Biomaterial Database

Model-based forecasting for Canadian COVID-19 data. 2021

Li-Pang Chen, and Qihuang Zhang, and Grace Y Yi, and Wenqing He

Department of Statistical and Actuarial Sciences, University of Western Ontario, London, Ontario, Canada.

Since March 11, 2020 when the World Health Organization (WHO) declared the COVID-19 pandemic, the number of infected cases, the number of deaths, and the number of affected countries have climbed rapidly. To understand the impact of COVID-19 on public health, many studies have been conducted for various countries. To complement the available work, in this article we examine Canadian COVID-19 data for the period of March 18, 2020 to August 16, 2020 with the aim to forecast the dynamic trend in a short term. We focus our attention on Canadian data and analyze the four provinces, Ontario, Alberta, British Columbia, and Quebec, which have the most severe situations in Canada. To build predictive models and conduct prediction, we employ three models, smooth transition autoregressive (STAR) models, neural network (NN) models, and susceptible-infected-removed (SIR) models, to fit time series data of confirmed cases in the four provinces separately. In comparison, we also analyze the data of daily infections in two states of USA, Texas and New York state, for the period of March 18, 2020 to August 16, 2020. We emphasize that different models make different assumptions which are basically difficult to validate. Yet invoking different models allows us to examine the data from different angles, thus, helping reveal the underlying trajectory of the development of COVID-19 in Canada. The examinations of the data dated from March 18, 2020 to August 11, 2020 show that the STAR, NN, and SIR models may output different results, though the differences are small in some cases. Prediction over a short term period incurs smaller prediction variability than over a long term period, as expected. The NN method tends to outperform other two methods. All the methods forecast an upward trend in all the four Canadian provinces for the period of August 12, 2020 to August 23, 2020, though the degree varies from method to method. This research offers model-based insights into the pandemic evolvement in Canada.

UI	MeSH Term	Description	Entries
D009026	Mortality	All deaths reported in a given population.	CFR Case Fatality Rate,Crude Death Rate,Crude Mortality Rate,Death Rate,Age Specific Death Rate,Age-Specific Death Rate,Case Fatality Rate,Decline, Mortality,Determinants, Mortality,Differential Mortality,Excess Mortality,Mortality Decline,Mortality Determinants,Mortality Rate,Mortality, Differential,Mortality, Excess,Age-Specific Death Rates,Case Fatality Rates,Crude Death Rates,Crude Mortality Rates,Death Rate, Age-Specific,Death Rate, Crude,Death Rates,Determinant, Mortality,Differential Mortalities,Excess Mortalities,Mortalities,Mortality Declines,Mortality Determinant,Mortality Rate, Crude,Mortality Rates,Rate, Age-Specific Death,Rate, Case Fatality,Rate, Crude Death,Rate, Crude Mortality,Rate, Death,Rate, Mortality,Rates, Case Fatality
D002170	Canada	The largest country in North America, comprising 10 provinces and three territories. Its capital is Ottawa.
D003710	Demography	Statistical interpretation and description of a population with reference to distribution, composition, or structure.	Demographer,Demographic,Demographic and Health Survey,Population Distribution,Accounting, Demographic,Analyses, Demographic,Analyses, Multiregional,Analysis, Period,Brass Technic,Brass Technique,Demographers,Demographic Accounting,Demographic Analysis,Demographic Factor,Demographic Factors,Demographic Impact,Demographic Impacts,Demographic Survey,Demographic Surveys,Demographic and Health Surveys,Demographics,Demography, Historical,Demography, Prehistoric,Factor, Demographic,Factors, Demographic,Family Reconstitution,Historical Demography,Impact, Demographic,Impacts, Demographic,Multiregional Analysis,Period Analysis,Population Spatial Distribution,Prehistoric Demography,Reverse Survival Method,Stable Population Method,Survey, Demographic,Surveys, Demographic,Analyses, Period,Analysis, Demographic,Analysis, Multiregional,Demographic Analyses,Demographies, Historical,Demographies, Prehistoric,Distribution, Population,Distribution, Population Spatial,Distributions, Population,Distributions, Population Spatial,Family Reconstitutions,Historical Demographies,Method, Reverse Survival,Method, Stable Population,Methods, Reverse Survival,Methods, Stable Population,Multiregional Analyses,Period Analyses,Population Distributions,Population Methods, Stable,Population Spatial Distributions,Prehistoric Demographies,Reconstitution, Family,Reconstitutions, Family,Reverse Survival Methods,Spatial Distribution, Population,Spatial Distributions, Population,Stable Population Methods,Technic, Brass,Technique, Brass
D006801	Humans	Members of the species Homo sapiens.	Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000086382	COVID-19	A viral disorder generally characterized by high FEVER; COUGH; DYSPNEA; CHILLS; PERSISTENT TREMOR; MUSCLE PAIN; HEADACHE; SORE THROAT; a new loss of taste and/or smell (see AGEUSIA and ANOSMIA) and other symptoms of a VIRAL PNEUMONIA. In severe cases, a myriad of coagulopathy associated symptoms often correlating with COVID-19 severity is seen (e.g., BLOOD COAGULATION; THROMBOSIS; ACUTE RESPIRATORY DISTRESS SYNDROME; SEIZURES; HEART ATTACK; STROKE; multiple CEREBRAL INFARCTIONS; KIDNEY FAILURE; catastrophic ANTIPHOSPHOLIPID ANTIBODY SYNDROME and/or DISSEMINATED INTRAVASCULAR COAGULATION). In younger patients, rare inflammatory syndromes are sometimes associated with COVID-19 (e.g., atypical KAWASAKI SYNDROME; TOXIC SHOCK SYNDROME; pediatric multisystem inflammatory disease; and CYTOKINE STORM SYNDROME). A coronavirus, SARS-CoV-2, in the genus BETACORONAVIRUS is the causative agent.	2019 Novel Coronavirus Disease,2019 Novel Coronavirus Infection,2019-nCoV Disease,2019-nCoV Infection,COVID-19 Pandemic,COVID-19 Pandemics,COVID-19 Virus Disease,COVID-19 Virus Infection,Coronavirus Disease 2019,Coronavirus Disease-19,SARS Coronavirus 2 Infection,SARS-CoV-2 Infection,Severe Acute Respiratory Syndrome Coronavirus 2 Infection,COVID19,2019 nCoV Disease,2019 nCoV Infection,2019-nCoV Diseases,2019-nCoV Infections,COVID 19,COVID 19 Pandemic,COVID 19 Virus Disease,COVID 19 Virus Infection,COVID-19 Virus Diseases,COVID-19 Virus Infections,Coronavirus Disease 19,Disease 2019, Coronavirus,Disease, 2019-nCoV,Disease, COVID-19 Virus,Infection, 2019-nCoV,Infection, COVID-19 Virus,Infection, SARS-CoV-2,Pandemic, COVID-19,SARS CoV 2 Infection,SARS-CoV-2 Infections,Virus Disease, COVID-19,Virus Infection, COVID-19
D015233	Models, Statistical	Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.	Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model
D016571	Neural Networks, Computer	A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming.	Computational Neural Networks,Connectionist Models,Models, Neural Network,Neural Network Models,Neural Networks (Computer),Perceptrons,Computational Neural Network,Computer Neural Network,Computer Neural Networks,Connectionist Model,Model, Connectionist,Model, Neural Network,Models, Connectionist,Network Model, Neural,Network Models, Neural,Network, Computational Neural,Network, Computer Neural,Network, Neural (Computer),Networks, Computational Neural,Networks, Computer Neural,Networks, Neural (Computer),Neural Network (Computer),Neural Network Model,Neural Network, Computational,Neural Network, Computer,Neural Networks, Computational,Perceptron