How is the COVID-19 pandemic impacting our life, mental health, and well-being? Design and preliminary findings of the pan-Canadian longitudinal COHESION study.

BACKGROUND: With the advent of the COVID-19 pandemic, in-person social interactions and opportunities for accessing resources that sustain health and well-being have drastically reduced. We therefore designed the pan-Canadian prospective COVID-19: HEalth and Social Inequities across Neighbourhoods (COHESION) cohort to provide a deeper understanding of how the COVID-19 pandemic context affects mental health and well-being, key determinants of health, and health inequities. METHODS: This paper presents the design of the two-phase COHESION Study, and descriptive results from the first phase conducted between May 2020 and September 2021. During that period, the COHESION research platform collected monthly data linked to COVID-19 such as infection and vaccination status, perceptions and attitudes regarding pandemic-related measures, and information on participants' physical and mental health, well-being, sleep, loneliness, resilience, substances use, living conditions, social interactions, activities, and mobility. RESULTS: The 1,268 people enrolled in the Phase 1 COHESION Study are for the most part from Ontario (47%) and Quebec (33%), aged 48 ± 16 years [mean ± standard deviation (SD)], and mainly women (78%), White (85%), with a university degree (63%), and living in large urban centers (70%). According to the 298 ± 68 (mean ± SD) prospective questionnaires completed each month on average, the first year of follow-up reveals significant temporal variations in standardized indexes of well-being, loneliness, anxiety, depression, and psychological distress. CONCLUSIONS: The COHESION Study will allow identifying trajectories of mental health and well-being while investigating their determinants and how these may vary by subgroup, over time, and across different provinces in Canada, in varying context including the pandemic recovery period. Our findings will contribute valuable insights to the urban health field and inform future public health interventions.

Identifying Potential Lyme Disease Cases Using Self-Reported Worldwide Tweets: Deep Learning Modeling Approach Enhanced With Sentimental Words Through Emojis.

BACKGROUND: Lyme disease is among the most reported tick-borne diseases worldwide, making it a major ongoing public health concern. An effective Lyme disease case reporting system depends on timely diagnosis and reporting by health care professionals, and accurate laboratory testing and interpretation for clinical diagnosis validation. A lack of these can lead to delayed diagnosis and treatment, which can exacerbate the severity of Lyme disease symptoms. Therefore, there is a need to improve the monitoring of Lyme disease by using other data sources, such as web-based data. OBJECTIVE: We analyzed global Twitter data to understand its potential and limitations as a tool for Lyme disease surveillance. We propose a transformer-based classification system to identify potential Lyme disease cases using self-reported tweets. METHODS: Our initial sample included 20,000 tweets collected worldwide from a database of over 1.3 million Lyme disease tweets. After preprocessing and geolocating tweets, tweets in a subset of the initial sample were manually labeled as potential Lyme disease cases or non-Lyme disease cases using carefully selected keywords. Emojis were converted to sentiment words, which were then replaced in the tweets. This labeled tweet set was used for the training, validation, and performance testing of DistilBERT (distilled version of BERT [Bidirectional Encoder Representations from Transformers]), ALBERT (A Lite BERT), and BERTweet (BERT for English Tweets) classifiers. RESULTS: The empirical results showed that BERTweet was the best classifier among all evaluated models (average F1-score of 89.3%, classification accuracy of 90.0%, and precision of 97.1%). However, for recall, term frequency-inverse document frequency and k-nearest neighbors performed better (93.2% and 82.6%, respectively). On using emojis to enrich the tweet embeddings, BERTweet had an increased recall (8% increase), DistilBERT had an increased F1-score of 93.8% (4% increase) and classification accuracy of 94.1% (4% increase), and ALBERT had an increased F1-score of 93.1% (5% increase) and classification accuracy of 93.9% (5% increase). The general awareness of Lyme disease was high in the United States, the United Kingdom, Australia, and Canada, with self-reported potential cases of Lyme disease from these countries accounting for around 50% (9939/20,000) of the collected English-language tweets, whereas Lyme disease-related tweets were rare in countries from Africa and Asia. The most reported Lyme disease-related symptoms in the data were rash, fatigue, fever, and arthritis, while symptoms, such as lymphadenopathy, palpitations, swollen lymph nodes, neck stiffness, and arrythmia, were uncommon, in accordance with Lyme disease symptom frequency. CONCLUSIONS: The study highlights the robustness of BERTweet and DistilBERT as classifiers for potential cases of Lyme disease from self-reported data. The results demonstrated that emojis are effective for enrichment, thereby improving the accuracy of tweet embeddings and the performance of classifiers. Specifically, emojis reflecting sadness, empathy, and encouragement can reduce false negatives.

Leveraging machine learning approaches for predicting potential Lyme disease cases and incidence rates in the United States using Twitter.

BACKGROUND: Lyme disease is one of the most commonly reported infectious diseases in the United States (US), accounting for more than [Formula: see text] of all vector-borne diseases in North America. OBJECTIVE: In this paper, self-reported tweets on Twitter were analyzed in order to predict potential Lyme disease cases and accurately assess incidence rates in the US. METHODS: The study was done in three stages: (1) Approximately 1.3 million tweets were collected and pre-processed to extract the most relevant Lyme disease tweets with geolocations. A subset of tweets were semi-automatically labelled as relevant or irrelevant to Lyme disease using a set of precise keywords, and the remaining portion were manually labelled, yielding a curated labelled dataset of 77, 500 tweets. (2) This labelled data set was used to train, validate, and test various combinations of NLP word embedding methods and prominent ML classification models, such as TF-IDF and logistic regression, Word2vec and XGboost, and BERTweet, among others, to identify potential Lyme disease tweets. (3) Lastly, the presence of spatio-temporal patterns in the US over a 10-year period were studied. RESULTS: Preliminary results showed that BERTweet outperformed all tested NLP classifiers for identifying Lyme disease tweets, achieving the highest classification accuracy and F1-score of [Formula: see text]. There was also a consistent pattern indicating that the West and Northeast regions of the US had a higher tweet rate over time. CONCLUSIONS: We focused on the less-studied problem of using Twitter data as a surveillance tool for Lyme disease in the US. Several crucial findings have emerged from the study. First, there is a fairly strong correlation between classified tweet counts and Lyme disease counts, with both following similar trends. Second, in 2015 and early 2016, the social media network like Twitter was essential in raising popular awareness of Lyme disease. Third, counties with a high incidence rate were not necessarily related with a high tweet rate, and vice versa. Fourth, BERTweet can be used as a reliable NLP classifier for detecting relevant Lyme disease tweets.

Socio-demographic determinants of COVID-19 vaccine uptake in Ontario: Exploring differences across the Health Region model.

The COVID-19 pandemic continues to be a worldwide public health concern. Although vaccines against this disease were rapidly developed, vaccination uptake has not been equal across all the segments of the population, particularly in the case of underrepresented groups. However, there are also differences in vaccination across geographical areas, which might be important to consider in the development of future public health vaccination policies. In this study, we examined the relationship between vaccination status (having received the first dose of a COVID-19 vaccine), socio-economic strata, and the Health Regions for individuals in Ontario, Canada. Our results show that between October of 2021 and January of 2022, individuals from underrepresented communities were three times less likely to be vaccinated than White/Caucasian individuals across the province of Ontario, and that in some cases, within these groups, individuals in low-income brackets had significantly higher odds of vaccination when compared to their peers in high income brackets. Finally, we identified significantly lower odds of vaccination in the Central, East and West Health Regions of Ontario within certain underrepresented groups. This study shows that there is an ongoing need to better understand and address differences in vaccination uptake across diverse segments of the population of Ontario that the pandemic has largely impacted.

Predicting malaria risk considering vector control interventions under climate change scenarios.

Many studies have projected malaria risks with climate change scenarios by modelling one or two environmental variables and without the consideration of malaria control interventions. We aimed to predict the risk of malaria with climate change considering the influence of rainfall, humidity, temperatures, vegetation, and vector control interventions (indoor residual spraying (IRS) and long-lasting insecticidal nets (LLIN)). We used negative binomial models based on weekly malaria data from six facility-based surveillance sites in Uganda from 2010-2018, to estimate associations between malaria, environmental variables and interventions, accounting for the non-linearity of environmental variables. Associations were applied to future climate scenarios to predict malaria distribution using an ensemble of Regional Climate Models under two Representative Concentration Pathways (RCP4.5 and RCP8.5). Predictions including interaction effects between environmental variables and interventions were also explored. The results showed upward trends in the annual malaria cases by 25% to 30% by 2050s in the absence of intervention but there was great variability in the predictions (historical vs RCP 4.5 medians [Min-Max]: 16,785 [9,902-74,382] vs 21,289 [11,796-70,606]). The combination of IRS and LLIN, IRS alone, and LLIN alone would contribute to reducing the malaria burden by 76%, 63% and 35% respectively. Similar conclusions were drawn from the predictions of the models with and without interactions between environmental factors and interventions, suggesting that the interactions have no added value for the predictions. The results highlight the need for maintaining vector control interventions for malaria prevention and control in the context of climate change given the potential public health and economic implications of increasing malaria in Uganda.

Models and data used to predict the abundance and distribution of Ixodes scapularis (blacklegged tick) in North America: a scoping review.

Tick-borne diseases (TBD) remain prevalent worldwide, and risk assessment of tick habitat suitability is crucial to prevent or reduce their burden. This scoping review provides a comprehensive survey of models and data used to predict I. scapularis distribution and abundance in North America. We identified 4661 relevant primary research articles published in English between January 1st, 2012, and July 18th, 2022, and selected 41 articles following full-text review. Models used data-driven and mechanistic modelling frameworks informed by diverse tick, hydroclimatic, and ecological variables. Predictions captured tick abundance (n = 14, 34.1%), distribution (n = 22, 53.6%) and both (n = 5, 12.1%). All studies used tick data, and many incorporated both hydroclimatic and ecological variables. Minimal host- and human-specific data were utilized. Biases related to data collection, protocols, and tick data quality affect completeness and representativeness of prediction models. Further research and collaboration are needed to improve prediction accuracy and develop effective strategies to reduce TBD.

Mathematical modeling of mpox: A scoping review.

Background: Mpox (monkeypox), a disease historically endemic to Africa, has seen its largest outbreak in 2022 by spreading to many regions of the world and has become a public health threat. Informed policies aimed at controlling and managing the spread of this disease necessitate the use of adequate mathematical modeling strategies. Objective: In this scoping review, we sought to identify the mathematical models that have been used to study mpox transmission in the literature in order to determine what are the model classes most frequently used, their assumptions, and the modelling gaps that need to be addressed in the context of the epidemiological characteristics of the ongoing mpox outbreak. Methods: This study employed the methodology of the PRISMA guidelines for scoping reviews to identify the mathematical models available to study mpox transmission dynamics. Three databases (PubMed, Web of Science and MathSciNet) were systematically searched to identify relevant studies. Results: A total of 5827 papers were screened from the database queries. After the screening, 35 studies that met the inclusion criteria were analyzed, and 19 were finally included in the scoping review. Our results show that compartmental, branching process, Monte Carlo (stochastic), agent-based, and network models have been used to study mpox transmission dynamics between humans as well as between humans and animals. Furthermore, compartmental and branching models have been the most commonly used classes. Conclusions: There is a need to develop modeling strategies for mpox transmission that take into account the conditions of the current outbreak, which has been largely driven by human-to-human transmission in urban settings. In the current scenario, the assumptions and parameters used by most of the studies included in this review (which are largely based on a limited number of studies carried out in Africa in the early 80s) may not be applicable, and therefore, can complicate any public health policies that are derived from their estimates. The current mpox outbreak is also an example of how more research into neglected zoonoses is needed in an era where new and re-emerging diseases have become global public health threats.

Identification of the elements of models of antimicrobial resistance of bacteria for assessing their usefulness and usability in One Health decision making: a protocol for scoping review.

INTRODUCTION: Antimicrobial resistance (AMR) is a complex problem that requires the One Health approach, that is, a collaboration among various disciplines working in different sectors (animal, human and environment) to resolve it. Mathematical and statistical models have been used to understand AMR development, emergence, dissemination, prediction and forecasting. A review of the published models of AMR will help consolidate our knowledge of the dynamics of AMR and will also facilitate decision-makers and researchers in evaluating the credibility, generalisability and interpretation of the results and aspects of AMR models. The study objective is to identify and synthesise knowledge on mathematical and statistical models of AMR among bacteria in animals, humans and environmental compartments. METHODS AND ANALYSIS: Eligibility criteria: Original research studies reporting mathematical and statistical models of AMR among bacteria in animal, human and environmental compartments that were published until 2022 in English, French and Spanish will be included in this study. SOURCES OF EVIDENCE: Database of PubMed, Agricola (Ovid), Centre for Agriculture and Bioscience Direct (CABI), Web of Science (Clarivate), Cumulative Index to Nursing and Allied Health Literature (CINAHL) and MathScinet. Data charting: Metadata of the study, the context of the study, model structure, model process and reporting quality will be extracted. A narrative summary of this information, gaps and recommendations will be prepared and reported in One Health decision-making context. ETHICS AND DISSEMINATION: Research ethics board approval was not obtained for this study as neither human participation nor unpublished human data were used in this study. The study findings will be widely disseminated among the One Health Modelling Network for Emerging Infections network and stakeholders by means of conferences, and publication in open-access peer-reviewed journals.

Modelling the transmission of dengue, zika and chikungunya: a scoping review protocol.

INTRODUCTION: Aedes mosquitoes are the primary vectors for the spread of viruses like dengue (DENV), zika (ZIKV) and chikungunya (CHIKV), all of which affect humans. Those diseases contribute to global public health issues because of their great dispersion in rural and urban areas. Mathematical and statistical models have become helpful in understanding these diseases' epidemiological dynamics. However, modelling the complexity of a real phenomenon, such as a viral disease, should consider several factors. This scoping review aims to document, identify and classify the most important factors as well as the modelling strategies for the spread of DENV, ZIKV and CHIKV. METHODS AND ANALYSIS: We will conduct searches in electronic bibliographic databases such as PubMed, MathSciNet and the Web of Science for full-text peer-reviewed articles written in English, French and Spanish. These articles should use mathematical and statistical modelling frameworks to study dengue, zika and chikungunya, and their cocirculation/coinfection with other diseases, with a publication date between 1 January 2011 and 31 July 2023. Eligible studies should employ deterministic, stochastic or statistical modelling approaches, consider control measures and incorporate parameters' estimation or considering calibration/validation approaches. We will exclude articles focusing on clinical/laboratory experiments or theoretical articles that do not include any case study. Two reviewers specialised in zoonotic diseases and mathematical/statistical modelling will independently screen and retain relevant studies. Data extraction will be performed using a structured form, and the findings of the study will be summarised through classification and descriptive analysis. Three scoping reviews will be published, each focusing on one disease and its cocirculation/co-infection with other diseases. ETHICS AND DISSEMINATION: This protocol is exempt from ethics approval because it is carried out on published manuscripts and without the participation of humans and/or animals. The results will be disseminated through peer-reviewed publications and presentations in conferences.

Differential Influence of Environmental Factors on Malaria Due to Vector Control Interventions in Uganda.

BACKGROUND: Few studies have explored how vector control interventions may modify associations between environmental factors and malaria. METHODS: We used weekly malaria cases reported from six public health facilities in Uganda. Environmental variables (temperature, rainfall, humidity, and vegetation) were extracted from remote sensing sources. The non-linearity of environmental variables was investigated, and negative binomial regression models were used to explore the influence of indoor residual spraying (IRS) and long-lasting insecticidal nets (LLINs) on associations between environmental factors and malaria incident cases for each site as well as pooled across the facilities, with or without considering the interaction between environmental variables and vector control interventions. RESULTS: An average of 73.3 weekly malaria cases per site (range: 0-597) occurred between 2010 and 2018. From the pooled model, malaria risk related to environmental variables was reduced by about 35% with LLINs and 63% with IRS. Significant interactions were observed between some environmental variables and vector control interventions. There was site-specific variability in the shape of the environment-malaria risk relationship and in the influence of interventions (6 to 72% reduction in cases with LLINs and 43 to 74% with IRS). CONCLUSION: The influence of vector control interventions on the malaria-environment relationship need to be considered at a local scale in order to efficiently guide control programs.

School and community reopening during the COVID-19 pandemic: a mathematical modelling study.

Operating schools safely during the COVID-19 pandemic requires a balance between health risks and the need for in-person learning. Using demographic and epidemiological data between 31 July and 23 November 2020 from Toronto, Canada, we developed a compartmental transmission model with age, household and setting structure to study the impact of schools reopening in September 2020. The model simulates transmission in the home, community and schools, accounting for differences in infectiousness between adults and children, and accounting for work-from-home and virtual learning. While we found a slight increase in infections among adults (2.2%) and children (4.5%) within the first eight weeks of school reopening, transmission in schools was not the key driver of the virus resurgence in autumn 2020. Rather, it was community spread that determined the outbreak trajectory, primarily due to increases in contact rates among adults in the community after school reopening. Analyses of cross-infection among households, communities and schools revealed that home transmission is crucial for epidemic progression and safely operating schools, while the degree of in-person attendance has a larger impact than other control measures in schools. This study suggests that safe school reopening requires the strict maintenance of public health measures in the community.

Effectiveness of non-pharmaceutical interventions to reduce SARS-CoV-2 transmission in Canada and their association with COVID-19 hospitalization rates.

Background: Non-pharmaceutical interventions (NPIs) aim to reduce the incidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections mostly by limiting contacts between people where virus transmission can occur. However, NPIs limit social interactions and have negative impacts on economic, physical, mental and social well-being. It is, therefore, important to assess the impact of NPIs on reducing the number of coronavirus disease 2019 (COVID-19) cases and hospitalizations to justify their use. Methods: Dynamic regression models accounting for autocorrelation in time series data were used with data from six Canadian provinces (British Columbia, Alberta, Saskatchewan, Manitoba, Ontario, Québec) to assess 1) the effect of NPIs (measured using a stringency index) on SARS-CoV-2 transmission (measured by the effective reproduction number), and 2) the effect of the number of hospitalized COVID-19 patients on the stringency index. Results: Increasing stringency index was associated with a statistically significant decrease in the transmission of SARS-CoV-2 in Alberta, Saskatchewan, Manitoba, Ontario and Québec. The effect of stringency on transmission was time-lagged in all of these provinces except for Ontario. In all provinces except for Saskatchewan, increasing hospitalization rates were associated with a statistically significant increase in the stringency index. The effect of hospitalization on stringency was time-lagged. Conclusion: These results suggest that NPIs have been effective in Canadian provinces, and that their implementation has been, in part, a response to increasing hospitalization rates of COVID-19 patients.