Rapid Generation of Pulmonary Organoids from Induced Pluripotent Stem Cells by Co-Culturing Endodermal and Mesodermal Progenitors for Pulmonary Disease Modelling.

Differentiation of induced pluripotent stem cells to a range of target cell types is ubiquitous in monolayer culture. To further improve the phenotype of the cells produced, 3D organoid culture is becoming increasingly prevalent. Mature organoids typically require the involvement of cells from multiple germ layers. The aim of this study was to produce pulmonary organoids from defined endodermal and mesodermal progenitors. Endodermal and mesodermal progenitors were differentiated from iPSCs and then combined in 3D Matrigel hydrogels and differentiated for a further 14 days to produce pulmonary organoids. The organoids expressed a range of pulmonary cell markers such as SPA, SPB, SPC, AQP5 and T1α. Furthermore, the organoids expressed ACE2 capable of binding SARS-CoV-2 spike proteins, demonstrating the physiological relevance of the organoids produced. This study presented a rapid production of pulmonary organoids using a multi-germ-layer approach that could be used for studying respiratory-related human conditions.

Analyzing the effects of quarantine, isolation, and vaccination on the spread of COVID-19 via a mathematical model

The main COVID-19 control strategies presently practiced are maintaining social distancing, quarantin-ing suspected exposures, and isolating infectious people. In this paper, a deterministic compartmental mathematical model is proposed considering these three control strategies. Based on the proposed model the effect of vaccination on the suppression of the disease is discussed. Critical vaccination rate and vaccinated population size relevant to disease suppression are determined based on the proposed mathematical model. Different forms of the most used key term in infectious disease modelling, reproduction number, are determined relevant to the proposed model. Sensitivity analysis of the reproduction numbers is done to identify model parameters mostly affecting the spread of the disease. Based on the reproduction number of the model disease controlling parameter regions are determined and graphical representations of those parameter regions are presented. Based on the results of the proposed mathematical model, it is observed that earlier implementation of the vaccination process is helpful to better control the disease. However, it takes considerable time to invent successful vaccinations for newly out-breaking diseases like COVID-19. Therefore, it took considerable time to start the vaccination process for COVID-19. It is observed that after starting a vaccination process at a particular rate it should continue until the vaccinated population reaches a critical size. © 2023, National Science Foundation. All rights reserved.

Microfluidic devices for the detection of disease-specific proteins and other macromolecules, disease modelling and drug development: A review.

Microfluidics is a revolutionary technology that has promising applications in the biomedical field.Integrating microfluidic technology with the traditional assays unravels the innumerable possibilities for translational biomedical research. Microfluidics has the potential to build up a novel platform for diagnosis and therapy through precise manipulation of fluids and enhanced throughput functions. The developments in microfluidics-based devices for diagnostics have evolved in the last decade and have been established for their rapid, effective, accurate and economic advantages. The efficiency and sensitivity of such devices to detect disease-specific macromolecules like proteins and nucleic acids have made crucial impacts in disease diagnosis. The disease modelling using microfluidic systems provides a more prominent replication of the in vivo microenvironment and can be a better alternative for the existing disease models. These models can replicate critical microphysiology like the dynamic microenvironment, cellular interactions, and biophysical and biochemical cues. Microfluidics also provides a promising system for high throughput drug screening and delivery applications. However, microfluidics-based diagnostics still encounter related challenges in the reliability, real-time monitoring and reproducibility that circumvents this technology from being impacted in the healthcare industry. This review highlights the recent microfluidics developments for modelling and diagnosing common diseases, including cancer, neurological, cardiovascular, respiratory and autoimmune disorders, and its applications in drug development.

Modelling the epidemiological implications for SARS-CoV-2 of Christmas household bubbles in England.

The emergence of SARS-CoV-2 saw severe detriments to public health being inflicted by COVID-19 disease throughout 2020. In the lead up to Christmas 2020, the UK Government sought an easement of social restrictions that would permit spending time with others over the Christmas period, whilst limiting the risk of spreading SARS-CoV-2. In November 2020, plans were published to allow individuals to socialise within 'Christmas bubbles' with friends and family. This policy involved a planned easing of restrictions in England between 23-27 December 2020, with Christmas bubbles allowing people from up to three households to meet throughout the holiday period. We estimated the epidemiological impact of both this and alternative bubble strategies that allowed extending contacts beyond the immediate household. We used a stochastic individual-based model for a synthetic population of 100,000 households, with demographic and SARS-CoV-2 epidemiological characteristics comparable to England as of November 2020. We evaluated five Christmas bubble scenarios for the period 23-27 December 2020, assuming our populations of households did not have symptomatic infection present and were not in isolation as the eased social restrictions began. Assessment comprised incidence and cumulative infection metrics. We tested the sensitivity of the results to a situation where it was possible for households to be in isolation at the beginning of the Christmas bubble period and also when there was lower adherence to testing, contact tracing and isolation interventions. We found that visiting family and friends over the holiday period for a shorter duration and in smaller groups was less risky than spending the entire five days together. The increases in infection from greater amounts of social mixing disproportionately impacted the eldest. We provide this account as an illustration of a real-time contribution of modelling insights to a scientific advisory group, the Scientific Pandemic Influenza Group on Modelling, Operational sub-group (SPI-M-O) for the Scientific Advisory Group for Emergencies (SAGE) in the UK, during the COVID-19 pandemic. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

Towards evolutionary predictions: Current promises and challenges.

Evolution has traditionally been a historical and descriptive science, and predicting future evolutionary processes has long been considered impossible. However, evolutionary predictions are increasingly being developed and used in medicine, agriculture, biotechnology and conservation biology. Evolutionary predictions may be used for different purposes, such as to prepare for the future, to try and change the course of evolution or to determine how well we understand evolutionary processes. Similarly, the exact aspect of the evolved population that we want to predict may also differ. For example, we could try to predict which genotype will dominate, the fitness of the population or the extinction probability of a population. In addition, there are many uses of evolutionary predictions that may not always be recognized as such. The main goal of this review is to increase awareness of methods and data in different research fields by showing the breadth of situations in which evolutionary predictions are made. We describe how diverse evolutionary predictions share a common structure described by the predictive scope, time scale and precision. Then, by using examples ranging from SARS-CoV2 and influenza to CRISPR-based gene drives and sustainable product formation in biotechnology, we discuss the methods for predicting evolution, the factors that affect predictability and how predictions can be used to prevent evolution in undesirable directions or to promote beneficial evolution (i.e. evolutionary control). We hope that this review will stimulate collaboration between fields by establishing a common language for evolutionary predictions.

Area-level socioeconomic deprivation, non-national residency, and Covid-19 incidence: A longitudinal spatiotemporal analysis in Germany.

Background: Socioeconomic conditions affect the dynamics of the Covid-19 pandemic. We analysed the association between area-level socioeconomic deprivation, proportion of non-nationals, and incidence of Covid-19 infections in Germany. Methods: Using linked nationally representative data at the level of 401 German districts from three waves of infection (January-2020 to May-2021), we fitted Bayesian spatiotemporal models to assess the association between socioeconomic deprivation, and proportion of non-nationals with Covid-19 incidence, controlling for age, sex, vaccination coverage, settlement structure, and spatial and temporal effects. We estimated risk ratios (RR) and corresponding 95% credible intervals (95% CrI). We further examined the deprivation domains (education, income, occupation), interactions between deprivation, sex and the proportion of non-nationals, and explored potential pathways from deprivation to Covid-19 incidence. Findings: Covid-19 incidence risk was 15% higher (RR=1·15, 95%-CrI=1·06-1·24) in areas classified with the highest deprivation quintile (Q5) compared to the least deprived areas (Q1). Medium-low (Q2), medium (Q3), and medium-high (Q4) deprived districts showed 6% (1·06, 1·00-1·12), 8% (1·08, 1·01-1·15), and 5% (1·05, 0·98-1·13) higher risk, respectively, compared to the least deprived. Districts with higher proportion of non-nationals showed higher incidence risk compared to districts with lowest proportion, but the association weakened across the three waves. During the first wave, an inverse association was observed with highest incidence risk in least deprived areas (Q1). Deprivation interacted with sex, but not with the proportion of non-nationals. Interpretation: Socioeconomic deprivation, and proportion of non-nationals are independently associated with the incidence of Covid-19. Regional planning of non-pharmaceutical interventions and vaccination strategies would benefit from consideration of area-level deprivation and non-national residency. Funding: The study was funded by the German Ministry of Health (ZMV I 1 - 25 20 COR 410).

Extracting Self-Reported COVID-19 Symptom Tweets and Twitter Movement Mobility Origin/Destination Matrices to Inform Disease Models

The emergence of the novel coronavirus (COVID-19) generated a need to quickly and accurately assemble up-to-date information related to its spread. In this research article, we propose two methods in which Twitter is useful when modelling the spread of COVID-19: (1) machine learning algorithms trained in English, Spanish, German, Portuguese and Italian are used to identify symptomatic individuals derived from Twitter. Using the geo-location attached to each tweet, we map users to a geographic location to produce a time-series of potential symptomatic individuals. We calibrate an extended SEIRD epidemiological model with combinations of low-latency data feeds, including the symptomatic tweets, with death data and infer the parameters of the model. We then evaluate the usefulness of the data feeds when making predictions of daily deaths in 50 US States, 16 Latin American countries, 2 European countries and 7 NHS (National Health Service) regions in the UK. We show that using symptomatic tweets can result in a 6% and 17% increase in mean squared error accuracy, on average, when predicting COVID-19 deaths in US States and the rest of the world, respectively, compared to using solely death data. (2) Origin/destination (O/D) matrices, for movements between seven NHS regions, are constructed by determining when a user has tweeted twice in a 24 h period in two different locations. We show that increasing and decreasing a social connectivity parameter within an SIR model affects the rate of spread of a disease. © 2023 by the authors.

Data pipelines in a public health emergency: The human in the machine.

In an emergency epidemic response, data providers supply data on a best-faith effort to modellers and analysts who are typically the end user of data collected for other primary purposes such as to inform patient care. Thus, modellers who analyse secondary data have limited ability to influence what is captured. During an emergency response, models themselves are often under constant development and require both stability in their data inputs and flexibility to incorporate new inputs as novel data sources become available. This dynamic landscape is challenging to work with. Here we outline a data pipeline used in the ongoing COVID-19 response in the UK that aims to address these issues. A data pipeline is a sequence of steps to carry the raw data through to a processed and useable model input, along with the appropriate metadata and context. In ours, each data type had an individual processing report, designed to produce outputs that could be easily combined and used downstream. Automated checks were in-built and added as new pathologies emerged. These cleaned outputs were collated at different geographic levels to provide standardised datasets. Finally, a human validation step was an essential component of the analysis pathway and permitted more nuanced issues to be captured. This framework allowed the pipeline to grow in complexity and volume and facilitated the diverse range of modelling approaches employed by researchers. Additionally, every report or modelling output could be traced back to the specific data version that informed it ensuring reproducibility of results. Our approach has been used to facilitate fast-paced analysis and has evolved over time. Our framework and its aspirations are applicable to many settings beyond COVID-19 data, for example for other outbreaks such as Ebola, or where routine and regular analyses are required.

Epidemic Diffusion Network of Spain: A Mobility Model to Characterize the Transmission Routes of Disease.

Human mobility drives the geographical diffusion of infectious diseases at different scales, but few studies focus on mobility itself. Using publicly available data from Spain, we define a Mobility Matrix that captures constant flows between provinces by using a distance-like measure of effective distance to build a network model with the 52 provinces and 135 relevant edges. Madrid, Valladolid and Araba/Álaba are the most relevant nodes in terms of degree and strength. The shortest routes (most likely path between two points) between all provinces are calculated. A total of 7 mobility communities were found with a modularity of 63%, and a relationship was established with a cumulative incidence of COVID-19 in 14 days (CI14) during the study period. In conclusion, mobility patterns in Spain are governed by a small number of high-flow connections that remain constant in time and seem unaffected by seasonality or restrictions. Most of the travels happen within communities that do not completely represent political borders, and a wave-like spreading pattern with occasional long-distance jumps (small-world properties) can be identified. This information can be incorporated into preparedness and response plans targeting locations that are at risk of contagion preventively, underscoring the importance of coordination between administrations when addressing health emergencies.

On the interplay of regional mobility, social connectedness and the spread of COVID-19 in Germany.

Since the primary mode of respiratory virus transmission is person-to-person interaction, we are required to reconsider physical interaction patterns to mitigate the number of people infected with COVID-19. While research has shown that non-pharmaceutical interventions (NPI) had an evident impact on national mobility patterns, we investigate the relative regional mobility behaviour to assess the effect of human movement on the spread of COVID-19. In particular, we explore the impact of human mobility and social connectivity derived from Facebook activities on the weekly rate of new infections in Germany between 3 March and 22 June 2020. Our results confirm that reduced social activity lowers the infection rate, accounting for regional and temporal patterns. The extent of social distancing, quantified by the percentage of people staying put within a federal administrative district, has an overall negative effect on the incidence of infections. Additionally, our results show spatial infection patterns based on geographical as well as social distances.

Voluntary risk mitigation behaviour can reduce impact of SARS-CoV-2: a real-time modelling study of the January 2022 Omicron wave in England.

BACKGROUND: Predicting the likely size of future SARS-CoV-2 waves is necessary for public health planning. In England, voluntary "plan B" mitigation measures were introduced in December 2021 including increased home working and face coverings in shops but stopped short of restrictions on social contacts. The impact of voluntary risk mitigation behaviours on future SARS-CoV-2 burden is unknown. METHODS: We developed a rapid online survey of risk mitigation behaviours ahead of the winter 2021 festive period and deployed in two longitudinal cohort studies in the UK (Avon Longitudinal Study of Parents and Children (ALSPAC) and TwinsUK/COVID Symptom Study (CSS) Biobank) in December 2021. Using an individual-based, probabilistic model of COVID-19 transmission between social contacts with SARS-CoV-2 Omicron variant parameters and realistic vaccine coverage in England, we predicted the potential impact of the SARS-CoV-2 Omicron wave in England in terms of the effective reproduction number and cumulative infections, hospital admissions and deaths. Using survey results, we estimated in real-time the impact of voluntary risk mitigation behaviours on the Omicron wave in England, if implemented for the entire epidemic wave. RESULTS: Over 95% of survey respondents (NALSPAC = 2686 and NTwins = 6155) reported some risk mitigation behaviours, with vaccination and using home testing kits reported most frequently. Less than half of those respondents reported that their behaviour was due to "plan B". We estimate that without risk mitigation behaviours, the Omicron variant is consistent with an effective reproduction number between 2.5 and 3.5. Due to the reduced vaccine effectiveness against infection with the Omicron variant, our modelled estimates suggest that between 55% and 60% of the English population could be infected during the current wave, translating into between 12,000 and 46,000 cumulative deaths, depending on assumptions about severity and vaccine effectiveness. The actual number of deaths was 15,208 (26 November 2021-1 March 2022). We estimate that voluntary risk reduction measures could reduce the effective reproduction number to between 1.8 and 2.2 and reduce the cumulative number of deaths by up to 24%. CONCLUSIONS: Predicting future infection burden is affected by uncertainty in disease severity and vaccine effectiveness estimates. In addition to biological uncertainty, we show that voluntary measures substantially reduce the projected impact of the SARS-CoV-2 Omicron variant but that voluntary measures alone would be unlikely to completely control transmission.

Using nanomaterials to address SARS-CoV-2 variants through development of vaccines and therapeutics

Nanomaterials have played a significant role in effectively combating the global SARS-CoV-2 pandemic that began in December 2019 through the development of vaccines as well as antiviral therapies. These versatile, tunable materials can interact and deliver a broad range of biologically relevant molecules for preventing COVID-19 infection, generating immunity against COVID-19, and treating infected patients. Application of these nanomaterials and nanotechnologies can further be investigated in conjunction with disease models of COVID-19 and this holds immense potential for accelerating vaccine or therapeutic process development further encouraging the elimination of animal model use during preclinical stages. This review examines the existing literature on COVID-19 related nanomaterial applications, including perspective on nanotechnology-based vaccines and therapeutics, and discusses how these tools can be adapted to address new SARS-CoV-2 variants of concern. We also analyze the limitations of current nanomaterial approaches to managing COVID-19 and its variants alongside the challenges posed when implementing this technology. We end by providing avenues for future developments specific to disease modelling in this ever-evolving field.

Differential Evolution to Optimize A Long-term Multi-strain Model of COVID-19 in South Africa

A limited number of studies have been conducted to investigate the dynamics of COVID-19 disease spread in South Africa and these existing studies have mostly focussed on mathematical analysis of a relatively short time period near the initial outbreak of COVID-19 in South Africa. The current study therefore attempted to extend on previous studies by applying a Susceptible- Exposed - Infected - Removed (SEIR) disease model to analyse the long-term dynamics of COVID-19 in South Africa, taking into account multiple waves of infection potentially caused by different virus strains. A Differential Evolution (DE) algorithm was used to fit the proposed model to real-world data, and this was done on both a geographically local and global scale to investigate the differences between these two approaches. Results revealed that a local approach provided a more accurate model fit to data than a global approach and that the method proposed in this work could give valuable insights into disease dynamics. © 2022 IEEE.

Optimal quarantine, isolation, and social distancing strategies for COVID-19 based on a mathematical model

Around 221 countries in the world are currently suffering from the COVID-19 pandemic and the World Health Organization reported there are 217.7 million confirmed cases with 4.5 million deaths tolls as of 31st August 2021. Until a cure is found, it is more appropriate to follow the health guidelines recommended by authorities. Theoretically, forecasting the courses and possible outcomes of such a pandemic is crucial for healthcare sectors to make decisions in advance. This paper aims to find optimal quarantine, isolation, and social distancing strategies for COVID-19 based on the SEIQJR mathematical model with a proper cost analysis. Minimising the cost of the controlling process of diseases is very important for public health policymakers. An optimal control problem is considered with a proposed cost functional which is minimised to yield optimal control strategies. We subsequently insert an inequality state constraint to the problem by considering the possible maximum capacities of hospitals. Copyright © 2022 Inderscience Enterprises Ltd.

Modelling of spatial infection spread through heterogeneous population: from lattice to partial differential equation models.

We present a simple model for the spread of an infection that incorporates spatial variability in population density. Starting from first-principle considerations, we explore how a novel partial differential equation with state-dependent diffusion can be obtained. This model exhibits higher infection rates in the areas of higher population density-a feature that we argue to be consistent with epidemiological observations. The model also exhibits an infection wave, the speed of which varies with population density. In addition, we demonstrate the possibility that an infection can 'jump' (i.e. tunnel) across areas of low population density towards areas of high population density. We briefly touch upon the data reported for coronavirus spread in the Canadian province of Nova Scotia as a case example with a number of qualitatively similar features as our model. Lastly, we propose a number of generalizations of the model towards future studies.

SARS-CoV-2 infection in cardiovascular disease: Unmet need of stem cell models.

This review aims to summarise new approaches in SARS-CoV-2-related research in cardiology. We provide a head-to-head comparison of models, such as animal research and human pluripotent stem cells, to investigate the pathomechanisms of COVID-19 and find an efficient therapy. In vivo methods were useful for studying systemic processes of the disease; however, due to differences in animal and human biology, the clinical translation of the results remains a complex task. In vitro stem cell research makes cellular events more observable and effective for finding new drugs and therapies for COVID-19, including the use of stem cells. Furthermore, multicellular 3D organoids even make it possible to observe the effects of drugs to treat SARS-CoV-2 infection in human organ models.

SARS-CoV-2 transmission in university classes.

We investigate transmission dynamics for SARS-CoV-2 on a real network of classes at Simon Fraser University. Outbreaks are simulated over the course of one semester across numerous parameter settings, including moving classes above certain size thresholds online. Regression trees are used to analyze the effect of disease parameters on simulation outputs. We find that an aggressive class size thresholding strategy is required to mitigate the risk of a large outbreak, and that transmission by symptomatic individuals is a key driver of outbreak size. These findings provide guidance for designing control strategies at other institutions, as well as setting priorities and allocating resources for disease monitoring. Supplementary Information: The online version contains supplementary material available at 10.1007/s13721-022-00375-1.

Lung Organoids-The Ultimate Tool to Dissect Pulmonary Diseases?

Organoids are complex multicellular three-dimensional (3D) in vitro models that are designed to allow accurate studies of the molecular processes and pathologies of human organs. Organoids can be derived from a variety of cell types, such as human primary progenitor cells, pluripotent stem cells, or tumor-derived cells and can be co-cultured with immune or microbial cells to further mimic the tissue niche. Here, we focus on the development of 3D lung organoids and their use as disease models and drug screening tools. We introduce the various experimental approaches used to model complex human diseases and analyze their advantages and disadvantages. We also discuss validation of the organoids and their physiological relevance to the study of lung diseases. Furthermore, we summarize the current use of lung organoids as models of host-pathogen interactions and human lung diseases such as cystic fibrosis, chronic obstructive pulmonary disease, or SARS-CoV-2 infection. Moreover, we discuss the use of lung organoids derived from tumor cells as lung cancer models and their application in personalized cancer medicine research. Finally, we outline the future of research in the field of human induced pluripotent stem cell-derived organoids.

Minimising the use of costly control measures in an epidemic elimination strategy: A simple mathematical model.

Countries such as New Zealand, Australia and Taiwan responded to the Covid-19 pandemic with an elimination strategy. This involves a combination of strict border controls with a rapid and effective response to eliminate border-related re-introductions. An important question for decision makers is, when there is a new re-introduction, what is the right threshold at which to implement strict control measures designed to reduce the effective reproduction number below 1. Since it is likely that there will be multiple re-introductions, responding at too low a threshold may mean repeatedly implementing controls unnecessarily for outbreaks that would self-eliminate even without control measures. On the other hand, waiting for too high a threshold to be reached creates a risk that controls will be needed for a longer period of time, or may completely fail to contain the outbreak. Here, we use a highly idealised branching process model of small border-related outbreaks to address this question. We identify important factors that affect the choice of threshold in order to minimise the expect time period for which control measures are in force. We find that the optimal threshold for introducing controls decreases with the effective reproduction number, and increases with overdispersion of the offspring distribution and with the effectiveness of control measures. Our results are not intended as a quantitative decision-making algorithm. However, they may help decision makers understand when a wait-and-see approach is likely to be preferable over an immediate response.

Predicting epidemics and the impact of interventions in heterogeneous settings: Standard SEIR models are too pessimistic

The basic reproduction number (R0) is an established concept to describe the potential for an infectious disease to cause an epidemic and to derive estimates of the required effect of interventions for successful control. Calculating R0 from simple deterministic transmission models may result in biased estimates when important sources of heterogeneity related to transmission and control are ignored. Using stochastic simulations with a geographically stratified individual-based SEIR (susceptible, exposed, infectious, recovered) model, we illustrate that if heterogeneity is ignored (i.e. no or too little assumed interindividual variation or assortative mixing) this may substantially overestimate the transmission rate and the potential course of the epidemic. Consequently, predictions for the impact of interventions then become relatively pessimistic. However, should such an intervention be suspended, then the potential for a consecutive epidemic wave will depend strongly on assumptions about heterogeneity, with more heterogeneity resulting in lower remaining epidemic potential, due to selection and depletion of high-risk individuals during the early stages of the epidemic. These phenomena have likely also affected current model predictions regarding COVID-19, as most transmission models assume homogeneous mixing or at most employ a simple age stratification, thereby leading to overcautious predictions of durations of lockdowns and required vaccine coverage levels. © 2022 The Authors. Journal of the Royal Statistical Society: Series A (Statistics in Society) published by John Wiley & Sons Ltd on behalf of Royal Statistical Society.