after inflicting more than 8 million infections and 425,000 deaths in less than 6 months. Currently, no definitive treatment or prevention therapy exists for COVID-19. The unprecedented rise of this pandemic has rapidly fueled research efforts to discover and develop new vaccines and treatment strategies against this novel coronavirus. While hundreds of vaccines/therapeutics are still in the preclinical or early stage of clinical development, a few of them have shown promising results in controlling the infection. Here, in this review, we discuss the promising vaccines and treatment options for COVID-19, their challenges, and potential alternative strategies.

scholarly journals Monitoring the Impact of Air Quality on the COVID-19 Fatalities in Delhi, India: Using Machine Learning Techniques

2020 ◽  
pp. 1-8
Author(s):  
Jasleen Kaur Sethi ◽  
Mamta Mittal
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Air Pollutants ◽  
Machine Learning Techniques ◽  
Environmental Restoration ◽  
The Novel ◽  
Ozone Pollution ◽  
Learning Techniques ◽  
Novel Coronavirus ◽  
The Impact

ABSTRACT Objective: The focus of this study is to monitor the effect of lockdown on the various air pollutants due to the coronavirus disease (COVID-19) pandemic and identify the ones that affect COVID-19 fatalities so that measures to control the pollution could be enforced. Methods: Various machine learning techniques: Decision Trees, Linear Regression, and Random Forest have been applied to correlate air pollutants and COVID-19 fatalities in Delhi. Furthermore, a comparison between the concentration of various air pollutants and the air quality index during the lockdown period and last two years, 2018 and 2019, has been presented. Results: From the experimental work, it has been observed that the pollutants ozone and toluene have increased during the lockdown period. It has also been deduced that the pollutants that may impact the mortalities due to COVID-19 are ozone, NH3, NO2, and PM10. Conclusions: The novel coronavirus has led to environmental restoration due to lockdown. However, there is a need to impose measures to control ozone pollution, as there has been a significant increase in its concentration and it also impacts the COVID-19 mortality rate.

scholarly journals Forecasting Flu Activity in the United States: Benchmarking an Endemic-Epidemic Beta Model

2020 ◽  
Vol 17 (4) ◽  
pp. 1381 ◽  
Author(s):  
Junyi Lu ◽  
Sebastian Meyer
Keyword(s):  
The United States ◽  
Conditional Density ◽  
Multiple Models ◽  
Epidemic Modeling ◽  
Short Term ◽  
Modeling Framework ◽  
Seasonal Peak ◽  
Peak Intensity ◽  
Probabilistic Forecasts ◽  
Overall Performance

Accurate prediction of flu activity enables health officials to plan disease prevention and allocate treatment resources. A promising forecasting approach is to adapt the well-established endemic-epidemic modeling framework to time series of infectious disease proportions. Using U.S. influenza-like illness surveillance data over 18 seasons, we assessed probabilistic forecasts of this new beta autoregressive model with proper scoring rules. Other readily available forecasting tools were used for comparison, including Prophet, (S)ARIMA and kernel conditional density estimation (KCDE). Short-term flu activity was equally well predicted up to four weeks ahead by the beta model with four autoregressive lags and by KCDE; however, the beta model runs much faster. Non-dynamic Prophet scored worst. Relative performance differed for seasonal peak prediction. Prophet produced the best peak intensity forecasts in seasons with standard epidemic curves; otherwise, KCDE outperformed all other methods. Peak timing was best predicted by SARIMA, KCDE or the beta model, depending on the season. The best overall performance when predicting peak timing and intensity was achieved by KCDE. Only KCDE and naive historical forecasts consistently outperformed the equal-bin reference approach for all test seasons. We conclude that the endemic-epidemic beta model is a performant and easy-to-implement tool to forecast flu activity a few weeks ahead. Real-time forecasting of the seasonal peak, however, should consider outputs of multiple models simultaneously, weighing their usefulness as the season progresses.

scholarly journals A Secondary Spontaneous Pneumothorax in a Patient with COVID-19

2021 ◽  
Author(s):  
Abdullah M. Al Alawi ◽  
Zakariya Al Naamani
Keyword(s):  
Pulmonary Fibrosis ◽  
Spontaneous Pneumothorax ◽  
Left Lung ◽  
University Hospital ◽  
Chest Drain ◽  
Good Resolution ◽  
Sultan Qaboos University ◽  
First Case ◽  
Novel Coronavirus ◽  
Global Threat

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel coronavirus, represents an unprecedented global threat. We report a 78 - year-old man who presented to the Emergency Department at Sultan Qaboos University Hospital in June 2020 with a one-day history of right chest pain and severe breathlessness. The patient was an ex-smoker and known to have idiopathic pulmonary fibrosis (IPF) with 2 previous pneumothoraces in the left lung. On presentation, the patient was breathless with oxygen saturation of 90% on room air. Chest X-ray demonstrated bilateral lung infiltrates and right-sided pneumothorax. The patient was tested for SARS-CoV-2 and positive results were reported. The patient had a chest drain that resulted in good resolution of the pneumothorax. The patient's condition improved remarkably, and he was discharged after 17 days of hospitalization. This is the first case of pneumothorax reported in a patient infected with COVID-19 who was known to have underlying IPF. Keywords: Spontaneous Pneumothorax; Pulmonary Fibrosis; SARS Coronavirus; Oxygen, Pleurodesis, COVID-19; Pleurodesis; Steroid.

scholarly journals A Mini Review on Current Clinical and Research Findings for Children Suffering from COVID-19

2020 ◽  
Author(s):  
Xiao Li ◽  
Kun Qian ◽  
Ling-ling Xie ◽  
Xiu-juan Li ◽  
Min Cheng ◽  
...  
Keyword(s):  
Pediatric Patients ◽  
Secondary Infection ◽  
Age Groups ◽  
Research Direction ◽  
Cochrane Library ◽  
Common Symptom ◽  
Ct Examination ◽  
Short Period ◽  
Novel Coronavirus ◽  
Global Threat

AbstractBackgroundAs the novel coronavirus triggering COVID-19 has broken out in Wuhan, China and spread rapidly worldwide, it threatens the lives of thousands of people and poses a global threat on the economies of the entire world. However, infection with COVID-19 is currently rare in children.ObjectiveTo discuss the latest findings and research focus on the basis of characteristics of children confirmed with COVID-19, and provide an insight into the future treatment and research direction.MethodsWe searched the terms “COVID-19 OR coronavirus OR SARS-CoV-2” AND “Pediatric OR children” on PubMed, Embase, Cochrane library, NIH, CDC, and CNKI. The authors also reviewed the guidelines published on Chinese CDC and Chinese NHC.ResultsWe included 25 published literature references related to the epidemiology, clinical manifestation, accessary examination, treatment, and prognosis of pediatric patients with COVID-19.ConclusionThe numbers of children with COVID-19 pneumonia infection are small, and most of them come from family aggregation. Symptoms are mainly mild or even asymptomatic, which allow children to be a risk factor for transmission. Thus, strict epidemiological history screening is needed for early diagnosis and segregation. This holds especially for infants, who are more susceptible to infection than other age groups in pediatric age, but have most likely subtle and unspecific symptoms. They need to be paid more attention to. CT examination is a necessity for screening the suspected cases, because most of the pediatric patients are mild cases, and plain chest X-ray do not usually show the lesions or the detailed features. Therefore, early chest CT examination combined with pathogenic detection is a recommended clinical diagnosis scheme in children. The risk factors which may suggest severe or critical progress for children are: Fast respiratory rate and/or; lethargy and drowsiness mental state and/or; lactate progressively increasing and/or; imaging showed bilateral or multi lobed infiltration, pleural effusion or rapidly expending of lesions in a short period of time and/or; less than 3 months old or those who underly diseases. For those critical pediatric patients with positive SARS-CoV-2 diagnosis, polypnea may be the most common symptom. For treatment, the elevated PCT seen in children in contrast to adults suggests that the underlying coinfection/secondary infection may be more common in pediatric patients and appropriate antibacterial treatment should be considered. Once cytokine storm is found in these patients, anti-autoimmune or blood-purifying therapy should be given in time. Furthermore, effective isolation measures and appropriate psychological comfort need to be provided timely.

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