Mathematical models of daphnia epidemics

Disease ecology studies the interactions among hosts, pathogens, and the environment and how these shape the spread of disease. These interactions can be quite complex and lead to fascinating dynamics. Our system of study, Daphnia has a lot of interesting and complex features that can be analyzed with precision both biologically and mathematically. By using mathematical models we can study the underlying biological mechanisms that drive and/or inhibit the spread of disease. This dissertation explores, through a range of models, the many aspects that play a role in Daphnia epidemics. We begin with simple models and build models with higher complexity by adding more realistic biological assumptions. From ordinary and partial differential equation models to stochastic models, through the chapters of this thesis, we zoom-in to the different aspects of Daphnia epidemics and and zoom-out to the bigger story that connects them. We give precise conditions under which short-term evolution of hosts can lead to the early termination of an epidemic. Moreover, overturning an assumption about hosts’ ability to recover, we showcase the role of recovery from an infection in reducing disease prevalence and the number of secondary infections. Through this thesis we have gained more insight into the biology of our system, and more importantly we open the door to new and exciting questions. As new biological insights are discovered, we can use mathematical models to continue to unravel the many aspects of Daphnia epidemics

To dine or not to dine: A visual exploration of Chicago’s food inspections

A visual exploration of Chicago food inspection dataset between 07/03/18 and 02/08/19 for HackCulture 2019.Ope

To dine or not to dine: A visual exploration of Chicago’s food inspections

A visual exploration of Chicago food inspection dataset between 07/03/18 and 02/08/19 for HackCulture 2019.Ope

An international observational study to assess the impact of the Omicron variant emergence on the clinical epidemiology of COVID-19 in hospitalised patients

Background: Whilst timely clinical characterisation of infections caused by novel SARS-CoV-2 variants is necessary for evidence-based policy response, individual-level data on infecting variants are typically only available for a minority of patients and settings. Methods: Here, we propose an innovative approach to study changes in COVID-19 hospital presentation and outcomes after the Omicron variant emergence using publicly available population-level data on variant relative frequency to infer SARS-CoV-2 variants likely responsible for clinical cases. We apply this method to data collected by a large international clinical consortium before and after the emergence of the Omicron variant in different countries. Results: Our analysis, that includes more than 100,000 patients from 28 countries, suggests that in many settings patients hospitalised with Omicron variant infection less often presented with commonly reported symptoms compared to patients infected with pre-Omicron variants. Patients with COVID-19 admitted to hospital after Omicron variant emergence had lower mortality compared to patients admitted during the period when Omicron variant was responsible for only a minority of infections (odds ratio in a mixed-effects logistic regression adjusted for likely confounders, 0.67 [95% confidence interval 0.61-0.75]). Qualitatively similar findings were observed in sensitivity analyses with different assumptions on population-level Omicron variant relative frequencies, and in analyses using available individual-level data on infecting variant for a subset of the study population. Conclusions: Although clinical studies with matching viral genomic information should remain a priority, our approach combining publicly available data on variant frequency and a multi-country clinical characterisation dataset with more than 100,000 records allowed analysis of data from a wide range of settings and novel insights on real-world heterogeneity of COVID-19 presentation and clinical outcome

Characteristics and outcomes of an international cohort of 600 000 hospitalized patients with COVID-19

Background: We describe demographic features, treatments and clinical outcomes in the International Severe Acute Respiratory and emerging Infection Consortium (ISARIC) COVID-19 cohort, one of the world's largest international, standardized data sets concerning hospitalized patients. Methods: The data set analysed includes COVID-19 patients hospitalized between January 2020 and January 2022 in 52 countries. We investigated how symptoms on admission, co-morbidities, risk factors and treatments varied by age, sex and other characteristics. We used Cox regression models to investigate associations between demographics, symptoms, co-morbidities and other factors with risk of death, admission to an intensive care unit (ICU) and invasive mechanical ventilation (IMV). Results: Data were available for 689 572 patients with laboratory-confirmed (91.1%) or clinically diagnosed (8.9%) SARS-CoV-2 infection from 52 countries. Age [adjusted hazard ratio per 10 years 1.49 (95% CI 1.48, 1.49)] and male sex [1.23 (1.21, 1.24)] were associated with a higher risk of death. Rates of admission to an ICU and use of IMV increased with age up to age 60 years then dropped. Symptoms, co-morbidities and treatments varied by age and had varied associations with clinical outcomes. The case-fatality ratio varied by country partly due to differences in the clinical characteristics of recruited patients and was on average 21.5%. Conclusions: Age was the strongest determinant of risk of death, with a ∼30-fold difference between the oldest and youngest groups; each of the co-morbidities included was associated with up to an almost 2-fold increase in risk. Smoking and obesity were also associated with a higher risk of death. The size of our international database and the standardized data collection method make this study a comprehensive international description of COVID-19 clinical features. Our findings may inform strategies that involve prioritization of patients hospitalized with COVID-19 who have a higher risk of death

The value of open-source clinical science in pandemic response: lessons from ISARIC

International audienc