Canada's provincial COVID-19 pandemic modelling efforts: A review of mathematical models and their impacts on the responses.

SETTING: Mathematical modelling played an important role in the public health response to COVID-19 in Canada. Variability in epidemic trajectories, modelling approaches, and data infrastructure across provinces provides a unique opportunity to understand the factors that shaped modelling strategies. INTERVENTION: Provinces implemented stringent pandemic interventions to mitigate SARS-CoV-2 transmission, considering evidence from epidemic models. This study aimed to summarize provincial COVID-19 modelling efforts. We identified modelling teams working with provincial decision-makers, through referrals and membership in Canadian modelling networks. Information on models, data sources, and knowledge translation were abstracted using standardized instruments. OUTCOMES: We obtained information from six provinces. For provinces with sustained community transmission, initial modelling efforts focused on projecting epidemic trajectories and healthcare demands, and evaluating impacts of proposed interventions. In provinces with low community transmission, models emphasized quantifying importation risks. Most of the models were compartmental and deterministic, with projection horizons of a few weeks. Models were updated regularly or replaced by new ones, adapting to changing local epidemic dynamics, pathogen characteristics, vaccines, and requests from public health. Surveillance datasets for cases, hospitalizations and deaths, and serological studies were the main data sources for model calibration. Access to data for modelling and the structure for knowledge translation differed markedly between provinces. IMPLICATION: Provincial modelling efforts during the COVID-19 pandemic were tailored to local contexts and modulated by available resources. Strengthening Canadian modelling capacity, developing and sustaining collaborations between modellers and governments, and ensuring earlier access to linked and timely surveillance data could help improve pandemic preparedness.

RéSUMé: CONTEXTE: La modélisation mathématique a joué un rôle de premier plan dans les ripostes sanitaires à la COVID-19 au Canada. Les différentes trajectoires épidémiques provinciales, leurs approches de modélisation et infrastructures de données représentent une occasion unique de comprendre les facteurs qui ont influencé les stratégies de modélisation provinciales. INTERVENTION: Les provinces ont mis en place des mesures de santé publique strictes afin d'atténuer la transmission du SRAS-CoV-2 en tenant compte des données probantes provenant des modèles épidémiques. Notre étude vise à décrire et résumer les efforts provinciaux de modélisation de la COVID-19. Nous avons identifié les équipes de modélisation travaillant avec les décideurs provinciaux parmi les réseaux Canadiens de modélisation et par référence. Les informations sur les modèles, leurs sources de données et les approches de mobilisation des connaissances ont été obtenues à l'aide d'instruments standardisés. RéSULTATS: Nous avons colligé les informations provenant de six provinces. Pour les provinces qui ont eu de la transmission communautaire soutenue, les efforts de modélisation initiaux se sont concentrés sur la projection des trajectoires épidémiques et des demandes de soins de santé et sur l'évaluation des impacts des interventions proposées. Dans les provinces où la transmission communautaire a été faible, les modèles visaient à quantifier les risques d'importation. La plupart des équipes ont développé des modèles à compartiments déterministes avec des horizons de projection de quelques semaines. Les modèles ont été régulièrement mis à jour ou remplacés par de nouveaux, s'adaptant aux dynamiques locales, à l'arrivée de nouveaux variants, aux vaccins et aux demandes des autorités de santé publique. Les données de surveillance des cas, des hospitalisations et des décès, ainsi que les études sérologiques, ont constitué les principales sources de données pour calibrer les modèles. L'accès aux données pour la modélisation et la structure de mobilisation des connaissances différaient considérablement d'une province à l'autre. IMPLICATION: Les efforts de modélisation provinciaux pendant la pandémie de la COVID-19 ont été adaptés aux contextes locaux et modulés par les ressources disponibles. Le renforcement de la capacité canadienne de modélisation, le développement et le maintien de collaborations entre les modélisateurs et les gouvernements, ainsi qu'un accès rapide et opportun aux données de surveillance individuelles et liées pourraient contribuer à améliorer la préparation aux futures pandémies.

Time to diagnosis and treatment of pulmonary tuberculosis in indigenous peoples: a systematic review.

BACKGROUND: Time to diagnosis and treatment is a major factor in determining the likelihood of tuberculosis (TB) transmission and is an important area of intervention to reduce the reservoir of TB infection and prevent disease and mortality. Although Indigenous peoples experience an elevated incidence of TB, prior systematic reviews have not focused on this group. We summarize and report findings related to time to diagnosis and treatment of pulmonary TB (PTB) among Indigenous peoples, globally. METHODS: A Systematic review was performed using Ovid and PubMed databases. Articles or abstracts estimating time to diagnosis, or treatment of PTB among Indigenous peoples were included with no restriction on sample size with publication dates restricted up to 2019. Studies that focused on outbreaks, solely extrapulmonary TB alone in non-Indigenous populations were excluded. Literature was assessed using the Hawker checklist. Registration Protocol (PROSPERO): CRD42018102463. RESULTS: Twenty-four studies were selected after initial assessment of 2021 records. These included Indigenous groups from five of six geographical regions outlined by the World Health Organization (all except the European Region). The range of time to treatment (24-240 days), and patient delay (20 days-2.5 years) were highly variable across studies and, in at least 60% of the studies, longer in Indigenous compared to non-Indigenous peoples. Risk factors associated with longer patient delays included poor awareness of TB, type of health provider first seen, and self-treatment. CONCLUSION: Time to diagnosis and treatment estimates for Indigenous peoples are generally within previously reported ranges from other systematic reviews focusing on the general population. However among literature examined in this systematic review that stratified by Indigenous and non-Indigenous peoples, patient delay and time to treatment were longer compared to non-Indigenous populations in over half of the studies. Studies included were sparse and highlight an overall gap in literature important to interrupting transmission and preventing new TB cases among Indigenous peoples. Although, risk factors unique to Indigenous populations were not identified, further investigation is needed as social determinants of health among studies conducted in medium and high incidence countries may be shared across both population groups. Trial registration N/a.

Mathematical modeling of the dynamics of COVID-19 variants of concern: Asymptotic and finite-time perspectives.

The COVID-19 pandemic has seen multiple waves, in part due to the implementation and relaxation of social distancing measures by the public health authorities around the world, and also caused by the emergence of new variants of concern (VOCs) of the SARS-Cov-2 virus. As the COVID-19 pandemic is expected to transition into an endemic state, how to manage outbreaks caused by newly emerging VOCs has become one of the primary public health issues. Using mathematical modeling tools, we investigated the dynamics of VOCs, both in a general theoretical framework and based on observations from public health data of past COVID-19 waves, with the objective of understanding key factors that determine the dominance and coexistence of VOCs. Our results show that the transmissibility advantage of a new VOC is a main factor for it to become dominant. Additionally, our modeling study indicates that the initial number of people infected with the new VOC plays an important role in determining the size of the epidemic. Our results also support the evidence that public health measures targeting the newly emerging VOC taken in the early phase of its spread can limit the size of the epidemic caused by the new VOC (Wu et al., 2139Wu, Scarabel, Majeed, Bragazzi, & Orbinski, ; Wu et al., 2021).

Why is it difficult to accurately predict the COVID-19 epidemic?

Since the COVID-19 outbreak in Wuhan City in December of 2019, numerous model predictions on the COVID-19 epidemics in Wuhan and other parts of China have been reported. These model predictions have shown a wide range of variations. In our study, we demonstrate that nonidentifiability in model calibrations using the confirmed-case data is the main reason for such wide variations. Using the Akaike Information Criterion (AIC) for model selection, we show that an SIR model performs much better than an SEIR model in representing the information contained in the confirmed-case data. This indicates that predictions using more complex models may not be more reliable compared to using a simpler model. We present our model predictions for the COVID-19 epidemic in Wuhan after the lockdown and quarantine of the city on January 23, 2020. We also report our results of modeling the impacts of the strict quarantine measures undertaken in the city after February 7 on the time course of the epidemic, and modeling the potential of a second outbreak after the return-to-work in the city.

Comparing methods to estimate incremental inpatient costs and length of stay due to methicillin-resistant Staphylococcus aureus in Alberta, Canada.

BACKGROUND: Methicillin-resistant Staphylococcus aureus (MRSA) is an opportunistic bacterial organism resistant to first line antibiotics. Acquisition of MRSA is often classified as either healthcare-associated or community-acquired. It has been shown that both healthcare-associated and community-acquired infections contribute to the spread of MRSA within healthcare facilities. The objective of this study was to estimate the incremental inpatient cost and length of stay for individuals colonized or infected with MRSA. Common analytical methods were compared to ensure the quality of the estimate generated. This study was performed at Alberta Ministry of Health (Edmonton, Alberta), with access to clinical MRSA data collected at two Edmonton hospitals, and ministerial administrative data holdings. METHODS: A retrospective cohort study of patients with MRSA was identified using a provincial infection prevention and control database. A coarsened exact matching algorithm, and two regression models (semilogarithmic ordinary least squares model and log linked generalized linear model) were evaluated. A MRSA-free cohort from the same facilities and care units was identified for the matched method; all records were used for the regression models. Records span from January 1, 2011 to December 31, 2015, for individuals 18 or older at discharge. RESULTS: Of the models evaluated, the generalized linear model was found to perform the best. Based on this model, the incremental inpatient costs associated with hospital-acquired cases were the most costly at $31,686 (14,169 - 60,158) and $47,016 (23,125 - 86,332) for colonization and infection, respectively. Community-acquired MRSA cases also represent a significant burden, with incremental inpatient costs of $7397 (2924 - 13,180) and $14,847 (8445 - 23,207) for colonization and infection, respectively. All costs are adjusted to 2016 Canadian dollars. Incremental length of stay followed a similar pattern, where hospital-acquired infections had the longest incremental stays of 35.2 (16.3-69.5) days and community-acquired colonization had the shortest incremental stays of 3.0 (0.6-6.3) days. CONCLUSIONS: MRSA, and in particular, hospital-acquired MRSA, places a significant but preventable cost burden on the Alberta healthcare system. Estimates of cost and length of stay varied by the method of analysis and source of infection, highlighting the importance of selecting the most appropriate method.

Correction to: Comparing methods to estimate incremental inpatient costs and length of stay due to methicillin-resistant Staphylococcus aureus in Alberta, Canada.

In the original publication of this article [1], the authors want to add the following sentence in the Acknowledgement section.

Strategies for halting the rise of multidrug resistant TB epidemics: assessing the effect of early case detection and isolation.

Background: The increasing rates of multidrug resistant TB (MDR-TB) have posed the question of whether control programs under enhanced directly observed treatment, short-course (DOTS-Plus) are sufficient or implemented optimally. Despite enhanced efforts on early case detection and improved treatment regimens, direct transmission of MDR-TB remains a major hurdle for global TB control. Methods: We developed an agent-based simulation model of TB dynamics to evaluate the effect of transmission reduction measures on the incidence of MDR-TB. We implemented a 15-day isolation period following the start of treatment in active TB cases. The model was parameterized with the latest estimates derived from the published literature. Results: We found that if high rates (over 90%) of TB case identification are achieved within 4 weeks of developing active TB, then a 15-day patient isolation strategy with 50% effectiveness in interrupting disease transmission leads to 10% reduction in the incidence of MDR-TB over 10 years. If transmission is fully prevented, the rise of MDR-TB can be halted within 10 years, but the temporal reduction of MDR-TB incidence remains below 20% in this period. Conclusions: The impact of transmission reduction measures on the TB incidence depends critically on the rates and timelines of case identification. The high costs and adverse effects associated with MDR-TB treatment warrant increased efforts and investments on measures that can interrupt direct transmission through early case detection.

Public health surveillance response following the southern Alberta floods, 2013.

OBJECTIVE: In June of 2013, southern Alberta underwent flooding that affected approximately 100,000 people. We describe the process put in place for public health surveillance and assessment of the impacts on health. METHODS: Public health surveillance was implemented for the six-week period after the flood to detect anticipated health events, including injuries, mental health problems and infectious diseases. Data sources were emergency departments (EDs) for presenting complaints, public health data on the post-exposure administration of tetanus vaccine/immunoglobulin, administrative data on prescription drugs, and reportable diseases. RESULTS: An increase in injuries was detected through ED visits among Calgary residents (rate ratio [RR] 1.28, 95% confidence interval [CI]: 1.14-1.43) and was supported by a 75% increase in the average weekly administration of post-exposure prophylaxis against tetanus. Mental health impacts in High River residents were observed among females through a 1.64-fold (95% CI: 1.11-2.43) and 2.32-fold (95% CI: 1.45-3.70) increase in new prescriptions for anti-anxiety medication and sleep aids respectively. An increase in sexual assaults presenting to EDs (RR 3.18, 95% CI: 1.29-7.84) was observed among Calgary residents. No increases in infectious gastrointestinal disease or respiratory illness were identified. Timely identification and communication of surveillance alerts allowed for messaging around the use of personal protective equipment and precautions for personal safety. CONCLUSION: Existing data sources were used for surveillance following an emergency situation. The information produced, though limited, was sufficiently timely to inform public health decision-making.