Characterizing responsiveness to the COVID-19 pandemic in the United States and Canada using mobility data

BackgroundMobile phone-derived human mobility data are a proxy for disease transmission risk and have proven useful during the COVID-19 pandemic for forecasting cases and evaluating interventions. We propose a novel metric using mobility data to characterize responsiveness to rising case rates. MethodsWe examined weekly reported COVID-19 incidence and retail and recreation mobility from Google Community Mobility Reports for 50 U.S. states and nine Canadian provinces from December 2020 to November 2021. For each jurisdiction, we calculated the responsiveness of mobility to COVID-19 incidence when cases were rising. Responsiveness across countries was summarized using subgroup meta-analysis. We also calculated the correlation between the responsiveness metric and the reported COVID-19 death rate during the study period. FindingsResponsiveness in Canadian provinces ({beta} = -1{middle dot}45; 95% CI: -2{middle dot}45, -0{middle dot}44) was approximately five times greater than in U.S. states ({beta} = -0{middle dot}30; 95% CI: -0{middle dot}38, -0{middle dot}21). Greater responsiveness was moderately correlated with a lower reported COVID-19 death rate during the study period (Spearmans{rho} = 0{middle dot}51), whereas average mobility was only weakly correlated the COVID-19 death rate (Spearmans{rho} = 0{middle dot}20). InterpretationOur study used a novel mobility-derived metric to reveal a near-universal phenomenon of reductions in mobility subsequent to rising COVID-19 incidence across 59 states and provinces of the U.S. and Canada, while also highlighting the different public health approaches taken by the two countries. FundingThis study received no funding. Research in contextO_ST_ABSEvidence before the studyC_ST_ABSThere exists a wide body of literature establishing the usefulness of mobile phone-derived human mobility data for forecasting cases and other metrics during the COVID-19 pandemic. We performed a literature search to identify studies examining the opposite relationship, attempting to quantify the responsiveness of human mobility to changes in COVID-19 incidence. We searched PubMed on October 21, 2022 using the keywords "COVID-19", "2019-nCoV", or "SARS-CoV-2" in combination with "responsiveness" and one or more of "mobility", "distancing", "lockdown", and "non-pharmaceutical interventions". We scanned 46 published studies and found one that used a mobile phone data-derived index to measure the intensity of social distancing in U.S. counties from January 2020 to January 2021. The authors of this study found that an increase in cases in the last 7 days was associated with an increase in the intensity of social distancing, and that this effect was larger during periods of lockdown/shop closures. Added value of the studyOur study developed a metric of the responsiveness of mobility to rising case rates for COVID-19 and calculated it for 59 subnational jurisdictions in the United States and Canada. While nearly all jurisdictions displayed some degree of responsiveness, average responsiveness in Canada was nearly five times greater than in the United States. Responsiveness was moderately associated with the reported COVID-19 death rate during the study period, such that jurisdictions with greater responsiveness had lower death rates, and was more strongly associated with death rates than average mobility in a jurisdiction. Implications of all the available evidenceMobile phone-derived human mobility data has proven useful in the context of infectious disease surveillance during the COVID-19 pandemic, such as for forecasting cases and evaluating non-pharmaceutical interventions. In our study, we derived a metric of responsiveness to show that mobility data may be used to track the efficiency of public health responses as the pandemic evolves. This responsiveness metric was also correlated with reported COVID-19 death rates during the study period. Together, these results demonstrate the usefulness of mobility data for making broad characterizations of public health responses across jurisdictions during the COVID-19 pandemic and reinforce the value of mobility data as an infectious disease surveillance tool for answering present and future threats.

Quantifying Contact Patterns in Response to COVID-19 Public Health Measures in Canada

BackgroundA variety of public health measures have been implemented during the COVID-19 pandemic in Canada to reduce contact between individuals. ObjectiveThe objective of this study was to construct contact patterns to evaluate the degree to which social contacts rebounded to normal levels, as well as direct public health efforts toward age- and location-specific settings. DesignFour population-based cross-sectional surveys. SettingCanada. ParticipantsMembers of a paid panel representative of Canadian adults by age, gender, official language, and region of residence. MethodsRespondents provided information about the age and setting for each direct contact made in a 24-hour period. Contact matrices were constructed and contacts for those under the age of 18 years imputed. The next generation matrix approach was used to estimate the reproduction number (Rt) for each survey. Respondents with children estimated the number of contacts their children made in school and extracurricular settings. ResultsEstimated Rt values were 0.49 (95% CI: 0.29-0.69) for May, 0.48 (95% CI: 0.29-0.68) for July, 1.06 (95% CI: 0.63-1.52) for September, and 0.81 (0.47-1.17) for December. The highest proportion of reported contacts occurred within the home (51.3% in May), in other locations (49.2% in July) and at work (66.3% and 65.4% in September and December). Respondents with children reported an average of 22.7 (95% CI: 21.1-24.3) (September) and 19.0 (95% CI 17.7-20.4) (December) contacts at school per day per child in attendance. ConclusionThe skewed distribution of reported contacts toward workplace settings in September and December combined with the number of reported school-related contacts suggest that these settings represent important opportunities for transmission emphasizing the need to ensure infection control procedures in both workplaces and schools.

SARS-CoV-2 Shedding Dynamics Across the Respiratory Tract, Sex, and Disease Severity for Adult and Pediatric COVID-19

BackgroundSARS-CoV-2 shedding dynamics in the upper (URT) and lower respiratory tract (LRT) remain unclear. ObjectiveTo analyze SARS-CoV-2 shedding dynamics across COVID-19 severity, the respiratory tract, sex and age cohorts (aged 0 to 17 years, 18 to 59 years, and 60 years or older). DesignSystematic review and pooled analyses. SettingMEDLINE, EMBASE, CENTRAL, Web of Science Core Collection, medRxiv and bioRxiv were searched up to 20 November 2020. ParticipantsThe systematic dataset included 1,266 adults and 136 children with COVID-19. MeasurementsCase characteristics (COVID-19 severity, age and sex) and quantitative respiratory viral loads (rVLs). ResultsIn the URT, adults with severe COVID-19 had higher rVLs at 1 DFSO than adults (P = 0.005) or children (P = 0.017) with nonsevere illness. Between 1-10 DFSO, severe adults had comparable rates of SARS-CoV-2 clearance from the URT as nonsevere adults (P = 0.479) and nonsevere children (P = 0.863). In the LRT, severe adults showed higher post-symptom-onset rVLs than nonsevere adults (P = 0.006). In the analyzed period (4-10 DFSO), severely affected adults had no significant trend in SARS-CoV-2 clearance from LRT (P = 0.105), whereas nonsevere adults showed a clear trend (P < 0.001). After stratifying for disease severity, sex and age (including child vs. adult) were not predictive of the duration of respiratory shedding. LimitationLimited data on case comorbidities and few samples in some cohorts. ConclusionHigh, persistent LRT shedding of SARS-CoV-2 characterized severe COVID-19 in adults. After symptom onset, severe cases tended to have higher URT shedding than their nonsevere counterparts. Disease severity, rather than age or sex, predicted SARS-CoV-2 kinetics. LRT specimens should more accurately prognosticate COVID-19 severity than URT specimens. Primary Funding SourceNatural Sciences and Engineering Research Council.

Anticipating the curve: can online symptom-based data reflect COVID-19 case activity in Ontario, Canada?

BackgroundLimitations in laboratory diagnostic capacity and reporting delays have hampered efforts to mitigate and control the ongoing COVID-19 pandemic globally. Syndromic surveillance of COVID-19 is an important public health tool that can help detect outbreaks, mobilize a rapid response, and thereby reduce morbidity and mortality. The primary objective of this study was to determine whether syndromic surveillance through self-reported COVID-19 symptoms could be a timely proxy for laboratory-confirmed case trends in the Canadian province of Ontario. MethodsWe retrospectively analyzed self-reported symptoms data collected using an online tool - Outbreaks Near Me (ONM) - from April 20th to Oct 11th, 2020 in Ontario, Canada. We estimated the correlation coefficient between the weekly proportion of respondents reporting a COVID-like illness (CLI) to both the weekly number of PCR-confirmed COVID-19 cases and the percent positivity in the same period for the same week and with a one-week lag. ResultsThere were 314,686 responses from 188,783 unique respondents to the ONM symptom survey. Respondents were more likely to be female and be in the 40-59 age demographic compared to the Ontario general population. There was a strong positive correlation between the weekly number of reported cases in Ontario and the percent of respondents reporting CLI each week (r = 0.89, p <0.01) and with a one-week lag (r = 0.89, p <0.01). InterpretationWe demonstrate a strong positive and significant correlation (r = 0.89, p <0.01) between percent of self-reported COVID-like illness and the subsequent weeks COVID-19 cases reported, highlighting that a rise in CLI may precede official statistics by at least 1 week. This demonstrates the utility of syndromic surveillance in predicting near-future disease activity. Digital surveillance systems are low-cost tools that may help measure the burden of COVID-19 in a community if there is under-detection of cases through conventional laboratory diagnostic testing. This additional information can be used to guide a healthcare response and policy decisions.

The impact of seasonal respiratory virus transmission on syndromic surveillance for COVID-19 in Ontario, Canada

BackgroundSyndromic surveillance systems for COVID-19 are being increasingly used to track and predict outbreaks of confirmed cases. Seasonal circulating respiratory viruses share syndromic overlap with COVID-19, and it is unknown how they will impact the performance of syndromic surveillance tools. Here we investigated the role of non-SARS-CoV-2 respiratory virus test positivity on COVID-19 two independent syndromic surveillance systems in Ontario, Canada. MethodsWe compared the weekly number of reported COVID-19 cases reported in the province of Ontario against two syndromic surveillance metrics: 1) the proportion of respondents with a self-reported COVID-like illness (CLI) from COVID Near You (CNY) and 2) the proportion of emergency department visits for upper respiratory conditions from the Acute Care Enhanced Surveillance (ACES) system. Separately, we plotted the percent positivity for other seasonal respiratory viruses over the same time period and reported Pearsons correlation coefficients before and after the uncoupling of syndromic tools to COVID-19 cases. ResultsThere were strong positive correlations of both CLI and ED visits for upper respiratory causes with COVID-19 cases up to and including a rise in entero/rhinovirus (r = 0.86 and 0.87, respectively). There was a strong negative correlation of both CLI and ED visits for upper respiratory causes with COVID-19 cases (r = -0.85 and -0.91, respectively) during a fall in entero/rhinovirus. InterpretationTwo methods of syndromic surveillance showed strong positive correlations with COVID-19 confirmed case counts before and during a rise in circulating entero/rhinovirus. However, as positivity for enterovirus/rhinovirus fell in late September 2020, syndromic signals became uncoupled from COVID-19 cases and instead tracked the fall in entero/rhinovirus. This finding provides proof-of-principle that regional transmission of seasonal respiratory viruses may complicate the interpretation of COVID-19 surveillance data. It is imperative that surveillance systems incorporate other respiratory virus testing data in order to more accurately track and forecast COVID-19 disease activity.

Heterogeneity in transmissibility and shedding SARS-CoV-2 via droplets and aerosols

Which virological factors mediate overdispersion in the transmissibility of emerging viruses remains a longstanding question in infectious disease epidemiology. Here, we use systematic review to develop a comprehensive dataset of respiratory viral loads (rVLs) of SARS-CoV-2, SARS-CoV-1 and influenza A(H1N1)pdm09. We then comparatively meta-analyze the data and model individual infectiousness by shedding viable virus via respiratory droplets and aerosols. Our analyses indicate heterogeneity in rVL as an intrinsic virological factor facilitating greater overdispersion for SARS-CoV-2 in the COVID-19 pandemic than A(H1N1)pdm09 in the 2009 influenza pandemic. For COVID-19, case heterogeneity remains broad throughout the infectious period, including for pediatric and asymptomatic infections. Hence, many COVID-19 cases inherently present minimal transmission risk, whereas highly infectious individuals shed tens to thousands of SARS-CoV-2 virions/min via droplets and aerosols while breathing, talking and singing. Coughing increases the contagiousness, especially in close contact, of symptomatic cases relative to asymptomatic ones. Infectiousness tends to be elevated between 1-5 days post-symptom onset. Our findings show how individual case variations influence virus transmissibility and present considerations for disease control in the COVID-19 pandemic. Significance StatementFor some emerging infectious diseases, including COVID-19, few cases cause most secondary infections. Others, like influenza A(H1N1)pdm09, spread more homogenously. The virological factors that mediate such distinctions in transmissibility remain unelucidated, prohibiting the development of specific disease control measures. We find that intrinsic case variation in respiratory viral load (rVL) facilitates overdispersion, and superspreading, for COVID-19 but more homogeneous transmission for A(H1N1)pdm09. We interpret the influence of heterogeneity in rVL on individual infectiousness by modelling likelihoods of shedding viable virus via respiratory droplets and aerosols. We analyze the distribution and kinetics of SARS-CoV-2 rVL, including across age and symptomatology subgroups. Our findings compare individual infectiousness across COVID-19 and A(H1N1)pdm09 cases and present quantitative guidance on triaging COVID-19 contact tracing.

Sociodemographic disparities in knowledge, practices, and ability to comply with COVID-19 public health measures in Canada

The effectiveness of public health interventions for mitigation of the coronavirus (COVID-19) pandemic depends on individual attitudes and the level of compliance toward these measures. We surveyed a representative sample of the Canadian population about risk perceptions, attitudes, and behaviours towards the Canadian COVID-19 public health response. Our analysis demonstrates that these risk perceptions, attitudes, and behaviours varied by several demographic variables identifying a number of areas in which policies could help address issues of public adherence. Examples include targeted messaging for men and younger age groups, social supports for those who need to self-isolate but may not have the means to do so, changes in workplace policies to discourage presenteeism, and provincially co-ordinated masking and safe school reopening policies. Taken together such measures are likely to mitigate the impact of the next pandemic wave in Canada.

Syndromic Surveillance for COVID-19 in Canada

BackgroundSyndromic surveillance through web or phone-based polling has been used to track the course of infectious diseases worldwide. Our study objective was to describe the characteristics, symptoms, and self-reported testing rates of respondents in three different COVID-19 symptom surveys in Canada. MethodsData sources consisted of two distinct Canada-wide web-based surveys, and phone polling in Ontario. All three sources contained self-reported information on COVID-19 symptoms and testing. In addition to describing respondent characteristics, we examined symptom frequency and the testing rate among the symptomatic, as well as rates of symptoms and testing across respondent groups. ResultsWe found that 1.6% of respondents experienced a symptom on the day of their survey, 15% of Ontario households had a symptom in the previous week, and 44% of Canada-wide respondents had a symptom in the previous month over March-April 2020. Across the three surveys, SARS-CoV-2-testing was reported in 2-9% of symptomatic responses. Women, younger and middle-aged adults (versus older adults) and Indigenous/First nations/Inuit/Metis were more likely to report at least one symptom, and visible minorities were more likely to report the combination of fever with cough or shortness of breath. InterpretationThe low rate of testing among those reporting symptoms suggests significant opportunity to expand testing among community-dwelling residents of Canada. Syndromic surveillance data can supplement public health reports and provide much-needed context to gauge the adequacy of current SARS-CoV-2 testing rates.

Reduced COVID-19-Related Critical Illness and Death, and High Risk of Epidemic Resurgence, After Physical Distancing in Ontario, Canada

BackgroundInsights from epidemiological models have helped to both guide and better understand COVID-19 mitigation policies that have been adopted across the globe. Many early models focussed on initial control options and were less reliant on fitting to observed data. As the pandemic progresses, models can be used to quantify the impact that control measures have had and what may unfold when such measures are relaxed. ObjectiveTo explore the impact of physical distancing measures on COVID-19 transmission in the population of Ontario, Canada.

Mathematical modeling of COVID-19 transmission and mitigation strategies in the population of Ontario, Canada

BackgroundWe evaluated how non-pharmaceutical interventions could be used to control the COVID-19 pandemic and reduce the burden on the healthcare system. MethodsUsing an age-structured compartmental model of COVID-19 transmission in the population of Ontario, Canada, we compared a base case with limited testing, isolation, and quarantine to scenarios with: enhanced case finding; restrictive social distancing measures; or a combination of enhanced case finding and less restrictive social distancing. Interventions were either implemented for fixed durations or dynamically cycled on and off, based on projected ICU bed occupancy. We present median and credible intervals (CrI) from 100 replicates per scenario using a two-year time horizon. ResultsWe estimated that 56% (95% CrI: 42-63%) of the Ontario population would be infected over the course of the epidemic in the base case. At the epidemic peak, we projected 107,000 (95% CrI: 60,760-149,000) cases in hospital and 55,500 (95% CrI: 32,700-75,200) cases in ICU. For fixed duration scenarios, all interventions were projected to delay and reduce the height of the epidemic peak relative to the base case, with restrictive social distancing estimated to have the greatest effect. Longer duration interventions were more effective. Dynamic interventions were projected to reduce the proportion of the population infected at the end of the two-year period. Dynamic social distancing interventions could reduce the median number of cases in ICU below current estimates of Ontarios ICU capacity. InterpretationWithout significant social distancing or a combination of moderate social distancing with enhanced case finding, we project that ICU resources would be overwhelmed. Dynamic social distancing could maintain health system capacity and also allow periodic psychological and economic respite for populations.

Estimation of COVID-2019 burden and potential for international dissemination of infection from Iran

The Coronavirus Disease 2019 (COVID-19) epidemic began in Wuhan, China in late 2019 and continues to spread globally, with exported cases confirmed in 28 countries at the time of writing. During the interval between February 19 and 23, 2020, Iran reported its first 43 cases with eight deaths. Three exported cases originating in Iran were identified, suggesting a underlying burden of disease in that country than is indicated by reported cases. A large epidemic in Iran could further fuel global dissemination of COVID-19. We sought to estimate COVID-19 outbreak size in Iran based on known exported case counts and air travel links between Iran and other countries, and to anticipate where infections originating in Iran may spread to next. We assessed interconnectivity between Iran and other countries using using International Air Transport Association (IATA) data. We used the methods of Fraser et al. to estimate the size of the underlying epidemic that would result in cases being observed in the United Arab Emirates (UAE), Lebanon, and Canada. Time at risk estimates were based on a presumed 6 week epidemic age, and length of stay data for visitors to Iran derived from the United Nations World Tourism Organization (UNWTO). We evaluated the relationship between the strength of travel links with Iran, and destination country rankings on the Infectious Disease Vulnerability Index (IDVI), a validated metric that estimates the capacity of a country to respond to an infectious disease outbreak. Scores range between 0-1, with higher scores reflecting greater capacity to manage infectious outbreaks. UAE, Lebanon, and Canada ranked 3rd, 21st, and 31st, respectively, for outbound air travel volume from Iran in February 2019. We estimated that 18,300 (95% confidence interval: 3770 to 53,470) COVID-19 cases would have had to occur in Iran, assuming an outbreak duration of 1.5 months in the country, in order to observe these three internationally exported cases reported at the time of writing. Results were robust under varying assumptions about undiagnosed case numbers in Syria, Azerbaijan and Iraq. Even if it were assumed that all cases were identified in all countries with certainty, the "best case" outbreak size was substantial (1820, 95% CI: 380-5320 cases), and far higher than reported case counts. Given the low volumes of air travel to countries with identified cases of COVID-19 with origin in Iran (such as Canada), it is likely that Iran is currently experiencing a COVID-19 epidemic of significant size for such exportations to be occurring. This is concerning, both for public health in Iran itself, and because of the high likelihood for outward dissemination of the epidemic to neighbouring countries with lower capacity to respond to infectious diseases epidemics.