scholarly journals Assessing the Spatio-temporal Spread of COVID-19 via Compartmental Models with Diffusion in Italy, USA, and Brazil

2021 ◽  
Author(s):  
Malú Grave ◽  
Alex Viguerie ◽  
Gabriel F. Barros ◽  
Alessandro Reali ◽  
Alvaro L. G. A. Coutinho
Keyword(s):  
Differential Equation ◽  
Epidemiological Data ◽  
Compartmental Models ◽  
Modeling Framework ◽  
Differential Equation Models ◽  
The World ◽  
Spatio Temporal ◽  
Qualitative Characteristics ◽  
Good Agreement ◽  
Temporal Spread

AbstractThe outbreak of COVID-19 in 2020 has led to a surge in interest in the mathematical modeling of infectious diseases. Such models are usually defined as compartmental models, in which the population under study is divided into compartments based on qualitative characteristics, with different assumptions about the nature and rate of transfer across compartments. Though most commonly formulated as ordinary differential equation models, in which the compartments depend only on time, recent works have also focused on partial differential equation (PDE) models, incorporating the variation of an epidemic in space. Such research on PDE models within a Susceptible, Infected, Exposed, Recovered, and Deceased framework has led to promising results in reproducing COVID-19 contagion dynamics. In this paper, we assess the robustness of this modeling framework by considering different geometries over more extended periods than in other similar studies. We first validate our code by reproducing previously shown results for Lombardy, Italy. We then focus on the U.S. state of Georgia and on the Brazilian state of Rio de Janeiro, one of the most impacted areas in the world. Our results show good agreement with real-world epidemiological data in both time and space for all regions across major areas and across three different continents, suggesting that the modeling approach is both valid and robust.

scholarly journals Spatio-Temporal Spread Pattern of COVID-19 in Italy

Mathematics ◽  
2021 ◽  
Vol 9 (19) ◽  
pp. 2454
Author(s):  
Nicoletta D’Angelo ◽  
Antonino Abbruzzo ◽  
Giada Adelfio
Keyword(s):  
Temporal Evolution ◽  
Virus Infections ◽  
Mobility Patterns ◽  
Modeling Framework ◽  
Spatial Trend ◽  
Italian Government ◽  
Italian Provinces ◽  
Spread Pattern ◽  
Spatio Temporal ◽  
Temporal Spread

This paper investigates the spatio-temporal spread pattern of COVID-19 in Italy, during the first wave of infections, from February to October 2020. Disease mappings of the virus infections by using the Besag–York–Mollié model and some spatio-temporal extensions are provided. This modeling framework, which includes a temporal component, allows the studying of the time evolution of the spread pattern among the 107 Italian provinces. The focus is on the effect of citizens’ mobility patterns, represented here by the three distinct phases of the Italian virus first wave, identified by the Italian government, also characterized by the lockdown period. Results show the effectiveness of the lockdown action and an inhomogeneous spatial trend that characterizes the virus spread during the first wave. Furthermore, the results suggest that the temporal evolution of each province’s cases is independent of the temporal evolution of the other ones, meaning that the contagions and temporal trend may be caused by some province-specific aspects rather than by the subjects’ spatial movements.

scholarly journals A small number of early introductions seeded widespread transmission of SARS-CoV-2 in Québec, Canada

2021 ◽  
Author(s):  
Carmen Lía Murall ◽  
Eric Fournier ◽  
Jose Hector Galvez ◽  
Arnaud N’Guessan ◽  
Sarah J. Reiling ◽  
...  
Keyword(s):  
Epidemiological Data ◽  
Founder Effects ◽  
Canadian Province ◽  
Genomic Epidemiology ◽  
Spring Break ◽  
Health Measures ◽  
Travel Restrictions ◽  
Spatio Temporal ◽  
Pandemic Wave ◽  
Temporal Spread

AbstractUsing genomic epidemiology, we investigated the arrival of SARS-CoV-2 to Québec, the Canadian province most impacted by COVID-19, with >280,000 positive cases and >10,000 deaths in a population of 8.5 million as of March 1st, 2021. We report 2,921 high-quality SARS-CoV-2 genomes in the context of >12,000 publicly available genomes sampled globally over the first pandemic wave (up to June 1st, 2020). By combining phylogenetic and phylodynamic analyses with epidemiological data, we quantify the number of introduction events into Québec, identify their origins, and characterize the spatio-temporal spread of the virus. Conservatively, we estimated at least 500 independent introduction events, the majority of which happened from spring break until two weeks after the Canadian border closed for non-essential travel. Subsequent mass repatriations did not generate large transmission lineages (>50 cases), likely due to mandatory quarantine measures in place at the time. Consistent with common spring break and ‘snowbird’ destinations, most of the introductions were inferred to have originated from Europe via the Americas. Fewer than 100 viral introductions arrived during spring break, of which 5-10 led to the largest transmission lineages of the first wave (accounting for 36-58% of all sequenced infections). These successful viral transmission lineages dispersed widely across the province, consistent with founder effects and superspreading dynamics. Transmission lineage size was greatly reduced after March 11th, when a quarantine order for returning travelers was enacted. While this suggests the effectiveness of early public health measures, the biggest transmission lineages had already been ignited prior to this order. Combined, our results reinforce how, in the absence of tight travel restrictions or quarantine measures, fewer than 100 viral introductions in a week can ensure the establishment of extended transmission chains.

Profile likelihood-based analyses of infectious disease models

2018 ◽  
Vol 27 (7) ◽  
pp. 1979-1998 ◽  
Author(s):  
Christian Tönsing ◽  
Jens Timmer ◽  
Clemens Kreutz
Keyword(s):  
Infectious Disease ◽  
Differential Equation ◽  
Ordinary Differential Equation ◽  
Parameter Estimation ◽  
Profile Likelihood ◽  
Disease Models ◽  
Optimization Approach ◽  
Modeling Framework ◽  
Differential Equation Models ◽  
Infectious Disease Models

Ordinary differential equation models are frequently applied to describe the temporal evolution of epidemics. However, ordinary differential equation models are also utilized in other scientific fields. We summarize and transfer state-of-the art approaches from other fields like Systems Biology to infectious disease models. For this purpose, we use a simple SIR model with data from an influenza outbreak at an English boarding school in 1978 and a more complex model of a vector-borne disease with data from the Zika virus outbreak in Colombia in 2015–2016. Besides parameter estimation using a deterministic multistart optimization approach, a multitude of analyses based on the profile likelihood are presented comprising identifiability analysis and model reduction. The analyses were performed using the freely available modeling framework Data2Dynamics (data2dynamics.org) which has been awarded as best performing within the DREAM6 parameter estimation challenge and in the DREAM7 network reconstruction challenge.

Overview of Gene Regulatory Network Inference Based on Differential Equation Models

2020 ◽  
Vol 21 (11) ◽  
pp. 1054-1059
Author(s):  
Bin Yang ◽  
Yuehui Chen
Keyword(s):  
Differential Equation ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Network Inference ◽  
New Drugs ◽  
Gene Regulatory Network Inference ◽  
Ode Models ◽  
Differential Equation Models ◽  
Linear Ode ◽  
Gene Regulatory

: Reconstruction of gene regulatory networks (GRN) plays an important role in understanding the complexity, functionality and pathways of biological systems, which could support the design of new drugs for diseases. Because differential equation models are flexible androbust, these models have been utilized to identify biochemical reactions and gene regulatory networks. This paper investigates the differential equation models for reverse engineering gene regulatory networks. We introduce three kinds of differential equation models, including ordinary differential equation (ODE), time-delayed differential equation (TDDE) and stochastic differential equation (SDE). ODE models include linear ODE, nonlinear ODE and S-system model. We also discuss the evolutionary algorithms, which are utilized to search the optimal structures and parameters of differential equation models. This investigation could provide a comprehensive understanding of differential equation models, and lead to the discovery of novel differential equation models.

scholarly journals Climate drivers of plague epidemiology in British India, 1898–1949

2020 ◽  
Vol 287 (1928) ◽  
pp. 20200538
Author(s):  
Warren S. D. Tennant ◽  
Mike J. Tildesley ◽  
Simon E. F. Spencer ◽  
Matt J. Keeling
Keyword(s):  
Predictive Models ◽  
Control Measures ◽  
Climate Data ◽  
British India ◽  
Middle Income ◽  
Middle Income Countries ◽  
Complex Interactions ◽  
The World ◽  
Spatio Temporal

Plague, caused by Yersinia pestis infection, continues to threaten low- and middle-income countries throughout the world. The complex interactions between rodents and fleas with their respective environments challenge our understanding of human plague epidemiology. Historical long-term datasets of reported plague cases offer a unique opportunity to elucidate the effects of climate on plague outbreaks in detail. Here, we analyse monthly plague deaths and climate data from 25 provinces in British India from 1898 to 1949 to generate insights into the influence of temperature, rainfall and humidity on the occurrence, severity and timing of plague outbreaks. We find that moderate relative humidity levels of between 60% and 80% were strongly associated with outbreaks. Using wavelet analysis, we determine that the nationwide spread of plague was driven by changes in humidity, where, on average, a one-month delay in the onset of rising humidity translated into a one-month delay in the timing of plague outbreaks. This work can inform modern spatio-temporal predictive models for the disease and aid in the development of early-warning strategies for the deployment of prophylactic treatments and other control measures.

scholarly journals Cerebral venous thrombosis in a patient with Down syndrome and coronavirus disease 2019: a case report

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Henry Robayo-Amortegui ◽  
Natalia Valenzuela-Faccini ◽  
Cesar Quecano-Rosas ◽  
Darlyng Zabala-Muñoz ◽  
Michel Perez-Garzon
Keyword(s):  
Risk Factors ◽  
Down Syndrome ◽  
Case Report ◽  
Venous Thrombosis ◽  
Cerebral Venous Thrombosis ◽  
Epidemiological Data ◽  
Thromboembolic Disease ◽  
Hispanic Woman ◽  
Case Presentation ◽  
The World

Abstract Background The new coronavirus disease 2019 pandemic has spread throughout most of the world. Cerebral venous thrombosis is a rare thromboembolic disease that can present as an extrapulmonary complication in coronavirus disease 2019 infection. Case presentation We report the case of a Hispanic woman with Down syndrome who has coronavirus disease 2019 and presents as a complication extensive cerebral venous thrombosis. Conclusions Cerebral venous thrombosis is a rare thromboembolic disease that can present as an extrapulmonary complication in coronavirus disease 2019 infection. In the absence of clinical and epidemiological data, it is important to carry out further investigation of the risk factors and pathophysiological causes related to the development of cerebrovascular thrombotic events in patients with Down syndrome with coronavirus disease 2019 infection.

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