Influence of spatial heterogeneity on an emerging infectious disease: the case of dengue epidemics

The importance of spatial heterogeneity and spatial scales (at a village or neighbourhood scale) has been explored with individual-based models. Our reasoning is based on the Chilean Easter Island (EI) case, where a first dengue epidemic occurred in 2002 among the relatively small population localized in one village. Even in this simple situation, the real epidemic is not consistent with homogeneous models. Conversely, including contact heterogeneity on different scales (intra-households, inter-house, inter-areas) allows the recovery of not only the EI epidemiological curve but also the qualitative patterns of Brazilian urban dengue epidemic in more complex situations.     

Rapid generation of inducible mouse mutants

We have generated an optimized inducible recombination system for conditional gene targeting based on a Cre recombinase–steroid receptor fusion. This configuration allows efficient Cre-mediated recombination in most organs of the mouse upon induction, without detectable background activity. An ES cell line, was established that carries the inducible recombinase and a loxP-flanked lacZ reporter gene. Out of this line, completely ES cell-derived mice were efficiently produced through tetraploid blastocyst complementation, without the requirement of mouse breeding. Our findings provide a new concept allowing the generation of inducible mouse mutants within 6 months, as compared to 14 months using the current protocol.     

Clinical progress in MSC-based therapies for the management of severe COVID-19

Considering the high impact that severe Coronavirus disease 2019 (COVID-19) cases still pose on public health and their complex pharmacological management, the search for new therapeutic alternatives is essential. Mesenchymal stromal cells (MSCs) could be promising candidates as they present important immunomodulatory and anti-inflammatory properties that can combat the acute severe respiratory distress syndrome (ARDS) and the cytokine storm occurring in COVID-19, two processes that are mainly driven by an immunological misbalance. In this review, we provide a comprehensive overview of the intricate inflammatory process derived from the immune dysregulation that occurs in COVID-19, discussing the potential that the cytokines and growth factors that constitute the MSC-derived secretome present to treat the disease. Moreover, we revise the latest clinical progress made in the field, discussing the most important findings of the clinical trials conducted to date, which follow 2 different approaches: MSC-based cell therapy or the administration of the secretome by itself, as a cell-free therapy.     

The Rapid Assessment and Early Warning Models for COVID-19

Human beings have experienced a serious public health event as the new pneumonia (COVID-19), caused by the severe acute respiratory syndrome coronavirus ha     

Empirical model for short-time prediction of COVID-19 spreading

The appearance and fast spreading of Covid-19 took the international community by surprise. Collaboration between researchers, public health workers, and politicians has been established to deal with the epidemic. One important contribution from researchers in epidemiology is the analysis of trends so that both the current state and short-term future trends can be carefully evaluated. Gompertz model has been shown to correctly describe the dynamics of cumulative confirmed cases, since it is characterized by a decrease in growth rate showing the effect of control measures. Thus, it provides a way to systematically quantify the Covid-19 spreading velocity and it allows short-term predictions and longer-term estimations. This model has been employed to fit the cumulative cases of Covid-19 from several European countries. Results show that there are systematic differences in spreading velocity among countries. The model predictions provide a reliable picture of the short-term evolution in countries that are in the initial stages of the Covid-19 outbreak, and may permit researchers to uncover some characteristics of the long-term evolution. These predictions can also be generalized to calculate short-term hospital and intensive care units (ICU) requirements.     

On realized serial and generation intervals given control measures: The COVID-19 pandemic case

The SARS-CoV-2 pathogen is currently spreading worldwide and its propensity for presymptomatic and asymptomatic transmission makes it difficult to control. The control measures adopted in several countries aim at isolating individuals once diagnosed, limiting their social interactions and consequently their transmission probability. These interventions, which have a strong impact on the disease dynamics, can affect the inference of the epidemiological quantities. We first present a theoretical explanation of the effect caused by non-pharmaceutical intervention measures on the mean serial and generation intervals. Then, in a simulation study, we vary the assumed efficacy of control measures and quantify the effect on the mean and variance of realized generation and serial intervals. The simulation results show that the realized serial and generation intervals both depend on control measures and their values contract according to the efficacy of the intervention strategies. Interestingly, the mean serial interval differs from the mean generation interval. The deviation between these two values depends on two factors. First, the number of undiagnosed infectious individuals. Second, the relationship between infectiousness, symptom onset and timing of isolation. Similarly, the standard deviations of realized serial and generation intervals do not coincide, with the former shorter than the latter on average. The findings of this study are directly relevant to estimates performed for the current COVID-19 pandemic. In particular, the effective reproduction number is often inferred using both daily incidence data and the generation interval. Failing to account for either contraction or mis-specification by using the serial interval could lead to biased estimates of the effective reproduction number. Consequently, this might affect the choices made by decision makers when deciding which control measures to apply based on the value of the quantity thereof.     

Host genetic variability and determinants of severe COVID-19

Since the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak, convergent studies have provided evidence that host genetic background may contribute to the development of severe coronavirus disease (COVID-19). Here, we summarize how some genetic variations, such as in SARS-CoV-2 receptor angiotensin-converting enzyme 2 or interferon signaling pathway, may help to understand why some individuals can develop severe COVID-19.     

Performance of early warning signals for disease re-emergence: A case study on COVID-19 data

Developing measures for rapid and early detection of disease re-emergence is important to perform science-based risk assessment of epidemic threats. In the past few years, several early warning signals (EWS) from complex systems theory have been introduced to detect impending critical transitions and extend the set of indicators. However, it is still debated whether they are generically applicable or potentially sensitive to some dynamical characteristics such as system noise and rates of approach to critical parameter values. Moreover, testing on empirical data has, so far, been limited. Hence, verifying EWS performance remains a challenge. In this study, we tackle this question by analyzing the performance of common EWS, such as increasing variance and autocorrelation, in detecting the emergence of COVID-19 outbreaks in various countries. Our work illustrates that these EWS might be successful in detecting disease emergence when some basic assumptions are satisfied: a slow forcing through the transitions and not-fat-tailed noise. In uncertain cases, we observe that noise properties or commensurable time scales may obscure the expected early warning signals. Overall, our results suggest that EWS can be useful for active monitoring of epidemic dynamics, but that their performance is sensitive to certain features of the underlying dynamics. Our findings thus pave a connection between theoretical and empirical studies, constituting a further step towards the application of EWS indicators for informing public health policies.     

Bradyarrhythmias in patients with COVID-19: A case series

Novel coronavirus disease (COVID-19) can have variety of cardiac manifestations; however, less is known about the prevalence, clinical characteristics and outcomes of bradyarrhythmias in patients with COVID-19. In the present case series of bradyarrhythmia in patients with COVID-19, we report complete heart block requiring intervention in 5 patients and sinus node dysfunction in 2 patients.     

Drosophila, an emerging model for cardiac disease

A variety of studies that are currently underway may validate the fruit fly as an in vivo model for analyzing genes involved in cardiac function. Many mutations in conserved genetic pathways have been found, including those controlling development and physiology. Because homologous genes control early developmental events as well as functional components of the Drosophila and vertebrate hearts, the fly is the simplest existing model system that can be used to assay genes involved in human congenital heart disease (CHD). The wide variety of genetic tools available to Drosophila researchers offers many technical advantages for rapidly screening through large numbers of candidate genes. Thus, an important future and long-term direction is likely to be the use of Drosophila as a vehicle for analyzing polygenic traits as an aid in human genetics. One can anticipate a time in the not too distant future when mutant lines exist for every gene in vertebrate systems, such as mice and zebrafish. However, one of the enduring problems that will not easily be addressed by such resources will be the tracking of complex traits defined by polygenic variants. For this level of genetic analysis, simple genetic model systems including yeast, Caenorhabditis elegans, and Drosophila melanogaster will undoubtedly play a crucial ongoing role. Of them, Drosophila will be critical for examining gene networks involved in organogenesis and is clearly the system of choice for studying cardiac development, function and aging, since among the simple genetic models it is the only one with a fluid pumping heart.     

A critical evaluation of glucocorticoids in the management of severe COVID-19

The viral infection by SARS-CoV-2 has irrevocably altered the life of the majority of human beings, challenging national health systems worldwide, and pushing researchers to rapidly find adequate preventive and treatment strategies. No therapies have been shown effective with the exception of dexamethasone, a glucocorticoid that was recently proved to be the first life-saving drug in this disease. Remarkably, around 20 % of infected people develop a severe form of COVID-19, giving rise to respiratory and multi-organ failures requiring subintensive and intensive care interventions. This phenomenon is due to an excessive immune response that damages pulmonary alveoli, leading to a cytokine and chemokine storm with systemic effects. Indeed glucocorticoids’ role in regulating this immune response is controversial, and they have been used in clinical practice in a variety of countries, even without a previous clear consensus on their evidence-based benefit.     

Surveillance of emerging infectious diseases for biosecurity

Science China Life Sciences - Emerging infectious diseases, such as COVID-19, continue to pose significant threats to human beings and their surroundings. In addition, biological warfare,...     

Metatranscriptomic analysis of host response and vaginal microbiome of patients with severe COVID-19

Science China Life Sciences -     

Age-related model for estimating the symptomatic and asymptomatic transmissibility of COVID-19 patients

Estimation of age-dependent transmissibility of COVID-19 patients is critical for effective policymaking. Although the transmissibility of symptomatic cases has been extensively studied, asymptomatic infection is understudied due to limited data. Using a dataset with reliably distinguished symptomatic and asymptomatic statuses of COVID-19 cases, we propose an ordinary differential equation model that considers age-dependent transmissibility in transmission dynamics. Under a Bayesian framework, multi-source information is synthesized in our model for identifying transmissibility. A shrinkage prior among age groups is also adopted to improve the estimation behavior of transmissibility from age-structured data. The added values of accounting for age-dependent transmissibility are further evaluated through simulation studies. In real-data analysis, we compare our approach with two basic models using the deviance information criterion (DIC) and its extension. We find that the proposed model is more flexible for our epidemic data. Our results also suggest that the transmissibility of asymptomatic infections is significantly lower (on average, 76.45% with a credible interval (27.38%, 88.65%)) than that of symptomatic cases. In both symptomatic and asymptomatic patients, the transmissibility mainly increases with age. Patients older than 30 years are more likely to develop symptoms with higher transmissibility. We also find that the transmission burden of asymptomatic cases is lower than that of symptomatic patients.