基于Prophet与ARIMA模型的四川省COVID-19累计确诊病例预测研究

目的 探讨Prophet与ARIMA模型2种预测方法在四川省COVID-19累计确诊病例的预测价值。方法 通过四川省卫生健康委员会官网收集2022年1月1日至4月8日四川省COVID-19累计确诊病例，分别采用ARIMA模型与Prophet模型进行建模预测，采用MAE、MAPE、RMSE等3个指标评价预测效果。结果 ARIMA(0,2,1)为最优模型，且模型参数差异有统计学意义（P<0.001），模型的残差为白噪声序列（P=0.095）；建立了包括趋势、节假日、周、日成分的Prophet模型；Prophet模型在训练集和测试集的MAE、MAPE和RMSE值均小于ARIMA(0,2,1)模型。结论 Prophet模型能够较好的预测四川省COVID-19累计确诊病例，在传染病领域具有较好的推广应用价值。     

ARIMA与SVM组合模型在新型冠状病毒肺炎预测中的应用

目的 基于差分自回归移动平均模型(ARIMA)通过引入支持向量机(SVM)方法,构建一个组合模型对新型冠状病毒肺炎(COVID-19)的发病趋势进行预测。方法 应用ARIMA模型对江苏省2020年1月22日-2月18日每日新增确诊病例数据中线性部分进行预测,捕捉时间序列数据的线性变化趋势,采用SVM对数据的非线性变化趋势进行预测,通过平均绝对误差(MAE)、均方误差(MSE)和平均绝对百分比误差(MAPE)评估两种组合模型的预测结果,比较模型的优劣。结果 在模型的拟合阶段,与单一ARIMA模型和SVM模型相比,ARIMA-SVM组合模型对COVID-19发病预测的MSE、MAE和MAPE均最小,分别为0.004、0.055和0.004;在模型的预测阶段,MSE、MAE和MAPE分别为7.811、2.730和0.764,在3个模型中也均是最小的。结论 与单ARIMA或SVM相比,ARIMA-SVM组合模型对COVID-19发病趋势具有更高预测精度。     

ARIMA模型与指数平滑法预测门诊量效果比较

目的比较差分自回归移动平均(ARIMA)模型与指数平滑法对医院门诊量的预测效果。方法利用扬州市某综合性三甲医院2010—2016年门诊量数据分别拟合ARIMA模型和指数平滑模型,以2017年该院门诊量数据评价两种模型的预测效果。结果拟合最佳的ARIMA模型为ARIMA (2, 1, 0)(2, 1, 0)12,拟合的均方根误差(RMSE)、平均绝对百分比误差(MAPE)和平均绝对误差(MAE)分别为5 062.47、 2.83%和3 473.96;对2017年门诊量预测的RMSE、 MAPE和MAE分别为8 243.26、 4.42%和6 084.00。拟合最佳的指数平滑模型为Holt-Winters加法指数平滑模型,拟合的RMSE、 MAPE和MAE分别为4 605.15、 2.79%和3 296.90;对2017年门诊量预测的RMSE、 MAPE和MAE分别为9 585.25、 5.50%和7 733.58。ARIMA (2, 1, 0)(2, 1, 0)12预测的3个误差指标值均小于Holt-Winters加法指数平滑模型。结论 ARIMA模型预测精度更高,可应用于医院每月门诊量的短期预测。     

三种模型应用于中国2011—2020年梅毒发病趋势分析的比较

  目的  比较差分自回归移动平均(autoregressive integrated moving average model, ARIMA)模型、非线性自回归神经网络(nonlinear autoregressive neural network, NAR)模型和长短期记忆神经网络(long-short term memory neural network, LSTM)模型应用于梅毒报告发病预测的效果, 优化疫情预测模型。  方法  以中国31个省、自治区、直辖市(未包含中国台湾、香港和澳门)2011-2019年梅毒月报告发病率为拟合集, 建立ARIMA模型、NAR模型和LSTM模型, 比较3种模型的拟合效果。以2020年梅毒月报告发病率为预测集, 比较3种模型的预测效果。  结果  ARIMA模型、NAR模型和LSTM模型拟合所得的平均绝对误差(mean absolute deviation, MAD)分别为0.013、0.011和0.002, 均方根误差(root mean squared error, RMSE)分别为0.015、0.018和0.003, 平均绝对百分比误差(mean absolute percentage error, MAPE)分别为4.266%、3.810%和0.692%;预测所得的MAD分别为0.064、0.049和0.044, RMSE分别为0.069、0.068和0.060, MAPE分别为23.310%、17.629%和18.575%。  结论  LSTM模型拟合预测梅毒报告发病率的效果更好, 为梅毒疫情的防控提供数据支撑。     

ARIMA模型和指数平滑法在我国北方流感样病例预测中的应用和比较

目的 比较ARIMA模型和指数平滑法对我国北方流感样病例的预测效果,为流感防控提供科学依据。方法 利用我国北方2012年第1周—2018年第17周的每周流感样病例比例数据拟合建立ARIMA模型和指数平滑模型,预测 2018年第18周—2019年第17周的流感样病例比例,对预测值与实际值进行比较。结果 ARIMA最优模型为ARIMA(0,1,1)(2,1,0)₅₂,预测的均方根误差(root mean square error,RMSE)、平均绝对百分比误差(mean absolute percentage error,MAPE)和平均绝对误差(mean absolute error,MAE)分别为0.57%、8.98%、0.34%;指数平滑法的最优模型为简单季节性模型,预测的均方根误差(RMSE)、平均绝对百分比误差(MAPE)和平均绝对误差(MAE)分别为0.83%、15.24%、0.55%。结论 ARIMA(0,1,1)(2,1,0)₅₂模型预测精度更高,可用于我国北方流感样病例的短期预测。     

ARIMA模型和Holt-Winters指数平滑法在自杀死亡中的预测效果比较

目的 探讨比较自回归求和滑动平均(autoregressive integrated moving average,ARIMA)模型和Holt-Winters指数平滑法在自杀死亡预测中的应用。 方法 利用河北省2014年1月—2018年6月自杀月度死亡资料分别建立ARIMA模型和Holt-Winters指数平滑模型,对2018年7—12月自杀月度死亡例数进行预测,并与实际死亡人数进行验证比较,然后根据2个模型的均方根误差(root mean square error,RMSE)、平均绝对误差(mean absolute error,MAE)以及平均绝对百分比误差(mean absolute percentage error,MAPE)评价模型的预测效果。 结果 2014—2018年河北省累计报告自杀死亡人数2 882例,自杀死亡水平整体呈现下降趋势,构建的ARIMA最佳模型是ARIMA(0,1,1)(1,1,0)₁₂,预测结果的RMSE、MAE和MAPE分别为5.99、4.67和9.80%;Holt-Winters指数平滑法最佳拟合模型是乘法模型,预测结果的RMSE、MAE和MAPE分别为6.03、5.17和11.44%。 结论 ARIMA模型预测效果优于Holt-Winters指数平滑法,更适用于自杀死亡趋势的短期预测。     

时变SEIQDR和ARIMA模型在上海市COVID-19预测中的应用和比较

目的 根据时变易感者-潜伏者-感染者-隔离者-死亡者-康复者(susceptible-exposed-infected-quarantined-dead-removed, SEIQDR)模型和差分自回归移动平均(autoregressive integrated moving average, ARIMA)模型,针对上海市Omicron感染数据选择适合上海市疫情判断的预测模型。方法 选用2022年3月1日―4月20日上海市COVID-19新增阳性感染者的数据进行拟合,选用2022年4月21日―5月30日的数据评估模型的预测效果。分别构建时变SEIQDR模型与ARIMA模型,通过比较决定系数(coefficient of determination, R²)、平均绝对误差(mean absolute error, MAE)和均方根误差(root mean squared error, RMSE)的大小评价模型的拟合及预测效果。结果 时变SEIQDR模型和ARIMA模型的拟合效果均较优,R²分别为0.990和0.984。2个模型5 d的预测效果均...     

自回归求和滑动平均(ARIMA)模型在全球新型冠状病毒肺炎发病人数预测中的应用

  目的   应用自回归求和滑动平均(autoregressive integrated moving average, ARIMA)模型对全球新型冠状病毒肺炎(coronavirus disease 2019, COVID-19)发病人数进行预测, 为各国提出的防控策略与措施提供参考和评价依据。   方法   收集2020年2月22日-3月19日各国(意大利、西班牙、德国、法国等)COVID-19每日累计确诊人数, 用SPSS 17.0和R 3.6.1软件拟合ARIMA模型, 对5日前数据进行回带评价拟合效果, 同时利用该模型预测各国后10日数据。   结果   ARIMA模型预测值和实际值动态趋势基本一致, 实际值在预测值的95% CI内。   结论   ARIMA模型能够较好的对全球COVID-19发病人数进行预测, 在指导疫情防控方面有实际意义。     

Elman神经网络与ARIMA模型对甲肝月发病率的预测研究

目的 两模型对甲肝月发病率数据进行拟合预测,比较最优模型。 方法 通过软件实现ARIMA模型和Elman神经网络对甲肝发病率进行拟合,并对2017年月发病率进行仿真。 结果 两模型拟合预测效果较好,ARIMA模型平均绝对误差(mean absolute error,MAE)、均方根误差(root-mean-square error,RMSE)、平均绝对百分比误差(mean absolute percentage error,MAPE)分别为0.013、0.002 9、9.29;Elman神经网络MAE、RMSE、MAPE分别为0.012、0.000 22、8.695。Elman神经网络预测结果优于ARIMA模型。 结论 两模型均能够拟合预测甲肝月发病率,Elman神经网络拟合预测效果更好。     

基于指数平滑模型的湖北省新冠肺炎疫情预测分析

目的分析和预测湖北省新冠肺炎(COVID-19)疫情变化趋势。方法采用平滑指数模型对累计确诊病例数、累计治愈出院病例数、累计死亡病例数、重症病例数及危重症病例数进行拟合和预测。结果湖北省COVID-19疫情逐渐得到缓解,在2月18日进入快速“缓解期”后,3月21日进入慢速“缓解期”。采用指数平滑模型获得的拟合值与实际值的趋势基本吻合,模型拟合较好,预测结果表明在4月2日现存确诊病例数将减少至1000例以内,且主要为重症和危重症病例。结论湖北省COVID-19疫情的防控措施是有效的,指数平滑法拟合效果较好,可用于COVID-19的疫情预测。     

The impact of COVID-19 on routine pediatric vaccination delivery in Brazil

IntroductionChildhood vaccination rates have decreased significantly during the COVID-19 pandemic. The Brazilian immunization program, Programa Nacional de Imunização (PNI), is a model effort, achieving immunization rates comparable to high-income countries. This study aimed to evaluate the impact of the COVID-19 pandemic in pediatric vaccinations administered by the PNI, as a proxy of adherence to vaccinations during 2020.MethodsData on the number of vaccines administered to children under 10 years of age nationally and in each of Brazil’s five regions were extracted from Brazil’s federal health delivery database. Population adjusted monthly vaccination rates from 2015 through 2019 were determined, and autoregressive integrated moving average (ARIMA) models were used to forecast expected vaccinated rates in 2020. We compared the forecasts to reported vaccine administrations to assess adequacy of pediatric vaccine delivery during the COVID-19 pandemic.ResultsFrom January 2015 to February 2020, the average rate of vaccine administration to children was 53.4 per 100,000. After February 2020, this rate decreased to 50.4, a 9.4% drop compared to 2019 and fell outside of forecasted ranges in December 2020. In Brazil's poorest region, the North, vaccine delivery fell outside of the forecasted ranges earlier in 2020 but subsequently rebounded, meeting expected targets by the end of 2020. However, in Brazil's wealthiest South and Southeast regions, initial vaccine delivery fell and remained well below forecasted rates through the end of 2020.ConclusionIn Brazil, despite a model national pediatric vaccination program with an over 95% national coverage, vaccination rates decreased during the COVID-19 pandemic. Coordinated governmental efforts have ameliorated some of the decrease, but more efforts are needed to ensure continued protection from preventable communicable diseases for children globally.     

基于SVEPIUHDR传染病动力学模型的新型冠状病毒肺炎疫情趋势分析

目的 对美国和英国新型冠状病毒肺炎（COVID-19）疫情的发展趋势进行模型拟合和预测,对疫苗接种的效果进行初步分析。方法 在SEIR模型基础上,增加症状前感染者、隔离措施及疫苗接种等要素,建立SVEPIUHDR模型。利用公开发布的数据建模,分别将美国2020年11月6日至2021年1月31日和英国2020年11月23日至2021年1月31日的数据进行拟合,2021年2月1日至4月1日的疫情数据评估预测效果,使用R 4.0.3软件进行分析,并预测在疫苗不同接种率下每日新增病例数的变化。结果 SVEPIUHDR模型对美国和英国的累计确诊病例数的拟合及预测平均偏差均<5%。按计划接种疫苗后,预计美国2021年4月COVID-19累计确诊人数达31 864 970人,若未接种疫苗,累积确诊人数达35 317 082人,相差345余万人。英国按计划接种疫苗后预计4月初累积确诊人数达4 195 538人,若未接种疫苗情况下累积确诊人数达4 268 786人,相差7万余人。结论 SVEPIUHDR模型对美、英两国COVID-19疫情的预测效果较好。     

Forecasting COVID-19 Hospital Census: A Multivariate Time-Series Model Based on Local Infection Incidence

BackgroundCOVID-19 has been one of the most serious global health crises in world history. During the pandemic, health care systems require accurate forecasts for key resources to guide preparation for patient surges. Forecasting the COVID-19 hospital census is among the most important planning decisions to ensure adequate staffing, number of beds, intensive care units, and vital equipment.ObjectiveThe goal of this study was to explore the potential utility of local COVID-19 infection incidence data in developing a forecasting model for the COVID-19 hospital census.MethodsThe study data comprised aggregated daily COVID-19 hospital census data across 11 Atrium Health hospitals plus a virtual hospital in the greater Charlotte metropolitan area of North Carolina, as well as the total daily infection incidence across the same region during the May 15 to December 5, 2020, period. Cross-correlations between hospital census and local infection incidence lagging up to 21 days were computed. A multivariate time-series framework, called the vector error correction model (VECM), was used to simultaneously incorporate both time series and account for their possible long-run relationship. Hypothesis tests and model diagnostics were performed to test for the long-run relationship and examine model goodness of fit. The 7-days-ahead forecast performance was measured by mean absolute percentage error (MAPE), with time-series cross-validation. The forecast performance was also compared with an autoregressive integrated moving average (ARIMA) model in the same cross-validation time frame. Based on different scenarios of the pandemic, the fitted model was leveraged to produce 60-days-ahead forecasts.ResultsThe cross-correlations were uniformly high, falling between 0.7 and 0.8. There was sufficient evidence that the two time series have a stable long-run relationship at the .01 significance level. The model had very good fit to the data. The out-of-sample MAPE had a median of 5.9% and a 95th percentile of 13.4%. In comparison, the MAPE of the ARIMA had a median of 6.6% and a 95th percentile of 14.3%. Scenario-based 60-days-ahead forecasts exhibited concave trajectories with peaks lagging 2 to 3 weeks later than the peak infection incidence. In the worst-case scenario, the COVID-19 hospital census can reach a peak over 3 times greater than the peak observed during the second wave.ConclusionsWhen used in the VECM framework, the local COVID-19 infection incidence can be an effective leading indicator to predict the COVID-19 hospital census. The VECM model had a very good 7-days-ahead forecast performance and outperformed the traditional ARIMA model. Leveraging the relationship between the two time series, the model can produce realistic 60-days-ahead scenario-based projections, which can inform health care systems about the peak timing and volume of the hospital census for long-term planning purposes.     

COVID-19疫情对北京市公立医院住院服务量的影响及其恢复情况分析

目的：了解COVID-19疫情对北京市公立医院住院服务的影响,为卫生健康管理决策提供参考。方法：采用描述性方法分析2020年上半年北京市公立医院出院量的变化情况,并利用ARIMA乘积季节模型假设未发生COVID-19情况下对2020—2021年的出院量进行预测,通过比较其与实际状态下出院量的差异,评估COVID-19疫情对住院服务的潜在影响。结果：2020年1—6月出院总量较2019年同期减少69.1万人次（48.0%）;外地患者出院量较去年同期下降28.2万人次（65.5%）,其中循环系统疾病与恶性肿瘤患者出院人次数下降最多。ARIMA模型结果显示,2020年1—6月实际出院总量与外地患者出院量较预测值分别减少77.3万人次（50.8%）与33.2万人次（69.1%）,住院服务的恢复压力不断增加。结论：疫情后期北京市住院医疗服务秩序的恢复将面临更为复杂的挑战,建议卫生健康部门充分利用互联网与现代化信息技术手段,在做好常态化防控的同时,重点做好外地患者与重点专科医院的住院需求应对。     

基于SARIMA - SVR模型的喀什地区流行性腮腺炎的流行趋势预测

目的 利用季节性自回归滑动平均模型（seasonal autoregressive integrated moving average，SARIMA）、支持向量回归模型（support vector regression， SVR）对喀什地区流行性腮腺炎( mumps)的月发病数进行预测，在上述两模型的基础上建立SARIMA - SVR组合模型，提高预测的精准度，为控制新疆喀什地区2021年流腮传播趋势提供科学预测。方法 以喀什地区2005年1月—2017年12月的流腮月发病数据为训练集，进行数据的拟合以及预测模型的训练，分别建立SARIMA、SVR、SARIMA - SVR组合模型。对2018年1月—2020年12月的流腮月发病数进行预测，并与实际值相比较，采用均方根误差(root mean square error，RMSE)衡量模型预测性能。结果 ARIMA(2，1，1)(0，0，1)12为最优的SARIMA模型，建立的SARIMA、SVR、SARIMA - SVR组合模型预测2018年1月—2020年12月的喀什地区流腮月发病数的RMSE分别为:9.611、9.545、3.427。结论 SARIMA - SVR组合模型对喀什地区流腮的预测精度高于单一预测模型，故选取该模型建立方式，利用2005年1月—2020年12月的喀什地区流腮月发病数据预测该地区2021年的月发病数。     

The Influence of Social Distancing on COVID-19 Mortality in US Counties: Cross-sectional Study

BackgroundPrevious studies on the impact of social distancing on COVID-19 mortality in the United States have predominantly examined this relationship at the national level and have not separated COVID-19 deaths in nursing homes from total COVID-19 deaths. This approach may obscure differences in social distancing behaviors by county in addition to the actual effectiveness of social distancing in preventing COVID-19 deaths.ObjectiveThis study aimed to determine the influence of county-level social distancing behavior on COVID-19 mortality (deaths per 100,000 people) across US counties over the period of the implementation of stay-at-home orders in most US states (March-May 2020).MethodsUsing social distancing data from tracked mobile phones in all US counties, we estimated the relationship between social distancing (average proportion of mobile phone usage outside of home between March and May 2020) and COVID-19 mortality (when the state in which the county is located reported its first confirmed case of COVID-19 and up to May 31, 2020) with a mixed-effects negative binomial model while distinguishing COVID-19 deaths in nursing homes from total COVID-19 deaths and accounting for social distancing– and COVID-19–related factors (including the period between the report of the first confirmed case of COVID-19 and May 31, 2020; population density; social vulnerability; and hospital resource availability). Results from the mixed-effects negative binomial model were then used to generate marginal effects at the mean, which helped separate the influence of social distancing on COVID-19 deaths from other covariates while calculating COVID-19 deaths per 100,000 people.ResultsWe observed that a 1% increase in average mobile phone usage outside of home between March and May 2020 led to a significant increase in COVID-19 mortality by a factor of 1.18 (P<.001), while every 1% increase in the average proportion of mobile phone usage outside of home in February 2020 was found to significantly decrease COVID-19 mortality by a factor of 0.90 (P<.001).ConclusionsAs stay-at-home orders have been lifted in many US states, continued adherence to other social distancing measures, such as avoiding large gatherings and maintaining physical distance in public, are key to preventing additional COVID-19 deaths in counties across the country.     

Algorithm for Individual Prediction of COVID-19–Related Hospitalization Based on Symptoms: Development and Implementation Study

BackgroundThe COVID-19 pandemic has placed a huge strain on the health care system globally. The metropolitan area of Milan, Italy, was one of the regions most impacted by the COVID-19 pandemic worldwide. Risk prediction models developed by combining administrative databases and basic clinical data are needed to stratify individual patient risk for public health purposes.ObjectiveThis study aims to develop a stratification tool aimed at improving COVID-19 patient management and health care organization.MethodsA predictive algorithm was developed and applied to 36,834 patients with COVID-19 in Italy between March 8 and the October 9, 2020, in order to foresee their risk of hospitalization. Exposures considered were age, sex, comorbidities, and symptoms associated with COVID-19 (eg, vomiting, cough, fever, diarrhea, myalgia, asthenia, headache, anosmia, ageusia, and dyspnea). The outcome was hospitalizations and emergency department admissions for COVID-19. Discrimination and calibration of the model were also assessed.ResultsThe predictive model showed a good fit for predicting COVID-19 hospitalization (C-index 0.79) and a good overall prediction accuracy (Brier score 0.14). The model was well calibrated (intercept –0.0028, slope 0.9970). Based on these results, 118,804 patients diagnosed with COVID-19 from October 25 to December 11, 2020, were stratified into low, medium, and high risk for COVID-19 severity. Among the overall study population, 67,030 (56.42%) were classified as low-risk patients; 43,886 (36.94%), as medium-risk patients; and 7888 (6.64%), as high-risk patients. In all, 89.37% (106,179/118,804) of the overall study population was being assisted at home, 9% (10,695/118,804) was hospitalized, and 1.62% (1930/118,804) died. Among those assisted at home, most people (63,983/106,179, 60.26%) were classified as low risk, whereas only 3.63% (3858/106,179) were classified at high risk. According to ordinal logistic regression, the odds ratio (OR) of being hospitalized or dead was 5.0 (95% CI 4.6-5.4) among high-risk patients and 2.7 (95% CI 2.6-2.9) among medium-risk patients, as compared to low-risk patients.ConclusionsA simple monitoring system, based on primary care data sets linked to COVID-19 testing results, hospital admissions data, and death records may assist in the proper planning and allocation of patients and resources during the ongoing COVID-19 pandemic.     

The Association of Work-related Stress According to the Demand–Control Model With Aggravation of Pre-existing Disease During the First State of COVID-19 Emergency in Japan

BackgroundThe job environment has changed a lot during the period of the coronavirus disease 2019 (COVID-19) pandemic. This study aimed to investigate the association between work-related stress and aggravation of pre-existing disease in workers during the first state of COVID-19 emergency in Japan.MethodsData were obtained from a large internet survey conducted between August 25 and September 30, 2020 in Japan. Participants who reported that they had a job as well as current history of disease(s) (ie, pre-existing conditions) were included (n = 3,090). Aggravation of pre-existing disease during the state of emergency was self-reported. Work-related stress from April 2020 (since the state of COVID-19 emergency) was assessed according to a job demand–control model. Multivariable logistic regression models were used to analyze the association.ResultsAggravation of pre-existing diseases was reported by 334 participants (11%). The numbers of participants with high demand and low control were 112 (18%) and 100 (14%), respectively. Compared to medium demand, high demand was significantly associated with aggravation of pre-existing diseases (odds ratio 1.77; 95% confidence interval, 1.30–2.42). Low control compared to medium control was also significantly associated with aggravation of pre-existing diseases (odds ratio 1.39; 95% confidence interval, 1.02–1.92).ConclusionWork-related stress during the first state of COVID-19 emergency was associated with aggravation of pre-existing disease during that period.Key words: work-related stress, pre-existing disease, job demand, job control, COVID-19     

Estimation of COVID-19 Period Prevalence and the Undiagnosed Population in Canadian Provinces: Model-Based Analysis

BackgroundThe development of a successful COVID-19 control strategy requires a thorough understanding of the trends in geographic and demographic distributions of disease burden. In terms of the estimation of the population prevalence, this includes the crucial process of unravelling the number of patients who remain undiagnosed.ObjectiveThis study estimates the period prevalence of COVID-19 between March 1, 2020, and November 30, 2020, and the proportion of the infected population that remained undiagnosed in the Canadian provinces of Quebec, Ontario, Alberta, and British Columbia.MethodsA model-based mathematical framework based on a disease progression and transmission model was developed to estimate the historical prevalence of COVID-19 using provincial-level statistics reporting seroprevalence, diagnoses, and deaths resulting from COVID-19. The framework was applied to three different age cohorts (< 30; 30-69; and ≥70 years) in each of the provinces studied.ResultsThe estimates of COVID-19 period prevalence between March 1, 2020, and November 30, 2020, were 4.73% (95% CI 4.42%-4.99%) for Quebec, 2.88% (95% CI 2.75%-3.02%) for Ontario, 3.27% (95% CI 2.72%-3.70%) for Alberta, and 2.95% (95% CI 2.77%-3.15%) for British Columbia. Among the cohorts considered in this study, the estimated total number of infections ranged from 2-fold the number of diagnoses (among Quebecers, aged ≥70 years: 26,476/53,549, 49.44%) to 6-fold the number of diagnoses (among British Columbians aged ≥70 years: 3108/18,147, 17.12%).ConclusionsOur estimates indicate that a high proportion of the population infected between March 1 and November 30, 2020, remained undiagnosed. Knowledge of COVID-19 period prevalence and the undiagnosed population can provide vital evidence that policy makers can consider when planning COVID-19 control interventions and vaccination programs.