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Background Information and emotions towards public health issues could spread widely through online social networks. Although aggregate metrics on the volume of information diffusion are available, we know little about how information spreads on online social networks. Health information could be transmitted from one to many (i.e. broadcasting) or from a chain of individual to individual (i.e. viral spreading). The aim of this study is to examine the spreading pattern of Ebola information on Twitter and identify influential users regarding Ebola messages. Methods Our data was purchased from GNIP. We obtained all Ebola-related tweets posted globally from March 23, 2014 to May 31, 2015. We reconstructed Ebola-related retweeting paths based on Twitter content and the follower-followee relationships. Social network analysis was performed to investigate retweeting patterns. In addition to describing the diffusion structures, we classify users in the network into four categories (i.e., influential user, hidden influential user, disseminator, common user) based on following and retweeting patterns. Results On average, 91% of the retweets were directly retweeted from the initial message. Moreover, 47.5% of the retweeting paths of the original tweets had a depth of 1 (i.e., from the seed user to its immediate followers). These observations suggested that the broadcasting was more pervasive than viral spreading. We found that influential users and hidden influential users triggered more retweets than disseminators and common users. Disseminators and common users relied more on the viral model for spreading information beyond their immediate followers via influential and hidden influential users. Conclusions Broadcasting was the dominant mechanism of information diffusion of a major health event on Twitter. It suggests that public health communicators can work beneficially with influential and hidden influential users to get the message across, because influential and hidden influential users can reach more people that are not following the public health Twitter accounts. Although both influential users and hidden influential users can trigger many retweets, recognizing and using the hidden influential users as the source of information could potentially be a cost-effective communication strategy for public health promotion. However, challenges remain due to uncertain credibility of these hidden influential users.

Spatio-temporal patterns of the spread of infectious diseases are commonly driven by environmental and ecological factors. This is particularly true for vector-borne diseases because vector populations can be strongly affected by host distribution as well as by climatic and landscape variables. Here, we aim to identify environmental drivers for bluetongue virus (BTV), the causative agent of a major vector-borne disease of ruminants that has emerged multiple times in Europe in recent decades. In order to determine the importance of climatic, landscape and host-related factors affecting BTV diffusion across Europe, we fitted different phylogeographic models to a dataset of 113 time-stamped and geo-referenced BTV genomes, representing multiple strains and serotypes. Diffusion models using continuous space revealed that terrestrial habitat below 300 m altitude, wind direction and higher livestock densities were associated with faster BTV movement. Results of discrete phylogeographic analysis involving generalized linear models broadly supported these findings, but varied considerably with the level of spatial partitioning. Contrary to common perception, we found no evidence for average temperature having a positive effect on BTV diffusion, though both methodological and biological reasons could be responsible for this result. Our study provides important insights into the drivers of BTV transmission at the landscape scale that could inform predictive models of viral spread and have implications for designing control strategies.

Transmissible gastroenteritis virus (TGEV) causes high mortality in young piglets (< 3 days of age). With aging, the susceptibility/morbidity/mortality rates drop. We previously hypothesized that the age-related changes in the intestinal mucus could be responsible for this resistance. Hence, this study investigated the effect of porcine intestinal mucus from 3-day and 3-week-old pigs on the free mobility of the virulent TGEV Miller strain, and on the infection in swine testicle (ST) cells. Single particle tracking (SPT) revealed that TGEV had significantly higher diffusion coefficients in 3-day mucus compared to 3-week mucus. TGEV and charged and uncharged control nanoparticles diffused freely in 3-day mucus but were hindered by 3-week mucus in the diffusion model; TGEV mimicked the diffusion behavior of negatively charged carboxylated particles. Inoculation of ST cells with TGEV in the presence of 3-week mucus resulted in a significantly lower average number of infected cells (30.9 ± 11.9/5 fields) compared with 3-day mucus (84.6 ± 16.4/5 fields). These results show that 3-week mucus has a significant TGEV-blocking activity compared to 3-day mucus in free diffusion and infection of the underlying susceptible cells. Additionally, a label-free proteomics analysis revealed an increased expression of mucin 13, known for negatively regulating the tight junctions in intestinal epithelium, in 3-day-old pigs. In 3-week-old pigs, a higher expression of mucin 2, a type of secreted mucin which is known for inhibiting coronavirus infection, was observed. Concludingly, this study demonstrated a protective effect of 3-week mucus against viral infections.

Background: SARS-CoV-2 virus is largely transmitted via respiratory droplets and the highest transmission risks arise when undertaking aerosol generating procedures like laparoscopy. Most national societies had advised the urgent suspension of elective surgery with the focus shifting to emergency and cancer surgery only during this pandemic. However very little is known regarding the risks to the health care professionals undertaking emergency laparoscopic procedures. Aims and Objective: To demonstrate safety at laparoscopy by modifying the technique for safe management of patients during the COVID-19 pandemic. Design and Setting: This is an observational cohort study. This study was done at a tertiary care reference hospital for minimal access gynaecological surgery. Safety of 42 semi-urgent and emergency laparoscopic surgeries in patients was evaluated for a period of 5 months after taking informed written consent of patients to participate in the study. Materials and Methods: Use of double closed circuit laparoscopic suction evacuation and filtration systems with closed circuit anaesthesia with specialized Heat and Moisture Exchangers (HME) bacterial & viral (BV) filters to make laparoscopic surgery safe. Results: 57.14% of the patients were 41 years or more. 47.6% presented either with menorrhagia, irregular vaginal bleeding or post-menopausal vaginal bleeding and 26.19 % patients were keen to conceive. In 50% patients, surgery was done in 60 minutes or less. Post-operatively, none of the patients had any complications and all were followed up for 14 days for COVID-19 infection. No staff, doctors or anaesthetist were detected COVID-19 positive during the follow up period. The limitation of the study was, that it was an observational study done in COVID-19 negative patients only. Conclusions: Safety at laparoscopy can be maintained when it is performed by an experienced surgeon who has full knowledge of safe laparoscopic techniques and performs it in the shortest time possible and with all due precautions.

Standard epidemiological models for COVID-19 employ variants of compartment (SIR or susceptible–infectious–recovered) models at local scales, implicitly assuming spatially uniform local mixing. Here, we examine the effect of employing more geographically detailed diffusion models based on known spatial features of interpersonal networks, most particularly the presence of a long-tailed but monotone decline in the probability of interaction with distance, on disease diffusion. Based on simulations of unrestricted COVID-19 diffusion in 19 US cities, we conclude that heterogeneity in population distribution can have large impacts on local pandemic timing and severity, even when aggregate behavior at larger scales mirrors a classic SIR-like pattern. Impacts observed include severe local outbreaks with long lag time relative to the aggregate infection curve, and the presence of numerous areas whose disease trajectories correlate poorly with those of neighboring areas. A simple catchment model for hospital demand illustrates potential implications for health care utilization, with substantial disparities in the timing and extremity of impacts even without distancing interventions. Likewise, analysis of social exposure to others who are morbid or deceased shows considerable variation in how the epidemic can appear to individuals on the ground, potentially affecting risk assessment and compliance with mitigation measures. These results demonstrate the potential for spatial network structure to generate highly nonuniform diffusion behavior even at the scale of cities, and suggest the importance of incorporating such structure when designing models to inform health care planning, predict community outcomes, or identify potential disparities.

We analyze the adoption of nonpharmaceutical interventions in the Organisation for Economic Co-operation and Development (OECD) countries during the early phase of the coronavirus disease 2019 (COVID-19) pandemic. Given the complexity associated with pandemic decisions, governments are faced with the dilemma of how to act quickly when their core decision-making processes are based on deliberations balancing political considerations. Our findings show that, in times of severe crisis, governments follow the lead of others and base their decisions on what other countries do. Governments in countries with a stronger democratic structure are slower to react in the face of the pandemic but are more sensitive to the influence of other countries. We provide insights for research on international policy diffusion and research on the political consequences of the COVID-19 pandemic.

For preventing the spread of epidemics such as the coronavirus disease COVID-19, social distancing and the isolation of infected persons are crucial. However, existing reaction-diffusion equations for epidemic spreading are incapable of describing these effects. In this work, we present an extended model for disease spread based on combining a susceptible-infected-recovered model with a dynamical density functional theory where social distancing and isolation of infected persons are explicitly taken into account. We show that the model exhibits interesting transient phase separation associated with a reduction of the number of infections, and allows for new insights into the control of pandemics. Existing models describing epidemic spreading need an update to capture effects of social distancing and isolation. An extended model is proposed by te Vrugt et al. by drawing an analogy between persons and diffusing particles with repulsive interactions that correspond to social distancing.

There has been growing attention on the effect of COVID‐19 on white‐matter microstructure, especially among those that self‐isolated after being infected. There is also immense scientific interest and potential clinical utility to evaluate the sensitivity of single‐shell diffusion magnetic resonance imaging (MRI) methods for detecting such effects. In this work, the performances of three single‐shell‐compatible diffusion MRI modeling methods are compared for detecting the effect of COVID‐19, including diffusion‐tensor imaging, diffusion‐tensor decomposition of orthogonal moments and correlated diffusion imaging. Imaging was performed on self‐isolated patients at the study initiation and 3‐month follow‐up, along with age‐ and sex‐matched controls. We demonstrate through simulations and experimental data that correlated diffusion imaging is associated with far greater sensitivity, being the only one of the three single‐shell methods to demonstrate COVID‐19‐related brain effects. Results suggest less restricted diffusion in the frontal lobe in COVID‐19 patients, but also more restricted diffusion in the cerebellar white matter, in agreement with several existing studies highlighting the vulnerability of the cerebellum to COVID‐19 infection. These results, taken together with the simulation results, suggest that a significant proportion of COVID‐19 related white‐matter microstructural pathology manifests as a change in tissue diffusivity. Interestingly, different b‐values also confer different sensitivities to the effects. No significant difference was observed in patients at the 3‐month follow‐up, likely due to the limited size of the follow‐up cohort. To summarize, correlated diffusion imaging is shown to be a viable single‐shell diffusion analysis approach that allows us to uncover opposing patterns of diffusion changes in the frontal and cerebellar regions of COVID‐19 patients, suggesting the two regions react differently to viral infection. We used simulations and experimental data to demonstrate the feasibility of the novel correlated diffusion imaging for detecting microstructural changes in human white matter. We demonstrate in the case of mild COVID‐19, correlated diffusion imaging is superior to diffusion tensor imaging when only single‐shell data are available. Moreover, correlated diffusion imaging may exhibit sensitivities to different pathologies at different b‐values.

Coronaviruses cause disease in animals and people around the world. Human coronaviruses (HCoV) are mainly known to cause infections of the upper and lower respiratory tract but the symptoms may also involve the nervous and digestive systems. Since the beginning of December 2019, there has been an epidemic of SARS-CoV-2, which was originally referred to as 2019-nCoV. The most common symptoms are fever and cough, fatigue, sputum production, dyspnea, myalgia, arthralgia or sore throat, headache, nausea, vomiting or diarrhea (30%). The best prevention is to avoid exposure. In addition, contact per-sons should be subjected to mandatory quarantine. COVID-19 patients should be treated in specialist centers. A significant number of patients with pneumonia require passive oxygen therapy. Non-invasive ventilation and high-flow nasal oxygen therapy can be applied in mild and moderate non-hypercapnia cases. A lung-saving ventilation strategy must be implemented in acute respiratory distress syndrome and mechanically ventilated patients. Extracorporeal membrane oxygenation is a highly specialized method, available only in selected centers and not applicable to a significant number of cases. Specific pharmacological treatment for COVID-19 is not currently available. Modern medicine is gearing up to fight the new coronavirus pandemic. The key is a holistic approach to the patient including, primar-ily, the use of personal protective equipment to reduce the risk of further virus transmission, as well as patient management, which consists in both quarantine and, in the absence of specific pharmacological therapy, symptomatic treatment.

The evolution of many microbes and pathogens, including circulating viruses such as seasonal influenza, is driven by immune pressure from the host population. In turn, the immune systems of infected populations get updated, chasing viruses even farther away. Quantitatively understanding how these dynamics result in observed patterns of rapid pathogen and immune adaptation is instrumental to epidemiological and evolutionary forecasting. Here we present a mathematical theory of coevolution between immune systems and viruses in a finite-dimensional antigenic space, which describes the cross-reactivity of viral strains and immune systems primed by previous infections. We show the emergence of an antigenic wave that is pushed forward and canalized by cross-reactivity. We obtain analytical results for shape, speed, and angular diffusion of the wave. In particular, we show that viral–immune coevolution generates an emergent timescale, the persistence time of the wave’s direction in antigenic space, which can be much longer than the coalescence time of the viral population. We compare these dynamics to the observed antigenic turnover of influenza strains, and we discuss how the dimensionality of antigenic space impacts the predictability of the evolutionary dynamics. Our results provide a concrete and tractable framework to describe pathogen–host coevolution.